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Author Sulaiman, Mohd Herwan ♦ Aliman, Omar ♦ Mustafa, Mohd Wazir ♦ Khalid, Saifulnizam Abd. ♦ Shareef, Hussain
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Support vector machines ♦ Genetic algorithms ♦ Training ♦ Generators ♦ Load flow ♦ Artificial neural networks ♦ Load modeling ♦ proportional sharing method ♦ deregulation ♦ genetic algorithm ♦ least squares support vector machine ♦ load flow
Abstract This paper proposes a new hybrid technique to allocate power flow in loads for pool market model in deregulated power industry. This paper focuses on creating an appropriate hybridization of Genetic Algorithm and Support Vector Machine (GA-SVM) to allocate the real power transfer from individual generator to loads by adopting machine learning approach. The IEEE 14-bus network is utilized as a test system to illustrate the effectiveness of GA-SVM output compared to that of conventional method used as a teacher. The well known proportional sharing method (PSM) is used as a teacher for calculating the contribution factors of individual generator to loads under pool model. The comparison with Artificial Neural Network (ANN) also will be presented in this paper.
Description Author affiliation: Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia (Mustafa, Mohd Wazir; Khalid, Saifulnizam Abd.) || Faculty of Electrical Engineering, and Build Environment Universiti Kebangsaan Malaysia, Bangi, Malaysia (Shareef, Hussain) || School of Electrical, System Engineering, Universiti Malaysia Perlis, Perlis, Malaysia (Sulaiman, Mohd Herwan) || Faculty of Electrical &, Electronic Engineering, Universiti Malaysia Pahang, Pahang, Malaysia (Aliman, Omar)
ISBN 9781424468898
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-11-21
Publisher Place Japan
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424468904
Size (in Bytes) 357.76 kB
Page Count 6
Starting Page 1
Ending Page 6


Source: IEEE Xplore Digital Library