Power Plant Availability Prediction Using Standard Bayesian Analysis of NERC/GADS Component Cause Code Failure Data

Prediction of power plant outage hours by cause can be very, effective in implementing a cost effective availability improvement program. If the predictions identify those causes for lost generation due to either full, pr partial outages most likely to happen in the next 3 years or less, attention can be focused on making improvements only in these areas and not for other causes that are not expected to be troublesome until later on in the life of the unit. With this approach, cognizance is given to the time value of money and overall less cost results because expenditures are deferred as long as possible. The necessity to differentiate between near-term causes of lost generation and long-term average expected performance, coupled with an incomplete data set, argues for the use of Bayesian analysis. A method of applying standard Bayesian analysis is demonstrated which uses as input only readily available data and produces as output, pre-dictions of forced outage hours (full or partial) for a unit during its next year of operation. The down hours are by cause and uncertainty of the predictions are indicated by plotting the results as probability densities (PD). How these PD can be used in an economic analysis to identify candidate availability improvement projects is demonstrated. Finally, a 7-year comparison between actual and predicted unit outage hours for sixth thermal units is presented as a measure of the "goodness" of the predictions and to serve as a base to measure future improvements to the prediction technique.