Abstract
Solar Photovoltaic (PV) system is an excellent renewable energy solution in today’s scenario. Harvesting maximum power from the solar PV system under dynamic meteorological conditions is a challenging task. Numerous bio-inspired Maximum Power Point Tracking (MPPT) strategies have been proposed in the literature. The conventional methods of MPPT control are easy and simple to implement, but has drawbacks such as steady state oscillations and inability to track the maximum power under swiftly varying irradiances and partial shading conditions. This paper proposes a Grasshopper Optimization Algorithm (GOA) tuned MPPT technique with the objective of obtaining optimal duty cycle, D, to control a DC–DC boost converter. The efficacy of the proposed system under start up transients, line disturbances, load disturbances, servo conditions and partial shading conditions are evaluated and compared with the conventional Perturb and Observe (P&O) based MPPT and the familiar Particle Swarm Optimization (PSO) based MPPT algorithm using MATLAB Simulink platform. It is observed that the proposed GOA tuned MPPT technique gives good steady state and dynamic response compared to P&O and PSO based MPPT algorithms, verified in terms of rise time, settling time, percentage maximum overshoot, Integral Squared Error and Integral Absolute Error.
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Subramanian, A., Raman, J. Grasshopper optimization algorithm tuned maximum power point tracking for solar photovoltaic systems. J Ambient Intell Human Comput 12, 8637–8645 (2021). https://doi.org/10.1007/s12652-020-02593-9
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DOI: https://doi.org/10.1007/s12652-020-02593-9