HYBRID METAHEURISTIC ALGORITHMS MPPT UNDER PARTIAL SHADING CONDITION
Abstract
This paper investigates the effectiveness of a hybrid metaheuristic optimization approach to achieving the maximum power from PV solar systems under partial shading. Stochastic metaheuristic optimization is utilized to guarantee the identification of optimal solutions within constrained timeframes. The proposed algorithms combine the Firefly Optimization Algorithm (FOA) and Salp Swarm Algorithm (SSA). Metaheuristic optimization proves advantageous due to its ability to tackle complex problems regardless of their structure. In this paper, settling time, speed convergence overshoot, and efficiency are considered under different values of irradiance. The sample time is carefully chosen to reach the optimal tracking time, making dynamic optimization the selected approach. The incorporation of FOA harnesses the search capability of SSA, leading to power outputs that closely align with those of the PV system. The utilization of SSA simplifies optimization complexity by utilizing a single control parameter. Additionally, the integration of FOA enhances the search capability of SSA, resulting in power outputs closely aligned with the PV system. A dc-dc boost converter is employed to achieve the desired output dc voltage. Matlab/Simulink is used to simulate the proposed system. The simulation results demonstrate satisfactory performance and the ability to achieve optimal Maximum Power Point (MPP) under partial shading conditions.
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References
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