Optimized Fault Diagnosis in Three-Phase Voltage Source Inverters Using Artificial Neural Network and Machine Learning Techniques

Three-phase voltage source inverters (VSI) reliability is critical for industrial and renewable energy applications, where open-circuit faults in switches can lead to performance degradation, energy loss, and equipment damage. This study presents a machine learning-based fault diagnosis framework integrating artificial neural networks (ANN) and ensemble classifiers to enhance fault detection accuracy under varying load conditions. Current signal waveforms were acquired using a Tektronix Digital Storage Oscilloscope and preprocessed using discrete wavelet transform to extract meaningful time-frequency features. Various machine learning algorithms, including support vector machines (SVM), k-nearest neighbors (KNN), decision trees (DT), random forest (RF), and Adaboost, were used for VSI fault classification. The classifiers' performance was compared using standard evaluation metrics, including precision, recall, F1-score, and accuracy, to ensure a comprehensive and fair assessment of fault classification effectiveness. Experimental results indicate that RF achieved the highest accuracy of 99.97%, followed by DT (99.49%), KNN with k = 3 (99.43%), and ANN (99.6%). The ANN model with an 18-10-1 architecture demonstrated strong classification performance, outperforming AdaBoost, which reached a peak accuracy of 96.95% at 500 estimators. SVM achieved an accuracy of 90.87%, making them less effective than ensemble-based classifiers. The study further validates that wavelet-based feature extraction combined with ANN reduces misclassification rates and improves real-time detection efficiency. Additionally, the proposed system reduced fault diagnosis time by 22.5% compared to conventional methods, making real-time implementation feasible. The findings confirm the effectiveness of the proposed ANN-based fault detection system in accurately classifying single and double open switch faults in VSI. Future work will integrate the system with IoT-based predictive maintenance and cloud-based fault detection for real-world applications.