Open-Circuit Fault Diagnosis in 3ϕ V/F-Controlled VSIs Under Variable Load Conditions at Different Frequencies Using Park’s Vector Normalization and Extreme Gradient Boosting

The open-circuit fault diagnosis of switching devices in 3ϕ V/F-controlled voltage source inverters is critical, since diagnostic parameters change with varying load conditions and frequency. Park’s vector transform-based approaches depend on threshold values for fault diagnosis, demanding continuous modifications based on load variations, making them prone to improper diagnosis. Artificial intelligence-based methods give good accuracy, but they require extensive data collection under varying load conditions, creating implementation efforts that are considerably high. This paper focuses on optimizing threshold-independent methods and reducing data requirements for the artificial intelligence-based open-circuit fault diagnosis of 3ϕ V/F-controlled VSIs. To mitigate the problem of fault misclassification under variable load conditions at different frequencies, the stator current is normalized using Park’s vector transform. Normalized currents ensure that the extracted features remain the same under all load conditions while providing distinctive features for faulty and healthy conditions. Feature extraction is implemented using the wavelet transform, and feature selection is carried out using a ReliefF algorithm, which enhances classification by selecting key features. The selected features are then used to diagnose faults using an extreme gradient boosting algorithm. In XGBoost, a random search is preferred over a grid search to find the best hyperparameters for optimal performance, as it speeds up tuning, explores more options, and efficiently balances accuracy. The proposed system outperforms current open-switch fault diagnosis approaches by providing high effectiveness, strong resistivity, and a fast detection time. The results are presented for different combinations of single and multiple open-switch faults under variable load conditions at different frequencies.