State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China.
Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300401, China.
Sensors (Basel). 2022 Nov 4;22(21):8490. doi: 10.3390/s22218490.
Currently, the online non-destructive testing (NDT) methods to measure the contact states of high-voltage circuit breakers (HVCBs) with SF gas as a quenching medium are lacking. This paper aims to put forward a novel method to detect the contact state of an HVCB based on the vibrational signal. First, for a 40.5-kV SF HVCB prototype, a mechanical vibration detection system along with a high-current generator to provide the test current is designed. Given this, vibration test experiments are carried out, and the vibration signal data under various currents and corresponding contact states are obtained. Afterward, a feature extraction method based on the frequency is designed. The state of the HVCB contacts is then determined using optimized deep neural networks (DNNs) along with the method of adaptive moment estimation (Adam) on the obtained experimental data. Finally, the hyperparameters for the DNNs are tuned using the Bayesian optimization (BO) technique, and a global HVCB contact state recognition model at various currents is proposed. The obtained results clearly depict that the proposed recognition model can accurately identify five various contact states of HVCBs for the currents between 1000 A and 3500 A, and the recognition accuracy rate is above 96%. The designed experimental and theoretical analysis in our study will provide the references for future monitoring and diagnosis of faults in HVCBs.
目前，缺乏用于测量以 SF6 气体为灭弧介质的高压断路器（HVCB）接触状态的在线无损检测（NDT）方法。本文旨在提出一种基于振动信号检测 HVCB 接触状态的新方法。首先，针对 40.5kV SF6 HVCB 样机，设计了一种带有大电流发生器以提供测试电流的机械振动检测系统。在此基础上，进行了振动测试实验，获得了各种电流和相应接触状态下的振动信号数据。然后，设计了一种基于频率的特征提取方法。利用自适应矩估计（Adam）方法和优化后的深度神经网络（DNN），根据获得的实验数据确定 HVCB 触头的状态。最后，使用贝叶斯优化（BO）技术对 DNN 的超参数进行调优，提出了一种在各种电流下的 HVCB 整体接触状态识别模型。所得结果清楚地表明，所提出的识别模型可以在 1000A 至 3500A 的电流范围内准确识别 HVCB 的五种不同接触状态，识别准确率高于 96%。本研究中的设计实验和理论分析将为未来 HVCB 的故障监测和诊断提供参考。
