基于双模式分解多通道输入的VSC-STATCOM逆变器故障诊断模型  

Fault Diagnosis Model of VSC-STATCOM Inverter Based on Dual-Mode Decomposition Multi-Channel Input

作　　者：孔凡文 毕贵红[1] 赵四洪 王祥伟 陈冬静 张靖超 陈仕龙[1] KONG Fanwen;BI Guihong;ZHAO Sihong;WANG Xiangwei;CHEN Dongjing;ZHANG Jingchao;CHEN Shilong(School of Electric Power Engineering,Kunming University of Science and Technology,Kunming 650500,China;Yunnan Power Grid Company Kunming Power Supply Bureau Power Control Center,Kunming 650041,China)

机构地区：[1]昆明理工大学电力工程学院,云南昆明650500 [2]云南电网公司昆明供电局电力控制中心,云南昆明650041

出　　处：《电机与控制应用》2024年第7期103-118,共16页Electric machines & control application

基　　金：天津市科委重点研发计划项目(18YFFCTG00040)。

摘　　要：针对传统电压源型静止同步补偿器中逆变器故障诊断存在的信号特征提取不充分,深度学习网络识别能力不足以及高噪声情况下识别率较低等问题,提出了一种基于双模式分解、多通道输入(MCI)、并行卷积神经网络(PCNN)、双向长短时记忆(BiLSTM)网络和自注意力(SA)机制组合的逆变器故障诊断方法。首先利用变分模态分解和时变滤波经验模态分解对逆变器输出的三相电流进行分解,降低原始信号复杂程度,实现不同模态分量间的规律互补;其次,利用MCI-PCNN-BiLSTM-SA组合模型对特征矩阵进行深层特征提取、学习和识别;最后,通过仿真进行验证,结果表明所提方法特征提取能力较强,在无噪声情况下平均识别率高达99.48%,在高噪声情况下平均识别率达95.59%。Aiming at the problems of insufficient signal feature extraction,insufficient recognition ability of deep learning network and low recognition rate under high noise condition in inverter fault diagnosis in traditional voltage source converter static synchronous compensator,an inverter fault diagnosis method based on the combination of dual-mode decomposition,multi-channel input(MCI),parallel convolutional neural network(PCNN),bi-directional long and short-term memory(BiLSTM)neural network and self-attention(SA)mechanism is proposed.Firstly,the three-phase current output of the inverter is decomposed by variational mode decomposition and time-varying filter empirical mode decomposition,which reduces the complexity of the original signal and realizes the law complementation between different modal components.Secondly,MCI-PCNN-BiLSTM-SA combined model is used to extract,learn and recognize the feature matrix.Finally,the proposed method is validated by simulation,and the results show that the proposed method has strong feature extraction ability,with an average recognition rate of 99.48%in the case of no noise and 95.59%in the case of high noise.

关 键 词：逆变器故障诊断 双模式分解 多通道输入 并行卷积神经网络 自注意力 

分 类 号：TM772[电气工程—电力系统及自动化]