## 4 Results: MPPT Techniques Review
Figure 7: Control strategy with different control loops or different types of controllers, each control signal is added to obtain an equivalent signal [48]. Figure 9: Selection of different proportional integral derivative (PID) controllers to obtain a steady state response in different operating ranges of the system [51]. On the other hand, controllers of the hierarchical type have been proposed. In [56] two controllers are proposed, the first one is a classic fractional order PI (FOPI) controller, this controller implements an FLC to change the real-time PI gains (FFOPI). The second is a PI, the integral part is FFOPI and is added to the proportional part represented by a gain Kp (FFOPI + I). A PSO algorithm determined initial gains. In [57], the author proposes a control model (PIFPI) based on the combination of PI-Fuzzy-PI controllers to control the angle of incidence of the blades in a wind turbine. The error of the generated power is used as control variable, which is multiplied by the gains of a first PI (or PD), the value obtained is the input to an FLC that calculates the integration constant of the second PI controller. PIFPI controller is presented in \[eP=P_{g}(t)-P_{g,rated}(t) \tag{13}\]0 In [58], a PID controller is presented, the values of proportional gain Kp and derivative Kd are the input for an FLC with an artificial organic controller (AOC) as the law of inference. The integration of the FLC output value represents Ki. The sum of the three constants is considered to be a controller (PID-AOC). In [59], an FLC is introduced that functions as a P&O controller. A P&O disturbs the control variable in small samples and observes the resulting changes. A large sample can quickly reach the desired solution, but can generate oscillation around the optimal solution. A small sample can reduce the disturbance near the optimal solution, but it would arrive at this solution slowly when the wind speed changes abruptly. An FLC is added that adjusts the size of the sample dynamically, according to the changes in wind speed. Different researchers have developed MPPT algorithms for pitch control for each blade. The authors of [60; 61; 62] propose using three FLC. One FLC controls the rotor torque by means collective pitch angle and, other two FLC control to blade moment. To adjust the blade pitch angles \(\beta 1\), \(\beta 2\) and \(\beta 3\), the individual blade pitch make activity within a certain range to achieve the purpose of fatigue load reduction. One of the proposed individual pitch controllers is presented in \[eP=P_{g}(t)-P_{g,rated}(t) \tag{13}\]1  
For the MPPT on the generator side [63], a control strategy with a FLC and an integral sliding mode current controller was implemented. FLC deduces the optimal current directly from the variations of current and voltage with good precision, it works as a harmonic compensator to eliminate voltage harmonics on the generator side. In [64; 65], vector control of the generator torque is performed by applying FLC. Table 2 contains a summary of the works of different authors who implemented an FLC in wind turbines and who present numerical results of their research. \[eP=P_{g}(t)-P_{g,rated}(t) \tag{13}\]2 Controllers of the hierarchical type, PI-Fuzzy-PI [57]. \[eP=P_{g}(t)-P_{g,rated}(t) \tag{13}\]3 Adaptive control algorithm calculates the gains of a PID controller in real time; the strategy to change the values of the gains is done with FLC [52; 53; 54].