A Physics-Informed Pattern Recognition Method for Open-Circuit Fault Detection of Inverters Under Unexpected Conditions

This paper introduces a novel physics-informed pattern recognition (PIPR) method for open-circuit fault detection in inverters. The proposed method unfolds in three stages: model analysis, offline training, and online validation. In the first stage, we construct an analytical model of power converters. This model is subsequently used to derive fault diagnosis variables. This step is followed by the collection of training samples via simulations. The gathered samples are then fed into pattern recognition neural networks, a process enabled by the prior extraction of model information. This architecture allows for efficient training of the neural network with fewer neurons and samples. The final stage involves the detection and diagnosis of faults by a well-trained online classifier. The robustness of the proposed PIPR method in dealing with unexpected conditions in classification problems shows its potential across diverse conditions.