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Tracing the real power transfer of individual generators to loads using Least Squares Support Vector Machine with Continuous Genetic Algorithm

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6 Author(s)
Mustafa, M.W. ; Fac. of Electr. Eng., Univ. Teknol. Malaysia, Skudai, Malaysia ; Khalid, S.A. ; Sulaiman, M.H. ; Rahim, S.R.A.
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This paper attempts to trace the real power transfer of individual generators to loads in pool based power system by incorporating the hybridization of Least Squares Support Vector Machine (LS-SVM) with Continuous Genetic Algorithm (CGA)- CGA-LSSVM. The idea is to use CGA to find the optimal values of regularization parameter, γ and Kernel RBF parameter, σ2, and adapt a supervised learning approach to train the LS-SVM model. The technique that uses proportional sharing principle (PSP) is utilized as a teacher. Based on converged load flow and followed by PSP technique for power tracing procedure, the description of inputs and outputs of the training data are created. The CGA-LSSVM will learn to identify which generators are supplying to which 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.

Published in:

Electrical, Control and Computer Engineering (INECCE), 2011 International Conference on

Date of Conference:

21-22 June 2011

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