Detecting and Classifying Line-to-Line Fault in Photovoltaic Arrays Under Challenging Conditions Using Machine Learning Classifiers.

Line-to-line fault (LLF) is considered one of the most common electrical faults occurring on the array side of the Photovoltaic System (PVS). This research proposes a diagnostic method to detect and classify LLFs under low mismatch levels, and higher fault impedances of up to 45 ohm when compared to other methods that did not exceed 25 ohm. The proposed method uses robust ML classifiers, namely Quadratic Discriminate Analysis (QDA) and Feed-Forward Neural Network (FNN), to design the required models. For this purpose, seven efficient features including array 2-voltages, array 2-currents, fill factor, maximum power to irradiance ratio, and voltage-temperature product, are extracted from the I-V characteristics curve of the employed Photovoltaic (PV) array. This is done under normal and faulty conditions, with a wide range of climate conditions. The faults are first detected by a detection module, then classified according to their mismatch levels by using classification module. Each module uses two Machine Learning (ML) models which represent the QAD and FNN classifiers. The results demonstrate that the proposed method can detect the LLFs, even under critical conditions of mismatch and impedance, with an average accuracy of 99.81% and 100% for QDA and FNN respectively. In addition, it classifies the severity of these faults with an average accuracy of 99.28% and 100% for both adopted models. [ABSTRACT FROM AUTHOR]