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A comprehensive study on symbolic expressions for fault detection-classification in photovoltaic farms

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  • Anđelić, Nikola
  • Baressi Šegota, Sandi
  • Mrzljak, Vedran

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.

Suggested Citation

  • Anđelić, Nikola & Baressi Šegota, Sandi & Mrzljak, Vedran, 2025. "A comprehensive study on symbolic expressions for fault detection-classification in photovoltaic farms," Applied Energy, Elsevier, vol. 383(C).
    • Handle: RePEc:eee:appene:v:383:y:2025:i:c:s030626192500100x
    DOI: 10.1016/j.apenergy.2025.125370
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