Genetic algorithms for optimal reactive power compensation of a power system with wind generators based on artificial neural networks
Joint Authors
Krichen, L.
Abd Allah, H. Hadj
Ouali, A.
Source
Issue
Vol. 3, Issue 2 (30 Jun. 2007), pp.1-12, 12 p.
Publisher
Publication Date
2007-06-30
Country of Publication
Algeria
No. of Pages
12
Main Subjects
Topics
Abstract EN
IIn this paper, we develop a method to maintain an acceptable voltages profile and minimization of active losses of a power system including wind generators in real time.
These tasks are ensured by acting on capacitor and inductance benches implemented in the consuming nodes.
To solve this problem, we minimize an objective function associated to active losses under constraints imposed on the voltages and the reactive productions of the various benches.
The minimization procedure was realised by the use of genetic algorithms (GA).
The major disadvantage of this technique is that it requires a significant computing time thus not making it possible to deal with the problem in real time.
After a training phase, a neural model has the capacity to provide a good estimation of the voltages, the reactive productions and the losses for forecast curves of the load and the wind speed, in real time.
American Psychological Association (APA)
Krichen, L.& Abd Allah, H. Hadj& Ouali, A.. 2007. Genetic algorithms for optimal reactive power compensation of a power system with wind generators based on artificial neural networks. Journal of Electrical Systems،Vol. 3, no. 2, pp.1-12.
https://search.emarefa.net/detail/BIM-173180
Modern Language Association (MLA)
Krichen, L.…[et al.]. Genetic algorithms for optimal reactive power compensation of a power system with wind generators based on artificial neural networks. Journal of Electrical Systems Vol. 3, no. 2 (Jun. 2007), pp.1-12.
https://search.emarefa.net/detail/BIM-173180
American Medical Association (AMA)
Krichen, L.& Abd Allah, H. Hadj& Ouali, A.. Genetic algorithms for optimal reactive power compensation of a power system with wind generators based on artificial neural networks. Journal of Electrical Systems. 2007. Vol. 3, no. 2, pp.1-12.
https://search.emarefa.net/detail/BIM-173180
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references: p. 11-12
Record ID
BIM-173180