Abstrakt

The Photovoltaic (PV) module converts only a small portion of irradiance into electrical energy. Most of the solar energy is wasted as heat, resulting in a rise in PV cell temperature and a decrease in solar cell efficiency. One way to harvest this freely available solar thermal energy and improve PV cell efficiency is by integrating PV systems with thermoelectric generators (TEG). This cogeneration approach of the hybrid PV-TEG system uses waste heat as an energy production source, resulting in higher output power density. Furthermore, as hybrid PV-TEG systems are frequently used in dynamic environments with temperature and irradiance variations, it is critical to modify the electrical operating points effectively and precisely to maximize the collected power, a process known as maximum power point tracking (MPPT). Under stochastic environmental conditions, a properly chosen MPPT technique can improve the generation efficiency from 10 to 15%. Therefore, in this study a novel implementation of atomic orbital search optimization algorithm is presented for MPPT tracking. Several case studies are designed to test AOS based MPPT algorithm performance under stochastic operating conditions. The effectiveness of the proposed algorithm is validated by comparing it with the conventional Perturb and Observe (P&O) algorithm and other highly-efficient metaheuristic algorithms such as Grasshopper Optimization (GHO), Particle swarm optimization (PSO), and Grey Wolf Optimization (GWO). AOS based controller extracts 6% more energy and tracks optimal power at an efficiency of 99.984% under various test scenarios. The tracking time is improved by 616.48% as compared to PSO. Experimental, quantitative, comparative, and statistical results demonstrate the AOS-based proposed technique’s superior performance under various practical conditions.

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