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One-Class Machine Learning Classifiers-Based Multivariate Feature Extraction for Grid-Connected PV Systems Monitoring under Irradiance Variations

Author

Listed:
  • Zahra Yahyaoui

    (Research Unit Advanced Materials and Nanotechnologies, Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kairouan 3100, Tunisia)

  • Mansour Hajji

    (Research Unit Advanced Materials and Nanotechnologies, Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kairouan 3100, Tunisia)

  • Majdi Mansouri

    (Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha 23874, Qatar)

  • Kais Bouzrara

    (Laboratory of Automatic Signal and Image Processing, National Engineering School of Monastir, Monastir 5019, Tunisia)

Abstract

In recent years, photovoltaic (PV) energy production has witnessed overwhelming growth, which has inspired the search for more effective operations. Nevertheless, different PV faults may appear, which leads to various degradation stages. Furthermore, under different irradiance levels, these faults may be misclassified as a healthy mode owing to the high resemblances between them, thus provoking serious challenges in terms of power losses and maintenance costs. Hence, interposing the irradiance variation in grid-connected PV (GCPV) systems modeling is important for monitoring tasks to ensure the effective operation of these systems, to increase their reliability and to prevent false alarms. Therefore, in this paper, a fault detection and diagnosis (FDD) method for the GCPV systems using machine learning (ML) based on principal component analysis (PCA) is proposed in order to ensure the reliability and security of the whole system under irradiance variations. The proposed strategy consists of three main steps: (i) introduce the irradiance variations in PV system modeling because of its great impact on power production; (ii) feature extraction and selection through PCA; and (iii) fault classification using ML techniques. In this study, we generate a database that is used to compare the proposed strategy with the standard strategy (considering a fixed irradiance during FDD), to make, at first, a complete and significant comparative assessment of fault diagnosis and to demonstrate the efficiency of the proposed strategy. The achieved results show the high effectiveness of the proposed one-class classification-based approach to detect and diagnose PV array anomalies, reaching an accuracy up to 99.68%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13758-:d:1240413
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    References listed on IDEAS

    as
    1. Das, Saborni & Hazra, Abhik & Basu, Mousumi, 2018. "Metaheuristic optimization based fault diagnosis strategy for solar photovoltaic systems under non-uniform irradiance," Renewable Energy, Elsevier, vol. 118(C), pages 452-467.
    2. Zahra Yahyaoui & Mansour Hajji & Majdi Mansouri & Kamaleldin Abodayeh & Kais Bouzrara & Hazem Nounou, 2022. "Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM," Energies, MDPI, vol. 15(17), pages 1-19, August.
    3. 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.
    4. 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.
    5. Rouani, Lahcene & Harkat, Mohamed Faouzi & Kouadri, Abdelmalek & Mekhilef, Saad, 2021. "Shading fault detection in a grid-connected PV system using vertices principal component analysis," Renewable Energy, Elsevier, vol. 164(C), pages 1527-1539.
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