Optimal Capacitor Allocation in Radial Distribution Networks for Annual Costs Minimization Using Hybrid PSO and Sequential Power Loss Index Based Method

Document Type : Research paper

Authors

1 Assistant Professor, Electrical Engineering Department, University of Zanjan, Zanjan, Iran.

2 Department of Electrical Engineering, University of Zanjan, Zanjan, Iran

3 West Mazandaran electrical power distribution company, Noshahr, Iran

Abstract

In the most recent heuristic methods, the high potential buses for capacitor placement are initially identified and ranked using loss sensitivity factors (LSFs) or power loss index (PLI). These factors or indices help to reduce the search space of the optimization procedure, but they may not always indicate the appropriate placement of capacitors. This paper proposes an efficient approach for the optimal capacitor placement in radial distribution networks with the aim of annual costs minimization based on the sequential placement of capacitors and calculation of power loss index. In the proposed approach, initially, the number of capacitors location is estimated using the total reactive power demand and the average range of capacitors available in the market. Then, the high potential buses can be identified using sequential power loss index-based method. This method leads to achieve the optimal or near optimal locations for the capacitors and decrease the search space of the optimization procedure significantly. The particle Swarm Optimization (PSO) algorithm takes the final decision for the optimum size and location of capacitors. To evaluate the efficiency of the conducted approach, it is tested on several well-known distribution networks, and the results are compared with those of existing methods in the literature. The comparisons verify the effectiveness of the proposed method in producing fast and optimal solutions.

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References

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Journal of Operation and Automation in Power Engineering
Volume 5, Issue 2
December 2017
Pages 117-130

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History

  • Receive Date: 09 September 2016
  • Revise Date: 20 November 2016
  • Accept Date: 28 December 2017
  • First Publish Date: 28 December 2017

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