NDLI: Probabilistic Methodologies for Determining the Optimal Number of Substation Spare Transformers

Content Provider	IEEE Xplore Digital Library
Author	da Silva, A.M.L. de Carvalho Costa, J.G. Chowdhury, A.A.
Copyright Year	1969
Abstract	This paper presents new probabilistic methodologies for computing the optimal number of transformer spares for power distribution substations. The basic idea consists of three steps: (1) the reliability evaluation of a given system of transformers with inventory of spares; (2) the calculation of investment and operational costs of the system for different alternatives of inventory composition; and (3) the identification of the number of spares that minimizes the total cost. Two new models are proposed for the reliability evaluation step. In the first one, the system operational states are represented by a Markov process. The second one uses a chronological Monte Carlo simulation model to assess the reliability performance of a system with inventory of spares. Both models are able to provide indices such as probability, frequency, and duration of failures, as well as estimates of energy not supplied and the corresponding costs. The proposed methodologies are applied to a 72-kV distribution transformer system, and the obtained results are compared to those from a widely used model based on a Poisson distribution.
Sponsorship	IEEE Power Engineering Society
Starting Page	68
Ending Page	77
Page Count	10
File Size	790509
File Format	PDF
ISSN	08858950
Volume Number	25
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Substations Transformers Costs Investments Frequency estimation Brazil Council Distributed computing Power distribution Power system reliability Markov processes spare transformers Catastrophic transformer failure inventory optimization probabilistic cost analysis
Content Type	Text
Resource Type	Article
Subject	Energy Engineering and Power Technology Electrical and Electronic Engineering

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