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