Preference comparison of ai power tracing techniques for deregulated power markets

Authors:
Hussain Shareef;Saifunizam Abd. Khalid;Mohd Wazir Mustafa;Azhar Khairuddin
Affiliations:
Faculty of Electrical Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia;Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia;Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia;Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
Venue:
Advances in Artificial Intelligence
Year:
2012

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Abstract

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.