Determination of generators' contributions to loads in pool based power system using least squares support vector machine

M. W. Mustafa, M. H. Sulaiman, H. Shareef, S. N. Abd Khalid

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

6 Citations (Scopus)

Abstract

This paper attempts to allocate the generators' contributions to loads in pool based power system by incorporating the Least Squares Support Vector Machine (LSSVM). The idea is to use supervised learning approach to train the LS-SVM. The technique that uses proportional tree method (PTM) which is applying the convention of proportional sharing principle is utilized as a teacher. Based on converged load flow and followed by PTM for power tracing procedure, the description of inputs and outputs of the training data for the LSSVM are created. The LS-SVM will learn to identify which generators are supplying to which loads. The proposed technique is demonstrated using IEEE 14-bus system to illustrate the effectiveness of the LS-SVM technique compared to that of the PTM. The comparison result with Artificial Neural Network (ANN) technique is also will be discussed.

Original languageEnglish
Title of host publicationPEOCO 2010 - 4th International Power Engineering and Optimization Conference, Program and Abstracts
Pages226-231
Number of pages6
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event4th International Power Engineering and Optimization Conference, PEOCO 2010 - Shah Alam, Malaysia
Duration: Jun 23 2010Jun 24 2010

Publication series

NamePEOCO 2010 - 4th International Power Engineering and Optimization Conference, Program and Abstracts

Conference

Conference4th International Power Engineering and Optimization Conference, PEOCO 2010
Country/TerritoryMalaysia
CityShah Alam
Period6/23/106/24/10

Keywords

  • Artificial neural network (ann)
  • Least squares support vector machine (ls-svm)
  • Pool based power system
  • Proportional tree method (ptm)
  • Supervised learning

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Control and Optimization

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