## 5 Discussion and Conclusions  
After making an analysis of different expert control systems applied in MPPT of wind turbines, FLC, ANN and ISA are considered the most analyzed strategies. It has been shown that they are very useful control strategies to solve the problem of non-linearity of the system variables. \begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Reference** & **Analysis of Results** \\ \hline [PERSON] et al., 2019 & The results show that the dynamic responses for (T, \(\omega\), i\({}_{\text{d}}\), i\({}_{\text{a}}\)) by using WOA based controller results have lower overshoot and smaller setting time than other methods. \\ \hline [PERSON] et al., 2018 & Experimental results reveal that the ACO based MPPT provides seven times faster convergence compared to the PSO algorithm for achievement of MPP and tracking efficiency. \\ \hline [PERSON] et al., 2018 & The proposed control method has a fast, dynamic response by controlling the generator d-axis current to get the maximum active power under normal wind velocities. In addition, the controller proves to have capability and fast transient response under grid fault condition. \\ \hline [PERSON] et al., 2016 & According to the results, an appreciable improvement of 17\% was calculated on power overshoot with intelligent GA respect to classic GA. Furthermore, stability time was 0.79 better, with controller proposed. \\ \hline [PERSON] et al., 2018 & According to simulations, the suggested design can guarantee system stability under increased mechanical torque perturbations and excessive wind speed with controller parameters uncertainties. Thus, the proposed approach succeeded in proving its capability to select the most robust PID controller. \\ \hline [PERSON], 2017 & The results demonstrate that the proposed strategy can restrict the frequency drop, decrease the disturbances PMSG. \\ \hline [PERSON] et al., 2016 & With the PSO method, the system achieves a new speed of rotor and stabilized in less than 0.1 s. The system also achieves new output power and is stabilized in less than 0.2 s. When compared with wind turbine system without MPPT, there has been an increasing average efficiency of 25\%. \\ \hline [PERSON] et al., 2016 & Simulation and experimental results show that LBBO is a successful optimization technique in control systems for wind turbine system. \\ \hline [PERSON] et al., 2016 & The standard deviation in angular speed of rotor is seen to be low with the proposed PI-PSO controller (0.0338 pu) as compared to the standard P control (0.0384 pu) and without control (0.0482 pu). Similarly, the pitch angle has increased less (5.0911\({}^{\circ}\)) as compared to P control (7.0107\({}^{\circ}\)). \\ \hline [PERSON] and [PERSON], 2015 & The performance of the PI-PSO controller produces less oscillations in every analyzed case, especially when wind speed is higher. However, the overshoots are bigger and settling times are longer. \\ \hline [PERSON] et al., 2015 & Graphic results shown that in below rated wind speed, optimal power is attained by regulating; thus, the pitch angle is kept at a mechanical minimum and rotor speed is controlled in such a way that it is always acquired, akin to MPP tracking. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary with Intelligent Search Algorithms (ISA) for MPPT for wind energy. Figure 21: In control systems, ISO algorithms are used to search the best values to perform a control strategy, in this case to search the best gains values for a PID controller. Conventional control systems based on mathematical models such as PID controllers have proven to be very reliable with stable systems, but they present a slow response when it is required to respond to major perturbations of the system.