Preference Comparison of AI Power Tracing Techniques for Deregulated Power Markets
Joint Authors
Shareef, Hussain
Mustafa, Mohd Wazir
Abdul Khalid, Saifulnizam Bin
Khairuddin, Azhar
Source
Advances in Artificial Intelligence
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-12-27
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Science
Abstract EN
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.
American Psychological Association (APA)
Shareef, Hussain& Abdul Khalid, Saifulnizam Bin& Mustafa, Mohd Wazir& Khairuddin, Azhar. 2012. Preference Comparison of AI Power Tracing Techniques for Deregulated Power Markets. Advances in Artificial Intelligence،Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-493247
Modern Language Association (MLA)
Shareef, Hussain…[et al.]. Preference Comparison of AI Power Tracing Techniques for Deregulated Power Markets. Advances in Artificial Intelligence No. 2012 (2012), pp.1-9.
https://search.emarefa.net/detail/BIM-493247
American Medical Association (AMA)
Shareef, Hussain& Abdul Khalid, Saifulnizam Bin& Mustafa, Mohd Wazir& Khairuddin, Azhar. Preference Comparison of AI Power Tracing Techniques for Deregulated Power Markets. Advances in Artificial Intelligence. 2012. Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-493247
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-493247