A Novel Simultaneous Diagnosis Method for IGBT Open-Circuit Faults and Current Sensor Faults of Three-Phase SPWM Inverter

This article presents a novel fault diagnosis method for open-circuit faults of insulated gate bipolar transistors (IGBTs) and current sensor faults in three-phase sinusoidal pulsewidth modulation inverters. The proposed method facilitates the simultaneous diagnosis of multiple IGBT and current sensor faults. A combination of Park transform and fast Fourier transform (FFT) is employed to extract fault features. The Park transform converts three-phase current into two-phase current, thereby reducing the dimensionality of the data. The FFT integrates time-domain and frequency-domain analyses to enhance the representation of fault characteristics. Additionally, fault features are extracted from the samples post-FFT to minimize the data volume of individual sample input by the model. For fault diagnosis, a temporal convolutional neural network model incorporating stacked residual blocks is developed to train and evaluate potential faults and demonstrate the model's validity and accuracy. The results of online simulations and hardware-in-the-loop experiments indicate that the proposed method can identify and locate the IGBT and sensor faults within 32 ms, and the accuracy is 98.75%. This method achieves higher training and testing accuracy and faster training and testing speed, which is superior to the existing diagnosis technologies.