[Objective] The optimization of electricity supply and demand matching and regulation in smart grids is becoming increasingly complex, and traditional static optimization methods cannot meet the optimization requirements of smart grids. To this end, a self-learning optimal control method was proposed to solve the optimal control problem of ice storage air conditioning (IAC) systems. [Methods] The adaptive dynamic programming-particle swarm optimization (ADP-PSO) algorithm was adopted to address the optimal control problem of the systems. A two-layer iterative adaptive dynamic programming method was designed to learn the optimal control strategy, where the inner iteration calculated the sequence of transformed iterative control laws, and the outer iteration optimized the iterative value function. Meanwhile, a parallel control scheme was developed to obtain the optimal control suitable for the IAC system, which could meet the cooling demand at the lowest operating cost. [Results] Simulation results and comparative studies verify the effectiveness of the proposed algorithm. [Conclusions] The proposed ADP-PSO algorithm can achieve optimal energy matching. This strategy can make the iterative value function converge to the optimum, thereby obtaining the optimal control strategy and minimizing the system operating cost.

[Objective] With the development of artificial intelligence (AI) and 5G technology, data centers are called important infrastructure facilities for future development. However, there is a prominent contradiction between the high energy consumption characteristics of data centers and current low-carbon development needs. The optimization of relying solely on clean energy power supply faces bottlenecks, such as difficult waste heat recovery and low efficiency. In response, a large-scale hydrogen production capacity configuration method was proposed to study waste heat recovery in data centers, offering a new approach for the green low-carbon development of data center energy supply systems and enhancing green hydrogen production efficiency. [Methods] Firstly, the energy consumption structure of the data center was analyzed. The mathematical model of the output heat energy of the water-cooled data center was constructed, and the mathematical model of the influence of the electrolyte temperature change on the efficiency of electrolytic hydrogen production was established. The electrolyte temperature rise coefficient was proposed as the coupling node of the data center and electrolytic hydrogen production, which laid the foundation for the subsequent establishment of the waste heat water coordination mechanism. Secondly, the data center′s energy consumption characteristics and hydrogen needs were analyzed. Using waste heat recovery and heating electrolyte theory, the data center+clean energy+green hydrogen operation architecture and the matched mode were built, and a dynamic supply-demand balance model was established. Based on clean energy output and data center operations, various electrolytic hydrogen production modes were formulated. Finally, considering the data center load characteristics, clean energy output fluctuations, hydrogen energy market demand, and other factors, a hydrogen production capacity configuration model was constructed with system economy, carbon emissions, and renewable energy consumption rate as optimization objectives. The multi-objective optimization method based on the improved timing difference algorithm and particle swarm optimization algorithm was designed, and simulation analysis was carried out with Matlab. [Results] The data center+clean energy+green hydrogen coordinated operation mode can reduce the annual electricity energy consumption by 2.59％ under the typical day scenario. The pricing method of the auxiliary peak shaving market can guide the system to operate according to different objectives and can deal with the economy of the system to varying degrees and low carbon demand. This study achieves the structural transformation of the data center energy system through multi-dimensional technological innovation. [Conclusions] The proposed large-scale electrolytic hydrogen production capacity configuration method meets waste heat utilization needs of data centers and reduces reliance degree of the energy supply system on fossil energy. It offers a new technical path for creating a new energy system of “adjustable load-energy storage-energy supply” in data centers, supporting digital transformation of China′s economy and the steady advancement of its “dual carbon” goals.

[Objective] Environmental perception is a core task in autonomous driving, and high-precision object detection is essential for ensuring the safety and stability of autonomous driving systems. In recent years, LiDAR has been widely adopted in autonomous driving as a key sensor for three-dimensional perception due to its advantages, such as immunity to lighting conditions and high ranging accuracy. [Methods] This study first reviewed traditional camera-based object detection methods and analyzed their limitations in complex environments, then introduced the development history, working principles, types, and key parameters of LiDAR, followed by a systematic review of object detection methods based on point cloud representations, voxel representations, and multi-sensor fusion strategies. The network architectures, advantages, and challenges of different methods were compared and analyzed, and quantitative performance evaluations were conducted based on experimental results from the KITTI detection dataset. In addition, this study introduced a perception framework based on the bird's eye view (BEV) perspective and the trend of multi-sensor fusion, and analyzed the trade-offs between detection accuracy, real-time performance, and environmental adaptability of the current algorithms. [Results] The advantages of LiDAR-based object detection were summarized, and key future research directions were proposed to address challenges such as point cloud sparsity, high computational overhead, and the complexity of multimodal fusion. [Conclusions] Continuous optimization of algorithms and hardware enhances the accuracy, robustness, and practicality of LiDAR object detection in complex scenes.

[Objective] The influence of photovoltaics (PV)-assisted current and extraction current on conventional relay protection hinders the effective functioning of relay protection equipment. A multi-objective setting method for relay protection in distribution networks suitable for conditions with high permeability of distributed PV was proposed to address this problem, which is aimed at enhancing the rapidity, sensitivity, and selectivity of protection, and ensuring economic viability and practicality, thus effectively safeguarding the power grid security and supporting the widespread access of distributed PV. [Methods] The influence of PV-assisted current and extraction current on the protection configuration of the distribution networks was analyzed, and the problem of unwanted operation and refuse operation of distribution network protection caused by PV access was avoided by introducing distance protection and instantaneous current protection as the protection criteria. A multi-objective optimization model with the optimal parameters of protection rapidity, sensitivity and selectivity was built, and the particle swarm optimization (PSO) algorithm was improved by adopting the dynamic splitting operator to make the solution of the protection setting meet the practical application requirements. [Results] High-permeability distributed PV results in unwanted operation or refuse operation of distribution network protection, which is effectively avoided by introducing distance protection and instantaneous current protection as the protection criteria. The multi-objective optimization protection configuration model was built, and the evaluation indexes of the overall protection effect of a certain area were formed, with the solution of the protection setting completed based on PSO algorithm. Finally, the overall evaluation of the protection effect under high-permeability PV access was realized, with the rapidity, sensitivity, and selectivity of protection improved. [Conclusions] The results show that the combination of distance protection and instantaneous current protection can effectively avoid the influence of the PV-assisted effect on the conventional instantaneous current protection. The protection performance can be effectively improved by the proposed multi-objective optimization scheme. Under the equilibrium strategy, the rapidity, sensitivity, and selectivity increase by about 82.2％, about 3.8％, and about 33.1％, respectively. The innovation of this study is that the combination of distance protection and instantaneous current protection was adopted to form the protection criteria, thus avoiding the problem of unwanted operation and refuse operation of the distribution network protection due to PV access. Additionally, a multi-objective optimization scheme for protection settings was constructed, and PSO algorithm was improved by employing the dynamic splitting operator, thereby avoiding the limitations of PSO algorithm and improving the reliability and applicability of protection settings.

[Objective] During verifying settings of relay protection equipment in substations, traditional methods mainly rely on manual verification or simple program verification. The manual verification accuracy varies, with relatively low verification efficiency. Simple program verification improves its efficiency to some extent, but there is room for further accuracy enhancement. To this end, a setting verification method for relay protection of intelligent substations based on deep learning was proposed. [Methods] Firstly, an improved convolutional recurrent neural network (CRNN) was employed to identify relay protection settings. Specifically, the convolutional neural network (CNN) was adopted to convert text images into feature sequences, followed by leveraging the recurrent neural network (RNN) to identify the feature sequences. Finally, the identification results were transcribed by adopting a dictionary-based connectionist temporal classification (CTC) loss function to obtain the setting text information. On this basis, the RNN module was enhanced by utilizing a convert gate unit, thus building a bidirectional convert gate long short-term memory (Bi-CGLSTM) model to achieve adaptive adjustment of data weights. Then, the setting verification was carried out by combining Chinese word segmentation technology. A complete dictionary of setting names was constructed, with the Levenshtein distance algorithm adopted to calculate the similarity between the text to be verified and the standard text. Additionally, an improved forward maximum matching algorithm was applied to match the setting text, thus completing the one-by-one setting verification of relay protection equipment in substations. [Results] 240 relay protection setting sheets from a power supply company that cover ten common equipment models were selected as experimental samples to validate the feasibility and effectiveness of the proposed method. The training parameter setting of the deep learning model was as follows:the iteration count of 100, learning rate of 0.001, and the Adam optimizer for adjusting weights and biases. The experimental results show that the identification accuracy of the improved CRNN model exceeds 97％, while the verification accuracy of the proposed method reaches 97.07％, with relatively shorter verification time and better overall performance than that of other comparative methods. [Conclusions] The identification accuracy of the setting text of substation relay protection in the context of big data can be effectively enhanced by the improved deep neural network. Additionally, verification accuracy can be ensured and verification efficiency can be significantly enhanced by the combination of the Levenshtein distance algorithm and the improved forward maximum matching algorithm. Powerful technical support is provided by the proposed method for the intelligent operation and maintenance of intelligent substations.

[Objective] Under the impetus of the carbon peaking and carbon neutrality goals, the high proportion of renewable energy grid connection has weakened system inertia and damping characteristics, posing a serious threat to grid stability. Although grid-forming virtual synchronous generator (VSG) control technology can actively provide inertia support for the grid, the complex nonlinear characteristics of renewable energy systems cause traditional VSG control to face risks of instability in angular frequency and output voltage under non-ideal operating conditions, resulting in suboptimal control performance. To address this issue, this paper proposed a grid-forming converter control strategy based on passivity-based control, overcoming the limitations of traditional linear control methods to enhance system dynamic response performance, interference resistance, and robustness. [Methods] This paper adopted a nonlinear control design framework. According to VSG control principles, the rotor motion equation of a synchronous generator was employed to achieve active frequency regulation, and the excitation system was used to achieve reactive voltage control. A Hamilton system model incorporating grid-forming control was built. Based on the core principles of passivity-based control theory, the Hamilton model was mathematically transformed into a dissipative Hamilton standard form with port characteristics. This model inherently possesses advantages for stability analysis, providing a theoretical foundation for controller design. In addition, based on the system′s stable operation requirements, the desired equilibrium operating point was set. To accelerate the dissipation of system energy toward the desired equilibrium point, effectively suppress oscillations, and enhance convergence speed, a damping term was introduced. Ultimately, the active and reactive control laws applicable to grid-forming converters were derived, achieving global asymptotic stability of the nonlinear system. [Results] Simulation test results show that under non-ideal conditions such as power change, grid voltage imbalance, short-circuit fault, and load variation, the grid-forming converter control strategy based on passivity-based control significantly outperforms traditional VSG control in terms of system angular frequency stability. The amplitude of frequency fluctuations is significantly reduced, and the time required to recover to the desired value is greatly shortened. The overshoot of the output voltage is reduced, the regulation process is smoother, and the voltage stabilizes faster, effectively enhancing voltage stability. [Conclusions] The proposed grid-forming converter stability control strategy establishes a nonlinear design framework based on the dissipative Hamilton model and designs a control strategy through energy shaping and damping injection. The derived control laws exhibit strong robustness and can accommodate complex operating conditions without requiring a precise system model. It effectively addresses the stability deficiencies and slow dynamic response of traditional VSG control when faced with system nonlinearities, overcoming the limitations of fixed linear control parameters. This provides a control strategy with strong interference resistance and fast dynamic response for renewable energy grid-connected systems, supporting the stable operation of new power systems.

[Objective] As the power system continues to expand, transmission lines, being a crucial channel for power transmission, require safe and stable operation. However, transmission lines, long exposed to the complex and variable natural environment, face multiple safety risks such as external force damage and equipment aging. To enable high-precision and high-efficiency automatic detection of potential hazards in transmission lines, this study proposed an intelligent identification technology for external damage risks in transmission lines, based on deep learning. [Methods] This study developed an integrated technical framework of “geometric correction-image enhancement-intelligent recognition”, systematically addressing key technical challenges in transmission line image recognition. In the geometric correction stage, a polynomial geometric correction model based on the least squares method was employed. By establishing an accurate coordinate mapping, this model effectively eliminated geometric distortions caused by factors such as shooting angles and lens distortion. In the image enhancement stage, a new image processing algorithm, combining bilateral filtering and the maximum between-class variance method, was proposed. This algorithm effectively removed image noise while retaining the edge features of transmission lines, providing high-quality data for subsequent recognition. In the intelligent recognition stage, a dual-optimized convolutional neural network (CNN) model was designed. The feature extraction process was optimized by dynamically adjusting the convolution kernel weights, and sparse constraints were introduced to enhance feature discriminability. Finally, precise recognition was achieved by integrating the support vector machine classifier. This method overcame the limitations of traditional technologies, such as insufficient geometric distortion correction and feature extraction, offering a comprehensive solution for intelligent identification of hidden dangers of transmission lines. [Results] Tested on real datasets containing multiple types of damage, this method demonstrates significantly higher recognition accuracy compared to mainstream algorithms such as YOLOv4 and Mask R-CNN. It shows greater robustness, especially in complex backgrounds. The method achieves an average positional offset of only 0.013 meters, fully meeting engineering application requirements. The floating point operations for processing 1 000 images reduce to 3.24×10⁹, significantly enhancing the real-time processing capability. [Conclusions] The proposed intelligent recognition technology for external damage risks in transmission lines has made significant improvements in recognition accuracy, positioning precision, and computational efficiency through innovative technical approaches and systematic optimizations. The theoretical contributions of this research include establishing a complete image processing system for transmission lines, providing a new approach for related studies; introducing a dual optimization mechanism that offers a viable solution for feature extraction in complex environments; adopting the lightweight network design, which serves as an important reference for applying deep learning models in engineering.

[Objective] In fields such as precision instruments and aerospace, low-frequency vibration control is crucial. In the field of low-frequency vibration isolation, the quasi-zero-stiffness vibration isolation system has attracted much attention in recent years due to its significant advantages. However, in practical engineering applications, it is still faced with the technical problems of insufficient range of quasi-zero-stiffness interval, which results in limited vibration isolation frequency band, and difficulty in further improving low-frequency vibration isolation performance. A novel quasi-zero-stiffness vibration isolator was designed in this paper to address these problems. [Methods] Based on the classic three-spring quasi-zero-stiffness structure, a new quasi-zero-stiffness vibration isolator based on a semi-circular cam and a rolling ball mechanism was proposed, which had a simple and compact structure. Firstly, through the static analysis of the system, the realization conditions of the quasi-zero-stiffness were determined, and the force-displacement and the stiffness-displacement relationship equations of the system were deduced, which revealed that the equivalent stiffness of the system near the equilibrium position was close to zero. Secondly, the dynamic characteristic equation of the system was established, and the amplitude-frequency response characteristics of the system were studied through theoretical analysis and numerical simulation, which revealed the nonlinear dynamic behavior of the system. Finally, the comprehensive effects of damping ratio, nonlinear term coefficient, and excitation amplitude changes on the vibration isolation performance of the system were deeply discussed by introducing force transmissibility and displacement transmissibility, and the system parameters were optimized to achieve the best vibration isolation effect. [Results] Compared with the traditional three-spring quasi-zero-stiffness vibration isolation system, the new vibration isolator significantly expands the quasi-zero-stiffness region, resulting in efficient low-frequency vibration isolation over a wider frequency range. The proposed vibration isolator has much lower force transmissibility than a linear vibration isolation system, and is able to isolate low-frequency vibrations more effectively. [Conclusions] The new vibration isolator designed in this paper effectively solves the technical problems of insufficient range of quasi-zero-stiffness interval and limited low-frequency vibration isolation performance faced by the traditional quasi-zero-stiffness vibration isolation system in practical engineering applications by optimizing structural design and parameter matching. Compared with the traditional linear vibration isolation system, the proposed vibration isolation system not only has a lower initial vibration isolation frequency but also shows better low-frequency vibration isolation performance, providing a new solution for low-frequency vibration control in the engineering field.

[Objective] The behavior of stress corrosion cracking (SCC) in super duplex stainless steel S32707 under deep-sea environments is a critical issue that significantly influences its engineering reliability. As a vital alloying element in steel, nitrogen (N) needs in-depth exploration regarding its role in regulating stress corrosion resistance. Revealing how N influences the evolution and mechanisms of stress corrosion in S32707 steel under simulated deep-sea environments, characterized by high pressure and chloride ion levels, will provide theoretical basis for developing better SCC-resistant solutions for deep-sea engineering materials. [Methods] Slow strain rate tension testing was adopted to analyze the tensile strength, yield strength, and elongation after fracture of S32707 with various N contents in air, simulated sea level, and deep-sea environments. Meanwhile, the SCC sensitivity was combined to evaluate the inhibitory influence of N content on SCC sensitivity, with the scanning electron microscope (SEM) adopted to characterize fracture morphologies and examine the crack propagation path. Furthermore, the stability of the passive film and corrosion kinetics were analyzed by employing potentiodynamic polarization curves. [Results] In simulating deep-sea environments, the mechanical properties of S32707 steel gradually improve with the increasing N content. The tensile strength rises significantly, while yield strength shows a slight decrease, and elongation after fracture increases obviously. The SCC sensitivity of S32707 steel drops from 16.8% to 10.3% with a decrease amplitude of 6.5%, which is significantly higher than the decrease amplitude of SCC sensitivity (1.8％) during simulating sea level environment. This means N improves the stress corrosion resistance of S32707 steel. Additionally, as N content increases, the electrochemical behavior of the steel gradually improves, shown by a decrease in pitting current density from 505.0 nA/cm² to 341.6 nA/cm² and an increase in the pitting potential from 60.2 mV to 101.0 mV, which helps inhibit cathodic reaction. Furthermore, as the N content increases, the microstructure exhibits gradual improvement, the area of the quasi-cleavage zone at the steel fracture decreases, quasi-cleavage characteristics diminish, and the number of cracks and crack length decrease, the section shrinkage rate increases, and the crack propagation degree gradually diminishes, suggesting that N suppresses crack initiation and propagation by enhancing fracture toughness. [Conclusions] N enhances the deep-sea stress corrosion resistance of super duplex stainless steel via various mechanisms. It reduces SCC sensitivity, boosts fracture toughness, and inhibits stress corrosion cracking. It can increase self-corrosion potential, decrease pitting current density, inhibit cathodic reaction, and slow local corrosion, thus improving the pitting-resistant performance of S32707 steel. Solid-solution N consumes H⁺ in the pitting corrosion pit and generates NH⁺₄, inhibiting pit acidification and hydrogen evolution corrosion. Therefore, the results show that adjusting N content is an effective method to enhance the stress corrosion resistance of S32707 in high-pressure deep-sea environment, thus providing a theoretical basis for the composition design and engineering application of highly corrosion-resistant duplex stainless steel.

[Objective] In the operation and management of power system, the medium and low-voltage distribution network serves as a key link between power sources and users. Its operating efficiency and stability are directly related to the safety and reliability of the entire power system. Three-phase line loss, as an important indicator of the distribution network′s operational efficiency, not only reflects the energy loss during the power transmission process but also directly affects the voltage quality, power consumption, and safe operation of the power grid. However, the three-phase line loss data in the distribution network exhibit complex distribution characteristics, such as multi-modal and asymmetric features. During dynamic changes, it is difficult to accurately capture the inherent patterns and structures in the data, which reduces the accuracy of anomaly detection. Therefore, this paper proposed an intelligent method for the anomaly detection of three-phase line loss in medium and low-voltage distribution networks. [Methods] During the data collection process of the distribution network′s three-phase line loss, the data can be influenced by multiple factors such as electromagnetic interference and equipment errors, leading to the presence of significant noise and outliers. These noises not only reduce the signal-to-noise ratio but also obscure the true features of the data, thereby affecting the accuracy of subsequent analysis. Therefore, a radial basis function (RBF) neural network was used to extract features from the collected three-phase line loss data. By performing nonlinear mapping of the input data, the method effectively suppressed the interference from noise, enhancing the signal-to-noise ratio. The preprocessed data were then normalized, which further improved the completeness and accuracy of the data collection. A loop current-based method was employed to decompose the circuits in the distribution network into multiple independent loops. In each loop, the real and imaginary parts of the voltage and current were calculated. By analyzing the temporal and phase variations of these values in detail, the operating status of the circuit was thoroughly understood, and potential anomaly patterns were accurately identified. Based on the real and imaginary part values of the voltage and current on the three-phase branch circuits, a Gaussian mixture distribution model was constructed. This model used multiple Gaussian distributions to describe the complex distribution features of the three-phase line loss data, allowing for more accurate capture of the inherent patterns and structures in the data. The maximum expectation algorithm was then used to fit the normalized line loss rate and construct a hybrid Gaussian model consisting of multiple Gaussian mixture distributions. The likelihood probability function of the eigenvector was calculated, and based on a preset probability threshold, the data were determined to be anomalous or normal. If the likelihood probability was below the threshold, it was classified as anomalous; otherwise, it was considered normal. This approach enabled the identification of line loss anomaly data. [Results] Experimental results show that the proposed method can accurately identify three-phase line loss buses, reducing the risk of misjudgment and missed detections. [Conclusions] This method can promptly detect and address faults in the distribution network, which is of significant importance in improving the operational efficiency and reliability of power systems.

[Objective] High-voltage circuit breakers are key control and protection devices in the power system, and their reliable operation is crucial for the safety and stability of power grids. However, during long-term operation, high-voltage circuit breakers may trigger various faults due to mechanical wear, component aging, and other problems. Currently, the detection of high-voltage circuit breakers faces challenges such as diverse detection signals, great difficulty in fault detection, and low accuracy. Therefore, studying an efficient and accurate mechanical status detection method for high-voltage circuit breakers is of great significance for ensuring the safe and stable operation of the power system. [Methods] This study proposed a mechanical status detection model for high-voltage circuit breakers based on multi-modal efficient Transformer. In the data acquisition stage, vibration sensors, current sensors, and displacement sensors were comprehensively employed to synchronously acquire vibration signals, current signals, and displacement signals during the operation of high-voltage circuit breakers, thus constructing a multi-modal signal dataset. In the signal preprocessing stage, wavelet transform technology was adopted to process the acquired multi-modal signals and decompose the signals into different frequency scales, thus effectively removing noise components in the signals, enhancing fault feature signals, and significantly improving signal quality. In terms of model building, an efficient Transformer module was introduced. With its powerful self-attention mechanism, the module could effectively capture long-distance dependency relationships in signal sequences and dig deeply into complex features in multi-modal signals. Additionally, by classifying the operation status of high-voltage circuit breakers into six categories, including normal operation, failure to maintain closing, loose soft connection, single-phase contact wear, loose insulating tie rod, and opening spring fracture, accurate diagnosis of the mechanical status of circuit breakers was realized. [Results] In the simulation experiments, simulation models of different fault types of high-voltage circuit breakers were built to simulate various working conditions during actual operation and generate multi-modal signal data. Inputting the data into the proposed detection model for testing shows that the model can accurately identify different fault types. In the actual experiments, multiple high-voltage circuit breakers were selected as test objects, and multi-modal signal data were collected under their normal operation and different fault settings. The experimental results reveal that the proposed method significantly improves the detection accuracy compared with traditional detection methods while ensuring the detection speed. [Conclusions] The proposed mechanical status detection model for high-voltage circuit breakers based on multi-modal efficient Transformer effectively solves the problems of complex detection signals and severe noise interference. By leveraging the powerful feature extraction and classification capabilities of the efficient Transformer model, accurate identification of multiple mechanical faults in high-voltage circuit breakers is realized. Simulation analysis and experimental results fully demonstrate that this method performs well in both detection accuracy and speed, providing reliable technical support for the status monitoring and fault diagnosis of high-voltage circuit breakers in the power system. It helps to timely detect potential faults of the equipment, and holds application significance and broad promotion prospects for ensuring the safe and stable operation of the power system.

[Objective] This paper addresses the consensus control problem of linear multi-agent systems with communication delays under an event-triggered mechanism to reduce communication frequency while ensuring system stability. Traditional consensus control schemes mostly rely on periodic information updates where frequent communication not only consumes resources but may also cause system instability. To address this issue, a distributed control method that integrated pinning control strategy with an integral-type event-triggered mechanism was proposed for multi-agent systems with communication delays, enabling consistent driving of agent states. [Methods] First, a virtual leader was introduced to transform the consensus problem into an asymptotic stabilization problem of the corresponding error system. Then, a feedback controller incorporating local agent states, neighboring information, and leader-guidance terms was designed. Combined with an integral triggering function, a triggering schedule was constructed to reduce communication updates. Subsequently, based on the constructed error-system model, a Lyapunov-Krasovskii functional was developed that accounted for the current state, communication-delay terms, double-integral terms, triggering-error terms, and delayed-derivative terms. Sufficient conditions were obtained to guarantee the global asymptotic stability of the closed-loop error system. On the basis of stability, the potential Zeno behavior induced by the event-triggered mechanism was further analyzed. By introducing an upper bound on the growth of the error energy and a dynamic constraint on the integral triggering function, a positive lower bound on the inter-event intervals was derived, and a set of sufficient conditions to exclude Zeno behavior were provided. [Results] Simulation results demonstrate that, even in the presence of communication delays, the proposed integral event-triggered mechanism achieves consensus while significantly reducing triggering frequency and control energy consumption achieving the lowest overall energy consumption. Moreover, the convergence curves become smoother and avoid excessive triggering caused by high-frequency small disturbances, demonstrating improved convergence stability and energy efficiency. [Conclusions] Compared with traditional and dynamic event-triggered schemes, the proposed control strategy achieves better overall performance in communication and energy utilization while guaranteeing consensus, demonstrating higher practical value for resource-constrained multi-agent systems. This study provides new theoretical support and a methodological framework for the distributed control of delayed multi-agent systems.

[Objective] With the high proportion integration of renewable energy sources such as wind and solar power into the grid, their inherent intermittency and volatility pose significant challenges to the voltage stability at the distribution end of the power grid. In particular, at the end of regional power grids, the uncertainty in wind-solar-load output increases the risk of rapid voltage drops, which may lead to equipment damage or even cascading failures. Existing studies have notable shortcomings in areas such as handling prediction errors in wind-solar-load output and multi-objective collaborative optimization. For example, the full-pure embedding sensitivity analysis method fails to adequately consider the influence of prediction errors, while the source-grid-load coordinated control framework ignores the interference of prediction errors on collaborative outcomes. To address these issues, this paper proposed a novel fast regulation algorithm for low voltage. By quantifying the uncertainty in wind-solar-load output, a multi-objective optimization model was developed that balances safety, performance, and cost. The model aims to achieve rapid and stable voltage regulation at the distribution end of the grid, thereby improving the reliability and adaptability of high-proportion renewable energy integration into the power grid. [Methods] The Collaborative Genetic Algorithm (CGA) was used as the core solution method. Firstly, precise probability density function models were established to account for the randomness in the output of wind power, photovoltaics, and load output. The output of wind power was quantified by combining the Weibull distribution of wind speed with the normal distribution of prediction errors. Photovoltaic output was associated with light intensity and photoelectric conversion efficiency, incorporating prediction errors. Load output was represented by a probability density function reflecting its volatility. Based on this, a low-voltage regulation model was developed with optimization objectives balancing safety, performance, and cost. The safety indicator quantified the total power loss at the distribution end of the grid, the performance indicator included the overall network loss and voltage deviation, while the cost indicator calculated the total lifecycle cost. Through integer-based mixed coding schemes and dynamically adjusted crossover and mutation probabilities, the algorithm effectively optimized the population and output the optimal solution that satisfied voltage stability margin requirements. [Results] Based on actual grid data from a region in Guangzhou, simulation experiments validate the effectiveness of the proposed algorithm. In terms of uncertainty handling, the proposed algorithm shows a significantly higher correlation between wind and photovoltaic output predictions and actual data compared to other traditional methods. This is due to the algorithm modeling output power prediction errors as random variables, which more accurately reflects the uncertainties in real-world systems. Regarding voltage regulation, when fluctuations in wind-solar-load output and increased load lead to voltage drops, the algorithm quickly and effectively restores node voltages to normal levels. Its performance outperforms traditional methods, such as those based on steady-state grid models and the double-loop voltage-current control algorithm. In terms of static voltage stability margin, the proposed algorithm maintains a high voltage stability margin of over 0.8 across various test scenarios, demonstrating strong voltage regulation capability. Furthermore, while ensuring voltage stability, the algorithm also considers the economic and performance efficiency of grid operations. [Conclusions] The fast regulation algorithm for low voltage effectively addresses the issue of low-voltage instability at the distribution end of power grids with high integration of renewable energy by deeply combining the wind-solar-load output uncertainty modeling and multi-objective optimization. This algorithm innovatively introduces probability density functions to quantify prediction errors, which significantly improves the accuracy of wind-solar-load output predictions. By using CGA for coordinated optimization of safety, performance, and cost targets, the algorithm achieves rapid dynamic voltage regulation. Experimental results show that the proposed algorithm outperforms traditional methods in terms of regulation speed, stability margin, and economic efficiency, which provides reliable technical support for intelligent grid control with high proportions of renewable energy integration. The research results not only have significant theoretical value but also demonstrate great potential in practical engineering applications. Future exploration will focus on voltage coordination control strategies across multiple time scales to continuously enhance the stability and economic efficiency of grid operations.

[Objective] With the longer service life of oil and gas pipelines, the failure to identify the pipeline material due to data deficiency has become increasingly prominent. Traditional detection methods are unable to meet the engineering requirements for material identification and aging status assessment. A novel non-destructive testing method is expected to be proposed based on the magnetic compression effect. By analyzing the base value variation of magnetic flux leakage (MFL) signals in different steels under an applied external magnetic field, a correlation model between material and magnetic signal was established to provide a theoretical basis and technical approach for achieving rapid, accurate, and non-contact identification of pipeline materials. [Methods] The magnetic charge theory was integrated with the magnetic compression effect to develop a mathematical model describing the MFL field on the surface of steel under the influence of an external magnetic field. A theoretical derivation was performed to establish the functional relationship among the MFL signal base values, the external magnetic field intensity, and the material′s magnetization intensity. The degree of the magnetic compression effect was proposed to be quantitatively characterized by the magnetic compression coefficient. Through the systematic experimental design, six representative structural and pipeline steels were selected as test materials, including Q235, Q345, X52, X65, X70, and X80. The specimens were machined into a standardized dimension of 270 mm×140 mm×10 mm. On a custom-built high-precision MFL testing platform, the external magnetic field was incrementally increased from 0 to 48 kA/m in steps of 1 kA/m, and the corresponding MFL signal base values were recorded in real time. To further validate the stability of the method, comparative tests were conducted between the signals obtained from original state steel plates and those obtained from polished plates with a surface roughness of 0.8 μm. All experiments were replicated to ensure both repeatability and accuracy of the results. [Results] According to the experimental results, with the enhancement of external magnetic field intensity, the MFL signal base values in all tested steel materials exhibit an initial increase followed by a subsequent decrease. The peak position varies significantly with the magnetic properties of the material. Specifically, the signal base values for Q235 and X65 peak at 24 kA/m, for X70 and X80 at 25 kA/m, while for Q345 and X52 with higher magnetization intensity, the peaks occur at 26 kA/m. This variation pattern highly coincides with the saturation magnetic characteristics of each steel′s M-H curve, which indicates the close relation between the critical field strength for the onset of the magnetic compression effect and the material′s magnetic properties. Furthermore, tests conducted under varying surface conditions of the steel plates reveal that, although the signal amplitudes differ, the critical point at which the base value begins to decline remains consistent for the same material. This finding confirms that the method is insensitive to surface conditions, with good anti-interference capability and adaptability to diverse operational environments. [Conclusions] A non-destructive testing method was proposed for pipeline material identification based on the magnetic compression effect. By establishing the correspondence between the MFL signal base values and the external magnetic field intensity, an effective differentiation between various structural and pipeline steels was achieved. This method exhibits not only good repeatability but also strong engineering applicability. The detection results are unaffected by complex factors such as internal pipeline surface roughness or corrosion state, making it suitable for complex working conditions such as internal pipeline detection. The research results provide a new technical means for material identification and safety assessment of aging pipelines, holding significant theoretical importance and engineering application value.

[Objective] CNC machine tools in the operating state feature high instantaneous power and low energy efficiency, and their machining energy consumption power changes with complex and variable processing tasks in real time, thus making it difficult to predict energy consumption of machine tool machining. The prediction mechanism model of machining energy consumption of CNC machine tools based on information flow and energy flow requires operators to be aware of the operating status of the machine tool and the characteristics of energy consumption changes of the machine tool, which results in difficult prediction of machining energy consumption of machine tools and long cycles. As the testing techniques and computational power of computers significantly improve, data-driven prediction methods have been introduced to the research on predicting the machining energy consumption of machine tools. Therefore, an adaptive incremental machine learning approach that combines stochastic configuration networks (SCNs) with a multi-mechanism-improved sand cat swarm optimization (SCSO) was proposed to achieve efficient and high-precision prediction of the machining energy consumption of machine tools. [Methods] By taking the helical groove CNC milling machine milling screw rotor as an example, based on the process parameters, the machining energy consumption milling experiments and collected machining energy consumption data were designed. Meanwhile, SCNs were optimized by adopting the multi-mechanism-improved SCSO algorithm to build a prediction model for machining energy consumption. The SCN algorithm was employed as the prediction model for machining energy consumption. The SCSO algorithm improved by combining the Tent population initialization strategy, variable helix search strategy and adaptive t-distribution strategy solved the scale factor and regularization parameter during SCN modeling to improve the prediction accuracy and prediction efficiency of SCNs. [Results] To verify the accuracy of the model, the root mean square error (RMSE) and mean absolute percentage error (MAPE) were employed as the evaluation indexes to compare the BP neural networks (SSA-BP) optimized by the SCSO-SCNs, SCNs, and squirrel search algorithm. Comparison results show that compared to SSA-BP and SCNs, SCSO-SCNs shows a decrease of 38.62％ and 46.03％ in RMSE respectively, while it presents a reduction of 40.47％ and 47.33％ in MAPE compared to SSA-BP and SCNs respectively, which proves the performance superiority of the SCSO-SCNs model in the prediction of machining energy consumption. [Conclusions] The proposed machine learning method, which integrates SCNs and multi-mechanism-improved SCSO algorithm, shows more obvious performance advantages in terms of machining energy consumption prediction for CNC machine tools. The improved SCSO algorithm enhances the search efficiency and the ability to jump out of local optimal solutions by optimizing the initial population of the algorithm and improving the population position updating strategy in the improvement and exploitation phases. The SCSO algorithm, based on multi-mechanism improvement, greatly improves the prediction accuracy of the model by seeking the optimal scale factor and generalization factor of SCNs. Comparison with existing machine learning algorithms shows that the proposed method has higher prediction accuracy and greatly improves the prediction efficiency of machining energy consumption.

[Objective] With the increasing importance of power transmission and transformation projects in distribution networks, traditional cost estimation methods face challenges such as large errors and excessive time consumption, making them inadequate for modern engineering management. To enhance the execution efficiency and estimation accuracy of power transmission and transformation projects, this study proposed a novel cost estimation method based on radial basis function neural network (RBFNN) and significance cost theory. This approach aims to address the limitations of traditional methods in complex cost estimation scenarios while enhancing the robustness and adaptability of the model. [Methods] This study applied significance cost theory to screen historical project data and identify the main factors affecting cost estimation for power transmission and transformation projects. These factors were used as input features for the neural network. The method introduced radial basis functions (RBFs) to restructure the traditional artificial neural network (ANN) architecture, creating a cost estimation model specifically for power transmission and substation projects. The model processed input data using Gaussian functions, initialized the hidden layer centers with the K-means clustering algorithm, and used least squares and gradient descent methods to train the output and hidden layers. To validate the model′s effectiveness, 100 sets of data from power transmission and transformation projects were used to compare the cumulative absolute error rate and average execution time of traditional methods (unit cost method and index estimation method) with the proposed model. Moreover, SHAP value analysis was employed to quantify the impact of key factors on estimation error rates. [Results] Simulation results demonstrate that the RBFNN-based cost estimation method outperforms traditional methods in both cumulative absolute error rate and execution time. When the test sample size increases to 20, the cumulative error rate for the unit cost method reaches 440％, while the index estimation method reaches 180％, and the proposed model maintains an error rate below 110％. In terms of execution time, traditional methods require an average of 5 s, while the proposed model reduces the time to just 0.5 s. In addition, SHAP value analysis reveals that factors such as wire cross-sectional area, steel pipe poles, and the number of circuits have the greatest influence on estimation error rates, with their SHAP values significantly higher than those of other factors. This finding provides critical insights for model optimization and cost control. [Conclusions] The cost estimation method proposed in this paper, based on RBFNN and significance cost theory, effectively improves the accuracy and efficiency of cost estimation for power transmission and transformation projects. Although the method still exhibits some errors in complex construction environments, it outperforms traditional methods in overall performance, making it highly practical with strong potential for widespread application. Future research will focus on integrating regression analysis, support vector machine (SVM), and other machine learning algorithms to further optimize model precision and better handle the complexity and variability of power transmission and transformation projects.

[Objective] Alien species invasion has emerged as a global ecological security problem. The resulting biodiversity loss and ecosystem degradation pose a serious threat to the sustainable development of human society. Traditional biological control methods often suffer from limited control accuracy and poor robustness when faced with environmental uncertainties and random disturbances. To address the dynamic characteristics of the stochastic biological system, this study proposed an adaptive fuzzy control (AFC) strategy based on Lyapunov stability theory. By building a stochastic dynamic model incorporating white noise disturbances and unknown nonlinearities, the study focused on solving the dual-objective problem of coordinated native species protection and invasive species suppression under coupled environmental uncertainty and stochastic perturbations. [Methods] Based on stochastic differential equation theory, a stochastic biological system dynamics model for alien species invasion was constructed. A fuzzy logic system (FLS) was employed to approximate the uncertain nonlinear term in the model. The backstepping method was integrated with adaptive fuzzy control and applied to stochastic biological systems. A fuzzy backstepping controller and an adaptive law with parameter self-tuning capabilities were designed using an appropriately chosen Lyapunov function. [Results] The proposed adaptive controller exhibits intelligent adjustment capabilities, allowing the population density of native species to effectively track the desired reference trajectory within a finite time. The tracking error converges to a neighborhood of zero, demonstrating the controller′s ability to monitor and analyze errors in real time. By dynamically adjusting control inputs, the system keeps tracking errors within acceptable bounds. Moreover, all system states under alien species invasion are proven to be semi-globally uniform and ultimately bounded. Notably, the system also maintains stable convergence characteristics and demonstrates strong adaptability under varying intensities of stochastic disturbance. [Conclusions] By combining FLS with nonlinear control theory, the proposed AFC strategy effectively addresses uncertainty control in stochastic biological systems. Numerical simulations further verify the strategy′s effectiveness in both protecting native species and managing invasive species, offering novel insights for intelligent regulation of complex ecosystems.

[Objective] DDoS attacks, as a highly destructive network threat, seriously threaten the stable operation of the power system. Due to the complexity and variability of data traffic in the power monitoring local area network (LAN), DDoS attack traffic and normal traffic have many similarities in their manifestations, making it difficult to effectively distinguish between the two. Although traditional static threshold methods can achieve traffic monitoring to a certain extent, misjudgments often occur due to their inability to adapt to the dynamic changes of traffic. The detection effect of DDoS attacks is thus weakened and reliable security guarantee cannot be provided for power monitoring LANs. Therefore, a DDoS attack matching detection method for power monitoring local area networks was proposed based on dynamic thresholds. [Methods] Real time network traffic data in the power monitoring local area network were collected through network traffic collection devices. The entropy value of these flows was calculated using information entropy theory. The chaos degree in data could be reflected by information entropy. Normal traffic usually had a certain regularity with relatively stable entropy values. Due to the influx of a large number of abnormal packets, however, DDoS attack traffic had significant fluctuations in entropy values. Based on this characteristic, a dynamic threshold was set, and the entropy value of the traffic was determined as abnormal when exceeding this dynamic threshold. The six-tuple feature set of the abnormal traffic was then extracted, including average flow packet count, average byte count, source IP address growth, flow table survival time variation, port growth, and convection ratio, and input into a pre-trained least squares support vector machine (LSSVM) classifier. The LSSVM classifier learned from the existing samples to establish the mapping relationship between features and classes. The abnormal traffic was then classified and judged to determine whether it was DDoS attack traffic. [Results] According to the test result, the proposed method shows significant improvements on both the ROC and PR curves, with higher receiver operating characteristic curve (ROC-AUC) and accuracy recall curve (PR-AUC) values than the traditional method. This fully demonstrates that the method, with higher accuracy and recall rate in detecting DDoS attacks, can effectively identify DDoS attack traffic hidden in normal traffic and reduce the misjudgment rate. [Conclusions] The detection method based on dynamic thresholds and LSSVM classifier can effectively overcome the difficulty in distinguishing DDoS attack traffic from normal traffic in power monitoring local area networks. By improving the accuracy and reliability of DDoS attack detection, it provides a more effective DDoS attack detection method for power monitoring local area networks, helps improve the security and stability of power systems, ensures the reliable operation of the power supply, and has important practical application value for network security protection in the power industry.

[Objective] For a long time, energy storage technology has received close attention in the academic and industrial fields. Compared to those made of other dielectric materials, dielectric capacitors using antiferroelectric materials exhibit higher energy storage densities and faster charge discharge rates. As a typical antiferroelectric material, PbZrO₃ (PZO) has a great potential in practical energy storage applications due to its unique field-induced phase transition behavior and high Curie temperature. [Methods] The energy storage performance of dielectric materials mainly depends on their polarization performance and electrical breakdown strength. To improve the energy storage density of the energy storage capacitor with PZO as the dielectric, a 3-nm-thick Al layer was deposited on the Pt(111)/Ti/SiO₂/Si substrate by thermal evaporation, and a PZO amorphous film was deposited on the Al-coated substrate surface by the sol-gel method. Then, PbZrO₃-Al₂O₃ (PZO-AO) nanocomposite films were prepared by two processes of microwave annealing (MA) and conventional annealing (CA). [Results] The results show that a nanocomposite film can be prepared by the method of this study, in which the Al₂O₃ nanoparticles are distributed in layers on the PZO matrix. The shape of the ferroelectric hysteresis loop and polarization performance of the film can be adjusted. The energy storage density of the PZO-AO nanocomposite film prepared by CA (CA PZO-AO film) is 13.52 J/cm³ in the electric field of 950 kV/cm, which is 83％ higher than that of the PZO film prepared by CA (CA PZO film). In addition, the experimental data show that MA can reduce the crystallization activation energy of the PZO film, which can not only make the amorphous PZO film crystallized at a low temperature of 650 ℃ but also shorten the annealing time to only one-third of CA time. Furthermore, MA can also stabilize the antiferroelectric properties of the PZO film, further improving the energy storage density of the film. Therefore, MA is used to further optimize the microstructure of the PZO-AO nanocomposite film, decrease the grain size of the film, and reduce the leakage current density. The leakage current density of the PZO-AO nanocomposite film prepared by MA (MA PZO-AO film) is in the orders of magnitude of about 10^-8, which is 1 order of magnitude lower than that of the CA PZO-AO film. This indicates that the grain size of perovskite and the distribution of Al₂O₃ nanoparticles are regulated by MA, so as to improve the electrical breakdown strength. The MA PZO-AO film finally has an energy storage density of 18.94 J/cm³ in an electric field of 1 550 kV/cm, which is 40.1％ higher than that of the CA PZO-AO film. [Conclusions] The experiment shows that high-quality dielectric nanocomposite films can be prepared by thermal evaporation combined with energy-saving and environmentally friendly MA technology. This research provides a new idea for the design of new energy storage capacitor materials by nanocomposite.

[Objective] With the explosive growth of network scale and complexity, traditional network operation and maintenance (O＆M) technologies face challenges such as low accuracy, severe noise interference, and difficulties in root cause localization when processing massive alarm data. Existing association rule algorithms such as Apriori and FP-Growth, fail to deeply analyze the correlations and hierarchical root causes in alarm data, resulting in low compression efficiency and high false positive rates. This paper aims to design an intelligent alarm data compression algorithm by introducing a graph convolutional network (GCN) to address the limitations of traditional methods in mining data correlations, suppressing noise, and analyzing multi-level root causes, thus enhancing the intelligence level of network O＆M and improving alarm processing efficiency. [Methods] To tackle the heterogeneity and redundancy of alarm data, a dynamic preprocessing mechanism based on sliding time windows was proposed. This mechanism constructed a high-precision alarm transaction database through time synchronization rules and redundancy removal operations. Next, the preprocessed alarm sequences were transformed into graph-structured data, where node feature matrices and adjacency matrices characterized alarm events and their correlations. A multi-layer GCN model was then designed: local convolution aggregated neighborhood node features, normalization techniques resolved structural imbalance in the graph data, and ReLU activation functions enhanced nonlinear feature extraction. Key parameter configurations included input feature dimensions, hidden layer structures, the Adam optimizer, and Dropout mechanisms, all balancing model complexity with generalization. Finally, performance differences between GCN and ResNet, Apriori, and FP-Growth were compared. [Results] Experimental results demonstrate that the proposed algorithm significantly outperforms traditional methods in both accuracy and runtime. Specifically, when the data volume reaches 6 000 entries, GCN achieves stable alarm accuracy exceeding 92％, surpassing Apriori (83％), FP-Growth (87％), and ResNet (84％—94％) with smaller fluctuations. In terms of efficiency, GCN′s average processing time is comparable to FP-Growth (with a difference of less than 5％ for data volumes exceeding 1 000 entries) and significantly lower than ResNet. Moreover, by capturing nonlinear correlations and hierarchical root causes in alarm data, GCN effectively suppresses the noise interference, validating its robustness in complex network environments. [Conclusions] The proposed GCN-based alarm compression algorithm achieves high-precision, low-redundancy alarm information extraction by deeply integrating spatiotemporal features and topological correlations of alarm data. Compared to traditional methods, it shows significant advantages in both accuracy and efficiency, providing reliable technical support for intelligent network O＆M. Future work will focus on lightweight model design and real-time optimization to further adapt to large-scale dynamic network scenarios.

[Objective] Transmission lines operating in regions with strong lightning activity are highly vulnerable to lightning strikes. Double-circuit lines on the same tower feature compact structures and significant electromagnetic coupling effects, resulting in consistently high lightning fault rates. Existing lightning protection measures rely heavily on statistical experience and cannot effectively distinguish different types of lightning faults such as shielding failures and back flashovers, making precise protection difficult. Consequently, line tripping accidents remain a recurring problem, posing a serious threat to the safe and stable operation of the power grid. To address this issue, this study proposed a deep learning-based lightning fault identification method with high-accuracy automatic identification of shielding failure and back flashover, providing effective technical support for differentiated lightning protection in transmission lines. [Methods] A lightning fault simulation model for 220 kV double-circuit transmission lines on the same tower was developed using the electromagnetic transient simulation software ATP-EMTP to obtain overvoltage response data under different lightning current amplitudes and grounding resistance conditions. To address the non-stationarity and mode mixing of lightning signals, ensemble empirical mode decomposition (EEMD) was introduced, where Gaussian white noise was added to suppress mode mixing. The first four intrinsic mode functions (IMFs) were extracted to preserve the major characteristic components. Subsequently, frequency slice wavelet transform (FSWT) was applied to compute multi-band energy ratios, which, together with lightning current amplitude and grounding resistance, formed a multidimensional feature set. In terms of classification modeling, a CNN-LSTM-Attention deep learning architecture was proposed:CNN extracted spatial features, LSTM modeled temporal dependencies, and the Attention mechanism focused on critical information, thus enabling effective fusion and identification of complex signal features. [Results] Experimental results demonstrate the excellent performance of the proposed method in distinguishing shielding failure from back flashover. The overall identification accuracy reaches 98.6％, with both precision and recall exceeding 98.5％, and an F₁-score of 0.99. Compared with benchmark models such as SVM and CNN, the proposed method exhibits a clear advantage in identification accuracy. Results from 10 independent comparative experiments show an average accuracy of 99.7％ and a variance of 0.000 93, fully verifying its stability and reliability. [Conclusions] The lightning fault identification method based on EEMD-FSWT feature extraction and the CNN-LSTM-Attention fusion model effectively characterizes the time-frequency features of lightning signals of double-circuit transmission lines on the same tower, achieving high-accuracy differentiation between lightning shielding failure and back flashover. This method not only improves the accuracy and timeliness of fault diagnosis but also provides important data support for formulating differentiated lightning protection strategies. The research results have significant engineering application value and strong potential for wide application in reducing lightning-induced line tripping and ensuring the safe and stable operation of power systems.

[Objective] To assess the security of unmanned aerial vehicle (UAV) communication environments in power applications, an effective security architecture and design scheme was proposed to address electromagnetic interference, data security, and other challenges UAV communications face during power inspections, ensuring efficient collaborative operations and secure data transmission of UAVs in complex electromagnetic environments. [Methods] Firstly, the structure of UAV collaborative wireless networks and security threats they face were analyzed, especially the influence of strong electromagnetic interference near power lines on UAV communications. Subsequently, an identity cryptography-based solution to UAV collaborative wireless networks was proposed. Additionally, by designing enhanced communication protocols and anti-interference mechanisms, stable transmission of critical data under strong electromagnetic interference was ensured. Meanwhile, the mutual authentication, signature, and identity verification mechanisms for communication data were introduced to enhance the overall security of UAV communications. [Results] Experimental results demonstrate that the proposed security assessment architecture and design scheme exhibits high data recovery rates and low resource consumption under varying numbers of UAVs, communication failure rates, electromagnetic interference intensities, and data packet sizes. In particular, the system maintains high data recovery rates and fault tolerance capabilities even under high electromagnetic interference, effectively resisting potential network intrusions and data tampering threats. [Conclusions] The proposed security assessment architecture and design scheme significantly enhances the security and reliability of UAV communications during power inspections, reducing the information leakage risk and enabling efficient collaborative operations in complex electromagnetic environments. The innovation of this study lies in combining identity cryptography and public key mechanisms to design a lightweight, efficient, and safe solution, providing effective security guarantees for UAV communication networks in power inspection tasks.

[Objective] In the actual construction of road engineering, it is often difficult for high-salt soft soil to meet the technical requirements of engineering construction directly due to its special physical and mechanical properties. Therefore, a systematic study on the solidification characteristics of high-salt soft soil should be conducted to provide theoretical support and technical references for the reasonable utilization of this type of soft soil in road engineering via an in-depth analysis of various characteristics presented during soft soil solidification. [Methods] Laboratory experiments were combined with theoretical analysis to conduct indoor solidification of high-salt soft soil, and the strength characteristics and micro-mechanism of the solidified soft soil were studied. Meanwhile, the cement single-mixing tests and the three-factor, three-level orthogonal tests of cement, lime and fly ash were set up, respectively. By focusing on the two types of tests, the effects of salt contents of 1.5％, 3.5％, 5.5％, and 7.5％ on the unconfined compression strength of high-salt solidified soft soil were explored. Then, the X-ray diffractometer (XRD) and scanning electron microscope (SEM) were adopted to further investigate the micro-mechanism of the solidified high-salt soft soil, and thus gain a deeper understanding of the mechanism. [Results] The cement single-mixing test shows that under the constant initial water content and salt content of soft soil, there is optimal mixing content for cement solidification of high-salt soft soil. Additionally, by setting different mixing contents of cement, lime, and fly ash for composite solidification treatment of soft soil with salt content of 7.5％, the maximum unconfined compression strength of the samples after 7, 14, and 28 days of curing can reach 326 kPa, 388 kPa, and 593 kPa, respectively, which can better meet the construction needs of actual projects. [Conclusions] When the combination of 7％ cement+3％ fly ash+3％ lime is employed as the inorganic gelling solidification agent, the relatively ideal solidification effect can be achieved for soft soil with different salt contents. It should be noted that under the premise of keeping the mix ratio of the solidification agent unchanged, the strength of the solidified soft soil shows a trend of continuous deterioration with the gradually increasing salt content in the soft soil. Additionally, the effect of chlorine salt on the strength of solidified soft soil becomes increasingly significant with the rising curing age.

[Objective] Given the widespread use of drone swarms in reconnaissance missions, optimizing the deployment of air defense countermeasure systems has become a critical issue for enhancing defensive capabilities. Drone swarms, with their high flexibility, robust survivability, and cost-effectiveness, pose a significant threat to traditional air defense frameworks. A single air defense system struggles to effectively address the coordinated multi-target nature of drone swarms, necessitating the collaborative deployment of multiple systems to maximize the flight cost of the swarm, thereby forcing path alterations or mission abandonment. This study aims to develop an efficient deployment method for air defense countermeasure systems to mitigate the security challenges posed by drone swarm reconnaissance. [Methods] This study introduced a deployment method for air defense countermeasure systems against drone swarms, based on water wave optimization (WWO) and the A^* algorithm, termed the water wave and A^* deployment (WAD) algorithm. The approach integrated two key sub-models:first, an optimal path planning model for drone swarms, which calculated the minimum flight cost under specified air defense countermeasure system positions; second, an air defense countermeasure system location optimization model that adjusted system positions to maximize the expected flight cost of the swarm. The WAD algorithm leveraged WWO′s balanced global and local search capabilities alongside the A^* algorithm′s efficiency in path planning, enhanced by an improved encoding-decoding scheme to boost search efficiency and avoid suboptimal solution spaces. [Results] The effectiveness of the WAD algorithm is confirmed through simulation experiments. The experimental scenario includes 4 flight starting points, 39 waypoints, and 3 air defense countermeasure systems. Results demonstrate that the WAD algorithm is able to obtain the maximum expected flight cost for the drone swarm and output optimized deployment positions for the air defense countermeasure systems and the swarm′s flight paths. The population′s best fitness converges rapidly with the increase of iteration counts, stabilizing within an average of 30 iterations, highlighting the algorithm′s high precision and computational efficiency, and significantly shortening the optimization time compared with traditional methods. [Conclusions] The WAD algorithm provides an efficient solution for optimizing the deployment of air defense countermeasure systems against drone swarms. By integrating the strengths of WWO and the A^* algorithm, it achieves an effective balance between global exploration and local exploitation, markedly improving convergence speed and optimization quality. The findings indicate that this method is applicable to defense requirements in complex reconnaissance scenarios. Future work may further investigates multi-objective optimization strategies in dynamic environments, explores coordination mechanisms among air defense countermeasure systems, and incorporates real-time threat assessment to adapt to the rapid evolution of drone swarm technologies.

[Objective] In the power system, transmission lines serving as a key component are often prone to failures caused by natural disasters such as lightning strikes due to their remote location and long distance. Lightning strokes on transmission lines in China account for about 50％ of the total accidents, which can cause tower insulation flashover, abnormal voltage, and even power supply interruption. Additionally, they can also damage electronic equipment and cause significant economic losses. An analysis of flashover voltage characteristics of 110 kV line insulators was conducted to improve the design of transmission lines and enhance their lightning resistance, thus strengthening the resistance of transmission lines to lightning strikes. [Methods] Based on the electric field theory and the energy conservation principle, the energy conservation equation of electrons in the electric field was constructed. Combined with the motion equations of three types of particles (positive attribute particles, negative attribute particles, and neutral particles) and Poisson′s equation, a mathematical model for lightning strokes on insulators was built. By adopting this model to calculate the lightning current overvoltage, the standard waveform of the current of the lightning impulse transmission line was obtained. At the same time, the relationship between the lightning resistance level of transmission lines and flashover voltage of insulator strings was analyzed. By analyzing the characteristics of lightning flashover voltage of insulators via overvoltage, the current value and lightning current overvoltage at the lightning strike point were obtained. Additionally, silicone rubber insulators for 110 kV lines were selected for experiments to simulate different parameters such as the call height, grounding resistance, lightning current waveform, and insulation distance, and analyze the influence of each parameter on the voltage waveform. [Results] The experimental results show that the voltage of the insulator gradually flattens after 12 μs when the insulator is subjected to different waveform lightning strikes. Specifically, the double exponential voltage peak is the largest, the oblique angle model has the best effect on voltage control, and the voltage waveform tends to be more stable under the insulation distance greater than 5 m. Compared with other methods, the voltage waveform analyzed by this method is closer to the actual situation, with an error of less than 1 kV and higher accuracy. [Conclusions] Under the tower height of 25 m, the insulator is the most sensitive to lightning impulse response, and the peak voltage increases with the rising grounding resistance. The oblique angle lightning current model has the best effect on voltage control, and the insulation distance greater than 5 m can improve the insulator′s ability to resist lightning impulse. In this study, mathematical models were combined with electric field theory, the influence of various parameters on insulators was comprehensively considered, and a more comprehensive and accurate voltage waveform and mathematical equation for insulator strings was established. The results provide scientific basis for the design of transmission lines, and hold engineering and reference significance for improving the lightning resistance of transmission lines and ensuring the safe and stable operation of power systems.

[Objective] There are still shortcomings in the transfer learning of gear fault diagnosis between different devices in existing research, especially under small sample conditions, and the diagnostic accuracy still needs to be improved. Therefore, a gear fault diagnosis method for small samples was proposed in this paper, which combined self-calibrated convolution integrated split attention network SCResNeSt50 and transfer learning strategy. [Methods] Firstly, the continuous wavelet transform was used to perform time-frequency analysis on the gear signal, generating a time-frequency graph as the model input. Secondly, based on the residual split attention network (ResNeSt) structure, a split attention mechanism and self-calibrated convolution were integrated to improve the linear processing mode of traditional convolutional neural networks for time-frequency graphs. The self-calibrated convolution was used to replace the conventional convolution in the ResNeSt module to achieve adaptive response calibration and multi-scale feature encoding, thereby expanding the receptive field and enhancing the ability to characterize fault features. Finally, a transfer learning strategy was adopted to fine tune the classifier parameters of the pre-trained model in the source domain, and the feature extraction layer was frozen, in order to achieve effective adaptation of the target task while preserving the general knowledge and feature representation of the source model and improving the accuracy of gear fault diagnosis under small sample conditions. [Results] Experiments were conducted on the gearbox dataset of Southeast University and the gear dataset of the University of Connecticut to verify the effectiveness of the method. The experiment included two scenarios: transfer learning under variable operating conditions and cross-dataset transfer learning. The proposed method was compared and analyzed with existing fault diagnosis methods. The results show that in the experiment of transfer learning under variable operating conditions, the diagnostic accuracy of the target domain reaches 98.7% and 98.9%. In the migration experiment from the dataset of Southeast University to that of the University of Connecticut, when the sample size of each gear state in the target domain training set is 25, 20, 16, 12, 8, and 6, the diagnostic accuracy reaches 98.1%, 98.1%, 97.8%, 97.5%, 96.5%, and 93.1%, respectively. [Conclusions] This method achieves better diagnostic accuracy than other methods in multiple experiments, which indicates that the improved self-calibrated convolution effectively enhances the characterization ability of gear fault features, and the transfer learning strategy significantly enhances the reliability of fault diagnosis under small sample conditions. This study provides a feasible solution to gear fault diagnosis under small sample conditions, promoting the development of intelligent fault diagnosis technology.

[Objective] Due to small scale and large specific surface area, nanomaterials have attracted increasing attention in the field of repair materials for earthen sites. Given the severe degradation of earthen sites in Liaoning that urgently require restoration, this study focuses on nano-titanium dioxide (TiO₂) and graphene oxide (GO), and investigates how TiO₂ and GO affect the unconfined compressive strength (UCS), triaxial shear properties, and microstructures of soil repair materials. It further clarifies the synergistic enhancement mechanism of the two nanomaterials. [Methods] Silicon-composite modified original soil (S-GLS) was used to prepare nano-TiO₂-GO-modified soil with different TiO₂ mass fractions (i.e., 1％, 3％ and 5％) and different GO mass fractions (i.e., 0.02％, 0.025％, and 0.03％), and the physical properties of nano-TiO₂-GO modified soil (SN-GLS) were studied. The mechanical behavior and full stress-strain curves of SN-GLS were investigated by UCS and triaxial shear tests. The microscopic morphology, pore characteristics, and pore-size distribution of SN-GLS were characterized by scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) tests. [Results] With the addition of TiO₂-GO, the compressive strength and shear strength of SN-GLS are significantly improved. Triaxial shear tests reveal a transition in the macroscopic failure mode of the specimen from oblique shear failure to a complex form that includes both swelling and multiple shear failures. The transition is characterized by lateral swelling in the central region of the specimen and the formation of cracks consisting of various shear bands, including vertical and transverse orientations. With 3％ nano-TiO₂ and 0.025％ GO at 28 days, the strain of SN-GLS peaks at the range from 2.5％ to 4％, and the residual strength retention rate is from 75％ to 90％. The incorporation of nano-TiO₂-GO increases the ductility of the original soil. SN-GLS has superior shear strength and deformation characteristics. Compared with S-GLS, cohesion increases by 61.8％ and 42.7％ at 7 and 28 days, respectively. The internal friction angle at 28 day increases by 1°. SEM and MIP results indicate that, compared with S-GLS, the optimization of microstructures and the enhancement of mechanical properties in SN-GLS arise from the synergistic action between GO and TiO₂. Specifically, the incorporation of GO produces a rough and wrinkled layered structure that increases the interlayer spacing of the original soil. Its oxygen-containing functional groups provide nucleation sites for hydration reactions and act as a “template”, thus promoting the formation of more hydration products (e.g., C-S-H) and markedly improving the mechanical properties. Nano-TiO₂ contributes through its nanoscale effect and filling effect, filling internal pores, strengthening the bonding between soil particles, and altering the arrangement of the soil skeleton to improve its orientation, which consequently increases the fractal dimension of SN-GLS by 0.045 9. These combined effects significantly optimize the pore-size distribution of SN-GLS, with the proportion of medium pores decreased by 18％, small pores increased by 16％, and micropores increased by 2％, indicating a transformation of medium pores into small and micropores, a denser microstructure, and an overall reduction of total porosity by 10.09％. [Conclusions] This study provides a new idea for the research and improvement of repair materials used in earthen sites. Nano-TiO₂ and GO have potential application value for the protection and reinforcement of earthen sites.

[Objective] As the requirements for operational precision and stability of rotating mechanical structures continue to increase, the dynamic performance of service bearings within the structures faces increasingly strict requirements. For enhancing the operational performance of rotating mechanical structures, it is crucial to reveal the change rules of dynamic contact characteristics of bearings under complex loading conditions. [Methods] Based on the classic Jones-Harris model and raceway control theory, a five-degree-of-freedom (5-DOF) dynamic contact characteristic analysis model was established by geometric and force analyses of components. The model systematically considered the effects of centrifugal force and gyroscopic moment and incorporated them into the analysis of bearing ball-raceway contact and separation states to comprehensively describe the dynamic contact characteristics of ball bearings under axial, radial, and moment loads and rotional speeds. In addition, a Newton-Raphson iterative algorithm combining inner and outer loops was established to solve local and global equations. The model was simplified according to the internal geometric and force relationships of bearings to solve variables so that its computational complexity could be effectively reduced. With the NSK 7013C bearing as an example, the change rules of dynamic contact characteristics of the bearing under specific load conditions were analyzed. [Results] Under a small axial load and a certain rotional speed, incomplete contact state occurs between the balls and raceways inside the bearing, with the incomplete contact region positively correlated with the rotional speed. Moreover, increasing the axial load appropriately reduces the variation in contact load amplitudes on the inner and outer races and eliminates the incomplete contact state. Only increasing the radial load leads to incomplete contact inside the bearing, but the incomplete contact region stops growing once the radial load reaches a certain level. Under the same load conditions, the contact angles and contact loads on the inner and outer races of the bearing vary with the change of azimuthal angle of the rolling elements. Radial and moment loads in different directions cause fluctuations in contact angles and loads, with their effects overlapping. [Conclusions] Axial loads can improve the load distribution within the bearing and suppress individual bearing ball separation. Axial, radial, and moment loads and rotational speed have varying degrees of impact on the dynamic contact characteristics of the bearing, with the moment load having the less influence. The innovation of this study lies in systematically considering the effects of external loads and the full working states of bearing ball-raceway contact and separation. It analyzes the change rules of dynamic contact characteristics under complex load conditions, providing new theoretical support for bearing selection, forward design, and operational performance improvement of rotating machinery.

[Objective] Firmware security in power Internet of Things (IoT) devices is crucial for ensuring the stable operation of critical infrastructure. However, existing vulnerability detection methods suffer from limited accuracy and adaptability due to complex firmware characteristics and reliance on a single analysis dimension. To address these issues, a multi-granularity vulnerability detection method for smart power IoT firmware suitable for the ubiquitous IoT background was proposed to improve the comprehensiveness and accuracy of vulnerability detection. [Methods] First, an i2vBi model was designed to map address space operands into eight classes to control the loading base address range, thus accurately generating instruction word vectors. The Softmax function was used to calculate contextual word probabilities, a maximum likelihood estimation model was trained, and instruction vectors were aggregated through a bidirectional long short-term memory (BiLSTM) network to obtain basic block embedding vectors containing forward and backward semantic information. Second, basic block embeddings were used to construct attribute control flow graphs to extract fine-grained structural features within functions. Furthermore, the principal neighborhood aggregation (PNA) algorithm was adopted, combining multiple aggregators and node-degree-based scalers to adaptively aggregate node neighbourhood information, generating more expressive graph embedding vectors and achieving function-level meso-granularity feature extraction. Subsequently, a convolutional neural network (CNN) and a self-attention mechanism were used to extract local pattern features of function execution order from graph embedding vectors, and these sequential features, together with attribute control flow graph features constructed from basic block embeddings, were input into a multilayer perceptron for fusion to form the final comprehensive feature vector. Finally, a semantic analysis dimension was introduced, in which known vulnerable functions were transformed into natural language text. A semantic embedding model based on bidirectional encoder representations from transformers (BERT) was used for masked modeling and mean pooling to generate semantic vectors. The cosine similarity between the semantic vectors and the comprehensive feature vectors of target functions was computed, and multi-granularity vulnerability detection based on semantic similarity was achieved by setting a threshold. [Results] To verify the effectiveness of the proposed method, experiments were conducted on a dataset containing real power IoT firmware images. The experimental results show that the AUC value of the proposed method remains stable between 0.85 and 0.95, which is significantly higher than that of comparative methods, demonstrating excellent overall classification performance. The Kappa coefficient lies in the high range of 0.85-0.95, indicating a high degree of consistency between detection results and actual conditions. The Hamming distance remains at a low level, indicating that false positive and false negative rates are effectively controlled, and prediction results are more accurate. [Conclusions] The proposed method effectively overcomes the limitation of a single feature dimension by integrating multiple levels of features, including instructions, basic blocks, function control flow, and semantics. This method not only significantly improves the accuracy and robustness of vulnerability detection but also exhibits better environmental adaptability due to its understanding of code semantics. The research results provide a reliable technical approach for automated and intelligent security analysis of smart power IoT firmware and have positive significance for enhancing the overall security and stability of power IoT systems.

[Objective] In industrial control systems (ICS), communication between devices rely heavily on industrial control protocols, and the security of these protocols is essential for stable ICS operation. Vulnerability detection and intrusion detection, as core components of the ICS defense framework, require accurate analysis of protocol structures and semantic functions. Protocol reverse engineering serves as a key technique for this purpose, and the precision of semantic inference directly determines the accuracy of protocol understanding. However, due to the absence of protocol documentation and strong format heterogeneity, existing semantic inference methods generally rely on expert knowledge, resulting in insufficient automation and limited cross-protocol generalization. Consequently, they fail to meet the high precision analysis needs of multi-source heterogeneous protocols in real industrial environments. [Methods] To solve the above problem, this study proposed a semantic inference method that integrated mBERT, multi-source domain adaptation, and a structured masking strategy. Cross-protocol semantic representations were achieved through the mBERT model. A structured masking strategy that combined attention weights and positional encoding was designed to enhance the model′s ability to capture intrinsic correlations between protocol structure and semantics, which improved the automation and efficiency of semantic inference. A progressive multi-source domain adaptation strategy with adversarial training further strengthened the model′s generalized semantic representation across multiple source protocols, enhanced its applicability to various industrial control protocols, and enabled effective inference of keyword semantics. [Results] Experiments were conducted in the target range for offensive and defensive drills in typical energy enterprises in the Key Laboratory of Information Security for the Petrochemical Industry in Liaoning Province. Data from three industrial control protocols, S7comm, Modbus/TCP, and EtherNet/IP, were collected, and a training dataset was built using a protocol-complexity scoring mechanism. The results show that the progressive multi-source domain adaptation strategy significantly improves model performance. When it is combined with the structured masking strategy, semantic inference accuracy is further enhanced. The proposed method achieves significantly higher precision, recall, and F₁-score compared with existing baseline methods. [Conclusions] This study proposes a semantic inference method that integrates mBERT, multi-source domain adaptation, and structured masking. High-dimensional spherical mapping and multi-task loss functions used in semantic inference improve the distinguishability of different semantic categories and enhance the model′s deeper recognition capability for protocol semantics. The proposed method significantly reduces reliance on manual prior knowledge, increases inference efficiency, and improves cross-protocol applicability. It provides a theoretically grounded new pathway for industrial control protocol reverse engineering and ICS security protection.

[Objective] Under the background of new power system construction, the randomness and fluctuation of the system are significantly exacerbated by the large-scale integration of high-proportion renewable energy and widespread popularity of flexible loads. Coupled with the continuously expanding power grid scale, control variables increase sharply, thus posing a severe challenge to the control strategies of traditional power grid voltage and tidal currents. The electrical distance between nodes is mainly relied on the existing power grid partitioning methods for reactive partitioning, which is difficult to adapt to the operation requirements for new power systems with drastic source-load changes. To this end, a power grid partitioning optimization method comprehensively considering multiple factors was proposed to lower the overall control difficulty of power grids under the penetration of high-proportion new energy and improve the autonomous operation capability of partitioning. [Methods] The core of this study is to build a set of partitioning index system and optimization model, and break through the traditional partitioning′s limitation of only focusing on the topological association. Additionally, the tightness of internal electrical connections and the degree of source-load matching were creatively considered, with the reactive partitioning indexes based on electrical distance and active partitioning indexes based on source-load matching constructed respectively. On this basis, the optimization model of power grid partitioning was built to minimize the reactive partitioning index, thus aiming to maximize the electrical tightness inside the partitioning and simplify reactive power and voltage control. Meanwhile, the key constraint that the active partitioning indexes satisfied the requirements was employed to limit the frequent interaction of active power between partitions, reduce the violent fluctuation of net loads within partitions, and ensure the source-load balance within partitions. At the same time, a bionic joint optimization algorithm was proposed, in which the global search ability of genetic algorithms and fast local refinement ability of firefly algorithms were fully used to efficiently solve the built nonlinear complex optimization model, improve the optimization speed, and avoid falling into the local optimal solutions. [Results] The standard IEEE 39-node system was adopted to verify the case example. The simulation results show that by adopting this algorithm, the source-load matching degree within partitions can be significantly improved, the fluctuation of net loads between and within partitions can be reduced, and unnecessary tidal current interaction can be decreased. Additionally, the difficulty of reactive control in the system can be lowered, the electrical tightness of nodes within the partitions can be enhanced, and the voltage and reactive regulation process within the partitions can be simplified by employing the algorithm. The proposed firefly-genetic bionic joint optimization algorithm exhibits excellent solution performance and can obtain the optimized partitioning scheme rapidly and effectively. [Conclusions] There are two main innovative points in this study. Firstly, the optimization objective of reactive control based on electrical distance and the constraint of active balance based on source-load matching are integrated in the power grid partitioning model, which overcomes the defect of insufficient adaptability of traditional methods to source-load changes. Secondly, an efficient and robust firefly-genetic bionic joint optimization algorithm was proposed to solve the partitioning model, thus effectively improving the optimization speed and accuracy. This algorithm provides a new technical way to solve the problem of partitioning operation control under the complex network structure of new power systems, and holds theoretical and practical significance for improving the safe and stable operation of power grids and promoting efficient consumption of new energy.

[Objective] Due to the low fault localization accuracy and efficiency of traditional fault self-healing methods, a fault self-healing method for distribution main stations based on the decision tree and multi-agent system (MAS) was proposed to improve the fault handling capability of distribution systems. [Methods] A hierarchical multi-agent technology was adopted to construct a fault self-healing system for distribution main stations, which included the feeder agent and node area agent. The distribution network data were collected and the gradient boosting decision tree (GBDT) algorithm was employed in the node area agent to complete fault localization, and the fault data were transmitted to the feeder agent. In the feeder agent, data were summarized, the influence of important load recovery sequence, transfer margin, and line loss was comprehensively considered to build a fault self-healing optimization model, and the model was solved via the multi-agent evolutionary algorithm to obtain the optimal fault self-healing recovery scheme for distribution main stations. [Results] Based on the IEEE-29 system, experimental analysis was conducted on the proposed method, and the results show that the accuracy of the GBDT fault localization algorithm is nearly 97％ after 150 iteration. The important load recovery amount, network loss, transfer capacity margin, and fault self-healing time of this method are 100％, 90.58 kW, 11.26 kW, and 2.79 s respectively. The self-healing recovery rate exceeds 91％, and the highest self-healing control operation complexity is no more than 5, all of which are superior to other comparative methods. [Conclusions] The GBDT fault localization algorithm can achieve more ideal accuracy and efficiency, and the proposed method can recover all important loads in the shortest time, ensuring minimal network loss. Additionally, the proposed method has relatively stable self-healing ability, which can better coordinate new energy generation, quickly adapt to the rapid development of new power systems, and achieve high-quality power supply. Aiming at traditional fault self-healing methods suffering from problems such as large workload and poor accuracy caused by centralized processing modes, the proposed method constructed a fault self-healing system for distribution main stations based on MAS, achieving fast and accurate fault detection and recovery via the distributed collaboration of the operating status of each node. Compared to the decision tree algorithm, the GBDT algorithm gradually improves analysis accuracy by fitting the residuals of the previous round in each round of iteration to construct a new learner. It is applicable to fault localization at the level of distribution main stations and provides accurate data support for fault self-healing. Compared with traditional optimization methods, the GBDT algorithm adopts the multi-agent evolutionary algorithm to solve the fault self-healing optimization model. By assigning the target to each agent for execution, the optimization efficiency is improved, and the excellent solutions of all agents are summarized to obtain the final solution, ensuring the global optimal effect.

[Objective] The stress state of steel components is a key parameter for assessing the safety and health of steel structures during long-term service. Existing stress detection methods based on the magnetically induced electromotive force effect do not consider the influence of the magnetic field direction, and variations in the magnetic field angles reduce the accuracy of detection results. Therefore, this study quantitatively investigates the influence of the magnetic field angle on the relationship between stress and magnetically induced electromotive force in steel structural columns through theoretical analysis and experimental research. [Methods] Axial compression tests on short Q235B H-shaped steel column specimens were designed. A manganese-zinc ferrite U-shaped electromagnetic sensor was fixed at the center of the steel flange, and its orientation was varied. During the graded loading process, magnetically induced electromotive force signals at different stress levels and magnetic field angles were collected simultaneously. All tests were repeated twice to ensure data reproducibility. Based on the experimental data, magnetically induced electromotive force-stress relationship curves at different magnetic field angles were established. The specific influence of magnetic field angle on this relationship was examined, and the quantitative relationship among magnetically induced electromotive force, stress, and magnetic field angle was clarified. In addition, the mean gradient of the magnetically induced electromotive force was used to quantify the effect of magnetic field angle on the electromotive force-stress relationship. [Results] Within the elastic range, the magnetically induced electromotive force shows a significant linear negative correlation with compressive stress. The slope of the relationship curve changes markedly with increasing magnetic field angle. The signal sensitivity is the highest when the magnetic field direction is parallel to the principal compressive stress direction. As the magnetic field angle increases, the sensitivity decreases nonlinearly and reaches its minimum value when the magnetic field direction is perpendicular to the stress direction. The characteristic curves corresponding to different angles do not intersect. Under different magnetic field angles, the gradient of the magnetically induced electromotive force tends toward a constant value with increasing stress, and its mean value decreases as the deviation angle between the magnetic field direction and the loading direction increases. [Conclusions] The magnetic field angle affects the accuracy of stress detection. The mathematical expression for the relationship between magnetically induced electromotive force and stress established based on the experimental results shows a good fit. Furthermore, the mean gradient of the magnetically induced electromotive force follows a cosine-function relationship with the magnetic field angle.

[Objective] In the surface of metal materials, the hot dipping method is adopted to produce pure zinc (Zn) layers, which is an efficient method of preparing corrosion-resistant protective layers. However, during the production of hot dipping Zn, since liquid Zn has a strong diffusion ability and reactivity, it will react with the iron elements in the immersed rolls and other components, thus generating iron and Zn alloy brittle intermetallic compounds. This will continue to consume the iron elements in the components, and the corroded iron slag attached to the immersed rolls will also scratch the cladding layer. [Methods] The plasma cladding method was adopted to prepare two cobalt-based alloy cladding layers with the thickness of about 5 mm on the 316L stainless steel substrate, and the two alloy cladding layers were placed in molten liquid Zn at 450 ℃ for 72 h, 120 h, and 168 h to analyze the morphologies, products and element loss after corrosion and investigate the corrosion behavior and mechanism of cobalt-based alloys. [Results] γ-Co and carbides Cr₇C₃ and Cr₂₃C₆ exist in the original organization of the two alloy cladding layers, but the number of carbides in 2^＃ alloy cladding layer is higher with a more diffuse distribution. The corrosion mode of the 1^＃ alloy cladding layer is homogeneous dissolution corrosion, and a diffusion layer with more pores and transverse cracks appears during corrosion. Due to the reaction of Zn with C, element C diffuses from inside to outside, leading to increased C content on the surface of the diffusion layer and cladding layer. After 168 h of corrosion, the action of thermal stress leads to the extension of transverse cracks, and the diffusion layer is finally peeled off into the upper and lower layers, with the intrusion of Zn between the layers. During the corrosion of the 2^＃ alloy cladding layer, the diffusion layer also appears, but there are few pores and cracks, with no occurrence of diffusion layer splitting. During the corrosion process, Zn first diffuses to the grain boundary between carbides and Co solid solution, and then dissolves and corrodes the Co solid solution and gradually bypasses the carbide phase, resulting in gradual thinning of the cladding layer thickness. The corrosion resistance of both alloy cladding layers is higher than that of the 316L stainless steel substrate, and the corrosion rates of the 1^＃ alloy cladding layer and 2^＃ alloy cladding layer are about 1/3 and 1/4 of that of the 316L stainless steel substrate respectively. Although the carbide types in the microstructure of the two alloy cladding layers are almost the same and the carbides do not react with Zn, the carbide content of the 2^＃ alloy cladding layer is relatively higher. Carbides can stop the diffusion of Zn, leading to a decrease in the base solid solution that can react with Zn. Therefore, the 2^＃ alloy cladding layer with higher carbide content has better corrosion resistance. [Conclusions] The carbides generated in situ during plasma cladding have a better protective effect on the cobalt-based alloys, and their corrosion rate decreases dramatically with the increasing content of carbide-forming elements. The corrosion results for different time show that the increase in carbide content can effectively improve the service time of the immersed rolls in liquid Zn.

[Objective] Relay protection equipment plays a crucial role in the operation of power systems. However, with the long-term operation of the equipment, aging and damage are inevitable, which will cause abnormal displacement data in relay protection equipment. If these abnormal data cannot be properly processed, the safe and stable operation of power systems will be affected. Therefore, how to effectively handle the abnormal displacement data of relay protection equipment has become a problem to be urgently solved. [Methods] An autonomous controllable fault-tolerant storage algorithm was proposed. Firstly, the state of the relay protection equipment was evaluated by this algorithm, with the potential disturbance factors analyzed. On this basis, the model predictive control (MPC) technology was adopted to predict the possible abnormal data. Based on the dynamic model of the system, decisions were made by MPC in advance via predicting the future state of the system. Then, the predicted abnormal data were corrected to restore the data to their original state. Meanwhile, the data elasticity theory and granularity rate were employed to calculate the compensation storage intensity. The data elasticity theory helped to measure the tolerance ability of the system in the face of faults, and the granularity rate was related to the data refinement degree. The accuracy and integrity of the data were ensured by the combination of the two. During the study, an experimental environment based on the above-mentioned algorithm was constructed, and the algorithm was tested by simulating the abnormal displacement data generated by the relay protection equipment in different working conditions. [Results] The algorithm′s efficacy was verified by the experiment. The fault-tolerant rate of the algorithm is above 0.89, which means that under a large amount of abnormal data, most of the data errors can be successfully handled by the algorithm. The proportion of memory required for storage is less than 20 MB, indicating that the algorithm occupies fewer memory resources for data storage. Under the data number of 10 000, the data transmission number is only 401, which reflects the high efficiency of the algorithm in data transmission. [Conclusions] It can be concluded from this study that the data fault-tolerance ability of relay protection equipment can be effectively enhanced by the proposed autonomous controllable fault-tolerant storage algorithm. The accuracy and integrity of the data are ensured by accurate prediction, correction of abnormal data and a reasonable storage strategy, thereby enhancing the ability of relay protection equipment to cope with equipment aging and damage. The reliability of relay protection equipment can be improved by the application of this algorithm in power systems to further ensure the safe and stable operation of power systems. The innovation of this study lies in the combination of MPC, data elasticity theory and the granularity rate to develop a brand-new fault-tolerant storage algorithm. This method of comprehensively adopting multiple technologies has unique advantages in handling the abnormal displacement data of relay protection equipment. By adopting the proposed algorithm, the data processing ability of relay protection equipment can be improved, and the risk of power system failures caused by data abnormalities can be reduced, which is of great significance for ensuring the safe and stable operation of power systems.

[Objective] With the gradual achievement of independent research and development of gas turbine engines, the research and development of turbine blades, known as the “jewel in the crown”, have attracted much attention. A part of the gas turbine blades are hollow in structure, and the ceramic cores are the main components that form the hollow blade cavity structure. Due to the large size of the hollow blade, the high-temperature casting time is much longer, and this leads to an extension of the high-temperature holding time of the ceramic core. To ensure that the ceramic core meets the casting requirements of large-sized hollow blades, it is particularly important to consider the long-term high-temperature creep behavior of the ceramic core. Therefore, the effect of alumina content on the long-term high-temperature creep behavior of silica-based ceramic cores was investigated in this paper. [Methods] First, fused silica was selected as the matrix, and the silica-based ceramic cores with different alumina contents were prepared. After sintering at 1 200 ℃, a creep test was conducted at 1 500 ℃ for 1 h using the suspension method, during which the creep deformation characteristics were observed and the deformation amount was measured. Scanning electron microscopy (SEM) was used to observe the fracture microstructures of the ceramic cores after creep at high temperature. X-ray diffraction (XRD) was used to analyze the phase compositions of the ceramic cores in different states. [Results] It is indicated that for the samples with 20％ alumina, the least porosity and the highest relative cristobalite content were detected after sintering and subsequent 1 500 ℃ for 1 h heat treatment. When the alumina content is 10％, the lowest creep deformation amount is obtained. The reaction between alumina and silica at high-temperature promotes viscous flow sintering, which increases the creep deformation of the ceramic cores. [Conclusions] Adding alumina can promote the phase transformation from fused silica to cristobalite during the sintering process. After high-temperature holding, as the content of alumina increases, alumina participates in the reaction, forming the mullite phase, causing viscous flow sintering and increasing the creep deformation amounts of the ceramic cores. To obtain silica-based ceramic cores with high strength and excellent long-term creep properties, it is necessary to reasonably control the content of alumina.

[Objective] Laser heat-assisted grinding can significantly improve the processibility of titanium alloys, with the laser preheating temperature field directly affecting the surface quality and grinding force of the workpiece. To reveal the laser-assisted belt grinding mechanism of titanium alloys, the influence laws of laser processing parameters on temperature field distribution and grinding force were investigated. [Methods] Based on heat conduction theory, a laser preheating simulation model of titanium alloys was established using finite element simulation software. The effects of laser power, scanning speed and spot radius on the workpiece temperature field distribution were analyzed. According to the micro-abrasive grinding theory, a laser-assisted single-abrasive grinding simulation model of titanium alloys was constructed to study the variation of grinding force under different preheating conditions. Additionally, laser-assisted belt grinding experiments of titanium alloys were designed to verify the simulation model. [Results] The laser absorption rate of titanium alloy was determined by comparing the experimentally measured temperature with the simulation results, thus further improving the laser preheating simulation model of titanium alloys. The simulation results of the laser preheating temperature field show that the temperature of the laser preheating zone goes up with the rise of laser power and decreases with the increase of the moving speed of the laser relative to the workpiece. When the workpiece moving speed is relatively low, the influence of speed variation on the preheating temperature is minor. The spot radius gets smaller with the higher temperature of the preheating zone, as a smaller spot radius leads to more concentrated laser energy and a higher temperature rise per unit time under the same laser power. The simulation results of laser-assisted single-abrasive grinding show that the grinding force decreases with the increase of laser power, because higher laser power leads to the elevated material temperature and reduced hardness of the grinding layer, thus lowering the processing resistance. The comparison between simulation and experimental results shows an average relative error of about 4.95％, indicating their good agreement and high reliability of the simulation model. [Conclusions] Laser irradiation can induce a sharp temperature rise on the material surface, forming an oxidized deteriorated layer. Due to the low thermal conductivity of titanium alloys, significant thermal stress can be generated by a large temperature gradient, which promotes the formation of microcracks in the preheating layer. If the deteriorated layer and thermal cracks induced by laser preheating are not removed by grinding, the workpiece surface quality will deteriorate. The established finite element simulation model is in high agreement with the experimental results, which verifies its reliability and accuracy, providing a reference for other laser-assisted material removal processes.

[Objective] Mainly relying on manual inspection, traditional power inspection methods have such problems as low efficiency, high cost, and great danger, and it is difficult for them to meet the requirements of modern power systems for efficient, safe, and intelligent inspection. In recent years, the rapid development of unmanned aerial vehicle (UAV) technology provides a new solution for power inspection. UAVs have the advantages of strong flexibility, wide coverage, and relatively low cost, which can effectively improve the inspection efficiency and reduce the manual inspection risk. However, UAV power inspection systems still face many challenges in practical applications, especially in terms of precise positioning, navigation, and data transmission under complex environments. As a global navigation satellite system independently developed by China, the BeiDou Navigation Satellite System (BDS) has the characteristics of high precision, high reliability and global coverage, providing powerful technical support for UAV power inspection systems. [Methods] By introducing BeiDou satellite technology, a UAV power inspection system that could maintain high precision and stability in complex scenarios was designed to improve the monitoring and maintenance efficiency of power equipment. The core of this paper is to reconstruct the hardware framework of UAV power inspection systems for the deep integration with BeiDou satellite technology. Based on the hardware framework reconstruction, a software algorithm based on the PPP-RTK function model of BeiDou satellite positioning was designed. This algorithm could obtain high-precision position information of UAVs in real time, thus effectively overcoming the influence of complex environments on inspection precision. By implementing this technical route, the stable and precise inspection of UAVs in complex scenarios was realized. In the research process, targeted reconstruction was conducted on the hardware framework to ensure that UAV could stably receive and efficiently process BeiDou satellite signals, and the system performance was fully verified by employing a large amount of experimental data. [Results] The experimental results show that in complex scenarios, the proposed UAV power inspection system can significantly improve the precision and stability of inspection results, and effectively reduce the influence of environmental factors on inspection quality. The effectiveness and superiority of the UAV power inspection system integrated with BeiDou satellite technology in complex scenarios were verified. [Conclusions] By conducting the reconstruction design of the hardware framework and software algorithm optimization, the inspection ability of UAVs in complex environments are notably improved, thus providing more reliable technical support for the monitoring and maintenance of power equipment. The innovation of this paper lies in the introduction of BeiDou satellite technology to the UAV power inspection system, and the realization of high-precision positioning based on the PPP-RTK function model, thereby effectively solving the inspection problem of traditional systems in complex scenarios. This paper not only improves the precision and stability of UAV power inspection but also provides a new technical path for the intelligent and precise monitoring and maintenance of power equipment, which has theoretical and practical significance.

[Objective] Traditional grey models are widely applied to short-term load prediction due to their sound adaptability to small-sample and information-poor data. However, when handling complex electricity consumption data featuring both exponential growth and linear trends, they suffer from inherent limitations, such as insufficient prediction accuracy, sensitivity to data noise, and weak generalization capability, thus making it difficult to meet the demands of modern refined power management. Given the shortcomings of traditional grey models, a comprehensively improved prediction framework was proposed to significantly enhance the accuracy and practicality of electricity consumption behavior prediction, thereby providing more reliable data support for intelligent management of power systems. [Methods] In the data preprocessing stage, the standard deviation method was adopted to identify and remove outliers, while the linear interpolation method was applied to fill missing values in electricity consumption data with dense collection cycles. During the stage of analyzing user consumption behavior, the K-means clustering algorithm was employed to process load curves, and the elbow method was utilized to determine the optimal number of clusters, identifying user groups with similar consumption patterns. In the stage of prediction model building, an improved grey model was proposed to integrate the traditional grey model with a linear regression model for building a fused grey-linear regression model. In the fused model, sequences were generated via accumulation, and fitting was conducted by employing the combined equation, with the parameters estimated via sequence transformation and the least squares method. Meanwhile, the fused model was utilized to predict the residual sequence, and the Fourier transform was introduced for spectral analysis and noise reduction. A Fourier basis matrix was constructed, and related coefficients were solved by adopting the least squares method to correct the original predicted values. [Results] Validation based on the actual data from 205 users in a specific region demonstrates that the improved model successfully identifies four typical electricity consumption patterns by clustering analysis. The proposed improved grey model was compared with the three baseline models of the traditional grey model, the grey model+linear model, and the grey model+residual correction model. The results show that the improved model exhibits significantly lower mean absolute error (MAE) and mean absolute percentage error (MAPE) than the other three models across all user categories and prediction time points. Its advantage is particularly pronounced during the initial prediction periods, indicating that the model is more suitable for short-term load prediction. [Conclusions] Clustering, linear compensation, and Fourier-based residual correction are integrated in the improved grey model. The classification foundation is provided for refined user management by K-means clustering. The traditional model's lack of linear fitting capability is effectively compensated for by linear regression, while noise and systematic errors are significantly reduced by Fourier-based residual correction. A substantial improvement in the model's accuracy and generalization capability is led to by the combination of the three elements. The model demonstrates excellent performance in short-term load prediction, holding practical significance in real-time electric power dispatch, demand response, economical energy usage, and cost reduction. The improved model is mainly applicable to short-term electric power load prediction, and future research will explore the integration with machine learning or the introduction of more factors to enhance its ability for medium-to-long-term electric power load prediction.

[Objective] The size and needs of the visually impaired groups cannot be ignored. The application of tactile sensing in the field of assisted reading is particularly noteworthy. This technology can not only be integrated into robots or prosthetic systems, but also provide an effective braille reading tool for blind or visually impaired groups. Therefore, research on vision aids and braille recognition technology with information interaction competency is significantly valuable for offering technical support for visually impaired groups. [Methods] Based on biomimetic principles, the function of biomimetic hair was simulated. With magnetic iron-gallium wire as biomimetic hair and Hall elements as receptors at the hair roots, a biomimetic electromagnetic tactile sensor was designed according to the size of braille dots. Based on magnetization intensity and magnetic induction intensity theories as well as mechanical equations, the relationship curve between the sensor's applied force and output voltage was deduced. A dynamic characteristic testing system was constructed, which consisting of a signal generator, a power amplifier, a vibration exciter, data acquisition card, a computer and a direct current stabilized voltage supply. The dynamic characteristics of the tactile sensor were tested. [Results] Test results show that the tactile sensor can convert applied force into an electrical signal within the range of 0-1.5 N. Within the applied force range of 0-1.5 N, the output voltage gradually increases with higher force. When the contact force is less than 0.5 N, the two approach a linear relationship. The sensor exhibits high stability in output voltage under an applied force of 0.2-1.4 N at a frequency of 1 Hz. Under an applied force of 1.0 N at 1 Hz, its sensitivity is 34.5 mV/N. When the applied force is 0.5 N at 1 Hz, the response time and recovery time are 20 ms and 18 ms, respectively. The designed biomimetic electromagnetic tactile sensor was applied to establish a braille recognition system consisting of a two-finger robotic hand, a motor-driven slide, a data acquisition card, and a computer. The correspondence between braille letters and the output voltage waveform was determined by scanning the braille dots. [Conclusions] The output characteristics of the developed biomimetic electromagnetic tactile sensor were tested. The experimental results show good agreement with the calculated values, indicating that the calculated model can describe the relationship between the applied force and the output voltage. The designed tactile sensor features high stability, high sensitivity, and fast response speed, making it suitable for detecting both static and dynamic applied forces. The braille recognition system was used to determine the voltage waveform corresponded to the braille letters. It is pointed out that voltage waveform peak count, peak intensity, and peak initiation time can serve as the criteria for recognizing the braille letters, demonstrating that the braille recognition system can recognize braille letters. The research results can provide new braille recognition tools and technical pathways for visually impaired groups, allowing for the deep integration of tactile sensors and recognition technology to build assistive technology support systems for visually impaired groups.