基于CNN-LSTM模型的绝缘架空导线局部放电识别研究

Research on Partial Discharge Recognition of Insulated Overhead Conductors Based on CNN-LSTM Model

绝缘架空导线相较于传统的裸露导线具有更好的绝缘性能,但当其掉到地上或被树枝等异物击中时,不会引起过电流,这使得标准保护设备往往难以检测到这些故障,而此类故障通常会引起局部放电(PD)现象。提出了一种基于卷积神经网络(CNN)和长期短期记忆神经网络(LSTM)的PD模式识别算法。该算法通过映射加权和学习参数矩阵赋予LSTM隐含状态不同的权重,从而减少历史信息的丢失并加强重要信息的影响作用,最终实现对PD活动的检测与识别。在VSB公开的ENET数据集上,提出的方法对正常类型和故障类型的识别精度分别达到了90.44%和90.33%,并与多种算法进行了比较,结果表明所提出的方法具有更高的识别精度。

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 algorithm assigns different weights to the LSTM hidden states through mapping weighting and learning parameter matrices,reducing the loss of historical information and enhancing the influence of important information,thereby detecting 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 compared with various algorithms,demonstrating higher recognition accuracy.