Bayesian estimates of transmission line outage rates that consider line dependencies

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

Applying Bayesian estimates of individual transmission line outage rates

Despite the important role transmission line outages play in power system reliability analysis, it remains a challenge to estimate individual line outage rates accurately enough from limited data. Recent work using a Bayesian hierarchical model shows how to combine together line outage data by exploiting how the lines partially share some common features in order to obtain more accurate estimates of line outage rates. Lower variance estimates from fewer years of data can be obtained. In this paper, we explore what can be achieved with this new Bayesian hierarchical approach using real utility data. In particular, we assess the capability to detect increases in line outage rates over time, quantify the influence of bad weather on outage rates, and discuss the effect of outage rate uncertainty on a simple availability calculation

Bayesian Estimates of Transmission Line Outage Rates That Consider Line Dependencies

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.This is a manuscript of an article published as Zhou, Kai, James R. Cruise, Chris J. Dent, Ian Dobson, Louis Wehenkel, Zhaoyu Wang, and Amy L. Wilson. "Bayesian estimates of transmission line outage rates that consider line dependencies." IEEE Transactions on Power Systems 36, no. 2 (2020): 1095-1106. DOI: 10.1109/TPWRS.2020.3012840 Copyright 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Posted with permission