Dynamic voltage collapse prediction in a practical power system with support vector machine

Muhammad Nizam, Azah Mohamed, Majid Al-Dabbagh, Aini Hussain

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.

Original languageEnglish
Title of host publicationIEEE Region 10 Annual International Conference, Proceedings/TENCON
DOIs
Publication statusPublished - 2008
Event2008 IEEE Region 10 Conference, TENCON 2008 - Hyderabad
Duration: 19 Nov 200821 Nov 2008

Other

Other2008 IEEE Region 10 Conference, TENCON 2008
CityHyderabad
Period19/11/0821/11/08

Fingerprint

Support vector machines
Electric potential
Multilayer neural networks
Neural networks
Computer simulation

Keywords

  • Artificial neural network
  • Dynamic voltage collapse
  • Prediction
  • Support vector machines

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications

Cite this

Nizam, M., Mohamed, A., Al-Dabbagh, M., & Hussain, A. (2008). Dynamic voltage collapse prediction in a practical power system with support vector machine. In IEEE Region 10 Annual International Conference, Proceedings/TENCON [4766855] https://doi.org/10.1109/TENCON.2008.4766855

Dynamic voltage collapse prediction in a practical power system with support vector machine. / Nizam, Muhammad; Mohamed, Azah; Al-Dabbagh, Majid; Hussain, Aini.

IEEE Region 10 Annual International Conference, Proceedings/TENCON. 2008. 4766855.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nizam, M, Mohamed, A, Al-Dabbagh, M & Hussain, A 2008, Dynamic voltage collapse prediction in a practical power system with support vector machine. in IEEE Region 10 Annual International Conference, Proceedings/TENCON., 4766855, 2008 IEEE Region 10 Conference, TENCON 2008, Hyderabad, 19/11/08. https://doi.org/10.1109/TENCON.2008.4766855
Nizam M, Mohamed A, Al-Dabbagh M, Hussain A. Dynamic voltage collapse prediction in a practical power system with support vector machine. In IEEE Region 10 Annual International Conference, Proceedings/TENCON. 2008. 4766855 https://doi.org/10.1109/TENCON.2008.4766855
Nizam, Muhammad ; Mohamed, Azah ; Al-Dabbagh, Majid ; Hussain, Aini. / Dynamic voltage collapse prediction in a practical power system with support vector machine. IEEE Region 10 Annual International Conference, Proceedings/TENCON. 2008.
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