## 1 Introduction
The impact of wind power generation on system frequency response has been discussed using \(H_{\infty}\) and \(\mu\)-synthesis [18], MPC [19], fuzzy logic control (FLC) with the quasi-oppositional harmony search (QOHS) algorithm for the PID controller [20], \(\epsilon\)-MOGA [21,22], and the polar fuzzy [23] approaches. These studies use simplified linear models neglecting the nonlinear specifications of wind turbines. Hence, they are very difficult to apply to large-scale power systems with nonlinearities. On the other hand, FLC with PSO [24], FLC with PSO for the PI controller [25], and the PI controller alone [26] have been studied for frequency regulation in the presence of wind turbines without considering the power and voltage stability of the power system. Recently, there have been studies on adaptive FLC approaches to multi-input multi-output (MIMO) nonlinear systems with time-varying delays and input saturation [27], unknown control directions and unknown dead-zones [28], single-input single-output (SISO) with input saturation [29], and packet dropouts and parameter uncertainties using the type-2 fuzzy model-based technique. However, the main drawback for these approaches is the fact that the design parameters [27, 28, 29] and the weighting functions [30] selection process affect the system performance significantly. Hence, the parameters should be adjusted carefully for achieving suitable transient performance and control action in practical applications. In [31], an adaptive fuzzy backstepping control of a nonlinear system with sampled and delayed measurements was presented. The major disadvantage of this scheme that it suffers from the problem of explosion of complexity, which is caused by the repeating differentiations of nonlinear functions. As a result, the complexity of the controller grows sharply as the order of the system increases, which makes it difficult for practical implementation, especially for large-scale systems. A complete review for hybrid system configuration, modeling, renewable energy sources, criteria for hybrid system optimization and control strategies, and software used for optimal sizing was presented in [32]. A real case study of comparative various standalone hybrid combinations for a remote area in Barwani, India was analyzed according to total net cost and emissions. Dynamic operation and control strategies for a microgrid hybrid wind-photovoltaic fuel cell power system were examined in [33]. In addition, a static var compensator (SVC) was utilized to supply reactive power and regulate the voltage of the hybrid system. Moreover, a general regression neural network (GRNN) with an improved PSO was applied to analyze the performance of the system. Furthermore, a high-performance online training radial basis function network sliding mode (RBFNSS) was designed to drive the optimal wind turbine speed for the maximum power point tracking (MPPT) control scheme. Two MPPT control approaches for both photovoltaic power and the WTG were presented in [34]. The pitch angle control of the WTG used the improved Eleman neural network (ENN) controller and the output was fed to wind turbine to achieve MPPT. In addition, the solar array was integrated with radial basis function network (RBFN) control algorithm to track maximum power. A design of fuzzy logic-based voltage-regulated solar power MPPT system was introduced in [35]. The voltage-regulated MPPT scheme was formulated as a feedback control system where the maximum power point condition and voltage regulation requirements were used as control systems input. Moreover, a buck-boost converter-based fuzzy controller was designed for the voltage-regulated MPPT system considering the error of the maximum power point condition and the error of the voltage regulation as two input variables for the fuzzy controller. A control approach to enhance the phase correction adjustment of distribution transformers in a grid was discussed in [36] using hybrid bird mating optimization (HBMO).