Partial Discharge Detection and Recognition in Insulated Overhead Conductor Based on Bi-LSTM with Attention Mechanism

Abstract

Insulated overhead conductor (IOC) faults cannot be detected by the ordinary protection devices due to the existence of the insulation layer. The failure of insulated overhead conductors is regularly accompanied by partial discharge (PD); thus, IOC faults are often judged by the PDs of insulated overhead conductors. In this paper, an intelligent PD detection model based on bidirectional long short-term memory with attention mechanism (AM-Bi-LSTM) is proposed for judging IOC faults. First, the original signals are processed using discrete wavelet transform (DWT) for de-noising, and then the signal statistical-feature and entropy-feature vectors are fused to characterize the PD signals. Finally, an AM-Bi-LSTM network is proposed for PD detection, in which the AM is able to assign the inputs different weights and highlight their effective characteristics; thus, the identification accuracy and computational complexity have been greatly improved. The validity and accuracy of the proposed model were evaluated with an ENET common dataset. The experiment results demonstrate that the AM-Bi-LSTM model exhibits a higher performance than the existing models, such as LSTM, Bi-LSTM, and AM-LSTM.

Pattern Recognition of Partial Discharge Faults in Switchgear Using a Back Propagation Neural Network Optimized by an Improved Mantis Search Algorithm

“…This model was used for enhancing PD data in power equipment and detecting PD signals. Xi et al, introduced an attention mechanism (AM) into the PDPR model to improve the recognition accuracy and the computational complexity by emphasizing its effective characteristics and combining the past and future information [15]. They proposed an intelligent partial discharge detection model based on the Bi-directional Long Short-Term Memory (AM-Bi-LSTM) network for identifying partial discharge faults in Insulated Overhead Conductors (IOC)s. Rizzi et al, used a genetic algorithm to extract key features and selected neuro-fuzzy classifiers for PDPR of cable partial discharge [16].…”