The energy consumption increases enormously because of the huge increase in population and industry; as a result, the power generation map should be changed through the replacement of the conventional generations by renewable ones [1] [2]. The majority of research in this field has focused on wind and solar farms, as well as ways to improve their grid interface [3]. Wind energy is the most favorable renewable energy source and has a great deal of potential to be a major player in the electrical power generation map due to its economic viability, environmental friendliness, and inexhaustibility. In fact, employing wind energy respects the environment and aids in avoiding the generation of unwanted carbon dioxide [4] [5] [6]. The development of wind turbine technology has increased the energy output, reduced costs, and boosted the deployment of wind turbines in both offshore and onshore facilities.[7] [8].
The wind generating capacity added globally in 2021 was 93.6 GW, increasing the generation capacity of wind power to 837 GW demonstrating 12% yearly growth [9]. By 2050, It is anticipated that wind power will have a significant impact on the global energy infrastructure, producing more electricity than any other source with eight thousand GW installed capacity [1] [9].
Variable speed wind turbines (VSWT) have recently taken the place of fixed speed wind turbines. WT can indeed extract the most power from the wind when it is working at a variable speed. DFIG, squirrel cage induction generator (SCIG), wound field synchronous generator (WFSG), and permanent magnet synchronous generator (PMSG) are the types of generators that may be utilized with variable speed wind turbines [6] [10] [11]. Due to the DFIG's effectiveness, minimal power losses, simplicity of operation, reduced cost, and very little maintenance. it has gained widespread application [12]. The integration of large wind power generators into current grids has brought various challenges related to efficiency, reliability, and grid integration. This has prompted researchers to propose and develop a variety of control systems that can generate high-quality wind energy [13]. The most used control technique is the PI controller because of its effectiveness under normal conditions and simplicity of usage [12] [14]. However, the PI controller-based control techniques don’t provide a good performance in terms of accuracy when faced with difficult operating situations such parameter changes and impact of wind disturbance [15]. The gains of the PI controller have been adjusted using a variety of optimization techniques [15–18]. In [16], the gains of a fractional order PI controller are fine-tuned using the genetic algorithm, which lowers steady-state error and speeds up the settling time. In [16], the writers suggested an intelligent PI controller that improved response time and reduced error through adaptive particle swarm optimization. In [17], the weighting matrices ensuring optimal performance at various wind speeds are determined using the whale optimization process. Gains are approximated in [18], pole compensation is used to compute gains, and Particle Swarm Optimization is used to build the RSC's control [1].
In recent years, many researchers focused on fuzzy logic control and its improvement. Genetic algorithm is used to improve the FLC installed in the RSC, system is validated by studying the system under different wind speeds and three phase faults [19]. An interval type-2 fuzzy logic controller applied on a DFIG in the case of disturbance in the wind speed and in case of changing the system parameters [6]. Fuzzy logic control was utilized to enhance the efficiency of direct power control technique and direct voltage control technique under several transient conditions [20]. FLC was used in vector control of DFIG-GSC, vector control of DFIG-RSC, control DC bus voltage and max power point tracking strategy [21].
The following is how the paper is set up: System model details are provided in Section II. The control system is described in Section III. The various controllers are illustrated in Section IV. In Section V, the simulation is covered. In Section VI, the robustness test is shown. Section VII comes to its results at the end.