Loading...
Generator maintenance scheduling of electric power systems using genetic algorithms with integer representations
Dahal, Keshav P. McDonald, J.R.
Dahal, Keshav P.
McDonald, J.R.
Publication Date
1997
End of Embargo
Supervisor
Rights
Copyright © [1997] IEEE. Reprinted from Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Glasgow 2-4 Sept 1997 (GALESIA 97). New York: IEEE.
This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bradford's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.
By choosing to view this document, you agree to all provisions of the copyright laws protecting it
Peer-Reviewed
Yes
Open Access status
Accepted for publication
Institution
Department
Awarded
Embargo end date
Additional title
Abstract
The effective maintenance scheduling of power system
generators is very important to a power utility for the
economical and reliable operation of a power system.
Many mathematical methods have been implemented for
generator maintenance scheduling (GMS). However,
these methods have many limitations and require many
approximations. Here a Genetic Algorithm is proposed
for GMS problems in order to overcome some of the
limitations of the conventional methods.
This paper formulates a general GMS problem using a
reliability criterion as an integer programming problem,
and demonstrates the use of GAs with three different
problem encodings: binary, binary for integer and
integer. The GA performances for each of these
representations are analysed and compared for a test
problem based on a practical power system scenario. The
effects of different GA parameters are also studied. The
results show that the integer GA is a very effective
method for GMS problems.
Version
published version paper
Citation
Dahal, K. P. and McDonald, J. R. (1997) Generator maintenance scheduling of electric power systems using genetic algorithms with integer representations. In: Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Glasgow 2-4 Sept 1997 (GALESIA 97). New York: IEEE. Conf. Publ. No. 446. pp.456-461
Link to publisher’s version
Link to published version
Link to Version of Record
Type
Conference paper