Data-driven design of fault diagnosis for three-phase PWM rectifier using random forests technique with transient synthetic features

TL;DR

This paper presents a data-driven online fault diagnosis method using random forests and transient synthetic features to accurately detect open-circuit faults in IGBTs of three-phase PWM rectifiers, enhancing system safety.

Contribution

It introduces a novel fault diagnosis approach combining random forests with transient synthetic features, outperforming other methods in accuracy and effectiveness.

Findings

Random forests outperform SVM and neural networks in fault diagnosis.

Transient synthetic features improve classification accuracy.

Fault diagnosis accuracy reaches 98.32% with the proposed method.

Abstract

A three-phase pulse-width modulation (PWM) rectifier can usually maintain operation when open-circuit faults occur in insulated-gate bipolar transistors (IGBTs), which will lead the system to be unstable and unsafe. Aiming at this problem, based on random forests with transient synthetic features, a data-driven online fault diagnosis method is proposed to locate the open-circuit faults of IGBTs timely and effectively in this study. Firstly, by analysing the open-circuit fault features of IGBTs in the three-phase PWM rectifier, it is found that the occurrence of the fault features is related to the fault location and time, and the fault features do not always appear immediately with the occurrence of the fault. Secondly, different data-driven fault diagnosis methods are compared and evaluated, the performance of random forests algorithm is better than that of support vector machine or artificial neural networks. Meanwhile, the accuracy of fault diagnosis classifier trained by transient synthetic features is higher than that trained by original features. Also, the random forests fault diagnosis classifier trained by multiplicative features is the best with fault diagnosis accuracy can reach 98.32%. Finally, the online fault diagnosis experiments are carried out and the results demonstrate the effectiveness of the proposed method, which can accurately locate the open-circuit faults in IGBTs while ensuring system safety.