Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review
DOI:
https://doi.org/10.55927/fjas.v3i10.11536Keywords:
Photovoltaic Fault Detection, Artificial Intelligence, Machine Learning, Deep Learning, Fault DiagnosisAbstract
The increasing global demand for renewable energy has propelled the adoption of photovoltaic systems as a key component of sustainable energy infrastructure. Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.
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