Localized overheating, also known as hot spotting, can occur in specific areas of a solar panel where excessive heat is generated within photovoltaic (PV) systems. This problem may occur as a result of uneven distribution of current, inconsistencies in shading, issues with soldering, or failures in the packaging, leading to a condition of reverse bias and localized heating. The presence of hot spots not only accelerates the degradation of PV systems but also increases the risk of permanent damage to the panels. Therefore, it is crucial to promptly identify and rectify any hot spot faults to ensure the safe and reliable operation of the PV system. This research investigates to analyzing hotspots series and parallel faults by incorporating advanced machine learning algorithms for fault detection and classification. Unlike conventional methods that rely solely on hotspots faults in general. Six distinct machine learning classifiers were used to the identification and categorization of early hot-spots in PV modules. Among these classifiers, the artificial neural network (ANN) emerges as the most suitable machine learning technique for early detection of PV hot-spots, exhibiting an impressive detection accuracy of 97% when appropriate data samples are employed. The data collection was done by real measuring instruments such as: current and voltage sensors, also environmental sensors for irradiance and temperature. Before the implementation of the machine learning tool, the data of each examined PV module was applied by preprocessing data methods such as: cleaning, deleting outliers, and transforming raw data into a format suitable for training and evaluating machine learning models.

Title

Engineering Research Express

Publisher

iopscience

Publisher Country

United Kingdom

Indexing

Thomson Reuters

Impact Factor

1.6

Publication Type

Both (Printed and Online)

Volume

--

Year

2025

Pages

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