论文标题
贝叶斯的估算值考虑线路依赖性的传输线中断率
Bayesian estimates of transmission line outage rates that consider line dependencies
论文作者
论文摘要
传输线的停电率是电力系统可靠性分析的基础。线路停电很少发生，每年仅发生一次，因此停电数据受到限制。我们提出了一个贝叶斯分层模型，该模型利用线路依赖性来更好地估计有限停机数据中单个传输线的中断率。贝叶斯估计的标准偏差要比仅通过将中断数除以数据年数来估算停电率的标准偏差要低，尤其是在中断数量较小时。贝叶斯模型产生更准确的单个线路停电率，并估计了这些速率的不确定性。更好地估计线路停电率可以改善系统风险评估，中断预测和维护计划。
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.
