Table of Contents
Advances in Artificial Intelligence
Volume 2012, Article ID 720463, 9 pages
Research Article

Preference Comparison of AI Power Tracing Techniques for Deregulated Power Markets

1Faculty of Electrical Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia
2Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia

Received 25 May 2012; Revised 4 December 2012; Accepted 9 December 2012

Academic Editor: Thomas Mandl

Copyright © 2012 Hussain Shareef et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper compares the two preference artificial intelligent (AI) techniques, namely, artificial neural network (ANN) and genetic algorithm optimized least square support vector machine (GA-LSSVM) approach, to allocate the real power output of individual generators to system loads. Based on solved load flow results, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the AI techniques compared to those of the MNE method. The AI methods provide the results in a faster and convenient manner with very good accuracy.