Intelligent data analysis for power systems

Wen Fan; Yuan Liao; Theo Laughner; Bruce Rogers; George Pitts; Joshua L. Wooten; James Rossman; Fred Elmendorf

doi:10.1109/PESGM.2012.6344953

Intelligent data analysis for power systems

Wen Fan, Yuan Liao, Theo Laughner, Bruce Rogers, George Pitts, Joshua L. Wooten, James Rossman, Fred Elmendorf

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

1 Scopus citations

Abstract

This paper presents potential techniques for automated analysis of various types of data collected in power systems. Diverse recording devices have been widely deployed in modern power systems, and as a result more data have been obtained. It is necessary to extract useful and actionable information from the captured data. This paper focuses on discussing intelligent techniques for automatically analyzing such data, including disturbance classification, fault events correlation, fault type classification, fault cause identification, fault location, generator monitoring and parameter estimation, incipient fault detection, and line parameter estimation.

Original language	English
Title of host publication	2012 IEEE Power and Energy Society General Meeting, PES 2012
DOIs	https://doi.org/10.1109/PESGM.2012.6344953
State	Published - 2012
Event	2012 IEEE Power and Energy Society General Meeting, PES 2012 - San Diego, CA, United States Duration: Jul 22 2012 → Jul 26 2012

Publication series

Name	IEEE Power and Energy Society General Meeting
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2012 IEEE Power and Energy Society General Meeting, PES 2012
Country/Territory	United States
City	San Diego, CA
Period	7/22/12 → 7/26/12

Keywords

Disturbance analysis
Fault location
Power quality
Substation automation

ASJC Scopus subject areas

Energy Engineering and Power Technology
Nuclear Energy and Engineering
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/PESGM.2012.6344953

Cite this

@inproceedings{7b2d960ed6ec48459dd68db5996d0c36,

title = "Intelligent data analysis for power systems",

abstract = "This paper presents potential techniques for automated analysis of various types of data collected in power systems. Diverse recording devices have been widely deployed in modern power systems, and as a result more data have been obtained. It is necessary to extract useful and actionable information from the captured data. This paper focuses on discussing intelligent techniques for automatically analyzing such data, including disturbance classification, fault events correlation, fault type classification, fault cause identification, fault location, generator monitoring and parameter estimation, incipient fault detection, and line parameter estimation.",

keywords = "Disturbance analysis, Fault location, Power quality, Substation automation",

author = "Wen Fan and Yuan Liao and Theo Laughner and Bruce Rogers and George Pitts and Wooten, {Joshua L.} and James Rossman and Fred Elmendorf",

year = "2012",

doi = "10.1109/PESGM.2012.6344953",

language = "English",

isbn = "9781467327275",

series = "IEEE Power and Energy Society General Meeting",

booktitle = "2012 IEEE Power and Energy Society General Meeting, PES 2012",

note = "2012 IEEE Power and Energy Society General Meeting, PES 2012 ; Conference date: 22-07-2012 Through 26-07-2012",

}

Fan, W, Liao, Y, Laughner, T, Rogers, B, Pitts, G, Wooten, JL, Rossman, J & Elmendorf, F 2012, Intelligent data analysis for power systems. in 2012 IEEE Power and Energy Society General Meeting, PES 2012., 6344953, IEEE Power and Energy Society General Meeting, 2012 IEEE Power and Energy Society General Meeting, PES 2012, San Diego, CA, United States, 7/22/12. https://doi.org/10.1109/PESGM.2012.6344953

TY - GEN

T1 - Intelligent data analysis for power systems

AU - Fan, Wen

AU - Liao, Yuan

AU - Laughner, Theo

AU - Rogers, Bruce

AU - Pitts, George

AU - Wooten, Joshua L.

AU - Rossman, James

AU - Elmendorf, Fred

PY - 2012

Y1 - 2012

N2 - This paper presents potential techniques for automated analysis of various types of data collected in power systems. Diverse recording devices have been widely deployed in modern power systems, and as a result more data have been obtained. It is necessary to extract useful and actionable information from the captured data. This paper focuses on discussing intelligent techniques for automatically analyzing such data, including disturbance classification, fault events correlation, fault type classification, fault cause identification, fault location, generator monitoring and parameter estimation, incipient fault detection, and line parameter estimation.

AB - This paper presents potential techniques for automated analysis of various types of data collected in power systems. Diverse recording devices have been widely deployed in modern power systems, and as a result more data have been obtained. It is necessary to extract useful and actionable information from the captured data. This paper focuses on discussing intelligent techniques for automatically analyzing such data, including disturbance classification, fault events correlation, fault type classification, fault cause identification, fault location, generator monitoring and parameter estimation, incipient fault detection, and line parameter estimation.

KW - Disturbance analysis

KW - Fault location

KW - Power quality

KW - Substation automation

UR - http://www.scopus.com/inward/record.url?scp=84870616737&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84870616737&partnerID=8YFLogxK

U2 - 10.1109/PESGM.2012.6344953

DO - 10.1109/PESGM.2012.6344953

M3 - Conference contribution

AN - SCOPUS:84870616737

SN - 9781467327275

T3 - IEEE Power and Energy Society General Meeting

BT - 2012 IEEE Power and Energy Society General Meeting, PES 2012

T2 - 2012 IEEE Power and Energy Society General Meeting, PES 2012

Y2 - 22 July 2012 through 26 July 2012

ER -

Intelligent data analysis for power systems

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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