J Shanghai Jiaotong Univ Sci

J Shanghai Jiaotong Univ Sci ›› 2025, Vol. 30 ›› Issue (2): 300-308.doi: 10.1007/s12204-023-2644-5

• Engieering and Technology • Previous Articles     Next Articles

Genetic Clustering-Based Equivalent Model of Wind Farm with Doubly Fed Induction Generator

基于遗传聚类的双馈风电场等效模型

蔡振华1, 2, 黎灿兵3, 吴秋伟4, 阳同光2, 李振恺3   

  1. 1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; 2. Key Laboratory Energy Monitoring and Edge Computing of Smart City, Hunan City University, Yiyang 413000, Hunan, China; 3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 4. Center for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
  2. 1. 湖南大学 电气与信息工程学院, 长沙 410082； 2. 湖南城市学院 智慧城市能源感知与边缘计算重点实验室，湖南益阳, 413000；3. 上海交通大学 电子信息与电气工程学院, 上海 200240；4. 丹麦理工大学 电气工程系 电力和能源中心，丹麦灵比 2800
  • Accepted:2022-12-08 Online:2025-03-21 Published:2025-03-21

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Abstract: With increasing the number of wind power generators, the consumption time of electromagnetic simulation of the wind farm explodes. To reduce the simulation time while meeting the accuracy requirement, a genetic clustering-based equivalent model is proposed for the wind farm with numerous doubly fed induction generators. In the proposed model, active power together with the reactive power and the wind speed are selected to form the set of clustering indicators. A normalization technique is utilized to cope with the multiple orders of magnitude in these factors. An exponential fitness value is formulated as a function of the sorting number of the primary fitness value, and the fitness-based selection probability is constructed to overcome the property of premature and slow convergence of the genetic clustering algorithm. The sum of squares due to error is used to determine the optimal clustering number. In addition, a decoupled parameter equivalence method is adopted to obtain the equivalent parameters of the collection network. Simulation results and comparisons with various methods under different voltage scenarios show the feasibility and effectiveness of the proposed model.

Key words: electromagnetic simulation, genetic clustering-based equivalent model, doubly fed induction generators, sum of squares due to error, collection network

摘要： 随着风力发电机组数量的不断增加，风电场电磁仿真时间剧烈增长。为了满足仿真精度同时减少仿真时间，本文为包含多个双馈风电机组的风电场提出基于遗传聚类的等值模型。在该模型中，有功功率、无功功率和风速用来构建聚类指标集，采用归一化技术处理指标间的数量级差异问题。构建以初始适应度值排序号作为指数值的指数适应度值，同时，构造基于适应度的选择概率以解决遗传聚类算法中收敛过早和收敛速度慢的问题，并通过误差平方和来确定最优聚类数。 另外，通过采用解耦参数等值法获得集电网络的等值参数。不同电压情况下，不同方法的仿真结果及比较表明该模型的可行性和有效性。

关键词: 电磁仿真，基于遗传聚类的等值模型，双馈感应发电机，误差平方和，集电网络

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