Diagnosing faults in a photovoltaic system using the Extra Trees ensemble algorithm

Abstract

The application of machine learning techniques for monitoring and diagnosing faults in photovoltaic (PV) systems has been shown to enhance the reliability of PV power generation. This research introduced a novel machine learning classifier for fault diagnosis in PV systems, utilizing an ensemble algorithm known as extra trees (ETC). The study initially proposed a system with two PV modules and developed a low-cost Arduino-based data logger to gather data from the PV system in free-fault and faulty conditions. Subsequently, the study evaluated six other advanced classifiers for fault diagnosis in PV systems, namely logistic regression (LR), k-nearest neighbor (kNN), support vector machine (SVM), decision tree (DT), AdaBoost, and random forest (RF) models using the collected data from the proposed PV system. The assessment of the various models' performance indicated that the extra trees model exhibits superior classification capabilities for partial shading (PS), open circuit (OCF), partial shading with bypass diode disconnected (PSBD), and combined partial shading with bypass diode disconnected plus open circuit (PSBDOC) faults. The results demonstrated that the new ETC classifier achieves an accuracy of 92%, surpassing the 91%, 87%, 7%, and 59% accuracy of the RF, DT, kNN, and LR classifiers, respectively. This highlights the effectiveness of the extra trees model in enhancing fault detection and classification by distinguishing between open circuits and twin faults. Consequently, these results can be utilized to develop advanced diagnostic tools for photovoltaic systems, thereby improving the reliability of solar technology and accelerating the rate of installation.