## 4 Results: MPPT Techniques Review

Table 3: Summary with ANN algorithms for MPPT for wind energy. In [86], a GA is suggested for the optimization of PID parameters. The algorithm determines the mutation rate, the crossing point and the number of iterations for an optimal solution. The criteria of validation of the objective function are the error of the controlled variable is within the minimum allowed and the generated energy is greater than the established value. In [87], a pitch controller based on the Moth-Flame Optimization algorithm (MFO) is proposed. MFO mimics the navigation sense of moths. The moths fly at night and maintain a fixed angle to the moon to travel in straight lines. This technique assumes that the candidate solutions are moths and that the moths fly in a three-dimensional space where each of the axes represent the control gains Kp, Ki and Kd. In [88], a frequency regulation strategy is introduced for the PMSGs connected to the grid. The controller extracts the active power and regulates the discharge to the grid. In addition, it dampens the instability in the wind turbine due to the excessive discharge of kinetic energy (KE). A PSO optimization method is used that maximizes the KE reserves in the turbines for reference of the power discharged to the network. In [89], the authors proposed controlling the work cycle in a Buck-boost converter through PSO, so that the wind power can be extracted to the maximum. The voltage and current values generated are the verifiable input that is used to determine the duty cycle and thus obtain an ideal generated power. With this controller, the wind turbine is more efficient at changes in wind speed, reducing losses in changes in speed of rotation. In [90], the authors introduce a technique for optimally obtaining the gains of the proportional integral (PI) controllers. The technique is based on developing a space of states model and obtaining the eigenvalues of the matrix A of the state space model. These eigenvalues are utilized for determining the optimum values of the PI controller's gains in order to perform the optimal system operation using Linearized Biogeography-Based Optimization (LBBO) technique. LBBO deals with the migration of species and animals between their habitats. Every habitat in LBBO represents a possible solution. The Habitat Suitability Index (HSI) of each habitat is used for measuring the fitness of this solution. A high HSI is considered as a good solution. A suitability index variable (SIV) is a number of features related to any solution. The problem dimension is obtained using the number of SIVs in each solution. Two main operations have to be performed in BBO to obtain the offspring generation. The first operation is the migration strategy, which is the way of the recombination between the solutions. The second operation is the mutation process. In [91; 92], the authors use an Integral Proportional controller with gain Kp and Ki in the control loop of the tilt angle. However, the proportional gain Kp and the integral gain Ki are tuned through the PSO algorithm. In [93], the author proposes a GA method that generates a set of diffuse IF-THEN rules for genetic operations such as: crossing, mutation, selection; and calculates the resulting solution. Through an ANN the generated rules are compared with the optimal values, the rules that satisfy most of the values are good rules. The algorithm replaces the worst fuzzy rules with the new fuzzy rules that generate better solutions. These rules establish the pitch angle in the best configuration to optimally control the wind turbine. In [94], a gain search control (GSC) is used for a PID controller that regulates pitch angle. The algorithm uses a set of linear quadratic Gaussian operations to obtain a fast and accurate response at several operating points that represent a de-wind speed. An optimization algorithm changes the controller used according to the point of operation, so the controller suppresses the transient oscillations and achieves a good and fast stabilization.