Partial Discharge Detection and Classification Using Low-Noise UHF Sensing Frontend and Wavelet Scattering Feature Extraction Network
Shu Zhou, Zhenyu Zhao, Jinsheng Ji, Ting Shi, Wensong Wang, Yuanjin Zheng, Yong‐Xin Guo
Abstract
Fast and accurate recognition of partial discharge (PD) patterns is essential to prevent insulation failure-related outages of industrial equipment. The ultrahigh-frequency (UHF) methods have been widely used for PD diagnosis due to their good versatility and noncontact capabilities. However, in harsh environments with significant noise and electromagnetic interference (EMI), existing UHF methods are difficult for acquiring high-quality PD signals and differentiating PD types. To overcome this issue, this article proposes an innovative method by combining a low-noise UHF sensing frontend and a wavelet scattering feature extraction network (WSN). The developed sensing frontend consists of a UHF antenna sensor and a conditioning circuit with bandpass configuration and stability compensation. The equivalent noise circuit of the frontend is modeled to analyze and optimize output noise. In addition, the constructed WSN with improved configuration directly derives low-variance features from noise-corrupted time series UHF PD signals. Subsequently, a low-complex and robust majority voting-based support vector machine (MVSVM) is trained to identify different PD types and EMI. In accordance with IEC 62478, experimental case studies validate the noise performance of the developed UHF frontend and demonstrate the effectiveness of the proposed WSN for PD classification. The proposed method achieves 91.3% accuracy on noisy datasets. Moreover, it surpasses comparison methods by 8.9% to 24.1% on insufficient datasets with significant noise and EMI.