International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Development of an Intelligent Layer to Improve Solar Power System Performance

Author(s) Mritunjay Piplaha, M. S. Dash, Devendra Sharma
Country India
Abstract The growing demand for renewable energy has led to rapid advancements in intelligent solar power technologies. This study proposes a Machine Learning–based High-Efficiency SEPIC Model for Context-Aware Duty Cycle Control (MLSCADCC) to enhance the efficiency, adaptability, and reliability of solar photovoltaic (PV) systems. The developed intelligent layer integrates Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Q-Learning to achieve adaptive, real-time control of SEPIC converter parameters under varying load and irradiance conditions. The proposed system dynamically adjusts the duty cycle and inductor ratings to maintain optimal power delivery and minimize losses. Simulation results demonstrate that the MLSCADCC model improves power efficiency by up to 15.4%, reduces energy consumption by 18.5%, and achieves comparable Total Harmonic Distortion (THD) levels with significantly reduced operating delay compared to existing BSTI, DB-TAC, and QRHGHE converter models. The outcomes confirm that the integration of machine learning in converter control enhances the performance and sustainability of solar power systems, contributing to the development of next-generation intelligent renewable energy frameworks.
Keywords SEPIC, MPPT, PV System, Machine Learning, GA, PSO, Q-Learning, THD, Renewable Energy
Field Engineering
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-31
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.59334
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E-ISSN 2582-2160
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