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
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626192500100X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:383:y:2025:i:c:s030626192500100x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.125370

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().