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Prediction of line voltage stability index using supervised learning
Joint Authors
Sharma, Ankit Kumar
Saxena, Akash
Source
Issue
Vol. 13, Issue 4 (31 Dec. 2017), pp.696-708, 13 p.
Publisher
Publication Date
2017-12-31
Country of Publication
Algeria
No. of Pages
13
Main Subjects
Abstract EN
In deregulated environment, stability issues have become dominant.
Reliability of the power is essential for successful operation of the power system.
Often high and dynamic loading conditions present new challenges in terms of decision of the control strategies to the system operator at energy management centre.
For the achievement of voltage stability, identification of weak buses is very important.
Line stability indices are important predictors of the weak buses in the over loaded system.
Identification of the weak buses is the first step of control strategy.
This paper presents an effective methodology based on Artificial Neural Network (ANN) to predict the Fast Voltage Stability Index (FVSI).
Comparative analysis of different topologies of ANN is carried out based on the capability of the prediction of FVSI.
Results are validated through offline Newton Raphson (NR) simulation method.
The proposed methodology is tested over IEEE-14 and IEEE-30 test bus System.
American Psychological Association (APA)
Sharma, Ankit Kumar& Saxena, Akash. 2017. Prediction of line voltage stability index using supervised learning. Journal of Electrical Systems،Vol. 13, no. 4, pp.696-708.
https://search.emarefa.net/detail/BIM-792812
Modern Language Association (MLA)
Sharma, Ankit Kumar& Saxena, Akash. Prediction of line voltage stability index using supervised learning. Journal of Electrical Systems Vol. 13, no. 4 (2017), pp.696-708.
https://search.emarefa.net/detail/BIM-792812
American Medical Association (AMA)
Sharma, Ankit Kumar& Saxena, Akash. Prediction of line voltage stability index using supervised learning. Journal of Electrical Systems. 2017. Vol. 13, no. 4, pp.696-708.
https://search.emarefa.net/detail/BIM-792812
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 707-708
Record ID
BIM-792812