Abstract
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.
Highlights
T RANSMISSION line outage rates are foundational for many reliability calculations, but in historical data the counts of outages for the more reliable lines are low, and estimated individual line outage rates are highly uncertain
We use the software Stan, which implements Monte Carlo Markov Chain (MCMC) as Hamiltonian Monte Carlo (HMC) [22] with the algorithm adaptively tuned by the No-U-Turn Sampler (NUTS) [23]
We use a Bayesian hierarchical model to improve the estimation of annual outage rates for individual transmission lines
Summary
T RANSMISSION line outage rates are foundational for many reliability calculations, but in historical data the counts of outages for the more reliable lines are low, and estimated individual line outage rates are highly uncertain. There are several ways in which individual transmission lines are partially similar, including their length, rating, geographical location, and their proximity. We leverage these partial similarities with a Bayesian hierarchical method to improve the estimation of line outage rates from historical data. To exploit the partial dependencies of line outage rates, this paper proposes a Bayesian hierarchical method to estimate outage rates of individual transmission lines. Our method can leverage the multiple partial dependencies in line length, rating, network proximity, and geographical area to give better outage rates of individual lines. Between lines, including proximity, length, and rated voltage, especially when the annual outage counts are low or the data is limited.
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