MVMO-based approach for optimal placement and tuning of supplementary damping controller
This paper introduces an approach based on the Swarm Variant of the Mean-Variance Mapping Optimization (MVMO-S) to solve the multi-scenario formulation of the optimal placement and coordinated tuning of power system supplementary damping controllers (POCDCs). The effectiveness of the approach is eva...
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Main Authors: | , |
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Format: | Default Article |
Published: |
2015
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Subjects: | |
Online Access: | https://hdl.handle.net/2134/19795 |
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Summary: | This paper introduces an approach based on the Swarm Variant of the Mean-Variance Mapping Optimization (MVMO-S) to solve the multi-scenario formulation of the optimal placement and coordinated tuning of power system supplementary damping controllers (POCDCs). The effectiveness of the approach is evaluated based on the classical IEEE 39-bus (New England) test system. Numerical results include performance comparisons with other metaheuristic optimization techniques, namely, 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 reinitialization (LBBO), and covariance matrix adaptation evolution strategy (CMA-ES). |
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