Priya patelSolar photovoltaic (PV) technology is a promising alternative for meeting the rising global energy demands. However, there is an information gap in the PV module parameters provided by vendors and manufacturers, making it difficult to optimize their performance. Metaheuristic algorithms, such as the nutcracker optimizer and Kepler optimization algorithm (KOA), are being developed to accurately identify the unknown parameters of PV models. These algorithms can extract near-optimal values for PV parameters, improving the overall efficiency of solar energy systems.
Machine learning algorithms also play a crucial role in optimizing solar energy systems, particularly in solar irradiance prediction. Accurate solar irradiance forecasts are essential for maximizing the efficiency of solar power systems, and machine learning algorithms can provide accurate predictions.
Metaheuristic algorithms have also shown effectiveness in maximum power point tracking (MPPT) applications in solar PV models. Researchers have developed control algorithms that demonstrate high MPPT efficiency in experimental studies.
Detection techniques for PV panel faults have been developed, including image processing and deep learning methods. These techniques help ensure the proper functioning of PV panels for optimal energy production.
Researchers have also developed the Improved Salp Swarm Algorithm (ISSA) for the techno-economic optimization of isolated microgrids. This algorithm helps strike the right balance between technical criteria and environmental concerns when deciding on the sizes of PV panels, wind turbines, battery energy storage units, and diesel generators.
Overall, the use of metaheuristic and machine learning algorithms in solar PV technology is transforming the way solar energy is harnessed. These algorithms improve the efficiency and effectiveness of solar energy systems, contributing to a greener and more sustainable future.
