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

Author

Listed:
  • 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
    Department of Mathematical Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • 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)

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.

Suggested Citation

  • Amal Hichri & Mansour Hajji & Majdi Mansouri & Kamaleldin Abodayeh & Kais Bouzrara & Hazem Nounou & Mohamed Nounou, 2022. "Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems," Sustainability, MDPI, vol. 14(17), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10518-:d:895800
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    References listed on IDEAS

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    1. Saba Gul & Azhar Ul Haq & Marium Jalal & Almas Anjum & Ihsan Ullah Khalil, 2019. "A Unified Approach for Analysis of Faults in Different Configurations of PV Arrays and Its Impact on Power Grid," Energies, MDPI, vol. 13(1), pages 1-23, December.
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    3. Kouadri, Abdelmalek & Hajji, Mansour & Harkat, Mohamed-Faouzi & Abodayeh, Kamaleldin & Mansouri, Majdi & Nounou, Hazem & Nounou, Mohamed, 2020. "Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 150(C), pages 598-606.
    4. Fonseca Alves, Ricardo Henrique & Deus Júnior, Getúlio Antero de & Marra, Enes Gonçalves & Lemos, Rodrigo Pinto, 2021. "Automatic fault classification in photovoltaic modules using Convolutional Neural Networks," Renewable Energy, Elsevier, vol. 179(C), pages 502-516.
    5. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
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    Cited by:

    1. Paweł Ziółkowski & Marta Drosińska-Komor & Jerzy Głuch & Łukasz Breńkacz, 2023. "Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence," Energies, MDPI, vol. 16(17), pages 1-28, August.
    2. Zahra Yahyaoui & Mansour Hajji & Majdi Mansouri & Kais Bouzrara, 2023. "One-Class Machine Learning Classifiers-Based Multivariate Feature Extraction for Grid-Connected PV Systems Monitoring under Irradiance Variations," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    3. Ana-Maria Moldovan & Mircea Ion Buzdugan, 2023. "Prediction of Faults Location and Type in Electrical Cables Using Artificial Neural Network," Sustainability, MDPI, vol. 15(7), pages 1-19, April.
    4. Manel Marweni & Mansour Hajji & Majdi Mansouri & Mohamed Fouazi Mimouni, 2023. "Photovoltaic Power Forecasting Using Multiscale-Model-Based Machine Learning Techniques," Energies, MDPI, vol. 16(12), pages 1-16, June.

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