[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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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.