Journal of Electrical Engineering and Automation

High-voltage DC cables play a crucial role in power transmission, and the analysis of partial discharge signals in their accessories is vital for the stability of power systems. This study focuses on signal feature extraction and pattern recognition, employing methods such as wavelet transform and empirical mode decomposition to accurately extract features from the time domain, frequency domain, and time-frequency domain. By integrating machine learning algorithms like support vector machines and random forests with deep learning models such as convolutional neural networks and recurrent neural networks, the study achieves accurate identification of partial discharge signals. Experiments have shown that this method performs well in complex environments, providing robust support for cable condition monitoring and fault prediction.

High-voltage DC cable; Partial discharge signal; Feature extraction; Pattern recognition; Machine learning