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JACIII Vol.17 No.5 pp. 681-689
doi: 10.20965/jaciii.2013.p0681
(2013)

Paper:

Improving the Search Ability of Tabu Search in the Distribution Network Reconfiguration Problem

Hirotaka Takano*, Junichi Murata*, Yukino Maki**,
and Makoto Yasuda**

*Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka-shi, Fukuoka 819-0395, Japan

**Department of Electrical and Information Engineering, Gifu National College of Technology, 2236-2 Kamimakuwa, Motosu-shi, Gifu 501-0495, Japan

Received:
March 19, 2013
Accepted:
May 20, 2013
Published:
September 20, 2013
Keywords:
distribution network reconfiguration, distribution loss minimization, metaheuristics, tabu search (TS), combinatorial optimization problem
Abstract

The distribution network reconfiguration problem is to decide whether each sectionalizing switch is to be open or closed in order to maintain or improve electrical power supply reliability, power quality and network operation efficiency. Obtaining the optimal network configuration is, however, extremely difficult because the network reconfiguration problem is actually a large-size combinatorial optimization problem. Many optimization algorithms have thus been applied to the reconfiguration problem to support power utility’s decision-making. This paper proposes a local search-based solution for the reconfiguration problem in which tabu search – one of the most widely used local search-based metaheuristics – is employed to solve the problem. Tabu search is improved by introducing an effective search strategy that utilizes the properties of this kind of problems. Numerical simulations are performed on a complex actual-scale distribution network model in order to verify the validity of the proposed solution.

Cite this article as:
H. Takano, J. Murata, Y. Maki, and <. Yasuda, “Improving the Search Ability of Tabu Search in the Distribution Network Reconfiguration Problem,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.5, pp. 681-689, 2013.
Data files:
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Last updated on Dec. 07, 2018