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

José L. Rueda, F. González-Longatt

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings of the 2015 IEEE Innovative Smart Grid Technologies - Asia, ISGT ASIA 2015
Place of PublicationPiscataway, NJ
Number of pages5
ISBN (Electronic)978-1-5090-1238-1
Publication statusPublished - 2016
Event2015 IEEE Innovative Smart Grid Technologies - Bangkok, Thailand
Duration: 3 Nov 20156 Nov 2015


Conference2015 IEEE Innovative Smart Grid Technologies
Abbreviated titleISGT ASIA 2015
Internet address


  • Damping control
  • evolutionary mechanism
  • metaheuristics
  • small-signal stability

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