A Random Forest and Current Fault Texture Feature-Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR. (arXiv:2211.03789v1 [eess.SP])

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.