Artificial-Intelligence-Based Open-Circuit Fault Diagnosis in VSI-Fed PMSMs and a Novel Fault Recovery Method

Artificial intelligence (AI) techniques are widely used in fault diagnosis because they are superior in detection and prediction. The detection of faults in power systems containing electronic components is critical. The switch faults of the voltage source inverter (VSI) have a severe impact on the driving system. Short-circuit switches increase the thermal stress due to their fast and high stator currents. Additionally, open-circuit switches cause unstable motor operation. However, these issues are not sufficiently addressed or accurately predicted for VSI switch faults in the literature. Thus, this paper investigates the use of different AI classifiers for three-phase VSI fault diagnosis. Various AI methods are used, such as naïve Bayes, support vector machine (SVM), artificial neural network (ANN), and decision tree (DT) techniques. These methods are applied to a VSI-fed permanent magnet synchronous motor (PMSM) to detect the faults in the inverter switches. These methods use the drain–source voltage and PWM signals to decide whether the switch is healthy or unhealthy. In addition, they are compared in terms of their detection accuracy. In this regard, the comparative results show that the DT method has the highest accuracy as compared to other methods in the fault diagnosis process. Moreover, this paper proposes a novel and universal voltage compensation loop to compensate for the absence of the voltage portion due to the open switch fault. Thus, the driving system is assisted in operating under its normal operating conditions. The universal term is used because the proposed voltage compensation loop can be implemented in any type of inverter. To validate the results, the proposed system is implemented using two software programs, LTSPICE XVII-USA, WEKA 3.9-New Zealand