A comprehensive study on symbolic expressions for fault detection-classification in photovoltaic farms
Nikola Anđelić,
Sandi Baressi Šegota and
Vedran Mrzljak
Applied Energy, 2025, vol. 383, issue C, No S030626192500100X
Abstract:
Large-scale photovoltaic (solar) farms play a crucial role in harnessing solar energy for electricity generation through photovoltaic (PV) technology. However, the control and management of such systems pose significant challenges, particularly in fault detection. This paper introduces the application of a genetic programming symbolic classifier (GPSC) to a publicly available dataset for fault detection in photovoltaic farms. Given the imbalanced nature of the original dataset, the study necessitated the application of oversampling techniques to achieve a balanced representation of class samples. Additionally, the impact of scaling and normalizing techniques on the performance of the GPSC was thoroughly investigated.
Keywords: Data preprocessing and oversampling; Genetic programming symbolic classifier; Random hyperparameter value search method; Photovoltaic farms fault detection and classification; Threshold based voting ensemble (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:383:y:2025:i:c:s030626192500100x
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DOI: 10.1016/j.apenergy.2025.125370
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