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, Mohamed Nounou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

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.

Original languageEnglish
Article number10518
JournalSustainability (Switzerland)
Volume14
Issue number17
DOIs
Publication statusPublished - Sept 2022
Externally publishedYes

Keywords

  • artificial neural network (ANN)
  • fault detection and diagnosis (FDD)
  • feature selection (FS)
  • genetic algorithm (GA)
  • grid connected photovoltaic (GCPV) systems

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