摘要
振荡模态的精准捕捉对有效抑制低频振荡有重要意义,基于量测的低频振荡模态辨识方法在在线监测识别领域具有广阔应用前景。本文针对模态识别算法定阶困难、易存在虚假模态等问题,提出了基于模糊C均值聚类的多阶随机子空间算法。通过多阶子空间计算可捕捉所有可能的系统模态,并通过模糊C均值算法确定实际最低阶数,经虚假模态筛除确定最终振荡主导模态,并且能降低干扰,提升辨识抗噪性能。本文算法与Prony算法进行了性能对比,并通过四机两区系统和实际电网相量测量单元量测数据验证了算法的适用性和鲁棒性。
The accurate capture of oscillation modes is of great significance to effectively suppressing the low-frequency oscillations,and the low-frequency oscillation mode recognition method based on measurement has a broad prospect in the field of online monitoring and identification. In this paper,a multi-order stochastic subspace algorithm based on fuzzy C-means(FCM)clustering is proposed to solve problems such as the difficulty in order determination using the modal recognition algorithm and the existence of false modes. This method can capture all possible system modes through multi-order subspace calculations,determine the actual minimum order using the FCM algorithm,and determine the final dominant oscillation mode through false mode screening. In addition,it can reduce the interference and improve the anti-noise recognition performance. The comparison of performance between the proposed algorithm and the Prony algorithm is conducted,and the applicability and robustness of the novel algorithm are tested by simulations of a four-machine two-area system and the actual phasor measurement unit(PMU)measurement data of power grid.
作者
王志远
龙呈
常晓青
江晓东
WANG Zhiyuan;LONG Cheng;CHANG Xiaoqing;CHIANG Hsiao-dong(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China;Electric Power Research Institute,State Grid Sichuan Electric Power Company,Chengdu 610041,China;School of Electrical and Computer Engineering,Cornell University,Ithaca 14853,USA)
出处
《电力系统及其自动化学报》
CSCD
北大核心
2020年第4期69-75,共7页
Proceedings of the CSU-EPSA
基金
国家电网公司科技资助项目(521999180001)。
关键词
低频振荡
模态识别
模糊C均值聚类
随机子空间
low-frequency oscillation
modal recognition
fuzzy C-means(FCM)clustering
stochastic subspace
