Transient stability assessment of a large actual power system using probabilistic neural network with enhanced feature selection and extraction

Noor Izzri Abdul Wahab, Azah Mohamed

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

5 Citations (Scopus)

Abstract

This paper presents transient stability assessment of a large actual power system using the probabilistic neural network (PNN) with enhanced feature selection and extraction method. The investigated large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the amount of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations carried out by considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN. An enhanced feature selection and extraction methods are then incorporated to reduce the input features to the PNN which is used as a classifier to determine whether the power system is stable or unstable. It can be concluded that the PNN with enhanced feature selection and extraction methods reduces the time taken to train the PNN without affecting the accuracy of the classification results.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009
Pages519-524
Number of pages6
Volume2
DOIs
Publication statusPublished - 2009
Event2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009 - Selangor
Duration: 5 Aug 20097 Aug 2009

Other

Other2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009
CitySelangor
Period5/8/097/8/09

Fingerprint

Feature extraction
Neural networks
Classifiers
Rotors

Keywords

  • Dynamic security assessment
  • Feature extraction
  • Feature selection
  • Transient stability assessment

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

Wahab, N. I. A., & Mohamed, A. (2009). Transient stability assessment of a large actual power system using probabilistic neural network with enhanced feature selection and extraction. In Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009 (Vol. 2, pp. 519-524). [5254758] https://doi.org/10.1109/ICEEI.2009.5254758

Transient stability assessment of a large actual power system using probabilistic neural network with enhanced feature selection and extraction. / Wahab, Noor Izzri Abdul; Mohamed, Azah.

Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. Vol. 2 2009. p. 519-524 5254758.

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

Wahab, NIA & Mohamed, A 2009, Transient stability assessment of a large actual power system using probabilistic neural network with enhanced feature selection and extraction. in Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. vol. 2, 5254758, pp. 519-524, 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009, Selangor, 5/8/09. https://doi.org/10.1109/ICEEI.2009.5254758
Wahab NIA, Mohamed A. Transient stability assessment of a large actual power system using probabilistic neural network with enhanced feature selection and extraction. In Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. Vol. 2. 2009. p. 519-524. 5254758 https://doi.org/10.1109/ICEEI.2009.5254758
Wahab, Noor Izzri Abdul ; Mohamed, Azah. / Transient stability assessment of a large actual power system using probabilistic neural network with enhanced feature selection and extraction. Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. Vol. 2 2009. pp. 519-524
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