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| Defect Identification Method for Energy Storage Springs in On-Load Tap Changers Based on High-Frequency Current Characteristics |
| Liu Haoyu1, Gao Shuguo1, Fang Tiancheng2, Zhang Fan2, Guo Meng3, Ji Shengchang2 |
1. State Grid Hebei Electric Power Research Institute Shijiazhuang 050021 China;
2. State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong University Xi’an 710049 China;
3. State Grid Hebei Electric Power Co. Ltd Shijiazhuang 050021 China |
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Abstract The on-load tap changer (OLTC) is the only component inside a transformer that requires frequent operation. Its energy storage spring is prone to loosening and degradation after repeated switching, which results in insufficient release restoring force and may cause severe mechanical failures such as switching malfunctions. To address this issue, this paper proposes a defect identification method for OLTC energy storage springs based on high-frequency current characteristics.
First, a simulated test platform under rated OLTC operating conditions was constructed using the resonance method. Experimental observations showed that during the switching process, the critical make-and-break actions of the moving and stationary contacts induced high-frequency current spikes coupled onto the grounding wire, and a correspondence between the occurrence timings of these current spikes and the switching sequence was established.
Subsequently, based on an original spring with a restoring force of 600 N, three types of defective energy storage springs were fabricated with restoring forces of 545 N, 470 N, and 400 N, respectively. The experimental results indicated that the reduction of restoring force led to prolonged bridging time, an extended interval before the first high-frequency current spike, and a significant increase in the total number of spikes. In addition, spring defects caused a decrease in the rotational kinetic energy of the main shaft and slower movement of the contacts, which resulted in multiple pulse-like high-frequency current waveforms with intervals of approximately 10 ms and a notable increase in the number of arc reignitions between contacts.
On this basis, a peak screening algorithm combining time interval and amplitude saliency was proposed. The algorithm includes three steps: noise filtering, extraction of significant high-frequency current timings, and elimination of asynchronous switching peaks. Four time-domain features were introduced for current characterization, namely the coefficient of variation of peak values, the number of peaks, the maximum inter-peak interval, and the peak range.
In practical OLTC switching tests, the heating of transition resistors prevented continuous switching operations and resulted in low testing efficiency. To address the problem of long test cycles, a data augmentation method based on an improved conditional generative adversarial network (CGAN) was developed. The traditional CGAN was optimized by incorporating label embedding (LE) and mini-batch discrimination (MBD), which enhanced both stability and generalization ability of the model. An adaptive boosting algorithm (AdaboostM2) was then employed for defect classification using the augmented dataset.
Experimental results demonstrated that the minimum distinction error between generated and original data was only 1.13%. After data augmentation, the balanced accuracy of defect identification reached 96.67%, representing a 19.60% improvement compared with the original dataset. Moreover, the CGAN+LE+MBD approach significantly reduced the interference of noisy data with large variance during the augmentation process. When different levels of additive noise were applied, the proposed model still maintained strong robustness under a signal-to-noise ratio of 20 dB. This work provides an effective framework for capturing the degradation signatures of OLTC springs and offers significant technical value for accurate defect identification and early warning of potential failures.
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Received: 23 June 2025
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[1] 高煜, 赵学童, 李刚, 等. 换流变压器用有载分接开关过渡电阻温升的动态仿真[J]. 电工技术学报, 2025, 40(7): 2226-2235.
Gao Yu, Zhao Xuetong, Li Gang, et al.Simulation on dynamic temperature rise of transition resistance in on-load tap-changer used in converter transformer[J]. Transactions of China Electrotechnical Society, 2025, 40(7): 2226-2235.
[2] 柴建龙, 厉璇, 杨鸣, 等. 换流变压器真空有载分接开关切换暂态特性分析与过渡电阻选型方法[J]. 电工技术学报, 2026, 41(5): 1781-1794.
Chai Jianlong, Li Xuan, Yang Ming, et al.Switching transient characteristics analysis and transition resistance selection method for on-load tap-changer of converter transformer[J]. Transactions of China Electrotechnical Society, 2026, 41(5): 1781-1794.
[3] 蔡宇琦, 方瑞明, 彭长青, 等. 基于ANHGA-VMD和耦合隐马尔可夫模型的有载分接开关机械故障诊断[J]. 高电压技术, 2021, 47(11): 3949-3959.
Cai Yuqi, Fang Ruiming, Peng Changqing, et al.Mechanical fault diagnosis of on-load tap-changer based on ANHGA-VMD and coupled hidden Markov model[J]. High Voltage Engineering, 2021, 47(11): 3949-3959.
[4] Secic A, Aizpurua J I, Garro U, et al.Transformer OLTC operation monitoring framework through acoustic signal processing and convolutional neural networks[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 3520314.
[5] Shi Yanhui, Yang Yang, Ruan Yanjun, et al.The denoising method of OLTC vibration signals based on ensemble empirical mode decomposition and wavelet thresholding[C]//2024 IEEE 2nd International Conference on Power Science and Technology (ICPST), Dali, China, 2024: 660-664.
[6] 王蕾, 袁洪跃, 王季琴, 等. 变压器有载分接开关技术和故障诊断发展现状及展望[J]. 高压电器, 2022, 58(3): 171-180.
Wang Lei, Yuan Hongyue, Wang Jiqin, et al.Development status and prospect of transformer on-load tap-changer technology and fault diagnosis[J]. High Voltage Apparatus, 2022, 58(3): 171-180.
[7] 闫晨光, 张芃, 徐雅, 等. 换流变压器有载分接开关级间短路故障差动保护动作特性[J]. 电工技术学报, 2023, 38(21): 5878-5888, 5912.
Yan Chenguang, Zhang Peng, Xu Ya, et al.Differential protection performance for converter transformer intertap short-circuit faults in on-load tap changers[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5878-5888, 5912.
[8] 欧强, 罗隆福, 李勇, 等. 一种电力变压器短路累积机械损伤评价方法[J]. 电工技术学报, 2024, 39(8): 2578-2590.
Ou Qiang, Luo Longfu, Li Yong, et al.An evaluation method for short-circuit cumulative mechanical damage of power transformer[J]. Transactions of China Electrotechnical Society, 2024, 39(8): 2578-2590.
[9] 翟雨佳, 刘浩, 戴昀翔, 等. 并联电容器组干式空心电抗器匝间电弧性短路故障监测及保护[J]. 电气工程学报, 2025, 20(3): 290-299.
Zhai Yujia, Liu Hao, Dai Yunxiang, et al.Monitoring and protection of inter-turn arc short circuit fault in dry-type air-core reactors of shunt capacitor bank[J]. Journal of Electrical Engineering, 2025, 20(3): 290-299.
[10] 谢庆, 蔡扬, 谢军, 等. 基于ALBERT的电力变压器运维知识图谱构建方法与应用研究[J]. 电工技术学报, 2023, 38(1): 95-106.
Xie Qing, Cai Yang, Xie Jun, et al.Research on construction method and application of knowledge graph for power transformer operation and maintenance based on ALBERT[J]. Transactions of China Electro-technical Society, 2023, 38(1): 95-106.
[11] He Junda, Chen Bingxin, Zhang Yajie, et al.Detection of faulty vibration in oil-immersed OLTC by fiber Bragg grating based on double cantilever beam type sensitization[J]. IEEE Access, 2025, 13: 34639-34648.
[12] 王丽丽, 高树国, 刘宏亮, 等. 基于Savitzky-Golay滤波器的有载分接开关振动信号包络线提取方法研究[J]. 高压电器, 2025, 61(1): 54-62, 71.
Wang Lili, Gao Shuguo, Liu Hongliang, et al.Study on envelope extraction method of vibration signal of OLTC based on Savitzky-Golay filter[J]. High Voltage Apparatus, 2025, 61(1): 54-62, 71.
[13] 袁耀, 黄克捷, 陈建兴, 等. 基于WSO-VMD样本熵和SSA-SVM算法的有载分接开关故障诊断方法研究[J]. 西南大学学报(自然科学版), 2024, 46(11): 203-216.
Yuan Yao, Huang Kejie, Chen Jianxing, et al.Fault diagnosis method of OLTC based on WSO-VMD sample entropy and SSA-SVM[J]. Journal of Southwest University (Natural Science Edition), 2024, 46(11): 203-216.
[14] Saveliev A, Tsukao S, Schmitten P, et al.Detection of on-load tap-changer contact wear using vibroacoustic measurements[J]. IEEE Transactions on Power Delivery, 2024, 39(6): 3441-3449.
[15] Dabaghi-Zarandi F, Ezzaidi H, Gauvin M, et al.Convolutional variational autoencoder for anomaly detection in on-load tap changers[J]. IEEE Access, 2025, 13: 50838-50848.
[16] Erbrink J J, Gulski E, Smit J J, et al.Diagnosis of onload tap changer contact degradation by dynamic resistance measurements[J]. IEEE Transactions on Power Delivery, 2010, 25(4): 2121-2131.
[17] Li Shuaibing, Dou Lilong, Li Hongwei, et al.An innovative electromechanical joint approach for contact pair fault diagnosis of oil-immersed on-load tap changer[J]. Electronics, 2023, 12(17): 3573.
[18] 崔建, 孙帅, 张国钢, 等. 基于双温度磁流体电弧仿真改进Mayr电弧模型的特快速暂态过电压仿真方法[J]. 电工技术学报, 2024, 39(16): 5149-5161.
Cui Jian, Sun Shuai, Zhang Guogang, et al.The very fast transient overvoltage simulation method based on two-temperature MHD arc simulation to improve Mayr arc model[J]. Transactions of China Electro-technical Society, 2024, 39(16): 5149-5161.
[19] Seo J, Ma Hui, Saha T K.A joint vibration and arcing measurement system for online condition monitoring of onload tap changer of the power transformer[J]. IEEE Transactions on Power Delivery, 2017, 32(2): 1031-1038.
[20] Feizifar B, Usta O.A new arc-based model and condition monitoring algorithm for on-load tap-changers[J]. Electric Power Systems Research, 2019, 167: 58-70.
[21] 国家市场监督管理总局, 国家标准化管理委员会. 分接开关第1部分:性能要求和试验方法: GB/T 10230.1—2019[S]. 北京: 中国标准出版社, 2020.
[22] 张凡, 左浩明, 汪翔宇, 等. 真空有载分接开关切换开关多体动力学仿真及其在机械状态评估中的应用[J]. 工程力学, 2025, 42(8): 241-250.
Zhang Fan, Zuo Haoming, Wang Xiangyu, et al.Multibody dynamics simulation of diverter switch of vacuum on-load tap changer and its application in mechanical condition evaluation[J]. Engineering Mechanics, 2025, 42(8): 241-250.
[23] Goodfellow I J, Pouget-Abadie J, Mirza M, et al.Generative adversarial nets[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS 2014), Montreal, Canada, 2014, 2: 2672-2680.
[24] 贾轩, 许吉凯, 任艺婧, 等. 基于样本扩容和数据驱动的台区理论线损计算方法[J]. 山东大学学报(工学版), 2025, 55(3): 158-164.
Jia Xuan, Xu Jikai, Ren Yijing, et al.Calculation method of theoretical line loss in transformer districts based on sample expansion and data-driven[J]. Journal of Shandong University (Engineering Science), 2025, 55(3): 158-164.
[25] Chen Yang, Kempton D J, Angryk R A.Examining effects of class imbalance on conditional GAN training[C]//22nd International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, 2023, 1: 475-486.
[26] 邵振国, 张承圣, 陈飞雄, 等. 生成对抗网络及其在电力系统中的应用综述[J]. 中国电机工程学报, 2023, 43(3): 987-1004.
Shao Zhenguo, Zhang Chengsheng, Chen Feixiong, et al.A review on generative adversarial networks for power system applications[J]. Proceedings of the CSEE, 2023, 43(3): 987-1004.
[27] 李思奇, 夏卯, 鲁思兆, 等. 基于二次分解时频图和SE-DSMC-BSA的轻量化有载分接开关机械故障识别方法[J]. 振动与冲击, 2025, 44(11): 268-279, 308.
Li Siqi, Xia Mao, Lu Sizhao, et al.Lightweight OLTC mechanical fault recognition method based on secondary decomposition time-frequency maps and SE-DSMC-BSA[J]. Journal of Vibration and Shock, 2025, 44(11): 268-279, 308. |
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2026, Vol. 41 
