基于分布级频率信息的电网扰动分析

L. Liu, Jidong Chai, H. Qi, Yilu Liu
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引用次数: 8

摘要

扰动分析对输电系统的研究具有重要的意义,它为输电系统的建模、运行和规划提供了方便。传统上,干扰被描述为兆瓦级(MW)事件,但由于监控设备的安装和授权过程缓慢,对数据的访问效率低下。本文利用频率监测网(FNET/GridEye)的频率干扰记录仪(FDRs)的频率记录,基于频率变化与干扰功率损失之间的线性关系,提出了一种在分布水平上进行干扰分析的新方法。首先分析了北美地区1992 ~ 2009年的实际扰动记录(单位兆瓦),确定了其幂律分布;同时我们发现小扰动是对数正态分布的。然后基于2011 - 2013年的实际记录(EI),对兆瓦级扰动和相应的频率变化记录进行并行研究。证明当扰动较大时,扰动的频率变化及其兆瓦记录具有相似的幂律分布;当扰动较小时,频率变化可以用对数正态分布及其数值近似系数来描述。这些发现使得在分布水平上监测频率变化时的干扰分析具有更好的分辨率和明显更快的数据访问速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power grid disturbance analysis using frequency information at the distribution level
Disturbance analysis is important to the study of the power transmission system because it facilitates the modeling, operation and planning of the system. Traditionally, disturbances are described as megawatt (MW) events, but the access to data is inefficient due to the slow installation and authorization process of the monitoring device. In this paper, we propose a novel approach to disturbance analysis conducted at the distribution level by exploiting the frequency recordings from Frequency Disturbance Recorders (FDRs) of the Frequency Monitoring Network (FNET/GridEye), based on the relationship between frequency change and the power loss of disturbances, which is linearly associated by the Frequency Response. We first analyze the real disturbance records (in megawatt) of North America from the year 1992 to 2009 and confirm the power law distribution; meanwhile we discover that small disturbances are log-normal distributed. Then based on the real records from 2011 to 2013 (EI), the disturbances in megawatt and the corresponding frequency change records are studied in parallel. We prove that the frequency change of disturbances and its megawatt records share similar power law distribution when the disturbances are large; the frequency change can be delineated by a log-normal distribution with its numerically approximated coefficient when the disturbances are small. These findings have enabled the analysis of disturbances as frequency changes monitored at the distribution level with much better resolution and significantly faster access of data.
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