Application of swarm mean-variance mapping optimization on location and tuning damping controllers
conference contributionposted on 23.03.2016 by Jose L. Rueda, Francisco Gonzalez-Longatt
Any type of content contributed to an academic conference, such as papers, presentations, lectures or proceedings.
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
- Centre for Renewable Energy Systems Technology (CREST)