Explainable Signature-based Machine Learning Approach for Identification of Faults in Grid-Connected Photovoltaic Systems

Transformation of conventional power networks into smart grids with the heavy penetration level of renewable energy resources, particularly grid-connected Photovoltaic (PV) systems, has increased the need for efficient fault identification systems. Malfunctioning any single component in grid-connected PV systems may lead to grid instability and other serious consequences, showing that a reliable fault identification system is the utmost requirement for ensuring operational integrity. Therefore, this paper presents a novel fault identification approach based on statistical signatures of PV operational states. These signatures are unique because each fault has a different nature and distinctive impact on the electrical system. Thus, the Random Forest Classifier trained on these extracted signatures showed 100% accuracy in identifying all types of faults. Furthermore, the performance comparison of the proposed framework with other Machine Learning classifiers depicts its credibility. Moreover, to elevate user trust in the predicted outcomes, SHAP (Shapley Additive Explanation) was utilized during the training phase to extract a complete model response (global explanation). This extracted global explanation can help in the assessment of predicted outcomes' credibility by decoding each prediction in terms of features contribution. Hence, the proposed explainable signature-based fault identification technique is highly credible and fulfills all the requirements of smart grids.