With the large-scale integration of renewable energy sources such as wind and solar power into the power system, there is an increased demand for precise characterization of their operational characteristics. Due to the randomness and volatility of wind energy, higher performance requirements are imposed on wind turbine control systems. By rapidly and accurately identifying the parameters of wind turbine control systems and precisely describing the characteristics of wind turbines, it contributes to power system analysis. After analyzing intelligent identification methods such as genetic algorithms, a new approach combining genetic algorithms with tabu search algorithms is proposed to improve the accuracy and speed of wind turbine parameter identification. Through parameter identification of the inner and outer loop parameters of the turbine-side converter and grid-side converter in the wind power generation system, and by comparing the convergence errors and iteration counts of different control algorithms, experimental results demonstrate that the proposed genetic-tabu search algorithm exhibits significant advantages in global optimization and rapid search capabilities compared to parameter identification methods such as the simplex method, genetic algorithm, and tabu search.