Research on Partial Discharge Recognition of Insulated Overhead Conductors Based on CNN-LSTM Model
Insulated overhead conductors,compared to traditional bare conductors,exhibit better insulation per-formance.However,faults such as the insulation conductor falling to the ground or being struck by foreign objects like tree branches do not cause overcurrent,making them difficult for standard protection devices to detect.Such faults often lead to partial discharge(PD)phenomena.This paper proposes a PD pattern recognition algorithm based on Convolutional Neural Networks(CNN)and Long Short-Term Memory networks(LSTM).The algo-rithm assigns different weights to the LSTM hidden states through mapping weighting and learning parameter ma-trices,reducing the loss of historical information and enhancing the influence of important information,thereby de-tecting and recognizing PD activities.Using the VSB publicly available ENET dataset,the proposed method achieves recognition accuracies of 90.44％for normal types and 90.33％for fault types,respectively,and is com-pared with various algorithms,demonstrating higher recognition accuracy.
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