Real power transfer allocation via continuous genetic algorithm-least squares support vector machine technique

Mohd Wazir Mustafa, Mohd Herwan Sulaiman, Saifulnizam Abd Khalid, Hussain Shareef

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

5 Citations (Scopus)

Abstract

This paper proposes a new hybrid technique, Continuous Genetic Algorithm and Least Squares Support Vector Machine to allocate the real power transfer from generators to loads, namely CGA-LSSVM. CGA is used to obtain the optimal value of hyper-parameters of LS-SVM and supervised learning approach is adopted in the training of LSSVM model. The technique that uses proportional sharing principle (PSP) is utilized as a teacher. Based on load profile of the system and followed by PSP technique for power tracing procedure, the description of inputs and outputs of the training data are created. The CGA-LSSVM is expected to be able to assess which generators are supplying to which specific loads. In this paper, the 25-bus equivalent system of southern Malaysia is used to illustrate the effectiveness of the CGA-LSSVM technique compared to that of the PSP technique.

Original languageEnglish
Title of host publicationPECon2010 - 2010 IEEE International Conference on Power and Energy
Pages12-17
Number of pages6
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 IEEE International Conference on Power and Energy, PECon2010 - Kuala Lumpur, Malaysia
Duration: Nov 29 2010Dec 1 2010

Publication series

NamePECon2010 - 2010 IEEE International Conference on Power and Energy

Conference

Conference2010 IEEE International Conference on Power and Energy, PECon2010
Country/TerritoryMalaysia
CityKuala Lumpur
Period11/29/1012/1/10

Keywords

  • Continuous genetic algorithm (CGA)
  • Least Squares Support Vector Machine (LS-SVM)
  • Proportional sharing principle (PSP)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology

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