## 4 Results: MPPT Techniques Review

Table 2: Summary of works done with FLC algorithms. The control systems based on ANN have been used in wind energy; these systems work in any scenario, since they can adapt to the mode of operation in specific wind conditions. In [66] an adaptive integral sliding mode controller is proposed to obtain the optimum rotation speed of the rotor by means of an ANN. An RNN is used for online learning. The integral sliding mode control is designed to calculate the control voltage \(\mathrm{u_{q}}\) and the control voltage \(\mathrm{u_{d}}\), the value of these tensions are sent to a PWM controller that controls the power converter of the generator. This work is compared with a RBFNN used in [67]. The RBFN is used to determine the optimal current that controls the PMSG. The RBFN parameters are optimized using a PSO-modified algorithm to improve learning. In [68], an algorithm is proposed that adapts the Bat Algorithm and improved Extreme Learning Machine (BA-ELM) for forecasting wind speed to alleviate the slow response of anemometers and sensors and eliminate the discontinuity of wind speed sequence, considering that the change of wind speed requires a very short response time. A state feedback control is proposed to achieve the rotor flux and rotor speed tracking. This method could decouple the current and voltage of induction generator to track the reference of stator current and flux linkage. This novel control strategy is shown in Figure 13.  
In [69], a method of pitch control is proposed, an ANN with an Online Learning Approach (OLA) is used. As an input variable, we have the error of the control variable; the error is previously filtered to transform a complex system into a simple one. In addition, a large sample observer is implemented to determine the rotor acceleration estimation, which avoids the use of additional sensors. The method must guarantee the monitoring of the rotor speed, the power of the generator and the limit value of the system variables. In [70], an ANN topology based on RBFNN is proposed for learning from wind speed. To achieve maximum power generation, the system is implemented with a quadratic reinforcement converter and the inverter efficiency is validated with a primary and single-ended primary inductance converter (SEPIC). In [71], an ANN based on RBFNN is used. The controller uses the wind speed, generator speed and power generated as input variables. The work cycle and the angle of the blade are considered as the controlled variables. The selection of the output variable is based on the wind speed. In region II, the duty cycle is used to control the power electronics converter, thus obtaining the maximum available power. In region III, the pitch control is performed to limit the output power. A block diagram of this strategy is presented in Figure 14.  
Figure 12: Control model that involves a combination of individual pitch control for each blade, this reduces the aerodynamic loads in the structure so it offers higher performance, and a collective pitch control to control the rotation of the turbine [61]. Figure 13: Control strategy where an Artificial Neural Network (ANN) is used to estimate the wind speed and thus buffer the discontinuity and slow response of an anemometer [68]. In [72], a Multy Layer Perceptron Neural Network (MLPNN)-based pitch angle controller and a non-linear speed controller based on Integral Sliding Mode Control (ISMC) are designed. MLPNN pitch controller limits the rotational speed when the wind speed exceeds its rated value; the weights connecting the neurons in different layers are trained by Back Propagation (BP) algorithm. ISMC adaptively changes the structure of the predefined controller according to the state of the system. Hence, the system response depends only on the sliding surface and is insensitive to variations of system parameters and external disturbances. The scheme of this control proposal is shown in Figure 15.  
In [73], an adaptive controller based on RBFNN is suggested without previous adjustment and with updating of the weights in line.