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On Estimation of Equipment Failures in Electric Distribution Systems Using Bayesian Inference

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Title:On Estimation of Equipment Failures in Electric Distribution Systems Using Bayesian Inference
Authors:Peerzada, Aaqib
Begovic, Miroslav M.
Rohouma, Wesam
Balog, Robert
Keywords:Distributed, Renewable, and Mobile Resources
asset management
bayesian parameter estimation
renewable generation
weibull distribution
Date Issued:05 Jan 2021
Abstract:This paper presents a new statistical parametric model to predict the times-to-failure of broad classes of identical devices such as on-load tap changers, switched capacitors, breakers, etc. A two-parameter Weibull distribution with scale parameter given by the inverse power law is employed to model the survivor functions and hazard rates of on-load tap changers. The resulting three-parameter distribution, referred to as IPL-Weibull, is flexible enough to assume right, left, and even symmetrical modal distribution. In this work, we propose an inferential method based on Bayes’ rule to derive the point estimates of model parameters from the past right-censored failure data. Using the Monte Carlo integration technique, it is possible to obtain such parameter estimates with high accuracy.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/70996
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.381
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Distributed, Renewable, and Mobile Resources


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