摘要
提出一种基于广域测量系统(WAMS)的方法对日常扰动引起的小幅振荡进行在线识别和统计,从而可以在系统正常运行中,利用实测数据找到系统的固有振荡模式,对其中易激发的危险模式进行节点同调分群、振荡中心识别和贡献因子计算等模态分析并告警,从而补充或纠正了根据数学模型和特定工况进行振荡分析时,由于模型和参数不准确导致的遗漏和误差。以这一功能在山东电力调度中心应用的实例说明了该方法的有效性和推广应用的重要性。
A wide area measurement system （WAMS） based low frequency analysis method is proposed to make identifications and statistics of the small low frequency oscillations excited by daily random disturbances in power systems. By analyzing statistical data, the intrinsic oscillation mode of the power systems can be found from the daily field data. Moreover, the occurrence probabilities of the oscillation modes can be measured, and the modes which can be excited most easily are analyzed further to group the coherent buses, to identify the oscillation centers, to calculate the contribution factors, and to give dispatchers or analyzers an early-warning. While other oscillation analysis methods are based on mathematical models and specific operation conditions, the proposed method is independent of specific operation conditions and the accuracy or correctness of power system models. It will be a useful supplement to mathematical model based methods and experimental based methods. The application examples in Shandong Power Grid are presented to show the validity and the importance to apply this oscillation analysis method.
出处
《电力系统自动化》
EI
CSCD
北大核心
2008年第6期95-98,共4页
Automation of Electric Power Systems
关键词
广域测量系统
低频振荡
同调分群
随机扰动
wide area measurement system （WAMS）
low frequency oscillation
coherence grouping
random disturbances
