Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network.

Jie Wu,Xiaojuan Chen,Zhaohua Zhang

doi:10.1155/2021/6036118

Abstract

Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis methods of IGBT devices are studied in this paper, and their classification accuracy is tested. Due to the shortcomings of incomplete data application in the traditional method of characterizing the device state based on point frequency parameters, a fault diagnosis method based on full frequency threshold screening was proposed in this paper, and its classification accuracy was detected by the Extreme Learning Machine (ELM) algorithm. The randomly generated input layer weight ω and hidden layer deviation lead to the change of output layer weight β and then affect the overall output result. In view of the errors and instability caused by this randomness, an improved Finite Impulse Response Filter ELM (FIR-ELM) training algorithm is proposed. The filtering technique is used to initialize the input weights of the Single Hidden Layer Feedforward Neural Network (SLFN). The hidden layer of SLFN is used as the preprocessor to achieve the minimum output error. To reduce the structural risk and empirical risk of SLFN, the simulation results of 300 groups of spectral data show that the improved FIR-ELM algorithm significantly improves the training accuracy and has good robustness compared with the traditional extreme learning machine algorithm.

Highlights

Insulated Gate Bipolar Transistor (IGBT) is a power semiconductor device consisting of BJT and MOS, which has been fully applied to high-precision fields, such as new energy power generation, wind power, electric locomotive traction, and electric vehicles [1, 2]
It is a machine learning method based on a single hidden layer feedforward neural network (SLFN) [13], but it is different from Single Hidden Layer Feedforward Neural Network (SLFN)
In order to verify the performance of the Extreme Learning Machine (ELM) algorithm for fault classification accuracy detection, this paper chooses to conduct experiments on the MatlabR2017a platform

Summary

Research Article

Received 24 June 2021; Revised August 2021; Accepted August 2021; Published 1 October 2021. Its reliability is closely related to system stability Once failure occurs, it may cause irreparable loss. Due to the shortcomings of incomplete data application in the traditional method of characterizing the device state based on point frequency parameters, a fault diagnosis method based on full frequency threshold screening was proposed in this paper, and its classification accuracy was detected by the Extreme Learning Machine (ELM) algorithm. E filtering technique is used to initialize the input weights of the Single Hidden Layer Feedforward Neural Network (SLFN). To reduce the structural risk and empirical risk of SLFN, the simulation results of 300 groups of spectral data show that the improved FIR-ELM algorithm significantly improves the training accuracy and has good robustness compared with the traditional extreme learning machine algorithm

Introduction

MP Vdc

Input layer Hidden layer Output layer

Expected output Predictive output

Oj Hidden layer βRm Output layer

Testing accuracy