Fault Identification of Photovoltaic Array Based on Machine Learning Classifiers
AuthorsBadr, Mohamed M.
Hamad, Mostafa S.
Abdel-Khalik, Ayman S.
Hamdy, Ragi A.
KAUST DepartmentElectrical and Computer Engineering Program
Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/673975
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AbstractFault 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.
CitationBadr, M. M., Hamad, M. S., Abdel-Khalik, A. S., Hamdy, R. A., Ahmed, S., & Hamdan, E. (2021). Fault Identification of Photovoltaic Array Based on Machine Learning Classifiers. IEEE Access, 9, 159113–159132. doi:10.1109/access.2021.3130889
SponsorsThis work was supported by the Information Technology Industry Development Agency’s (ITIDA) Information Technology Academia Collaboration (ITAC) Collaborative Funded Project through Advanced Research Projects (ARP) under Grant ARP2020.R28.18.