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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-24627 |
|---|---|
| record_format |
dspace |
| 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 |
13.188404 |