23 October 2024, Volume 43 Issue 10
    

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    Intelligent Diagnosis of Power Equipment Faults
  • LI Hao, WEI Fanrong, WANG Hao, LI Xudong
    Advanced Technology of Electrical Engineering and Energy. 2024, 43(10): 1-12. https://doi.org/10.12067/ATEEE2404070
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Aiming at the problem of low accuracy of real-time diagnosis of power transformer mechanical faults, this paper proposes a power transformer fault diagnosis method based on vibration signal and deep learning. Firstly, the vibration signal on the surface of the power transformer case is decomposed by the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) to obtain the reconstructed signal, and the fuzzy entropy value is introduced to construct the vibration eigenvectors. Then, a Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) is used to form a basic classification network to achieve feature classification, and an Efficient Channel Attention Mechanism (ECAM) is introduced to improve the CNN learning performance. Finally, a Multi-strategy Co-optimization Bald Eagle Search (MSCOBES) algorithm is designed based on the hybrid improvement of ICMIC chaotic mapping, adaptive dynamic perturbation and elite inverse learning, and the improved algorithm is applied to realize hyper-parameter optimization of CNN-BiGRU to obtain the optimization of power transformer fault diagnosis based on MSCOBES-CNN-BiGRU-ECAM model. In the experiment for the test transformer, the experimental results show that the proposed method can reach an accuracy up to 994% for the power transformer with different types of mechanical fault.
  • DENG Xudong, ZHANG Zhanlong, XIA Yuancan, WANG Li, WU Chen, FANG Jin, WANG Fan
    Advanced Technology of Electrical Engineering and Energy. 2024, 43(10): 13-23. https://doi.org/10.12067/ATEEE2404072
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    In recent years, mechanical failures in domestic power transformer windings have been frequent. Utilizing vibration analysis to monitor the mechanical operation of power transformer windings can effectively mitigate the risk of transformer failures. This article first analyzes and solves the vibration sources and acceleration theory of power transformer windings, establishes a finite element model of a 110 kV power transformer, studies the vibration characteristics of the transformer windings under rated normal load conditions, and finds that the radial vibration intensity of the windings under load conditions is much greater than the axial vibration, with a main frequency of 100 Hz in the winding vibration signal. With fixed constraints at the upper and lower ends of the windings, the vibration intensity at the upper and lower ends is less than that in the middle of the windings. Next, by altering the geometric shape of the high-voltage windings to cause buckling deformation, a comparison analysis of the vibration signals in the undeformed state of the windings reveals that when deformation occurs, the vibration signal spectrum shows a significant amount of high-order harmonic components. As the degree of deformation of the windings increases, the main frequency of the vibration signal continuously increases, and the distribution of high-order harmonic components becomes more complex. Finally, by defining characteristic parameters such as the fundamental frequency ratio and high-frequency ratio, the article further analyzes the changes in vibration characteristics before and after the deformation of the transformer windings, providing a basis for evaluating the mechanical state of transformer windings using vibration analysis in actual field conditions.
  • ZHANG Rongbin, XU Yaosong, NIU Yuanping
    Advanced Technology of Electrical Engineering and Energy. 2024, 43(10): 24-42. https://doi.org/10.12067/ATEEE2403014
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    Aiming at the characteristics of transformer faults, a new transformer fault diagnosis model is proposed by combining the Weighted Kernel Principal Component Analysis (WKPCA) technique with IEDO-XGBoost. The method mainly combines the dissolved gas analysis technique with the non-coded ratio method to obtain the fault characteristics of the transformer, use WKPCA to reduce its dimension, and use the processed normalized fault sample data as the input of the IEDO-XGBoost model to output the transformer fault diagnosis type and its diagnostic accuracy. The 20-dimensional transformer fault feature data are selected for WKPCA dimension reduction processing, which accelerates the convergence speed of the model; the exponential distribution optimizer algorithm is improved by using the adaptive sine-cosine strategy and Gaussian variance strategy, and the performance of the improved exponential distribution optimization algorithm is tested by using 10 typical test functions. The results show that the improved exponential distribution optimization algorithm has faster convergence speed and global search ability. Then, the improved exponential distribution algorithm is used to determine multiple optimal parameters in the XGBoost model. Simulation results show that the diagnostic accuracy of the model is 91.82%, which is 2.73%, 3.64%, 5.46%, 8.18% and 10.91% higher than that of EDO-XGBoost, NGO-XGBoost, GJO-XGBoost, GWO-XGBoost and WOA-XGBoost fault diagnosis models, respectively, which verifies that the proposed method can effectively improve transformer fault diagnosis performance.
  • CHEN Yicong, CHEN Zongrang, ZHANG Peng, XU Haodong, SUO Jun, ZHANG Yusheng, ZHU Shuyou, YAN Chenguang
    Advanced Technology of Electrical Engineering and Energy. 2024, 43(10): 43-51. https://doi.org/10.12067/ATEEE2309022
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    In recent years, internal short-circuit faults inside ultra-high voltage (UHV) converter transformers have led to a spate of explosive fires, posing a serious threat to the safe and reliable operation of power systems. In this paper, taking a typical single-phase four-limb converter transformer of ±800 kV UHV converter station as a research case, modeling and simulation of winding interturn short-circuit fault are carried out on ANSYS Maxwell & Simplorer platform. The variation characteristics of radial leakage flux, the short-circuit current and the arcing fault energy under different fault scenarios are investigated. The results show that the normal axial flux distribution is disturbed by a large amount of radial leakage flux inside the converter transformer with the occurrence of interturn short-circuit fault, and high-amplitude fault circulating current emerges in the short-circuit loop. Under a 1.98% interturn short-circuit fault, the magnetic leakage flux density reaches 2.76 T at peak, the short-circuit circulating current reaches 80.59 kA at peak, and the fault energy released over four cycles reaches 1.22 MJ. According to the law of ampere-turn balance, the more the shorted turn number, the lower the circulating current amplitude. However, since the average arc voltage increases monotonically with the arc length, the overall arc energy is positively correlated with the number of shorted turns. In addition, interturn short-circuit faults closer to the middle part of the winding are more serious due to the difference in leakage inductance caused by the leakage distortion.
  • PAN Zhicheng, ZHANG Zhanlong, LYU Jinzhuang , DENG Jun, XIE Zhicheng, LI Dingyuan
    Advanced Technology of Electrical Engineering and Energy. 2024, 43(10): 52-61. https://doi.org/10.12067/ATEEE2404008
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    When there is too much residual magnetism in the iron core, it can cause a large amplitude of excitation inrush current, leading to the burnout or misoperation of the converter protection device, and seriously affecting its normal operation. In order to eliminate the influence of residual magnetism on converter transformers, this paper proposes a residual magnetism measurement method for converter transformers based on the energy change on one side of the induction coil of the converter transformer, which is the induced energy. The purpose is to calculate the residual magnetism of the converter transformer core by fitting the relationship between the induced energy and the residual magnetism of the converter core based on the energy change generated on one side of the induction coil of the converter transformer, while applying external excitation to the converter transformer. The method proposed is easy to execute, has strong operability, and has little impact on the properties of the converter. It can provide reliable support for the measurement and elimination of residual magnetism in the converter. In addition, the experimental results also confirmed the time saving and economic benefits of this method.
  • JIANG Peiyu, FANG Shuqi, QIAN Menghao, LI Jialong, WANG Liming, TANG Xian, YIN Fanghui
    Advanced Technology of Electrical Engineering and Energy. 2024, 43(10): 62-70. https://doi.org/10.12067/ATEEE2405006
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    The multi-layer winding structure of the radial converter transformer grid side-valve side-voltage regulating winding is closely connected. When the windings vibrate, a coupling vibration effect will occur between the structures, thus affecting the vibration response characteristics of the box. At present, the theory of coupled vibration of winding radial structures is not perfect, and the relevant vibration models cannot effectively explain the multi-frequency response phenomenon when windings vibrate. To solve the above problems, this paper considers the structural coupling effect in the generation and transmission path of winding vibration, simplifies the coupled vibration process into the arch winding vibration problem under the action of simple harmonic load excitation, and then proposes a nonlinear vibration model of winding radial coupled vibration. The multi-scale method is used to derive the mathematical expression of coupled vibration acceleration, and the correctness of the model is proved through theoretical analysis and experiments. The research results show that under the excitation of 50 Hz power frequency current, the main frequency of winding vibration is concentrated at 100 Hz. Under the action of structural coupling, multi-octave harmonic components such as 200 Hz and 300 Hz appear, and there are also a small number of odd harmonics of 50 Hz such as 150 Hz and 250 Hz. The research conclusions provide theoretical references for engineering applications such as converter transformer vibration detection technology and structural vibration and noise reduction design.
  • XIE Qian, LIU Baize, DING Jinzhong, YAN Dapeng, YANG Xiaoping, DANG Jian
    Advanced Technology of Electrical Engineering and Energy. 2024, 43(10): 71-84. https://doi.org/10.12067/ATEEE2403048
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    In view of the problem that most existing deep learning methods can only work with limited labeled sample data, which makes the diagnosis model too serious, resulting in high accuracy when training the model but low fault identification accuracy when put into use, this paper studies isolation switches a high-accuracy diagnosis method for small data sample sets in different working conditions, and a Multi-Granular Attention Mechanism (MG-AM) network framework for checking isolation switch fault diagnosis under different working conditions is constructed. First, this framework preprocesses the isolation switch fault data to obtain enhanced data samples and data feature libraries. Next, the time comparison module is used to compare the fault data roughly, and several possibilities of the fault condition are preliminarily obtained. The original data are predicted by the multi-granularity context comparison module, and the predicted results are compared with the enhanced data. Then, making full use of the collected sample data, the labeled and unlabeled sample data are input into the network, and the network is optimized simultaneously through semi-supervised learning and unsupervised learning. Finally, the isolation switch fault diagnosis model is established to realize the accurate identification of the unknown sample fault data. The experimental results show that the MG-AM network framework can effectively use the inherent samples for fault diagnosis, and has a good recognition rate, with an average recognition rate of 96.47%.
  • insulation based on multi-spectral informationHU Die, LI Xiaofeng, JIANG Xiaofeng, YANG Ruilin, LIU Yang, DONG Ming
    Advanced Technology of Electrical Engineering and Energy. 2024, 43(10): 85-92. https://doi.org/10.12067/ATEEE2305020
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    In order to obtain more abundant discharge characteristic information and improve the efficiency of partial discharge diagnosis, a deep learning fusion method of partial discharge in oil-paper insulation based on multi-spectral information is proposed. Firstly, based on the micro-optical sensor and the spectral distribution of partial discharge, a multi-spectral synchronous detection platform for partial discharge is constructed, and the multi-spectral data of four discharge types are obtained through experiments. Then the convolution neural network model is constructed, and the partial discharge data of different spectral sections are used as the input of different channels of the model. The effective information in the multispectral signal is extracted by channel level fusion, and the partial discharge types of oil-paper insulation are accurately identified. The results show that the multi-spectral information of different discharge types can be used as an effective feature of pattern recognition; by the introduction of multi-spectral information, the recognition accuracy of the proposed method can reach more than 98 %, which is significantly improved compared with that of only using pulse current signals; compared with statistical characteristic parameter analysis and deep neural network, and the proposed method has better effect on multi-spectral information fusion and higher recognition accuracy.
  • Treatise and Report
  • HU Jie , ZHOU Xin , LI Jinghua, HE Xin, SUN Haoning , WANG Delin
    Advanced Technology of Electrical Engineering and Energy. 2024, 43(10): 93-101. https://doi.org/10.12067/ATEEE2212037
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    Based on the background of asynchronous interconnection between Yunnan Power Grid and Southern Power Grid, the impact of electrolytic aluminum load and frequency limit controller on the frequency stability of Yunnan Power Grid is discussed. Firstly, the electrolytic aluminum load with increasing load size was studied, a load model reflecting the frequency and voltage power response of the electrolytic aluminum load has been developed for its load composition and characteristics, and simulations have been conducted to verify it; secondly, the dynamic frequency regulation process of FLC and governor is analyzed after the high power disturbance of the system, and it is shown that if the dead zone of FLC is not set properly or the response of governor is too slow, the DC line will be overloaded for a long time, which is not conducive to the safe and stable operation of power system. Based on this, the deadband arrangement of FLC and the method to optimize the performance of governor are proposed. Finally, using the actual data of Yun-Guang DC project for simulation, the impact of the access of electrolytic aluminum load on the frequency stability of the system under single and double pole blocking fault is analyzed, and the effectiveness of the frequency regulation strategy proposed in this paper is verified.
  • CHEN Bing, LI Qun, WANG Xu, ZHU Yinfang, GUO Shiwei, XIONG Linyun
    Advanced Technology of Electrical Engineering and Energy. 2024, 43(10): 102-112. https://doi.org/10.12067/ATEEE2306023
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    The gradual penetration of new energy into the modern power system is causing more severe stability issues. Due to the unique merits in enhancing the system stability, grid forming control is becoming one of the mainstream control approaches in large scale integration of renewable power. Therefore, this paper aims to propose a novel sliding mod control method for grid-forming converters via the virtual flux orientation, which enables the converters with the characteristics of virtual synchronous generator to support grid integration and synchronized operation of renewable energy. First of all, the concept of virtual flux for the grid-forming converter is proposed, which is denoted by the time integral of the voltage source converter (VSC) terminal voltage. Via a virtual flux controller, the virtual flux is reoriented to the reference axis of the swing equation of the virtual synchronous generator, thereby the grid synchronization is achieved. Moreover, the proposed virtual flux reorientation scheme is capable of controlling the active/reactive power and voltage of the VSC. To prevent the overcurrent of the converters during grid fault condition, this paper also proposed a short circuit current limiting approach to enhance the low voltage ride through capability of the system. Finally, the effectiveness of the proposed method is validated in RTDS simulation platform.