Authors - K.Surya Teja, Immanuel Anupalli, P.Sudheer Abstract - Maximum power point tracking (MPPT) is a vital module of photovoltaic (PV) systems. Traditional maximum power MPPT techniques struggle in a complex and ever-changing scenarios, and the solar system's output characteristic curve shows multi-peak phenomena owing to dissimilarities in temperature and light concentration. This paper proposes an adaptive hybrid RIME optimization technique which enhances the exploratory capabilities of the method during the initialization phase by integrating tent mapping. The goal is to improve feature selection tasks and MPPT for PV systems under partial shading condition. It uses piecewise mapping to optimize the algorithm's parameters and attack a fair steadiness amongst global exploration and local exploitation. The search method is dynamically adjusted with an adaptive inertia weight introduced, which further increases convergence speed, search efficiency and algorithm's adaptability. In order to reduce computational costs and increase classification accuracy, the hybrid method employs natural-inspired metaheuristics for feature selection, resulting in optimal subsets. When it comes to tracking speed, precision, and stability in the PV MPPT environment, the method beats PSO-BOA, conventional RIME, IRIME and HRIME approaches.
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