中国电机工程学报2025，Vol.45Issue(4)：1369-1380,中插12,13.DOI:10.13334/j.0258-8013.pcsee.231736
基于CNN-BiLSTM-Attention的直流微电网故障诊断研究
Research on DC Microgrid Fault Diagnosis Based on CNN-BiLSTM-Attention
摘要
Abstract
In this paper,a fault diagnosis method is proposed for existing DC microgrids to address the challenges of speed and accuracy.The proposed method combines the strengths of convolutional neural network(CNN)and bidirectional long short-term memory(BiLSTM)network,incorporating an attention mechanism.Specifically,CNN is utilized to extract vertical detailed features from fault data at a specific moment,compressing the data length and reducing subsequent network training parameters to improve the speed of fault diagnosis.Furthermore,we construct a cascaded network with BiLSTM as the core,enabling the extraction of horizontal historical features from fault data during the fault evolution process.The attention mechanism is integrated to enhance the model's focus on the feature changes in fault data,thereby improving the accuracy of fault diagnosis.Simulation results demonstrate that the proposed method outperforms mainstream fault diagnosis methods in terms of accuracy and recognition speed.Additionally,the proposed method exhibits excellent diagnostic performance for fault record data under conditions of noise interference,imbalanced samples,and small sample sizes.关键词
故障诊断/直流微电网/卷积神经网络/双向长短期记忆网络/注意力机制Key words
fault diagnosis/DC microgrid/convolutional neural network/bidirectional long short-term memory network/attention mechanism分类
信息技术与安全科学引用本文复制引用
孟宏宇,张建良,蔡兆龙,李超勇..基于CNN-BiLSTM-Attention的直流微电网故障诊断研究[J].中国电机工程学报,2025,45(4):1369-1380,中插12,13.基金项目
国家自然科学基金项目(62127803). Project Supported by National Natural Science Foundation of China(62127803). （62127803）
