Research on partial discharge pattern recognition technology based on convolutional neural network and long short-term memory

Partial discharge (PD) is one of the common forms of faults in high-voltage equipment, and it is also one of the most important forms of fault manifestation, with different discharge modes for different types of defects. Its identification and localization are of great significance for equipment condition monitoring and fault prevention. This article proposes a local discharge pattern recognition method based on deep learning algorithms. The method uses two deep learning algorithms, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), to preprocess the sensor signals, extract effective feature information, and perform pattern recognition through the combination of the two algorithms, achieving good recognition results. This article describes in detail the principles and implementation process of the algorithm, and verifies the effectiveness of the algorithm in simulated experiments.