A Random Forest and Current Fault Texture Feature-Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR

TL;DR

This paper introduces a data-driven method using random forests and current texture features for diagnosing current sensor faults in three-phase PWM VSR systems, enhancing system reliability without additional hardware.

Contribution

It proposes a novel fault diagnosis approach based on current texture features and random forest classification, avoiding extra sensors and improving fault detection accuracy.

Findings

Faults are detected and located successfully in simulations.

The method effectively supports system maintenance and stability.

No additional hardware sensors are required.

Abstract

Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature-based method for current sensor fault diagnosis in three-phase PWM VSR systems. First, the three-phase alternating currents (ACs) of the three-phase PWM VSR are collected to extract the current fault texture features, and no additional hardware sensors are needed to avoid causing additional unstable factors. Then, the current fault texture features are adopted to train the random forest current sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven CSFDD classifier. Finally, the effectiveness of the proposed method is verified by simulation experiments. The result shows that the current sensor faults can be detected and located successfully and that it can effectively provide fault locations for maintenance personnel to keep the stable operation of the whole system.