AI-SafePV: An AI-Based Fault Detection Software Package to Provide Safety in Photovoltaic Arrays

Faults diagnosis in photovoltaic (PV) systems may not always be possible using conventional protection devices 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 AI-SafePV which is an intelligent and automatic fault diagnosis software package using a small dataset for the training process through feature extraction and selection processes, as well as using an ensemble learning algorithm to classify open-circuit (OC) and line-line (LL) faults in PV systems. For this purpose, firstly, the AI-SafePV software package extracts the key features of the operating current and voltage of the PV arrays. Secondly, the Lasso penalty is applied as a feature selection technique 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 (GA) to optimize the weights assigned to the algorithms in order to detect electrical faults in PV systems with higher accuracy. The experimental output of AI-SafePV software package demonstrate high efficiency and reliability to diagnose open-circuit (OC) and line-line (LL) faults in PV arrays under challenging conditions with an outstanding accuracy of 99%.