Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems

Amal Hichri, Mansour Hajji, Majdi Mansouri (), Kamaleldin Abodayeh, Kais Bouzrara, Hazem Nounou and Mohamed Nounou
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Amal Hichri: Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kasserine 1200, Tunisia
Mansour Hajji: Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kasserine 1200, Tunisia
Majdi Mansouri: Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha 23874, Qatar
Kamaleldin Abodayeh: Department of Mathematical Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Kais Bouzrara: Laboratory of Automatic Signal and Image Processing, National Engineering School of Monastir, Monastir 5000, Tunisia
Hazem Nounou: Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha 23874, Qatar
Mohamed Nounou: Chemical Engineering Program, Texas A&M University at Qatar, Doha 23874, Qatar

Sustainability, 2022, vol. 14, issue 17, 1-14

Abstract: Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting their dependability, availability, and necessary safety. As a result, the problem of FDD in grid-connected PV (GCPV) systems is discussed in this work. Tools for feature extraction and selection and fault classification are applied in the developed FDD approach to monitor the GCPV system under various operating conditions. This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artificial neural network (ANN) classifier is applied for fault diagnosis. Only the most important features are selected to be supplied to the ANN classifier. The classification performance is determined via different metrics for various GA-based ANN classifiers using data extracted from the healthy and faulty data of the GCPV system. A thorough analysis of 16 faults applied on the module is performed. In general terms, the faults observed in the system are classified under three categories: simple, multiple, and mixed. The obtained results confirm the feasibility and effectiveness with a low computation time of the proposed approach for fault diagnosis.

Keywords: grid connected photovoltaic (GCPV) systems; fault detection and diagnosis (FDD); artificial neural network (ANN); genetic algorithm (GA); feature selection (FS) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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