Current sensor fault detection and compensation system for wound rotor synchronous motor based on neural networks

The article presents the idea of using an artificial neural network-based fault classification system for the current sensor (CS) faults. The analysis includes a wound rotor synchronous motor drive system designed according to the idea of fault-tolerant control (FTC). Thanks to the use of the CS fault compensation strategy, the drive system ensures continued functionality, even after a failure has occurred. In the article, the simulation part of the implementation of a CS fault classifier is presented. The neural structure aims to determine the damage category (gain change, offset, saturation, open circuit) and localisation (phase A or B). The developed classifier uses the principal structure of a multilayer perceptron (MLP). The assessment of the type of CS fault is based on single samples of the stator phase current signal. The article shows the high precision of the neural-based classifier that uses the information from the mathematical model in the practical implementation of diagnostic systems for wound rotor synchronous machines.