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A multi-stage review framework for AI-driven predictive maintenance and fault diagnosis in photovoltaic systems

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  • Hamza, Ali
  • Ali, Zunaib
  • Dudley, Sandra
  • Saleem, Komal
  • Uneeb, Muhammad
  • Christofides, Nicholas

Abstract

The photovoltaic (PV) sector encounters challenges such as high initial costs, reliance on weather, susceptibility to faults, irregularities in the grid, and degradation of components. Predictive maintenance (PdM) aims to proactively identify issues, thereby enhancing reliability and efficiency but may lack specific fault details without additional diagnostic efforts. This research presents an advanced PdM and fault diagnosis framework that integrates fault pattern analysis, severity assessments, and critical fault predictions. It aims to improve the functionality of PV systems, minimize downtime, and enhance reliability by identifying and analyzing specific fault patterns. Consequently, our article provides a critical review of current Artificial Intelligence (AI) methodologies for PdM and fault diagnosis in PV systems. Moreover, this study highlights the significance of data standardization and offers recommendations on how PdM, when combined with fault diagnosis, can utilize various data sources to anticipate faults in advance, assess their severity, and optimize system performance and maintenance activities. To the best of the authors’ knowledge, no such review study exists.

Suggested Citation

  • Hamza, Ali & Ali, Zunaib & Dudley, Sandra & Saleem, Komal & Uneeb, Muhammad & Christofides, Nicholas, 2025. "A multi-stage review framework for AI-driven predictive maintenance and fault diagnosis in photovoltaic systems," Applied Energy, Elsevier, vol. 393(C).
    • Handle: RePEc:eee:appene:v:393:y:2025:i:c:s0306261925008384
    DOI: 10.1016/j.apenergy.2025.126108
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    1. Gamarra, Carlos & Guerrero, Josep M. & Montero, Eduardo, 2016. "A knowledge discovery in databases approach for industrial microgrid planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 615-630.
    2. Eleonora Arena & Alessandro Corsini & Roberto Ferulano & Dario Alfio Iuvara & Eric Stefan Miele & Lorenzo Ricciardi Celsi & Nour Alhuda Sulieman & Massimo Villari, 2021. "Anomaly Detection in Photovoltaic Production Factories via Monte Carlo Pre-Processed Principal Component Analysis," Energies, MDPI, vol. 14(13), pages 1-16, July.
    3. Qamar Navid & Ahmed Hassan & Abbas Ahmad Fardoun & Rashad Ramzan & Abdulrahman Alraeesi, 2021. "Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    4. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    5. Jufri, Fauzan Hanif & Oh, Seongmun & Jung, Jaesung, 2019. "Development of Photovoltaic abnormal condition detection system using combined regression and Support Vector Machine," Energy, Elsevier, vol. 176(C), pages 457-467.
    6. Muhammad Hussain & Hussain Al-Aqrabi & Richard Hill, 2022. "Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection," Energies, MDPI, vol. 15(15), pages 1-14, July.
    7. Andreas Livera & Georgios Tziolis & Jose G. Franquelo & Ruben Gonzalez Bernal & George E. Georghiou, 2022. "Cloud-Based Platform for Photovoltaic Assets Diagnosis and Maintenance," Energies, MDPI, vol. 15(20), pages 1-25, October.
    8. Zixia Yuan & Guojiang Xiong & Xiaofan Fu, 2022. "Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey," Energies, MDPI, vol. 15(22), pages 1-18, November.
    9. AlSkaif, Tarek & Dev, Soumyabrata & Visser, Lennard & Hossari, Murhaf & van Sark, Wilfried, 2020. "A systematic analysis of meteorological variables for PV output power estimation," Renewable Energy, Elsevier, vol. 153(C), pages 12-22.
    10. Khaled Bataineh & Naser Eid, 2018. "A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions," Resources, MDPI, vol. 7(4), pages 1-16, November.
    11. Ahmed A. Al-Katheri & Essam A. Al-Ammar & Majed A. Alotaibi & Wonsuk Ko & Sisam Park & Hyeong-Jin Choi, 2022. "Application of Artificial Intelligence in PV Fault Detection," Sustainability, MDPI, vol. 14(21), pages 1-25, October.
    12. Meiya Dong & Jumin Zhao & Deng-ao Li & Biaokai Zhu & Sihai An & Zhaobin Liu, 2021. "ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing," International Journal of Distributed Sensor Networks, , vol. 17(11), pages 15501477211, November.
    13. Fouzi Harrou & Bilal Taghezouit & Sofiane Khadraoui & Abdelkader Dairi & Ying Sun & Amar Hadj Arab, 2022. "Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems," Energies, MDPI, vol. 15(18), pages 1-28, September.
    14. Karunathilake, Hirushie & Hewage, Kasun & Prabatha, Tharindu & Ruparathna, Rajeev & Sadiq, Rehan, 2020. "Project deployment strategies for community renewable energy: A dynamic multi-period planning approach," Renewable Energy, Elsevier, vol. 152(C), pages 237-258.
    15. Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    16. 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.
    17. Kellil, N. & Aissat, A. & Mellit, A., 2023. "Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions," Energy, Elsevier, vol. 263(PC).
    18. Mellit, A. & Benghanem, M. & Kalogirou, S. & Massi Pavan, A., 2023. "An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things," Renewable Energy, Elsevier, vol. 208(C), pages 399-408.
    19. 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.
    20. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    21. G R Venkatakrishnan & R Rengaraj & S Tamilselvi & J Harshini & Ansheela Sahoo & C Ahamed Saleel & Mohamed Abbas & Erdem Cuce & C Jazlyn & Saboor Shaik & Pinar Mert Cuce & Saffa Riffat, 2023. "Detection, location, and diagnosis of different faults in large solar PV system—a review," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 18, pages 659-674.
    22. Mellit, Adel & Kalogirou, Soteris, 2022. "Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems," Renewable Energy, Elsevier, vol. 184(C), pages 1074-1090.
    23. Akram, M. Waqar & Li, Guiqiang & Jin, Yi & Chen, Xiao & Zhu, Changan & Zhao, Xudong & Khaliq, Abdul & Faheem, M. & Ahmad, Ashfaq, 2019. "CNN based automatic detection of photovoltaic cell defects in electroluminescence images," Energy, Elsevier, vol. 189(C).
    24. Masoud Emamian & Aref Eskandari & Mohammadreza Aghaei & Amir Nedaei & Amirmohammad Moradi Sizkouhi & Jafar Milimonfared, 2022. "Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques," Energies, MDPI, vol. 15(9), pages 1-25, April.
    25. Hamid Iftikhar & Eduardo Sarquis & P. J. Costa Branco, 2021. "Why Can Simple Operation and Maintenance (O&M) Practices in Large-Scale Grid-Connected PV Power Plants Play a Key Role in Improving Its Energy Output?," Energies, MDPI, vol. 14(13), pages 1-29, June.
    26. Varaha Satya Bharath Kurukuru & Frede Blaabjerg & Mohammed Ali Khan & Ahteshamul Haque, 2020. "A Novel Fault Classification Approach for Photovoltaic Systems," Energies, MDPI, vol. 13(2), pages 1-17, January.
    27. Mariam Ibrahim & Ahmad Alsheikh & Feras M. Awaysheh & Mohammad Dahman Alshehri, 2022. "Machine Learning Schemes for Anomaly Detection in Solar Power Plants," Energies, MDPI, vol. 15(3), pages 1-17, February.
    28. Mojgan Hojabri & Samuel Kellerhals & Govinda Upadhyay & Benjamin Bowler, 2022. "IoT-Based PV Array Fault Detection and Classification Using Embedded Supervised Learning Methods," Energies, MDPI, vol. 15(6), pages 1-18, March.
    29. Wang, Mengyuan & Xu, Xiaoyuan & Yan, Zheng, 2023. "Online fault diagnosis of PV array considering label errors based on distributionally robust logistic regression," Renewable Energy, Elsevier, vol. 203(C), pages 68-80.
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