摘要: |
相量测量单元(PMU)被广泛用于电力系统数据实时采集和状态监测,为电力系统的稳定运行提供了可靠的信息保障。在电力系统受到扰动的情况下,动态过程中会包含低频周期性衰减成分分量,也可能体现为低频振荡。基于PMU实时采集信号,构建了一种针对低频振荡的相关辨识方法,可有效辨识系统因短时扰动而产生的低频振荡模式,并通过量化系统动态过程振荡强度来准确辨识起振时间。通过仿真计算和针对实际变电站PMU采集信号的辨识计算,验证了此方法的可行性。此方法在实际变电站受到短时扰动的情况下可在短时内准确辨识出低频振荡成分的频率、幅值、衰减系数和动态过程的起止时间等参数,并能够较好地复原系统由受扰至恢复的动态过程,为电力系统的低频振荡辨识提供了一种可行的新方法。 |
关键词: 相关分析 相量测量单元(PMU) 低频振荡 动态过程 信号检测 |
DOI:10.7667/PSPC170559 |
投稿时间:2017-04-18修订日期:2017-12-27 |
基金项目:国家重点研发计划项目资助(2016YFB0900600)“大型交直流混联电网运行控制与保护” |
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An identification method for low-frequency oscillation based on signal correlation |
LI Shi,DING Renjie |
(Department of Electrical Engineering, Tsinghua University, Beijing 100084, China) |
Abstract: |
Phasor Measurement Unit (PMU) is widely used for real-time data acquisition and condition monitoring in power system, which provides reliable guarantee for the stable operation of power system. The dynamic process may contain low-frequency periodic attenuation components and it may also be low-frequency oscillation when a transient disturbance happens in power system. A correlation identification method for low-frequency oscillation based on PMU data is constructed, which can identify the oscillation mode caused by the short-time disturbance effectively, and can identify the starting time of oscillation by quantifying the oscillation intensity of dynamic process. The feasibility of this method is proved by simulation and identification of actual PMU signals in the substation. This method can identify the parameters of low-frequency oscillation timely and exactly, such as frequency, amplitude, attenuation coefficient, starting and stopping time of dynamic process, when the disturbance occurs in the substation, and it can rebuild the dynamic process of the system from disturbance to recovery, therefore it provides a new way of low-frequency oscillation identification in power system. This work is supported by National Key Research and Development Program of China (No. 2016YFB0900600). |
Key words: correlation analysis phasor measurement unit (PMU) low-frequency oscillation dynamic process signal detection |