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Application of swarm mean-variance mapping optimization on location and tuning damping controllers

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conference contribution
posted on 23.03.2016, 16:07 by Jose L. Rueda, Francisco Gonzalez-Longatt
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.



  • Mechanical, Electrical and Manufacturing Engineering

Research Unit

  • Centre for Renewable Energy Systems Technology (CREST)

Published in

IEEE PES Innovative Smart Grid Technologies 2015 Asian Conference (ISGT Asia 2015)


RUEDA, J.L. and GONZALEZ-LONGATT, F.M., 2015. Application of swarm mean-variance mapping optimization on location and tuning damping controllers. Proceedings of the 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), Bangkok, Thailand, 3rd-6th November 2015.




AM (Accepted Manuscript)

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