Optimal Placement and Sizing for Fault Current Limiters in the Transmission Network Using the Hybrid Optimization Algorithm of Particle Swarm and Gravity Search

Document Type : -

Authors

1 Electrical Engineering Faculty, Sahand University of Technology, Tabriz, Iran

2 student of electrical engineering faculty of Tabriz Sahand University of Technology

Abstract

Considering the high importance of the electricity industry as the infrastructure of other infrastructures of the country, increasing the reliability of the correct operation of the protection programs in the electricity transmission network is considered as one of the measures that can be mentioned in the direction of passive defense. One of these methods is the use of fault current limiters (FCL) in the transmission network. In power systems, the technical and economic benefits of using FCL depend on the type, number, installation locations and optimal parameters of limiters. In the present study, the number, location and impedance of FCLs in the network are determined to achieve various goals such as reducing the short circuit level, then, the minimum number of limiters along with the installation locations and the optimal parameter of each limiter are calculated in two steps using a meta-heuristic algorithm, called the Hybrid Optimization algorithm (PSOGSA) (Combination of the Particle Swarm Optimization Algorithm (PSO) and Gravitational Search Algorithm (GSA)). In the following, the proposed meta-heuristic algorithm is compared with genetic algorithms, PSO and GSA. According to the results of the numerical studies conducted to compare the proposed algorithm with GA, PSO and GSA algorithms, the proposed hybrid algorithm instead of installing more fault current limiters, by increasing its impedance optimally while reducing the total cost of installing this equipment; has better performance in terms of reducing the short circuit level of buses and it converges to the optimal point faster.

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References

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Journal of Advanced Defense Science & Technology
Volume 13, Issue 2 - Serial Number 48
October 2022
Pages 113-124

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History

  • Receive Date: 18 December 2021
  • Revise Date: 16 June 2022
  • Accept Date: 15 July 2022
  • Publish Date: 21 September 2022

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