KIEE International Transactions on Power Engineering

The Korean Institute of Electrical Engineers (대한전기학회)
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Parallel Genetic Algorithm-Tabu Search Using PC Cluster System for Optimal Reconfiguration of Distribution Systems

  • Mun Kyeong-Jun (Korea Atomic Energy Research institute(KAERI)) ;
  • Lee Hwa-Seok (Dept. of Electrical Engineering, Koje College) ;
  • Park June-Ho (Division of Electrical and computer Engineering, Pusan National University)
  • Published : 2005.06.01
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Abstract

This paper presents an application of the parallel Genetic Algorithm-Tabu Search (GA- TS) algorithm, and that is to search for an optimal solution of a reconfiguration in distribution systems. The aim of the reconfiguration of distribution systems 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 solve the optimal switch position because of its numerous local minima. This paper develops a parallel GA- TS algorithm for the reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10$\%$ of the population to enhance the local searching capabilities. With migration operation, the best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based rapid Ethernet. To demonstrate the usefulness of the proposed method, the developed algorithm was tested and is compared to a distribution system in the reference paper From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution quality, speedup, efficiency, and computation time.

Keywords

References

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