河南理工大学学报（自然科学版）2024，Vol.43Issue(5)：118-126,9.DOI:10.16186/j.cnki.1673-9787.2022060019
基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断
Fault diagnosis of bidirectional DC-DC power converter based on improved LSTM-SVM
摘要
Abstract
Objectives In order to solve the problem of low accuracy of soft fault diagnosis for bidirectional DC-DC power converter,Methods the fault diagnosis model of bidirectional DC-DC power converter based on improved LSTM-SVM was proposed.Firstly,the fault mechanisms of capacitors,inductors and MOSFET tubes in bidirectional DC-DC power converter were analyzed.The variations of the output electrical param-eters of the converter after the failure of each component were simulated by simulation experiment,and the fault characteristic parameters corresponding to the failure of different components of the converter were de-termined.Then,an improved LSTM-SVM bidirectional DC-DC power converter fault diagnosis model was constructed.The Mogrifier gate mechanism was added to LSTM to improve the ability of LSTM to extract weak features from the original time series data.Finally,since the end classifier of traditional LSTM was Soft-max,it mainly solved the problem of single component diagnosis,the converter had many fault types and high dimension,so SVM optimized by sparrow search algorithm was used instead of the original Softmax function to classify faults from LSTM output data and to improve the accuracy of fault diagnosis.24 groups of faults including electrolytic capacitor,inductor and MOSFET single and double tube faults were set up under two states of charge and discharge of bidirectional DC-DC power converter.The improved LSTM-SVM constructed in this paper and the original LSTM-SVM bidirectional DC-DC converter fault diagnosis model were respectively used for diagnosis.Results The average accuracy of the improved LSTM-SVM fault diagno-sis model was 99.71%,and the average accuracy of the original LSTM-SVM fault diagnosis model was 88.48%.The accuracy of the improved LSTM-SVM fault diagnosis model for each component was higher than that of the original LSTM-SVM fault diagnosis model.Conclusions The fault diagnosis model of bidi-rectional DC-DC power converter based on improved LSTM-SVM was realized to accurately diagnose the electrolytic capacitor,inductor and MOSFET single and double tube faults in bidirectional DC-DC power converter.关键词
双向DC-DC变换器/软故障/改进长短期记忆网络/麻雀搜索/支持向量机/故障诊断Key words
bidirectional DC-DC converter/soft fault/improved long and short term memory network/sparrow search/support vector machine/fault diagnosis分类
信息技术与安全科学引用本文复制引用
王福忠,任淯琳,张丽,王丹..基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断[J].河南理工大学学报（自然科学版）,2024,43(5):118-126,9.基金项目
国家自然科学基金资助项目(U1804143) （U1804143）
河南省科技攻关项目(232102241028 （）
202102210295) （）
河南省高校基本科研业务费专项项目(NSFRF210424) （NSFRF210424）
河南理工大学青年骨干教师资助项目(2019XQG-17). （2019XQG-17）
