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Power flow allocation method with the application of hybrid genetic algorithm-least squares support vector machine

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4 Author(s)
Mustafa, M.W. ; Fac. of Electr. Eng., Univ. Teknol. Malaysia, Skudai, Malaysia ; Khalid, S.A. ; Sulaiman, M.H. ; Shareef, H.

This paper proposes a new power flow allocation method in pool based power system with the application of hybrid genetic algorithm (GA) and least squares support vector machine (LS-SVM), namely GA-SVM. GA is utilized to find the optimal values of regularization parameter, γ and Kernel RBF parameter, σ2, which are embedded in LS-SVM model so that the power flow allocation problem can be solved by using machine learning adaptation approach. The supervised learning paradigm is used to train the LS-SVM model where the proportional sharing principle (PSP) method 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 GA-SVM model 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 proposed method. The comparison result with artificial neural network (ANN) technique is also will be presented.

Published in:

IPEC, 2010 Conference Proceedings

Date of Conference:

27-29 Oct. 2010