Particle swarm optimization-based maximum power point tracking algorithm for wind energy conversion system

An Algorithmic Approach for Maximum Power Point Tracking of Wind Turbine using Particle Swarm Optimization

“…The tracking algorithm, depending upon the location of the operating point and relation between the changes in power and speed, computes the desired optimum signal in order to drive the system to the point of maximum power [2].…”

“…Additionally, choosing an appropriate step-size in MPPT algorithm is not an easy task; though larger step-size means a faster response but it creates more oscillations around the peak point which reduces the system efficiency; a smaller step-size improves efficiency yet reduces the convergence speed [2], [6] . …”

“…Wind energy is a pollution-free and inexhaustible source. Therefore, a wind energy generation system could be one of the potential sources of alternative energy for the future [1], [2]. Besides being dependent on the wind speed, the amount of mechanical energy that can be extracted from the wind is governed by the ratio of the rotational speed to wind speed.…”

“…There is a specific optimal ratio for each wind turbine, which is called the optimal tip speed ratio (TSR) or at which the extracted power is maximum. As the wind speed is instantaneously varying, it is essential for the rotational speed to be variable to maintain the equality of the TSR to the desirable one at all times [2]. TSR calculation requires the measured value of wind speed and turbine speed data.…”

“…It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Compared with HCS, PSO increases the efficiency of the renewable energy conversion system, where it provides an adaptive step-size that may approach zero at the maximum point, so that it does not produce the oscillations around the peak point [2].…”