WANG F Z, REN Y L, ZHANG L,et al.Fault diagnosis of bidirectional DC-DC power converter based on improved LSTM-SVM[J].Journal of Henan Polytechnic University(Natural Science) ,2024,43(5):118-126.

doi:10.16186/j.cnki.1673-9787.2022060019

Received:2022/06/09

Revised:2023/04/20

Published:2024/07/31

Fault diagnosis of bidirectional DC-DC power converter based on improved LSTM-SVM

WANG Fuzhong1, REN Yulin1, ZHANG Li1, WANG Dan2

1.School of Electrical Engineering and Automation，Henan Polytechnic University，Jiaozuo  454000，Henan，China;2.School of Intelligent Engineering，Huanghe Jiaotong University，Jiaozuo  454950，Henan，China

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 parameters 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 determined.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 Softmax，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 diagnosis 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 bidirectional 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.

Key words:bidirectional DC-DC converter;soft fault;improved long and short term memory network;sparrow search;support vector machine;fault diagnosis