随着风光等新能源大规模接入电力系统，提高了对新能源运行特性的刻画精确度要求。由于风能的随机性和波动性，对风机控制系统性能提出了更高的要求。通过快速准确地辨识出风机控制系统参数，精确描述风电机组特性，有助于电力系统分析。在对智能辨识方法如遗传算法等方法进行分析后，为提高风机参数辨识准确度与快速性，提出了一种结合遗传算法与禁忌搜索算法的新方法。通过对风力发电系统机侧换流器和网侧换流器内外环参数进行参数辨识，对比不同控制算法收敛误差以及迭代次数，实验结果表明相比于单纯形法、遗传算法、禁忌搜索等参数辨识方法，所提遗传-禁忌搜索算法的全局优化及快速搜索能力有明显优势。

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.