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Fast Prediction of Angle Stability Using Support Vector Machine and Fault Duration Data

Automation; Forecasting; Intelligent systems; Phase measurement; Process control; Software testing; Stability; Support vector machines; System stability; Transients; Angle stability; Balanced faults; Different operating conditions; Fault clearance; Fault durations; Phasor measurement unit (PMUs); St...

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Main Authors: Shahriyari M., Khoshkhoo H., Pouryekta A., Ramachandaramurthy V.K.
Other Authors: 57211193618
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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id my.uniten.dspace-24627
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spelling my.uniten.dspace-246272023-05-29T15:25:17Z Fast Prediction of Angle Stability Using Support Vector Machine and Fault Duration Data Shahriyari M. Khoshkhoo H. Pouryekta A. Ramachandaramurthy V.K. 57211193618 24824311000 56119220300 6602912020 Automation; Forecasting; Intelligent systems; Phase measurement; Process control; Software testing; Stability; Support vector machines; System stability; Transients; Angle stability; Balanced faults; Different operating conditions; Fault clearance; Fault durations; Phasor measurement unit (PMUs); Stability of power system; Wide- area measurement systems (WAMS); Phasor measurement units This paper deals with the prediction of the transient stability of power systems using only pre-fault and fault duration data measured by Wide Area Measurement System (WAMS). In the proposed method, the time-synchronized values of voltage and current generated by synchronous generators (SGs) are measured by Phasor Measurement Units (PMUs) installed at generator buses, and given as input to the proposed algorithm in order to extract a proper feature set. Then, the proposed feature set is applied to Support Vector Machine (SVM) classifier to predict the transient stability status after fault occurrence and before fault clearance. The robustness and accuracy of the proposed method has been extensively examined under both unbalanced and balanced fault conditions as well as under different operating conditions. The results of simulation performed on an IEEE 14-bus test system using DIgSILENT PowerFactory software show that the proposed method can accurately predict the transient stability status against different contingencies using only pre-disturbance and fault duration data. � 2019 IEEE. Final 2023-05-29T07:25:17Z 2023-05-29T07:25:17Z 2019 Conference Paper 10.1109/I2CACIS.2019.8825052 2-s2.0-85072940030 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072940030&doi=10.1109%2fI2CACIS.2019.8825052&partnerID=40&md5=973176adb9c42206c70caa0766d1eef6 https://irepository.uniten.edu.my/handle/123456789/24627 8825052 258 263 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Automation; Forecasting; Intelligent systems; Phase measurement; Process control; Software testing; Stability; Support vector machines; System stability; Transients; Angle stability; Balanced faults; Different operating conditions; Fault clearance; Fault durations; Phasor measurement unit (PMUs); Stability of power system; Wide- area measurement systems (WAMS); Phasor measurement units
author2 57211193618
author_facet 57211193618
Shahriyari M.
Khoshkhoo H.
Pouryekta A.
Ramachandaramurthy V.K.
format Conference Paper
author Shahriyari M.
Khoshkhoo H.
Pouryekta A.
Ramachandaramurthy V.K.
spellingShingle Shahriyari M.
Khoshkhoo H.
Pouryekta A.
Ramachandaramurthy V.K.
Fast Prediction of Angle Stability Using Support Vector Machine and Fault Duration Data
author_sort Shahriyari M.
title Fast Prediction of Angle Stability Using Support Vector Machine and Fault Duration Data
title_short Fast Prediction of Angle Stability Using Support Vector Machine and Fault Duration Data
title_full Fast Prediction of Angle Stability Using Support Vector Machine and Fault Duration Data
title_fullStr Fast Prediction of Angle Stability Using Support Vector Machine and Fault Duration Data
title_full_unstemmed Fast Prediction of Angle Stability Using Support Vector Machine and Fault Duration Data
title_sort fast prediction of angle stability using support vector machine and fault duration data
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806425547428855808
score 13.226497

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