Application of Swarm Mean-Variance Mapping Optimization on location and tuning damping controllers

José L. Rueda; F. González-Longatt

doi:10.1109/ISGT-Asia.2015.7386968

Application of Swarm Mean-Variance Mapping Optimization on location and tuning damping controllers

José L. Rueda, F. González-Longatt

Intelligent Electrical Power Grids

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

Abstract

This paper introduces the use of the Swarm Variant of the Mean-Variance Mapping Optimization (MVMO-S) to solving the multi-scenario problem of the optimal placement and coordinated tuning of power system damping controllers (POCDCs). The proposed solution is tested using the classical IEEE 39-bus test system, New England test system. This papers includes performance comparisons with other emerging metaheuristic optimization: comprehensive learning particle swarm optimization (CLPSO), genetic algorithm with multi-parent crossover (GA-MPC), differential evolution DE algorithm with adaptive crossover operator, linearized biogeography-based optimization with re-initialization (LBBO), and covariance matrix adaptation evolution strategy (CMA-ES). Numerical results illustrates the feasibility and effectiveness of the proposed approach.

Original language	English
Title of host publication	Proceedings of the 2015 IEEE Innovative Smart Grid Technologies - Asia, ISGT ASIA 2015
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Electronic)	978-1-5090-1238-1
DOIs	https://doi.org/10.1109/ISGT-Asia.2015.7386968
Publication status	Published - 2016
Event	2015 IEEE Innovative Smart Grid Technologies - Bangkok, Thailand Duration: 3 Nov 2015 → 6 Nov 2015 http://www.ieee-pes.org/isgt-asia-2015-thailand

Conference

Conference	2015 IEEE Innovative Smart Grid Technologies
Abbreviated title	ISGT ASIA 2015
Country/Territory	Thailand
City	Bangkok
Period	3/11/15 → 6/11/15
Internet address	http://www.ieee-pes.org/isgt-asia-2015-thailand

Keywords

Damping control
evolutionary mechanism
metaheuristics
small-signal stability

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10.1109/ISGT-Asia.2015.7386968

Cite this

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title = "Application of Swarm Mean-Variance Mapping Optimization on location and tuning damping controllers",

abstract = "This paper introduces the use of the Swarm Variant of the Mean-Variance Mapping Optimization (MVMO-S) to solving the multi-scenario problem of the optimal placement and coordinated tuning of power system damping controllers (POCDCs). The proposed solution is tested using the classical IEEE 39-bus test system, New England test system. This papers includes performance comparisons with other emerging metaheuristic optimization: comprehensive learning particle swarm optimization (CLPSO), genetic algorithm with multi-parent crossover (GA-MPC), differential evolution DE algorithm with adaptive crossover operator, linearized biogeography-based optimization with re-initialization (LBBO), and covariance matrix adaptation evolution strategy (CMA-ES). Numerical results illustrates the feasibility and effectiveness of the proposed approach.",

keywords = "Damping control, evolutionary mechanism, metaheuristics, small-signal stability",

author = "Rueda, {Jos{\'e} L.} and F. Gonz{\'a}lez-Longatt",

year = "2016",

doi = "10.1109/ISGT-Asia.2015.7386968",

language = "English",

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booktitle = "Proceedings of the 2015 IEEE Innovative Smart Grid Technologies - Asia, ISGT ASIA 2015",

publisher = "IEEE",

address = "United States",

note = "2015 IEEE Innovative Smart Grid Technologies, ISGT ASIA 2015 ; Conference date: 03-11-2015 Through 06-11-2015",

url = "http://www.ieee-pes.org/isgt-asia-2015-thailand",

}

Rueda, JL & González-Longatt, F 2016, Application of Swarm Mean-Variance Mapping Optimization on location and tuning damping controllers. in Proceedings of the 2015 IEEE Innovative Smart Grid Technologies - Asia, ISGT ASIA 2015., 7386968, IEEE, Piscataway, NJ, pp. 1-5, 2015 IEEE Innovative Smart Grid Technologies, Bangkok, Thailand, 3/11/15. https://doi.org/10.1109/ISGT-Asia.2015.7386968

Application of Swarm Mean-Variance Mapping Optimization on location and tuning damping controllers. / Rueda, José L.; González-Longatt, F.
Proceedings of the 2015 IEEE Innovative Smart Grid Technologies - Asia, ISGT ASIA 2015. Piscataway, NJ: IEEE, 2016. p. 1-5 7386968.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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N2 - This paper introduces the use of the Swarm Variant of the Mean-Variance Mapping Optimization (MVMO-S) to solving the multi-scenario problem of the optimal placement and coordinated tuning of power system damping controllers (POCDCs). The proposed solution is tested using the classical IEEE 39-bus test system, New England test system. This papers includes performance comparisons with other emerging metaheuristic optimization: comprehensive learning particle swarm optimization (CLPSO), genetic algorithm with multi-parent crossover (GA-MPC), differential evolution DE algorithm with adaptive crossover operator, linearized biogeography-based optimization with re-initialization (LBBO), and covariance matrix adaptation evolution strategy (CMA-ES). Numerical results illustrates the feasibility and effectiveness of the proposed approach.

AB - This paper introduces the use of the Swarm Variant of the Mean-Variance Mapping Optimization (MVMO-S) to solving the multi-scenario problem of the optimal placement and coordinated tuning of power system damping controllers (POCDCs). The proposed solution is tested using the classical IEEE 39-bus test system, New England test system. This papers includes performance comparisons with other emerging metaheuristic optimization: comprehensive learning particle swarm optimization (CLPSO), genetic algorithm with multi-parent crossover (GA-MPC), differential evolution DE algorithm with adaptive crossover operator, linearized biogeography-based optimization with re-initialization (LBBO), and covariance matrix adaptation evolution strategy (CMA-ES). Numerical results illustrates the feasibility and effectiveness of the proposed approach.

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KW - metaheuristics

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Application of Swarm Mean-Variance Mapping Optimization on location and tuning damping controllers

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