Abstract
Probabilistic risk assessment (PRA) techniques are increasingly being used in electric power industry applications for better coping with uncertainties over deterministic approaches. One application where PRA techniques can add value is data analysis for parameters such as outage frequency. Focusing on a probabilistic contingency analysis (PCA), this study examines the issue of obtaining a point estimate of outage statistics by lumping or pooling outage data records together from different sources. A Pearson Chi-square test is adopted to determine the poolability of data, and a lognormal distribution is used to model the data source variability and capture variations of operation and maintenance practices among different utilities. The distribution parameters representing outage frequencies and durations are calculated from the raw outage data. An improved PCA scheme based on the outcomes of this study is proposed and being implemented.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
