晋萃萃, 李夕白, 刘柳, 李平, 申家锴, 温可瑞, 孙长海. 基于改进模糊C均值聚类的广域电网主动频率响应控制典型场景生成[J]. 现代电力, 2022, 39(5): 505-513. DOI: 10.19725/j.cnki.1007-2322.2021.0212
引用本文: 晋萃萃, 李夕白, 刘柳, 李平, 申家锴, 温可瑞, 孙长海. 基于改进模糊C均值聚类的广域电网主动频率响应控制典型场景生成[J]. 现代电力, 2022, 39(5): 505-513. DOI: 10.19725/j.cnki.1007-2322.2021.0212
JIN Cuicui, LI Xibai, LIU Liu, LI Ping, SHEN Jiakai, WEN Kerui, SUN Changhai. A Typical Scenario Generation Method of Active Frequency Response Control Based on Improved Fuzzy C-means Clustering for Bulk Power System[J]. Modern Electric Power, 2022, 39(5): 505-513. DOI: 10.19725/j.cnki.1007-2322.2021.0212
Citation: JIN Cuicui, LI Xibai, LIU Liu, LI Ping, SHEN Jiakai, WEN Kerui, SUN Changhai. A Typical Scenario Generation Method of Active Frequency Response Control Based on Improved Fuzzy C-means Clustering for Bulk Power System[J]. Modern Electric Power, 2022, 39(5): 505-513. DOI: 10.19725/j.cnki.1007-2322.2021.0212

基于改进模糊C均值聚类的广域电网主动频率响应控制典型场景生成

A Typical Scenario Generation Method of Active Frequency Response Control Based on Improved Fuzzy C-means Clustering for Bulk Power System

  • 摘要: 由于广域电网的主动频率响应控制所面临的运行场景数目巨大且控制策略涉及因素众多,需要对运行场景进行聚类,以在保证控制精度的前提下提高控制效率。为此,提出一种基于改进模糊C均值聚类的主动频率响应控制典型场景生成方法。首先,针对各场景下系统频率最低点求解过程复杂、在线求解难度大等问题。其次,依据聚类有效性指标改进模糊C均值聚类算法,求取场景聚类数,建立运行场景与类别间的隶属关系。最后,从保证系统频率安全角度出发,将类内最坏运行场景作为典型场景,为其制定主动频率响应控制策略,对其进行离线验证与在线应用。仿真分析表明,所提方法在主动频率响应控制离线分析与在线应用阶段均可行有效。

     

    Abstract: In view of the fact that active frequency response (abbr. AFR) of bulk power system is facing with a huge number of operation scenarios and its control strategy involves numerous factors, it is necessary to cluster its operation scenarios to improve the control efficiency on the promise of ensuring control accuracy. For this reason, a typical scene generation method of active frequency response control based on the improved fuzzy C-means clustering was proposed. Firstly, in allusion to such defects as complex solving process and difficulty of online solution, an estimation method of system frequency nadir under traditional frequency response control mode and active frequency response control mode was proposed. Secondly, according to clustering effectiveness index, the fuzzy C-means clustering algorithm was improved to calculate the number of scene clusters, and the membership function between the running scenes and the category was established. Finally, to ensure the security of system frequency, the worst operation scenario within the class was taken as a typical scenario, and an active frequency response control strategy for this scenario was enacted to conduct the off-line verification and on-line application. Results of simulation analysis show that the proposed method is feasible and effective in the stage of off-line analysis on active frequency response control and in the stage of online application.

     

/

返回文章
返回