Masters Theses

Abstract

"An Automated Meter Reading (AMR) system is a metering technology that enables power utility companies to receive customers' energy usage data centrally over a communication network. The installed automated meters also provide a daily log of outage events for each customer. A utility company can greatly benefit by using this information for outage management and to improve reliability. However, outage data is frequently corrupted and the outage flags registered by the customers' meters do not necessarily reflect true outages. This thesis focuses on developing methods to analyze outage data and building a model to identify good data versus spurious indications. Outage data analysis is accomplished by comparison with known occurrences of outage events. A histogram analysis is performed to study the distribution of multiple outages. This thesis also introduces a fuzzy logic-based algorithm to analyze AMR meter outages and predict a degree of accuracy for each outage indication. A generalized model is developed to gather essential network information pertinent to outage indications. This information is combined with data from the outage analysis system and is used as an input to the fuzzy logic system that analyzes the information and provides a confidence index for the AMR outage flags"--Abstract, page iii.

Advisor(s)

Crow, Mariesa

Committee Member(s)

Chowdhury, Badrul H.
Ferdowsi, Mehdi

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Sponsor(s)

AmerenUE

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2012

Pagination

viii, 78 pages

Note about bibliography

Includes bibliographical references (pages 45-47).

Rights

© 2012 Prasad Prabhakar Shinde, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

Electric power failures -- Data processingFuzzy logicPublic utilitiesUtility meters

Thesis Number

T 10072

Print OCLC #

829103089

Electronic OCLC #

800770448

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