Volume 20, No. 01, Month MARCH, Year 2020, Pages 87 - 100

Improved grey wolf assists mppt approach for solar photovoltaic system under partially shaded and gradually atmospheric changing condition

Dipanwita Debnath, Nirmala Soren, Arun Dev Pandey, Noman Hanif Barbhuiya

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Solar energy seems to be most sustainable way which can conserve the environment from negative impacts caused by electrical power generation from fossil fuels. For achieving the utmost power in output, photovoltaic (PV) array should operate on its optimal power point. When PV arrays are subjected to a partial shading state, several local peaks are experienced by power-voltage performance curve of PV system which significantly trims down the efficiency of complete system. So it becomes vital to locate the global peak of PV system for harvesting the upper limit power for better performance. This paper presents an improved grey wolf optimization (IGWO) technique which employs the concepts of parallel grey wolf optimization and opposition-based learning to trace the GMPP under partial shading situation and also figure out the performance for gradually varying with irradiance and temperature for PV system. This IGWO-based MPPT scheme has been implemented in MATLAB simulation environment. The outcome of the simulation is verified by the past-proposed MPPT techniques namely perturb and observe (P&O) and particle swarm optimization (PSO). It has been found from the simulation results that the performance of the suggested IGWO based MPPT design is advanced than the P&O and PSO techniques in terms of tracking speed, steady-state oscillation under partial shading conditions (PSCs) and accuracy.


maximum power point tracking, opposition-based learning, parallel grey wolf optimizer, partial shading conditions (PSCs), photovoltaic (PV).


Published by : Asian Institute of Technology
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