## 4 Results: MPPT Techniques Review  
This work gathers a selection of scientific articles, dedicated to the elaboration of control systems with ES for MPPT in wind turbines with PMSG. For the elaboration of this analysis, about 100 scientific articles of different journals were considered. The most novel works were selected and with at least 4 years old. The selected articles are divided according to the methodology used, starting with FL, ANN and finally GA. As a result, a review of the work carried out is presented and a table with the results obtained by different authors for each used methodology is presented, besides, the most interesting schemes of control models are presented. The development of FLCs begin with simple control loop with two inputs, a variable and its error value, and one output signal as show in Figure 6. Different authors [8; 36; 37; 38; 39] use as inputs the error between desired power and power measured in the generator (eP), in addition to the difference of the error in certain time \(\Delta\)(eP), these inputs are defined in (13) and (14). \[eP=P_{g}(t)-P_{g,rated}(t) \tag{13}\]  
\[\Delta(eP)=eP(t)-eP(t-1) \tag{14}\]  
With the same control logic, other authors [40; 41; 42; 43] change the inputs variable and they use generator shaft speed error (eu) and the error difference \(\Delta\)(eu); and in [44; 45] they use torque error (er) and the error difference \(\Delta\)(er). In [46] combine types of variables; the Power error (eP) and the generator shaft speed (eu) are used. In [47] is used one more input variable, the error power (eP), variation of the power generated error \(\Delta\)(eP) and generator shaft speed error (eu) are used. Another group of researchers integrated proportional integral derivative (PID) control together with FLC to give greater stability to their control response. In [48] the author works with two PD controllers, torque control and pitch control and three FLC modules, FLC-1 to control the angle of the blade, FLC-2 for torque control and FLC-3 to control the speed of the rotor. All these controllers work in parallel to solve the wind problem higher than the nominal. This control model is shown in Figure 7. The control model used in [49] includes a proportional integral (PI) controller and an FLC. A correlation factor, defined by the error and the error derivative, assigns the weight that each controller will have, if the error is small, it will give more weight to the controller PI to maintain stability, if the error is large, it will give more weight to the FLC to obtain a faster control response. This control model is shown in Figure 8. In [50] a model similar to [49] is used, however the author adds a feed-forward FLC to adjust the angle of blade when there are gusts of wind. Figure 6: Basic scheme of fuzzy logic controller (FLC) for pitch angle [36; 37; 38; 39]. In [51], the operating range of the turbine is divided into 5 sections according to the wind speed. In each section, a PID was implemented and the optimal gains were calculated previously. A FLC is used to switch between controllers. In the Figure 9, is shown this control strategy. However, in [52], an FLC is used to change the PID gains, according to the value of the error of the controlled variable, the gains are calculated previously. The authors of [53] proposed a Fuzzy adaptive PID, where an FLC is used to calculate the gains for a PID control. In [54], an adaptive PI control is proposed, where the gains of the PI controller are set, but the generator power is used as the control variable. An example of these control strategies is shown in Figure 10. In [55], a feed-forward FLC is integrated to accelerate the response of pitch control when there are increases in the wind speed change and then added to the output value of FLC-PID controller. Figure 8: Different types of controllers where the control method is selected according to the needs of the system, the controller selection depends on a different controller [49].