Fault detection in photovoltaic systems using machine learning technique
Khadija Attouri, Mansour Hajji, Majdi Mansouri, Mohamed Faouzi Harkat, Abdelmalek Kouadri, Hazem Nounou, Mohamed Nounou
Abstract
To ensure high reliability of the Grid-Connected Photovoltaic (GCPV) systems, promptly faults detection, diagnosis and automatic process monitoring are essential tools to keep the PV and the grid network under optimal functioning. Regardless of fault types, incipient faults are usually more difficult to detect and accurately isolate. As an alternative and effective method, the principal components analysis (PCA) is proposed to extract and select more relevant features and support vector machines (SVM) technique is applied to quickly detect the faults that occur in a GCPV system. The T2 and squared weighted errors (SWE) statistics, generally used as fault detection indices, are appropriately extracted and selected within the PCA framework. Both of these features are fed to a SVM classifier handling the incipient fault detection. This task is carried out on a simulated GCPV operating under maximum power point trackers (MPPT) and matching realistic outdoor to demonstrate the effectiveness and robustness of the proposed technique.