Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer

Abstract

The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the PSO and GA tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and the controller step input response. The GWO, the Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA) tuning methods were implemented in the Matlab 2016b to search the optimal gains of the Proportional and Integral controller through minimization of the objective function. A comparison was made between the results obtained from the GWO tuning method against PSO and GA tuning techniques. The GWO computed the smallest value of the objective function minimized. It exhibited faster convergence and better time response specification compared to other methods. These and more performance indicators show the superiority of the GWO tuning method.

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

“…Results of performance evaluation demonstrated superiority of GWO over PSO, by improving the dynamic performance of the system. Some other applications of GWO for PID tuning include robotics ( Şen and Kalyoncu, 2018), speed control of DC motor (Agarwal et al, 2018), inverted pendulum (Yadav et al, 2020), voltage regulation (Verma and Nagar, 2018) and wind turbine (Sule et al, 2020). A generic flowchart illustrating GWO algorithm is presented in Fig.…”

Time delay effect reduction on the wireless networked control system using an optimized FOPI-FOPD controller

“…The last class is omega wolf (ω) [40]. There are three hunting simulation operations, prey searching, prey encircling, and prey attacking [41]. GWO has been applied to obtain the controller's parameters' best values in the WNCS.…”

Packet loss compensation over wireless networked using an optimized FOPI-FOPD controller for nonlinear system

“…The fourth class is the omega wolf (ω) [40]. The simulation of the hunting process in GWO can be partitioned into three operations: searching for prey, surrounding the prey, and attacking the prey [41]. GWO has been utilized to get each controller's and compensator parameters' best values in the WNCS.…”