Engieering and Technology

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

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  • 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

Accepted date: 2022-12-08

  Online published: 2025-03-21

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.

Cite this article

Cai Zhenhua, Li Canbing, Wu Qiuwei, Yang Tongguang, Li Zhenkai . Genetic Clustering-Based Equivalent Model of Wind Farm with Doubly Fed Induction Generator[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(2) : 300 -308 . DOI: 10.1007/s12204-023-2644-5

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