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Feb 17, 2025Keywords:
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Main Article Content
A review of artificial intelligence tools for the management of cleaning and tracking in Photovoltaic systems
Abstract
Solar Photovoltaic System (SPV) is a rapidly expanding renewable energy source integrated into national power grids and off-grid systems worldwide. Therefore, the effective management of power systems and the energy industry needs accurate solar energy forecasting, ranging from short-term predictions of a few seconds to longer-term forecasts up to one week in advance. Hence it is well recognized that the presence of dust particles on the surface of photovoltaic (PV) modules substantially influences their overall performance. Subsequently, the resultant decrease in energy generation significantly impacts the financial earnings. A range of methodologies have been used to address the limitations associated with solar radiation forecasting. The use of machine learning (ML) algorithms provides a range of techniques for predicting solar radiation. Additionally, integrating artificial intelligence (AI) and ML may collectively enhance the optimization of cleaning intervals for photovoltaic (PV) modules. In the same manner, the development of a sun tracking algorithm aims to accurately determine the location of the sun in order to optimize the reception of solar energy. This article aims to critically review the current research on maintenance and tracking strategies for PV panels while presenting a novel application of AI and ML algorithms. Finally, this review presents a comprehensive analysis comparing traditional approaches with AI techniques to demonstrate the significant mpact of AI algorithms in PV systems.
