Design of a hybrid fuzzy classifier system for power system sensor status evaluation

Kang Lin; Keith Holbert

Design of a hybrid fuzzy classifier system for power system sensor status evaluation

Kang Lin, Keith Holbert

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

Correct estimation of the system operating states in the presence of uncertain measurement data is a crucial challenge for real-time power system monitoring. Proposed in this paper is a strategy that augments state estimation methods using an approach that can incorporate additional information that is not traditionally included as a part of the state variables. Specifically, fuzzy logic is utilized to create a hybrid fuzzy classifier system (HFCS). The HFCS aims to combine information from multiple domains in order to detect, isolate, identify, and mitigate threats to the power networks. Results from applying this HFCS to the IEEE 14-bus test system are presented.

Original language	English (US)
Title of host publication	2005 IEEE Power Engineering Society General Meeting
Pages	1351-1358
Number of pages	8
State	Published - 2005
Event	2005 IEEE Power Engineering Society General Meeting - San Francisco, CA, United States Duration: Jun 12 2005 → Jun 16 2005

Publication series

Name	2005 IEEE Power Engineering Society General Meeting
Volume	2

Other

Other	2005 IEEE Power Engineering Society General Meeting
Country/Territory	United States
City	San Francisco, CA
Period	6/12/05 → 6/16/05

Keywords

First principle methods
Fuzzy logic
Neural networks
Pattern recognition
State estimation

ASJC Scopus subject areas

General Engineering

Cite this

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title = "Design of a hybrid fuzzy classifier system for power system sensor status evaluation",

abstract = "Correct estimation of the system operating states in the presence of uncertain measurement data is a crucial challenge for real-time power system monitoring. Proposed in this paper is a strategy that augments state estimation methods using an approach that can incorporate additional information that is not traditionally included as a part of the state variables. Specifically, fuzzy logic is utilized to create a hybrid fuzzy classifier system (HFCS). The HFCS aims to combine information from multiple domains in order to detect, isolate, identify, and mitigate threats to the power networks. Results from applying this HFCS to the IEEE 14-bus test system are presented.",

keywords = "First principle methods, Fuzzy logic, Neural networks, Pattern recognition, State estimation",

author = "Kang Lin and Keith Holbert",

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series = "2005 IEEE Power Engineering Society General Meeting",

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AU - Holbert, Keith

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AB - Correct estimation of the system operating states in the presence of uncertain measurement data is a crucial challenge for real-time power system monitoring. Proposed in this paper is a strategy that augments state estimation methods using an approach that can incorporate additional information that is not traditionally included as a part of the state variables. Specifically, fuzzy logic is utilized to create a hybrid fuzzy classifier system (HFCS). The HFCS aims to combine information from multiple domains in order to detect, isolate, identify, and mitigate threats to the power networks. Results from applying this HFCS to the IEEE 14-bus test system are presented.

KW - First principle methods

KW - Fuzzy logic

KW - Neural networks

KW - Pattern recognition

KW - State estimation

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M3 - Conference contribution

AN - SCOPUS:27144447777

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SN - 9780780391567

T3 - 2005 IEEE Power Engineering Society General Meeting

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EP - 1358

BT - 2005 IEEE Power Engineering Society General Meeting

T2 - 2005 IEEE Power Engineering Society General Meeting

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ER -

Design of a hybrid fuzzy classifier system for power system sensor status evaluation

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

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