Please use this identifier to cite or link to this item: https://doi.org/10.1007/s40565-016-0249-9
Title: Pareto optimal allocation of fault current limiter based on immune algorithm considering cost and mitigation effect
Authors: Chen, B 
Wei, L
Zhu, Y
Zhong, Y
Yuan, J
Lei, Y
Keywords: Costs
Optimal systems
Pareto principle
Immune algorithms
Inversion operators
Mitigation effects
Optimal allocation
Pareto optimal solutions
Fault current limiters
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Citation: Chen, B, Wei, L, Zhu, Y, Zhong, Y, Yuan, J, Lei, Y (2017). Pareto optimal allocation of fault current limiter based on immune algorithm considering cost and mitigation effect. Journal of Modern Power Systems and Clean Energy 5 (5) : 820-829. ScholarBank@NUS Repository. https://doi.org/10.1007/s40565-016-0249-9
Rights: Attribution 4.0 International
Abstract: This paper presents a multi-objective Pareto optimal method for allocation of fault current limiters based on an immune algorithm, which takes into account two objectives of the cost and fault current mitigation effect. A sensitivity factor calculation method based on the rate of fault current mitigation is proposed to reduce the search space and improve the efficiency of the algorithm. In this approach, the objective functions related to the cost and fault current mitigation effect are established. A modified inversion operator based on equal cost is proposed to converge to global optimal solutions more effectively. The proposed algorithm is tested on the IEEE 39-bus system, and obtains the Pareto optimal solutions, from which the user can select the most suitable solutions according to the preferences and relative importance of the objective functions. Simulation results are used to verify the proposed method. © 2016, The Author(s).
Source Title: Journal of Modern Power Systems and Clean Energy
URI: https://scholarbank.nus.edu.sg/handle/10635/183505
ISSN: 2196-5625
DOI: 10.1007/s40565-016-0249-9
Rights: Attribution 4.0 International
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