Bayesian Estimates of Transmission Line Outage Rates That Consider Line Dependencies
Bayesian Estimates of Transmission Line Outage Rates That Consider Line Dependencies
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DOI：
10.1109/tpwrs.2020.3012840
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发表时间：
2020-01
影响因子：
6.6
通讯作者：
Kai Zhou;J. Cruise;C. Dent;I. Dobson;L. Wehenkel;Zhaoyu Wang;Amy L. Wilson
中科院分区：
文献类型：
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作者：
Kai Zhou;J. Cruise;C. Dent;I. Dobson;L. Wehenkel;Zhaoyu Wang;Amy L. Wilson
输电线路停运率是电力系统可靠性分析的基础。线路中断很少发生，大约一年只发生一次，因此中断数据有限。我们提出了一种贝叶斯分层模型，该模型利用线路依赖关系从有限的停运数据中更好地估计单个输电线路的停运率。贝叶斯估计比简单地通过将中断次数除以数据的年数来估计中断比率的标准差更低，特别是在中断次数较小时。贝叶斯模型提供了更准确的单条线路停运率，以及对这些停运率的不确定性的估计。更好地估计线路停运率可以改进系统风险评估、停运预测和维护计划。
Transmission line outage rates are fundamental to power system reliability analysis. Line outages are infrequent, occurring only about once a year, so outage data are limited. We propose a Bayesian hierarchical model that leverages line dependencies to better estimate outage rates of individual transmission lines from limited outage data. The Bayesian estimates have a lower standard deviation than estimating the outage rates simply by dividing the number of outages by the number of years of data, especially when the number of outages is small. The Bayesian model produces more accurate individual line outage rates, as well as estimates of the uncertainty of these rates. Better estimates of line outage rates can improve system risk assessment, outage prediction, and maintenance scheduling.