基于高频电流特征的有载分接开关储能弹簧缺陷识别方法

doi:10.19595/j.cnki.1000-6753.tces.251099

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摘要有载分接开关（OLTC）是变压器内部唯一频繁动作的部件,其储能弹簧在长期多次切换后极易劣化松动,可能引发切换失败等严重机械故障。为此,该文提出了一种基于高频电流特征的有载分接开关储能弹簧缺陷识别方法。首先,搭建了基于谐振法的有载分接开关额定工况模拟试验平台,发现有载分接开关切换时产生的电弧会使油箱接地线上耦合出现高频电流尖峰,并据此建立了高频电流峰值时刻与切换时序的对应关系;其次,特制了三种缺陷储能弹簧（释放回复力分别545、470、400 N）,试验发现储能弹簧释放回复力下降会导致桥接过程延长且电弧重燃次数增加,高频电流尖峰第一间隔时间延长,尖峰数量显著增加;然后,提出了联合时间间隔和幅值显著度的峰值筛选算法,实现了高频电流特征提取;最后,提出了基于生成对抗网络-标签嵌入-小批量判别的数据增强方法,生成数据和原始数据区分误差最低为1.13%,数据增强后缺陷识别平衡准确率达96.67%,较生成前提升19.60%。该文所提方法可用于有载分接开关储能弹簧缺陷的识别和预警。

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关键词 ：有载分接开关, 储能弹簧, 高频电流, 数据增强, 缺陷识别

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.

Key words： On-load tap changer (OLTC) energy storage springs high-frequency current data augmentation defect identification

收稿日期: 2025-06-23

PACS:

TM411

基金资助:国网河北省电力有限公司科技项目资助（kj2023-009）

通讯作者: 张凡男,1991年生,副教授,研究方向为电力设备多物理场耦合分析及数字孪生技术。E-mail：zhangfanee@xjtu.edu.cn

作者简介: 刘浩宇男,1993年生,博士,高级工程师,研究方向为电力变压器状态感知与智能评估技术。E-mail：1102132065@qq.com

引用本文:

刘浩宇, 高树国, 方天承, 张凡, 郭猛, 汲胜昌. 基于高频电流特征的有载分接开关储能弹簧缺陷识别方法[J]. 电工技术学报, 2026, 41(13): 4584-4597. Liu Haoyu, Gao Shuguo, Fang Tiancheng, Zhang Fan, Guo Meng, Ji Shengchang. Defect Identification Method for Energy Storage Springs in On-Load Tap Changers Based on High-Frequency Current Characteristics. Transactions of China Electrotechnical Society, 2026, 41(13): 4584-4597.

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