## 4 Results: MPPT Techniques Review
The block diagram of this strategy is presented in Figure 13.4  
In [82], is proposed a real-time wind speed estimation method and sensorless control for power generation. An extreme learning machine (ELM) realizes the wind speed estimation. A specific variable of wind turbine is used for improving its performance with considering the variable pitch angle. Since the design is independent of air density, the proposed ELM method for wind speed  
Figure 13.5 Individual pitch controller based on a neural network of radial base function (RBFNN) model, which measures wind speed with light detection and range (LIDAR) [75]. Figure 13.6 ANN trained by a reinforcement learning (RL) algorithm, the system changes to application mode and returns to learning mode when there are changes in the environment, until it is trained again [77]. estimation is robust to temperature variations. The estimated wind speed is calculated to determine the optimal pitch angle. Table 3 presents a summary with ANN algorithms for MPPT for wind energy. ISA is a method that imitates natural evolutionary processes to optimize a solution to a complex problem. In the control of wind turbines, it has been commonly found as a search tool to obtain optimal control parameters applied to other types of controllers. In [83], the author uses a whale optimization algorithm (WOA) to find optimal parameters of conventional PI controllers. WOA-PI is applied to a machine-side converter to find the optimum PMSG voltages under normal conditions. The WOA simulates the pursuit behavior of humpback whales, where they try to surround the prey near the surface of the water by making a spiral-shaped web of bubbles around the bank of fish. Despite a change in wind speed, optimum dynamic performance of PMSG is achieved. In [84], an Ant Colony Optimization (ACO) based algorithm is proposed. The ants initially walk randomly, and once they find food, they return to their colony leaving a trail of pheromones. If other ants find this trace, it is likely that they do not continue to walk randomly, they may follow the trail of pheromones, returning and reinforcing if they find food eventually. However, the pheromone trail evaporates, reducing its attractive force. The longer it takes an ant to travel the road and return, the longer pheromones have to evaporate. A short path, in comparison, is walked more frequently, so the density of pheromones becomes larger in short paths than in long ones. The ACO controller is responsible for the quick charge of the battery and then use the energy in the wind system. A converter is used to maximize the power stages. In [85], a system for controlling Matrix Converter (MC) based on hysteresis current controller is proposed. A Bacterial Foraging Optimization BFO is employed for better PI parameters to choose proper idref so that the controlled system has fast response performance. PI controller is employed for controlling the active and reactive current components of the PMSG. The algorithm mimics the foraging behavior of Escherichia coli (_E. coli_) bacteria present in the human intestine. Artificial bacteria perform three basic foraging activities: chemotaxis where the bacteria move to nutrients, reproduction, for which the bacteria are divided into two surviving the strongest, and elimination-dispersion, where bacteria can be eliminated or forced to move to another place. This process increases the diversity of the solutions and improves the search for local maximums and minima. Figure 13.7 A PID controller is used to regulate the pitch angle and perform the MPPT; an ANN is used to adapt the PID gains in any state of the system [80]. Figure 13.8 In control systems, ISO algorithms are used to train an ANN with greater speed, the purpose of the ANN is varied, and in this case, the reference of the pitch angle and generator torque is obtained [81].