Publication

Abstract : The wind turbine system WTS has a major challenge of highly variable wind speed. The maximum available power corresponding to each value of wind speed needs to be extracted at each variation for the efficient operation of the WTS. This assessment is achieved using a maximum power point tracking MPPT method. In this research work, the maximum available power in a 2-MW WTS is effectively tracked at each instant of wind variations using various metaheuristic algorithms so that the capital cost of the WTS can be achieved in a minimum period. Firstly, a 2-MW wind turbine model is modeled in MATLAB/Simulink and characteristics of power coefficient versus tip speed ratio (TSR) and turbine power versus turbine rotor speed are achieved to decide the optimal points for the effective MPPT. Secondly, in strategy to maximize the turbine power at the corresponding wind speeds, metaheuristic algorithms such as whale optimization (WOA), grasshopper optimization (GOA), gray wolf optimization (GWO), differential squirrel search algorithm DSSA, and hybrid particle swarm optimization PSO-GWO are implemented. Wind speeds are taken as input to each MPPT algorithm and corresponding optimal rotor speeds of the turbine are determined so that corresponding maximum power can be extracted at a pre-defined optimal TSR value. Furthermore, all the metaheuristic algorithms are well tested at various benchmark functions and exploration, exploitation, and convergence analysis have been carried out for the performance comparison. The MPPT performance is assessed on the power coefficient versus TSR curve that shows the effective tracking of MPP point by point in a simple way.