Abstract
Fault identification in Photovoltaic (PV) array is a contemporary research topic motivated by the higher penetration levels of PV systems in recent electrical grids. Therefore, this work aims to define an optimal Machine learning (ML) structure of automatic detection and diagnosis algorithm for common PV array faults, namely, permanent (Arc Fault, Line-to-Line, Maximum Power Point Tracking unit failure, and Open-Circuit faults), and temporary (Shading) under a wide range of climate datasets, fault impedances, and shading scenarios. To achieve the best-fit ML structure, three distinct ML classifiers are compared, namely, Decision Tree (DT) based on different splitting criteria, K-Nearest Neighbors (KNN) based on the different metrics of distance and weighting functions, and Support Vector Machine (SVM) based on different Kernel functions and multi-classification approaches. Also, Bayesian Optimization is adopted to assign the optimal hyperparameters to the fault classifiers. To investigate the performance of classifiers reported, both simulation and experimental case studies are carried out and presented.
Highlights
T HE SOLAR PHOTOVOLTAIC (PV) industry has experienced significant growth over the past years due to the technology’s clear economic and environmental benefits
The PV array is commonly subjected to a variety of faults including, Partial Shading (PS) conditions, Maximum Power Point Tracking (MPPT) unit failure, PV module hot spots/micro-cracks and electrical faults (Open-Circuit (OC), Line-to-Line (LL), Line-to-Ground (LG), and Arc Fault (AF)) [5]
This paper introduced a fault detection and diagnosis algorithm for PV array, which is able to discriminate different fault types, namely, AF, LL, OC, MPPT unit failure, and PS under a wide range of unexpected scenarios
Summary
T HE SOLAR PHOTOVOLTAIC (PV) industry has experienced significant growth over the past years due to the technology’s clear economic and environmental benefits. The PV array is commonly subjected to a variety of faults including, Partial Shading (PS) conditions, Maximum Power Point Tracking (MPPT) unit failure, PV module hot spots/micro-cracks and electrical faults (Open-Circuit (OC), Line-to-Line (LL), Line-to-Ground (LG), and Arc Fault (AF)) [5] These faults contribute to energy losses and/or system degradation, which, in turn, increase maintenance costs or fire hazards. The available studies have not shown how different hyperparameters for the mentioned classifiers are tuned, which has a high influence on the classifier(s) performance In this paper, these gaps have been taken into consideration in order to select the optimal ML models for the proposed FDD algorithm framework. A brief description of each circuit is given in the following subsections
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