Automation of Electric Power SystemsVol. 37, Issue 3, Pages: 38-42(2013)
作者机构:
东南大学电气工程学院,江苏省,南京市,210096
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Published:2013
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DING Tao, GU Wei, WAN Qiulan. [J]. Automation of electric power systems, 2013, 37(3): 38-42.
DOI:
DING Tao, GU Wei, WAN Qiulan. [J]. Automation of electric power systems, 2013, 37(3): 38-42.DOI:
基于MVEE理论和广域测量的扰动识别算法
摘要
利用相量测量单元所采集的实时数据,快速准确地识别电网扰动,为调度操作提供有用信息具有重要意义。文中提出了一种识别电网扰动的最小体积封闭椭球(MVEE)算法:将采集的数据映射到多维空间,并用KY(Kumar and Yildirim)一阶算法寻找最小椭球以包含整个数据;研究所找到椭球的体积和轴半径的变化量,以识别系统扰动。采用EPRI36节点的仿真系统,分别在冲击负荷扰动场景和短路/断线故障场景下,计算最小椭球的体积和轴半径。所得结果表明,在系统发生扰动后,最小椭球的体积和轴半径均会在同一时刻发生显著变化,可很好地用于识别各类扰动。
Abstract
Power system disturbances can be identified accurately with the real-time data measured by phasor measurement units(PMUs). This provides a fast and accurate approach for the system operators. A minimum volume enclosing ellipsoid(MVEE)algorithm is proposed to identify system disturbances. The measurement data are mapped to multidimensional space to search for a minimum volume ellipsoid for enclosing the sampling data using the KY(Kumar and Yildirim)algorithm. The system disturbances can then be identified using the volume and radius of the ellipsoid. The method has been tested with ERPI36-bus test system in the scenarios of short fault and abrupt load change impact. The results show that the volume and radius of ellipsoid would greatly change at the same time of the disturbance. The MVEE algorithm has good performance for identifying the system disturbances.