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A Novel GWO-CSA Based IOT Enabled Maximum Power Point Tracking System for Operational Efficiency Enhancement of Solar Photovoltaic Energy System

Published: 13 May 2024 Publication History

Abstract

This paper introduces a novel integration of Grey Wolf Optimization (GWO) and Crow Search Algorithm (CSA) within an IoT-enabled framework to enhance the operational efficiency of Solar Photovoltaic (PV) Energy Systems. The primary focus is on maximizing the power output through an advanced Maximum Power Point Tracking (MPPT) system. The proposed GWO-CSA algorithm synergizes the explorative capabilities of the Grey Wolf Optimization and the exploitative strengths of the Crow Search Algorithm, leading to a robust and efficient tracking mechanism. The implementation of IoT technology facilitates real-time monitoring and adaptive control, further optimizing the system's performance. Simulation results, conducted under varying environmental conditions, demonstrate a significant improvement in efficiency and stability over traditional MPPT methods. The GWO-CSA based system shows remarkable adaptability to fluctuating solar irradiance and temperature, ensuring optimal power extraction even under suboptimal conditions. This enhancement in operational efficiency not only increases the energy yield but also extends the lifespan of the solar panels. This research contributes to the field of renewable energy by providing a scalable and effective solution for maximizing solar energy utilization. The integration of IoT and advanced optimization algorithms presents a promising avenue for future developments in sustainable energy systems.

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ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine Intelligence
November 2023
1215 pages
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Published: 13 May 2024

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