A Fuzzy Clustering Algorithm-Based Dynamic Equivalent Modeling Method for Wind Farm With DFIG

• Published in: IEEE transactions on energy conversion Vol. 30; no. 4; pp. 1329 - 1337
• Main Authors: Zou, Jianxiao, Peng, Chao, Xu, Hongbing, Yan, Yan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active power characteristic analysis; Clustering algorithms; dynamic equivalent model; fuzzy clustering algorithm; Generators; Heuristic algorithms; Mathematical model; multimachine representation method; Stators; wind farm with double fed induction wind generator (DFIG); Wind farms; Wind speed; wind farm with double fed induction wind generator (DFIG); fuzzy clustering algorithm; multimachine representation method; dynamic equivalent model; Active power characteristic analysis
• ISSN: 0885-8969; 1558-0059
• DOI: 10.1109/TEC.2015.2431258

With the increasing capacity of grid connected wind farms, the influence of wind power to stable operation of an electric power system is becoming more and more important. In order to analyze the active power output characteristics of wind farm, a multimachine representation dynamic equivalent method based on the fuzzy clustering algorithm is proposed. First, indicators which can characterize the active power output performance of a doubly fed induction wind generator (DFIG) are researched. Second, a fuzzy C-means (FCM) clustering algorithm is first applied to the modeling of wind farm. DFIGs are divided into groups by analyzing the indicator data with FCM. Finally, DFIGs of the same group are equivalent as one DFIG to realize the dynamic equivalent modeling of wind farm with DFIG. Simulation results demonstrated that the established dynamic equivalent model can reflect the active power dynamic response characteristics of wind farm with DFIG effectively; meanwhile, the model of wind farm is simplified and computation complexity is reduced.