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    Electrical Engineering
  • Electrical Engineering
    Coordinated control method based on cloud-edge coordination for AC/DC hybrid power grid line overload
    YU Qiuling, LIANG Jinzhao, CHEN Kangping
    2024, 46(3): 241-247. https://doi.org/10.7688/j.issn.1000-1646.2024.03.01
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Aiming at the problem that many factors affect the formation of AC/DC hybrid power grid line overload, which makes it difficult to capture the line operation characteristics and achieve accurate control, a coordinated overload control method based on cloud-edge collaboration was proposed. The mathematical model of overload state was established, and the decoupling algorithm was used to solve the incremental values of power parameters under different degrees of overload and conduct feature extraction further. By employing it as the cloud computing reference value of cloud-edge collaborative algorithm, the coordinated control parameters that simultaneously meet the requirements of optimal power, minimum energy consumption and load shedding were obtained to achieve coordinated control. The experimental data prove that the cloud-edge collaborative method can accurately detect all nodes out of the blind area under the constraint of blind area, without missing detection. The changes in node power of photovoltaic and wind power output are significantly higher than those of normal nodes, showing the better overload control effect over all three types of nodes.
  • Electrical Engineering
    Lightweight detection network for grid worker recognition
    HU Gebiao, LIN Zhichi, GUO Zheng, ZHAO Wenshuo
    2024, 46(3): 248-254. https://doi.org/10.7688/j.issn.1000-1646.2024.03.02
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    Aiming at the problems of low monitoring accuracy and coverage caused by the diversity of power grid construction environment and the existence of monitoring blind areas in traditional methods, a lightweight detection network dedicated to the identification of power grid workers was proposed to monitor the work behavior of power grid workers through the high-altitude and ground three-dimensional patrol mode. A lightweight target detection network was used to detect the power grid workers in the surveillance video, and judge whether they wore helmets or not, and then the personal recognition network was used to identify the worker not wearing helmets. The simulation results show that the as-proposed method can realize the three-dimensional inspection of the work behavior of power grid workers. Compared with the traditional method, the as-proposed method has lesser computation and can achieve a recognition accuracy of 63.4％.
  • Electrical Engineering
    Abnormal data detection method based on IWOA-ELM-AE for power asset information management system
    LI Kai, JIN Shudong, LIU Hongzhi, WANG Yanmei, YANG Xiaoying
    2024, 46(3): 255-262. https://doi.org/10.7688/j.issn.1000-1646.2024.03.03
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    Aiming at the problem that the current power asset information management system is difficult to detect abnormal data accurately and independently, a method based on IWOA-ELM-AE for detecting abnormal data in the power asset information management system was proposed. The analysis for possible anomaly types under the framework of the management system was performed, the improved whale optimization algorithm (IWOA) was used to optimize the ELM-AE, and the corresponding abnormal data optimization detection model for power information system was established. The as-proposed model was applied to the detection of abnormal data of power asset information, and the performance evaluation index system was established to measure its effect. The results show that the test performance evaluation results of as-proposed method has remarkable advantages over the traditional model, and can detect the abnormal data in the power asset information more accurately.
  • Electrical Engineering
    Key data processing method based on hybrid intelligent optimization algorithm for full link of power transmission and transformation project
    HE Lin, HUANG Bo, SHEN Yabo, LI Shuang
    2024, 46(3): 263-269. https://doi.org/10.7688/j.issn.1000-1646.2024.03.04
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    In order to improve the quality and efficiency of data management for the full link of power transmission and transformation project, a key data processing method based on hybrid intelligent optimization algorithm for the full link of power transmission and transformation project was proposed. The cost data management was taken as the core, the engineering cost control assessment model using hierarchical analysis was established, and cost assessment indexes and index weights were obtained. Meanwhile, an improved random neighborhood embedding algorithm was designed to realize data dimensionality reduction, and an adaptively improved whale optimization algorithm and a particle swarm algorithm were introduced. Under the framework of crossover strategy, two algorithms were combined to formulate a hybrid whale particle swarm optimization algorithm. The experimental results show that the as-proposed method is better for the key data processing of the full link of transmission and transformation project, and its accuracy and efficiency have significant advantages over other methods and can improve the data management level.
  • Electrical Engineering
    Optimal operation strategy for cloud energy storage market based on multiple mixed games
    LIU Yutong, CHENG Mengzeng, ZHANG Mingli, ZHAO Lin, MA Guangchao, MA Shaohua
    2024, 46(3): 270-276. https://doi.org/10.7688/j.issn.1000-1646.2024.03.05
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    Aiming at the problem of unequal distribution of benefits of multiple distributed energy storage entities participating in the cloud energy storage market, a multi-objective cloud energy storage optimization operation strategy based on multiple mixed games was proposed. The traditional mode of leasing energy storage resources was broken, and the rights of both supervision and use were entrusted to the cloud energy storage aggregator for unified and coordinated management. A multi-objective optimization mathematical model considering low-carbon economy was established using the method of multiple mixed games. While ensuring the satisfaction of energy supply demands, the optimal allocation scheme under the highest cooperation overload was obtained. The simulation results show that compared to the commonly used research methods, the economic benefits of as-proposed method increases by 18.625％ and carbon emission amount reduces by 5.57％, providing theoretical support for the development of the cloud energy storage market.
    • Materials Science & Engineering
  • Materials Science & Engineering
    Influence of pre-twinning on adiabatic shear sensitivity of AZ31 magnesium alloy
    MAO Pingli, YANG Yusong
    2024, 46(3): 277-283. https://doi.org/10.7688/j.issn.1000-1646.2024.03.06
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    In order to study the effect of different twin volume fractions on the adiabatic shear sensitivity of materials, AZ31 magnesium alloys with different twin volume fractions were obtained by pre-compression. The hat-shaped samples were subjected to high speed impact at the temperature of 200 ℃ and the strain rate of 944 s-1 by using split Hopkinson pressure bar (SHPB). The microstructure evolution before and after high strain rate deformation was characterized by electron backscatter diffractometer (EBSD) and optical microscope (OM). The absorbed energy of samples during adiabatic shear process was calculated, and the micro-hardness within and without the adiabatic shear band(ASB)was tested. The results show that the adiabatic shear band can be found in samples with different twin volume fractions. With the increase of twin volume fraction, the width and absorb energy of the adiabatic shear band decrease, while the adiabatic shear sensitivity increases.
  • Materials Science & Engineering
    Effect of AlCuFe nanoparticles on magnetic properties and corrosion resistance of NdFeB
    LI Zhijie, ZHENG Yangyang, LI Renjun, BAI Bing, ZHANG Hongwei, ZHAO Danna
    2024, 46(3): 284-290. https://doi.org/10.7688/j.issn.1000-1646.2024.03.07
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    In order to improve the magnetic properties and corrosion resistance of sintered NdFeB, grain boundary doped Al65Cu20Fe15 nanoparticles were used to prepare sintered NdFeB magnets with high performances, and an electrochemical workstation was used to investigate the corrosion behaviour of doped magnets in 3.5％NaCl solution. The results show that the doping of Al65Cu20Fe15 nanoparticles can form a low melting point phase in the magnet, improve the wettability between the grain boundary and the main phase, inhibit the magnetic coupling among the hard magnetic phase, and enhance the magnetic properties of the magnets. When the mass fraction of Al65Cu20Fe15 nanoparticles is 0.4％, the magnet has the highest magnetic force with the coercivity of 978.1 kA/m, the maximum magnetic energy product of 270.3 kJ/m3 and the remanence of 1.208 T. Compared with the undoped magnet, the corrosion potential of magnet doped with 0.4％ Al65Cu20Fe15 nanoparticles increases from -0.881 6 V to -0.704 0 V, while the corrosion current density decreases from 78.292 9 μA/cm2 to 33.222 9 μA/cm2, and the arc radius of high frequency capacitive reactance is much larger than that of the undoped magnet. Therefore, the corrosion resistance of the magnet can be effectively improved.
  • Materials Science & Engineering
    Prediction of processing map based on machine learning for low-cost Ti-5Al-1.5Mo-1.8Fe titanium alloy
    MU Yiqiang, ZHANG Mingchuan, QIAO Ze, WANG Feng, QIN Meiling, XU Qinsi
    2024, 46(3): 291-297. https://doi.org/10.7688/j.issn.1000-1646.2024.03.08
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    In order to study the thermal deformation behavior of low-cost titanium alloy, a Ti-5Al-1.5Mo-1.8Fe alloy was compressed for hot compression experiment by using Instron 5869 thermal compressor. Six machine learning models taking deformation temperature, strain rate and the degree of strain as input variables and adopting flow stresses as output variables were established. The flow stress values of alloy under different conditions were predicted and the prediction performance of these models were evaluated. The predicted processing map was drawn according to the prediction data of LSTM neural network model with the best prediction performance, and the predictive ability of the model was evaluated and verified by its comparison with experimental processing map. The results show that the processable regions of Ti-5Al-1.5Mo-1.8Fe alloy with the strain of 0.499 can be accurately reflected by the predicted processing map, in good accordance with the experimental map. The as-proposed method has better prediction for the thermal deformation behavior of Ti-5Al-1.5Mo-1.8Fe alloy.
    • Mechanical Engineering
  • Mechanical Engineering
    Stability analysis of magnetic energy harvesting active suspension
    CHEN Li, XU Li, ZHOU Ran, SUN Feng
    2024, 46(3): 298-304. https://doi.org/10.7688/j.issn.1000-1646.2024.03.09
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    In order to improve the ride comfort and safety of vehicles, a new magnetic energy harvesting active suspension was proposed and its stability was studied. BP neural network PID control algorithm (BP-PID) was used to build a magnetic energy harvesting active suspension control system, and theoretical simulation was conducted by using MATLAB/Simulink. B-level and C-level random pavements were established to simulate and analyze the stability of magnetic energy harvesting active suspension at different speeds and pavement levels. The result shows that BP-PID control significantly improves suspension stability, in comparison with the passive suspension and the magnetic energy harvesting active suspension under PID control. The vertical acceleration of the vehicle body gets improved by 45.90％ at 60 km/h on B-level random road surfaces, demonstrating the rationality of BP-PID control, thus effectively improve the suspension stability.
  • Mechanical Engineering
    Asymmetric hysteresis model of spherical wire rope isolator
    SUN Ziqiang, GAO Liuyang, YAN Ming, ZHANG Chunhui
    2024, 46(3): 305-311. https://doi.org/10.7688/j.issn.1000-1646.2024.03.10
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    Aiming at the difficulty of hysteresis model characterization caused by the nonlinear stiffness and damping characteristics of spherical wire rope isolator, a normalized Bouc-Wen hysteresis model with the improved nonlinear elastic and correction factors was proposed. In addition, the as-proposed model was further enhanced in combination with Simulink simulation, and the least square method combined with genetic algorithm was used to identify the parameters. To verify the effectiveness of the as-proposed model, the hysteresis loops of GGQ25-62L spherical wire rope isolator were drawn by numerical simulation under different working conditions and compared with experimental data. The results show that the as-proposed parameter identification method is accurate and reliable, and the experimental data are in good agreement with the theoretical curve. The model can describe the dynamic characteristics of the isolator effectively.
    • Information Science & Engineering
  • Information Science & Engineering
    Data mining method based on decision tree for implicit user behavior in social network
    HAN Yongyin, WANG Xia, WANG Zhixiao
    2024, 46(3): 312-317. https://doi.org/10.7688/j.issn.1000-1646.2024.03.11
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    In order to solve the problem of social network that it is difficult to calculate the association similarity in the process of data mining for implicit user behavior, a data mining method based on decision tree for implicit user behavior in social network was proposed. Social network was regarded as a vector space containing different dimensions, and users′ interest space and interest points on specific dimensions were calculated. After determining the sample attribute set, the test branch was established according to the known behavior data, and the attribute weight of branch subset was calculated. In addition, it was iterated until the data points with the same attributes were mined. Test results show that the as-proposed method can ensure accurate mining in the face of different types of implicit user behavior, and the search for target behavior data is effective and practical.
  • Information Science & Engineering
    SOC estimation method based on AFEKF for lithium ion battery
    LIU Guangjun, WU Siqi, ZHANG Heng, DENG Zhou
    2024, 46(3): 318-323. https://doi.org/10.7688/j.issn.1000-1646.2024.03.12
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    In order to solve the problem that the cumulative error is easy to occur because of the influence of historical data when estimating the charge state of lithium battery by using extended Kalman filtering algorithm, a SOC (state of charge) estimation method based on adaptive fading extended Kalman filtering was proposed. Thevenin equivalent model and recursive least square method were employed to identify battery parameters. By introducing adaptive fading factor into EKF algorithm, the influence of historical data on current state estimation was suppressed, and the SOC estimation of lithium battery was completed. The results show that AFEKF (adaptive fading extended Kalman filtering) algorithm can effectively converge when it is repeated for 20 times, and it has better robustness. The average error of SOC estimation is 1.03％, the root mean square error is 1.21％, and the average running time is 1.476 s, showing a good simulation for the dynamic and static characteristics of batteries.
  • Information Science & Engineering
    Ultrasonic assisted nondestructive testing method for internal defects of automotive micro-parts
    GUAN Liangliang, TIAN Guohong
    2024, 46(3): 324-330. https://doi.org/10.7688/j.issn.1000-1646.2024.03.13
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    In order to analyze the defects of automotive micro-parts more accurately and completely, a nondestructive testing method based on ultrasonic assisted mode for internal defects was designed. According to the transmission and reflection of ultrasonic wave and the change of sound field, the images of micro-parts were collected. After the de-noising of image through the edge information fusion, the image was segmented and the edge information was recognized to determine the internal defect location. Kernel principal component analysis technology was used to detect the features of defective location through dimension reduction processing, in order to complete nondestructive testing. The results show that the images of micro-parts obtained by this method are relatively clear, the precision rate of defect detection is always higher than 95％, the detection time for bubble and pore defects is less than 6 s, and less than 8 s for crack defect detection, showing that this method has higher localization accuracy and detection efficiency for internal defects.
    • Architectural Engineering
  • Architectural Engineering
    Mechanical properties of MK strengthened BF-MPC
    JIN Shengji, XU Li, YANG Yuhao, XU Jingyan
    2024, 46(3): 331-338. https://doi.org/10.7688/j.issn.1000-1646.2024.03.14
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    Aiming at the low toughness of magnesium phosphate cement (MPC), magnesium phosphate cement mortar with various proportions of basalt fiber (BF) and metakaolin (MK) were prepared through experimental method to test MPC′s strength at all ages, and microscopic analysis was carried out by scanning electron microscopy (SEM) and X ray diffraction (XRD). Experimental results reveal that the flexural strength and compressive strength of MPC mortar increase with the content of MK. The addition of BF can significantly enhance the flexural strength of MPC mortar, but the effect on the compressive strength is not significant. Microscopic analysis shows that MK and fly ash (FA) react within the MPC mortar and produce the secondary hydration products, enhancing the bonding between BF and MPC and improving MPC mortar′s mechanical properties.
  • Architectural Engineering
    Bond performance between self-compacting recycled concrete and ribbed steel bar
    YU Fang, ZHU Xianglong, YAO Dali, WANG Jianzhen
    2024, 46(3): 339-346. https://doi.org/10.7688/j.issn.1000-1646.2024.03.15
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    To study the bond performance between self-compacting recycled concrete (RASCC) and ribbed steel bar, 18 sets of specimens were designed, and the effects of concrete strength, steel bar diameter, bond length and protective layer thickness on bond performance were studied by central pull-out test. The results show that the bond strength between RASCC and ribbed steel bar increases exponentially with the compressive strength of concrete, increases linearly with the splitting tensile strength, increases parabolically with the diameter and bond length of steel bar, and increases first and then stabilizes with the increase of protective layer thickness. According to the experimental results, the formulas for calculating the bond strength and critical anchorage length of RASCC and ribbed steel bar were proposed, and the bond-slip constitutive model of RASCC and ribbed steel bar was established.
  • Architectural Engineering
    Prediction of vibration response based on BP artificial neural network for simply supported beam bridge structures
    ZHAO Licai
    2024, 46(3): 347-352. https://doi.org/10.7688/j.issn.1000-1646.2024.03.16
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    To address the problems of large workload, time-consuming calculation and inability to achieve rapid prediction of structural response in finite element models, a structural stress and displacement prediction model based on BP neural network algorithm for simply supported beam bridge was proposed. Taking a simply supported beam bridge as the engineering background, a three-dimensional finite element model of the beam system was established by using Midas/Civil finite element software. Taking into account parameter uncertainty, a comprehensive training and testing sample dataset was obtained by using the finite element model, and an artificial-neural-network-based bridge structure response prediction model was established. The research results show that the artificial neural network prediction model can achieve rapid prediction of structural response on the prerequisite of satisfying prediction accuracy. The maximum error between the measured value of load test and the predicted value by the model is within 12.46％, and the goodness of fit is above 0.9. This prediction model can be combined with the bridge health monitoring system in the later stage to achieve real-time analysis of the structural response of simply supported beam bridges, and can be applied to the structural response evaluation of simply supported beams.
  • Architectural Engineering
    Dissolution effect and mechanical properties of carbonate rocks
    MENG Jinzhu, CHEN Sili, WANG Junxiang, ZHANG Jingyu
    2024, 46(3): 353-360. https://doi.org/10.7688/j.issn.1000-1646.2024.03.17
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    To investigate the influence of erosion on the mechanical properties of carbonate rocks under different occurrence environments, the erosion and uniaxial ultimate tensile strength tests were performed on carbonate rocks under the influence of various factors by using a self-developed rock hydrodynamic pressure dissolution test equipment. The dissolution effect, deterioration law of mechanical properties and chemical damage of carbonate rocks were investigated. The test results show that different degrees of mechanical damage emerge in the rock samples under different occurrence environments. The compressive strength and elastic modulus of rock samples decrease after dissolution. The peak stress and elastic modulus decrease with the increasing dissolution rate, and a deterioration fitting correlation was proposed further. In addition, various factors, in the sequence of pH, temperature, time, water velocity, hydrodynamic pressure, the microstructure and mineral composition of rock samples after dissolution, influencing the sensitivity of rock samples compressive strength can be obtained.