电器与能效管理技术Issue(9)：1-12,12.DOI:10.16628/j.cnki.2095-8188.2025.09.001
基于Filtering LSTM-Lightweight CNN的交流串联电弧故障检测方法
An AC Series Arc Fault Detection Method Based on Filtering LSTM-Lightweight CNN
摘要
Abstract
To address the insufficient generalization performance of deep learning-based arc fault detection methods in unknown multi-load circuits,a Filtering Long Short-Term Memory(Filtering LSTM)neural network driven by high-frequency coupled analog signals is proposed.By combining this network with a Lightweight convolutional neural network(Lightweight CNN),a Filtering LSTM-Lightweight CNN arc fault detection model is constructed.The high-frequency coupled signals of multi-load circuits can be simulated through the linear superposition of high-frequency coupled signals from single-load circuits.These analog signals are then used to drive the Filtering LSTM,which filters out unknown features in the multi-load circuit signals and reconstructs the signals.Finally,a Lightweight CNN optimized by the tree-structured Parzen estimator is employed to perform arc fault detection on the reconstructed signals.Experiments demonstrate that the Filtering LSTM-Lightweight CNN achieves an arc fault detection accuracy of 99.45％among 136 000 unknown multi-load circuit samples.Compared with detection algorithms that do not adopt Filtering LSTM,the proposed method improves the detection accuracy by up to 14.05％,significantly enhancing the generalization ability of the arc fault detection model.关键词
串联电弧故障/特征过滤/轻量级卷积神经网络/故障检测Key words
series arc fault/feature filtering/Lightweight convolutional neural network(Lightweight CNN)/fault detection分类
信息技术与安全科学引用本文复制引用
何键涛,王兆锐,鲍光海..基于Filtering LSTM-Lightweight CNN的交流串联电弧故障检测方法[J].电器与能效管理技术,2025,(9):1-12,12.基金项目
福建省科技计划项目(2023H0007) （2023H0007）
