Abstract
The data-driven fault detection techniques particularly artificial intelligent ones have many advantages over model-based methods is that not much information about system parameters is needed. In this work, a data-driven method based on machine learning random forest technique was proposed to instantaneous detecting and diagnosing a partial shading fault in a grid-connected PV system in real-time, a PV system installed in the desert area of Adrar, Algeria was used as a case study. The feasibility of the tree-based ensemble method (random forest) in detecting and diagnosing a partial shading fault in a grid-connected PV system was assured with high performance, the error was recorded with less than 1.3%.
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Ziane, A. et al. (2021). Detecting Partial Shading in Grid-Connected PV Station Using Random Forest Classifier. In: Hatti, M. (eds) Artificial Intelligence and Renewables Towards an Energy Transition. ICAIRES 2020. Lecture Notes in Networks and Systems, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-030-63846-7_10
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DOI: https://doi.org/10.1007/978-3-030-63846-7_10
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