Applications of Machine Learning Algorithms for Photovoltaic Fault Detection: a Review

  • Abdelilah Et-taleby Innovative Technologies Laboratory Sidi Mohamed Ben Abdellah University Fez
  • Yassine Chaibi Moroccan School of Engineering Sciences, Rabat, Morocco
  • Mohamed Benslimane Innovative Technologies Laboratory Sidi Mohamed Ben Abdellah University, Fez
  • Mohammed Boussetta Innovative Technologies Laboratory Sidi Mohamed Ben Abdellah University, Fez
Keywords: Machine Learning, photovoltaic system, PV fault detection, artificial neural network


Over the years, the boom of technology has caused the accumulation of a large amount of data, famously known as big data, in every field of life. Traditional methods have failed to analyse such a huge pile of data due to outdated techniques. In recent times, the use of photovoltaic systems has risen worldwide. The arena Photovoltaic (PV) system has witnessed the same unprecedented expansion of data owing to the associated monitoring systems. However, the faults created within the PV system cannot be detected, classified, or predicted by using conventional techniques. This necessitates the use of modern techniques such as Machine Learning. Its powerful algorithms, such as artificial neural networks (ANN), help in the accurate detection and classification of faults in the PV system. This review paper introduces and evaluates the applications of Machine Learning (ML) algorithms in PV fault detection. It provides a brief overview of Machine Learning and its concepts along with various widely used ML algorithms. This review various peer-reviewed studies to investigate various models of ML algorithms in the PV system with the main focus on its fault detection accuracy and efficiency.


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How to Cite
Et-taleby, A., Yassine Chaibi, Benslimane, M., & Boussetta, M. (2023). Applications of Machine Learning Algorithms for Photovoltaic Fault Detection: a Review. Statistics, Optimization & Information Computing, 11(1), 168-177.
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