Transient stability enhancement of power system based on wind turbines by power system stabilizer using crow search algorithm

Due to the lack of fuel costs and being an environmentally friendly energy source, the generation of electrical energy from wind power has recently gained great importance. Many studies have been carried out on wind turbines and generators for wind power generation and many different efforts to integrate the power systems of these systems is achieved. In this study, the transient stability of a power system including a wind turbine will be examined. Since various wind turbine generators are used in applications, in this study, the comparative transient stability of wind turbine generators will be analyzed. As a result of the analysis, it is aimed to determine the appropriate turbine generator pair for instability. Power system stabilizers (PSSs) are extensively used in generator units to enhance the transient stability of the power system. PSSs are designed to minimize low-frequency oscillations such as power angle, rotor speed, and field current deviation, etc. following a large disturbance. Hence, optimal tuning and placement of the parameters of PSS are crucial for the efficiency of the stabilizer. In this paper, Crow Search Algorithm (CSA), rely on the intelligence of crows, one of the metaheuristic methods was used for the first time in a Single Machine Infinite Bus (SMIB) system so as to find the optimum parameters of the PSS in order to improve the transient stability of the system. In addition, Artificial Bee Colony (ABC) and Ant Colony Optimization (ACO) algorithms were also applied to find the parameters of PSS in a multi-machine power system, and comparative analyzes were also carried out. In the study, stability analysis of the oscillation equations for the temporary failure conditions of a power system with a wind farm turbine generator system was carried out with CSA. Modeling and simulation of the SMIB and designing of PSS were made by Matlab/Simulink. The simulations reveal that the CSA-based PSS controller performs a dynamic response when the system is interrupted, far superior to the GA-based and PSO-based PSS controller designs. It was observed that CSA-based PSS settled 48.1% faster than PSO-based PSS and 55.7% faster than GA-based PSS. Evaluation time for CSA is 43.5% shorter than PSO and 48.6% shorter than GA. When using CSA-based PSS in the SMIB system compared to other approaches, overshoot and low-frequency oscillations are greatly reduced. When using CSA-based PSS in SMIB system compared to other approaches, overshoot and low-frequency oscillations are greatly reduced and low-frequency oscillations of active power, rotor speed, field current, and mechanical power are significantly reduced.