Feature Extraction of Partial Discharge Source with Complex Noise Based on Adaptive S-Transform and Truncated Compact Singular Value Decomposition
Ning Shuguang1, He Yigang1,2, Cheng Tongtong1, Sui Yongbo1, Huang Yuan1
1. School of Electrical Engineering and Automation Hefei University of Technology Hefei University of Technology Hefei 230009 China; 2. School of Electrical Engineering Wuhan University Wuhan 430072 China
Abstract：To solve the problem that the features of the partial discharge (PD) source are difficult to extract because the PD source signal is polluted by the complex noise, a PD source complex noise feature extraction method is proposed based on the adaptive S-transform and the truncated compact singular value decomposition (TCSVD). First, the S-transform is optimized and improved, and then applied for the PD source to obtain the time-frequency domain matrix. The narrow-band interference signal is filtered adaptively, and the useful time-frequency signal of partial discharge is extracted. Second, compact singular value decomposition is utilized to decompose the extracted time-frequency matrix. Then, the fitting derivative method is proposed to find the singular value threshold parameters and truncate the singular value, the white noise signal in PD source is filtered. Finally, the proposed PD source feature extraction method is verified and analyzed by theoretical simulation and field test. The results indicate that the feature extraction method has well feature extraction ability for the PD signal with complex noise, and can be utilized to effectively extract useful information of PD signal.
通讯作者:
何怡刚 男,1966年生,教授,博士生导师,研究方向为模拟和混合信号电路的测试与故障诊断,智能电网,自动测试与诊断装备,射频识别技术和智能信号处理等。E-mail：18655136887@163. com
作者简介: 宁暑光 男,1992年生,博士研究生,研究方向为局部放电检测,电力设备局部放电定位及故障诊断。E-mail：18726386659@163. com
引用本文:
宁暑光, 何怡刚, 程彤彤, 隋永波, 黄源. 基于自适应S变换与截断紧致奇异值分解的局部放电源复杂染噪特征提取方法[J]. 电工技术学报, 2022, 37(15): 3951-3962.
Ning Shuguang, He Yigang, Cheng Tongtong, Sui Yongbo, Huang Yuan. Feature Extraction of Partial Discharge Source with Complex Noise Based on Adaptive S-Transform and Truncated Compact Singular Value Decomposition. Transactions of China Electrotechnical Society, 2022, 37(15): 3951-3962.
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