[KSCI] Korea Science Citation Index Service

Distribution System Reconfiguration Using the PC Cluster based Parallel Adaptive Evolutionary Algorithm

Mun Kyeong-Jun (Korea Atomic Energy Research Institue(KAERI))
Lee Hwa-Seok (Dept. of Electrical Engineering, Koje College)
Park June Ho (School of Electrical and Computer Engineering, Pusan National University)
Hwang Gi-Hyun (Division of Internet Engineering, Dongseo University)
Yoon Yoo-Soo (Korea Electrotechnology Research Institue(KERI))

Publication Information

KIEE International Transactions on Power Engineering / v.5A, no.3, 2005 , pp. 269-279 More about this Journal

Abstract

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of a reconfiguration in distribution systems. The aim of the reconfiguration is to determine the appropriate switch position to be opened for loss minimization in radial distribution systems, which is a discrete optimization problem. This problem has many constraints and it is very difficult to find the optimal switch position because of its numerous local minima. In this investigation, a parallel AEA was developed for the reconfiguration of the distribution system. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of GA and the local search capability of ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After AEA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC-cluster system consisting of 8 PCs·was developed. Each PC employs the 2 GHz Pentium IV CPU, and is connected with others through switch based fast Ethernet. The new developed algorithm has been tested and is compared to distribution systems in the reference paper to verify the usefulness of the proposed method. From the simulation results, it is found that the proposed algorithm is efficient and robust for distribution system reconfiguration in terms of the solution quality, speedup, efficiency, and computation time.

Keywords

Adaptive Evolutionary Algorithm; Distribution System Reconfiguration; Evolution Strategy; Genetic Algorithm; PC-cluster;

Citations & Related Records

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