Hybrid sand cat-galactic swarm optimization-based adaptive maximum power point tracking and blade pitch controller for wind energy conversion system

Abstract

As wind energy is sustainable, pollution-free, easily available, and free of cost, it has become an efficient source of renewable energy for electricity generation. But, the problem with wind energy is that it varies with time, seasons, and location. This makes the Wind Energy Conversion System (WECS) unstable as it frequently needs to match the load demands. The balance in power generation by wind energy is essential since it has to be connected to various grids. So, this unbalanced energy production can affect the stability of the associated power grids as well. It also results in expensive regulatory measures, storage options, and load shedding. So, the stable operation of the WECS is highly essential to adapt it as a trustable source of electricity production. The stable operation of the WECS requires a robust and advanced system for control. Better control of the wind power extracting model is achieved by controlling the Maximum Power Point Tracking (MPPT) and blade pitch. So, an Adaptive MPPT and Blade Pitch Controller (BPC) for the WECS have been developed in this article, with the support of a hybrid optimization algorithm. In order to enhance the working principles of this controller, two effective algorithms such as Sand Cat Swarm Optimization (SCSO) and Galactic Swarm Optimization (GSO) are integrated and named Hybrid Sand Cat Galactic Swarm Optimization (HSC-GSO). With the help of the recommended HSC-GSO, the functionality of the controller is enhanced and also at the same time this algorithm helps to optimize the three gains in the Proportional Integral Differential (PID) controller of both MPPT and BPC, respectively. Moreover, with the support of the proposed HSC-GSO the damping oscillations in the WECS output power and voltage are minimized. In the end, the numerical analysis is conducted for the presented system by comparing it with the traditional techniques. From the overall result analysis, the stability of the recommended adaptive WECS is 97, which is higher than the conventional algorithms such as DHOA, SCSO, GSO, and DA. Thus, it has been proved that the proposed HSC-GSO algorithm for the parameters optimization in the PID controller of MPPT and the PID controller of BPC attains high robustness, increased steady-state stability, and efficient transient response than the traditional techniques.