In renewable systems, there may be conditions that can be either network error or power transmission line and environmental conditions such as when the wind speed is unbalanced and the wind turbine is connected to the grid. In this case, the control system is not damaged and will remain stable in the power transmission system. Voltage stability studies on an independent wind turbine at fault time and after fixing the error is one of the topics that can strengthen the future of independent collections. At the time of the fault, the network current increases dramatically, resulting in a higher voltage drop. Hence the talk of fast voltage recovery during error and after fixing the error and protection of rotor and grid side converters against the fault current and also protection against rising DC voltage (which sharply increases during error) is highly regarded. So, several improvements have been made to the construction of a doubly-fed induction generator (DFIG) turbine such as:

a) error detection system,

b) DC link protection,

c) crow bar circuit,

d) block of the rotor and stator side converters,

e) injecting reactive power during error,

f) nonlinear control design for turbine blades,

g) tuning and harmonization of controllers used to keep up the power quality and to stabilize the system output voltage in the power grid.

First, the dynamic models of a wind turbine, gearbox, and DFIG are presented. Then the controllers are modeled. The results of the simulation have been validated in MATLAB/Simulink.

One of the most important aims of the sizing and allocation of distributed generators (DGs) in power systems is to achieve the highest feasible efficiency and performance by using the least number of DGs. Considering the use of two DGs in comparison to a single DG significantly increases the degree of freedom in designing the power system. In this paper, the optimal placement and sizing of two DGs in the standard IEEE 33-bus network have been investigated with three objective functions which are the reduction of network losses, the improvement of voltage profiles, and cost reduction. In this way, by using the backward-forward load distribution, the load distribution is performed on the 33-bus network with the power summation method to obtain the total system losses and the average bus voltage. Then, using the iterative search algorithm and considering problem constraints, placement and sizing are done for two DGs to obtain all the possible answers and next, among these answers three answers are extracted as the best answers through three methods of fuzzy logic, the weighted sum, and the shortest distance from the origin. Also, using the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) and setting the algorithm parameters, thirty-six Pareto fronts are obtained and from each Pareto front, with the help of three methods of fuzzy logic, weighted sum, and the shortest distance from the origin, three answers are extracted as the best answers. Finally, the answer which shows the least difference among the responses of the iterative search algorithm is selected as the best answer. The simulation results verify the performance and efficiency of the proposed method.