Abstract
This journal presents the development of an innovative algorithm for Maximum Power Point Tracking (MPPT) utilizing the Enhanced Self Lift Luo Converter (ESLLC) developed through Queen Honey Bee Migration (QHBM). The QHBM used for MPPT employs a queen-based decision-making approach to determine the optimal point on solar panels. The queen continuously searches for the Maximum Power Point (MPP), and upon locating it, ceases tracking and starts building a nest. Once the nest is established, the queen resumes the search for MPP. The testing simulation evaluates computing speed, durability, and MPP's margin errors. MATLAB/Simulink is employed for verification. The simulation results demonstrate that the QHBM surpasses other algorithms like PSO, P&O, and FLC in terms of computing speed, durability, and MPP margin errors. The QHBM-based MPPT exhibits superior responsiveness to changes in irradiation and temperature compared to alternative algorithms. This proposed algorithm effectively adapts to varying environmental conditions that influence irradiation and temperature changes. Consequently, the suggested algorithm holds significant promise for practical implementation in dynamic environmental settings.
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