Renewable Energy Resources and Technologies
Zaiba Ishrat; Ankur Kumar Gupta; Seema Nayak
Abstract
The rapid rise in electrical energy demand and the depletion of fossil fuels have created a market for renewable energy. Among all the renewable energy resources, the most popular is solar energy, perceived as pollution-free, easily accessible, and low maintenance. In non-uniform solar irradiation or ...
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The rapid rise in electrical energy demand and the depletion of fossil fuels have created a market for renewable energy. Among all the renewable energy resources, the most popular is solar energy, perceived as pollution-free, easily accessible, and low maintenance. In non-uniform solar irradiation or partial shading conditions (PSC), the photovoltaic characteristics (PVC) of a solar panel system (SPS) exhibit multiple minor peaks (MP) with one global peak power point (GPPP). To extract the utmost energy from the SPS, the authors proposed an efficient hybrid algorithm integrating the advantages of machine learning and the classical algorithm fractional open circuit voltage (FOVA) to track the GPPP. To follow the GPPP of SPS under unstable environmental surroundings, this study tests ML-based hybrid MPPT algorithms, specifically squared multiple variable linear regression algorithms (SMVLRA), using Matlab/Simulink. Simulation through Matlab is employed to validate the efficiency of the SMVLRA-MPPT approach compared to existing popular conventional and modern MPPT algorithms, namely the Perturb and Observation algorithm (P&OA), the variable step size incremental conductance (VINC) algorithm, and an intelligent algorithm, Decision Tree Regression Algorithm (DTRA). The simulation results demonstrate that SMVLRA offers higher peak power and mean peak power efficiency in less tracking time, with lower error and almost negligible steady-state fluctuation under PSC. The proposed algorithm achieves 99.99% efficiency under standard test conditions (1000w/m2, 25°C), 99.95% under PSC1 (1000w/m2, 800w/m2, 25°C), and 98.89% under PSC2 (1000w/m2, 800w/m2, 600w/m2, 25°C)
