Genetic algorithms (GAs) are widely used for optimal allocation of capacitors in distribution systems When dealing with large-scale systems, such as in case of unbalanced multi-converter distribution systems, these algorithms can require significant computational efforts, which reduce their effectiveness In order to reduce processing time for GAs and simultaneously maintain adequate levels of accuracy, methods based on the reduction of the search space of GAs or based on micro-genetic algorithms have been proposed These methods generally guarantee good solutions with acceptable levels of computational effort In this paper, some fast, GA-based methods are compared and applied for solving the problem of optimal sizing and siting of capacitors in unbalanced multi-converter distribution systems The algorithms have been implemented and tested on the unbalanced IEEE 34-bus test distribution system, and their performances have been compared with the performance of the simple genetic algorithm technique.

Optimal Allocation of Capacitors in Unbalanced Multi-converter Distribution Systems: A Comparison of Some Fast Techniques Based on Genetic Algorithms

CARPINELLI, GUIDO;PROTO, DANIELA;
2010

Abstract

Genetic algorithms (GAs) are widely used for optimal allocation of capacitors in distribution systems When dealing with large-scale systems, such as in case of unbalanced multi-converter distribution systems, these algorithms can require significant computational efforts, which reduce their effectiveness In order to reduce processing time for GAs and simultaneously maintain adequate levels of accuracy, methods based on the reduction of the search space of GAs or based on micro-genetic algorithms have been proposed These methods generally guarantee good solutions with acceptable levels of computational effort In this paper, some fast, GA-based methods are compared and applied for solving the problem of optimal sizing and siting of capacitors in unbalanced multi-converter distribution systems The algorithms have been implemented and tested on the unbalanced IEEE 34-bus test distribution system, and their performances have been compared with the performance of the simple genetic algorithm technique.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/365645
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