A weighted ensemble learning-based autonomous fault diagnosis method for photovoltaic systems using genetic algorithm

Conventional protection devices may not be able to diagnose the faults in Photovoltaic (PV) systems due to the nonlinear behavior of PV characteristics, its dependency on the operating environment, and operation of Maximum Power Point Tracking (MPPT) algorithms. To date, numerous studies have been carried out to overcome this challenge through Artificial Intelligence (AI) techniques. However, most of the AI-based techniques require a large dataset and also suffer overfitting problem. In this study, we propose an intelligent and automatic fault diagnosis method using less dataset for the training process through feature extraction and selection algorithms, as well as using an ensemble learning algorithm to classify open-circuit (OC) and line-line (LL) faults in PV systems. For this purpose, the proposed model firstly extracts the key features of the operating current and voltage of the PV arrays. Secondly, the Lasso penalty as an embedded feature selection technique is applied to determine the best subset of features. Thirdly, an ensemble learning algorithm consisting of three individual learning algorithms namely Logistic Regression (LR), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) is used in the classification stage to predict conditions of PV systems based on a weighted voting approach. Moreover, we apply a genetic algorithm to optimize the weights assigned to the algorithms in order to detect electrical faults in PV systems with higher accuracy. The experimental results demonstrate the proposed model is very efficient and reliable to diagnose open-circuit (OC) and line-line (LL) faults in PV arrays under any challenging conditions with an accuracy of 99%.