By Topic

Real power transfer allocation via Continuous Genetic Algorithm-Least Squares Support Vector Machine technique

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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

This paper proposes a new hybrid technique, Continuous Genetic Algorithm and Least Squares Support Vector Machine to allocate the real power transfer from generators to loads, namely CGA-LSSVM. CGA is used to obtain the optimal value of hyper-parameters of LS-SVM and supervised learning approach is adopted in the training of LS-SVM model. The technique that uses proportional sharing principle (PSP) is utilized as a teacher. Based on load profile of the system and followed by PSP technique for power tracing procedure, the description of inputs and outputs of the training data are created. The CGA-LSSVM is expected to be able to assess which generators are supplying to which specific loads. In this paper, the 25-bus equivalent system of southern Malaysia is used to illustrate the effectiveness of the CGA-LSSVM technique compared to that of the PSP technique.

Published in:

Power and Energy (PECon), 2010 IEEE International Conference on

Date of Conference:

Nov. 29 2010-Dec. 1 2010