On-Line Approach for Loss Reduction in Electric Power Distribution Networks Using Learning Classifier Systems

Authors:
Patrícia Amâncio Vargas;Christiano LyraFilho;Fernando J. Von Zuben
Affiliations:
-;-;-
Venue:
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Year:
2001

Citing 7
Cited 0

Classifier systems and genetic algorithms

Artificial Intelligence
Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Network flows: theory, algorithms, and applications

Network flows: theory, algorithms, and applications
Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search

Tabu Search
Improving the Performance of Genetic Algorithms in Classifier Systems

Proceedings of the 1st International Conference on Genetic Algorithms

Quantified Score

Hi-index	0.00

Visualization

Abstract

The problem of minimization of energy losses in power distribution systems can be formulated as obtaining the "best" network configuration, through the manipulation of sectionalizing switches. Using graph terminology, we have a combinatorial optimization problem, whose solution corresponds to finding a minimum spanning tree for the network. As an on-line approach to loss reduction in electric power distribution networks, this paper relies on Learning Classifier Systems to continually proposed network configurations close to the one associated with minimum energy losses, in the case of time-varying profiles of energy requirement. In order to evolve the set of rules that composes the Classifier System, operators for selection, reproduction and mutation are applied. Case studies illustrate the possibilities of this approach.