A support vector machine (SVM) based voltage stability classifier

Rodel D. Dosano; Hwachang Song; Byongjun Lee

A support vector machine (SVM) based voltage stability classifier

Rodel D. Dosano
, Hwachang Song
, Byongjun Lee

Dept. of Electrical and Information Engineering

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

Abstract

This paper proposes a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. The excellent performance of the SVM in the classification related to time-series prediction matches the real-time data of local measurement for system responses by shortterm and long-term dynamics. The methodology for automatic monitoring of the system is initiated locally, which aims to leave sufficient time to perform immediate corrective actions to stop system degradation by the effect of major disturbances. This paper explains the procedure for fast classification of long-term voltage stability using the SVM algorithm.

Original language	English
Title of host publication	Proceedings of the 7th IASTED International Conference on Power and Energy Systems
Pages	265-271
Number of pages	7
State	Published - 2007
Event	7th IASTED International Conference on Power and Energy Systems - Palma de Mallorca, Spain Duration: 29 Aug 2007 → 31 Aug 2007

Publication series

Name	Proceedings of the IASTED International Conference on Energy and Power Systems

Conference

Conference	7th IASTED International Conference on Power and Energy Systems
Country/Territory	Spain
City	Palma de Mallorca
Period	29/08/07 → 31/08/07

Keywords

Classification
Local phasor measurement
Power system voltage stability
Real-time monitoring
Support vector machine

Cite this

@inproceedings{32d541814c984a56ae69a47e9a7cb2b0,

title = "A support vector machine (SVM) based voltage stability classifier",

abstract = "This paper proposes a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. The excellent performance of the SVM in the classification related to time-series prediction matches the real-time data of local measurement for system responses by shortterm and long-term dynamics. The methodology for automatic monitoring of the system is initiated locally, which aims to leave sufficient time to perform immediate corrective actions to stop system degradation by the effect of major disturbances. This paper explains the procedure for fast classification of long-term voltage stability using the SVM algorithm.",

keywords = "Classification, Local phasor measurement, Power system voltage stability, Real-time monitoring, Support vector machine",

author = "Dosano, \{Rodel D.\} and Hwachang Song and Byongjun Lee",

year = "2007",

language = "English",

isbn = "9780889866898",

series = "Proceedings of the IASTED International Conference on Energy and Power Systems",

pages = "265--271",

booktitle = "Proceedings of the 7th IASTED International Conference on Power and Energy Systems",

note = "7th IASTED International Conference on Power and Energy Systems ; Conference date: 29-08-2007 Through 31-08-2007",

}

Dosano, RD, Song, H & Lee, B 2007, A support vector machine (SVM) based voltage stability classifier. in Proceedings of the 7th IASTED International Conference on Power and Energy Systems. Proceedings of the IASTED International Conference on Energy and Power Systems, pp. 265-271, 7th IASTED International Conference on Power and Energy Systems, Palma de Mallorca, Spain, 29/08/07.

A support vector machine (SVM) based voltage stability classifier. / Dosano, Rodel D.; Song, Hwachang; Lee, Byongjun.
Proceedings of the 7th IASTED International Conference on Power and Energy Systems. 2007. p. 265-271 (Proceedings of the IASTED International Conference on Energy and Power Systems).

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

TY - GEN

T1 - A support vector machine (SVM) based voltage stability classifier

AU - Dosano, Rodel D.

AU - Song, Hwachang

AU - Lee, Byongjun

PY - 2007

Y1 - 2007

N2 - This paper proposes a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. The excellent performance of the SVM in the classification related to time-series prediction matches the real-time data of local measurement for system responses by shortterm and long-term dynamics. The methodology for automatic monitoring of the system is initiated locally, which aims to leave sufficient time to perform immediate corrective actions to stop system degradation by the effect of major disturbances. This paper explains the procedure for fast classification of long-term voltage stability using the SVM algorithm.

AB - This paper proposes a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. The excellent performance of the SVM in the classification related to time-series prediction matches the real-time data of local measurement for system responses by shortterm and long-term dynamics. The methodology for automatic monitoring of the system is initiated locally, which aims to leave sufficient time to perform immediate corrective actions to stop system degradation by the effect of major disturbances. This paper explains the procedure for fast classification of long-term voltage stability using the SVM algorithm.

KW - Classification

KW - Local phasor measurement

KW - Power system voltage stability

KW - Real-time monitoring

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/56149114746

M3 - Conference contribution

AN - SCOPUS:56149114746

SN - 9780889866898

T3 - Proceedings of the IASTED International Conference on Energy and Power Systems

SP - 265

EP - 271

BT - Proceedings of the 7th IASTED International Conference on Power and Energy Systems

T2 - 7th IASTED International Conference on Power and Energy Systems

Y2 - 29 August 2007 through 31 August 2007

ER -

A support vector machine (SVM) based voltage stability classifier

Abstract

Publication series

Conference

Keywords

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