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- W2063474911 abstract "In this paper, various novel heuristic stochastic search techniques have been proposed for optimization of proportional–integral–derivative gains used in Sugeno fuzzy logic based automatic generation control of multi-area thermal generating plants. The techniques are classical particle swarm optimization, hybrid particle swarm optimizations and hybrid genetic algorithm simulated annealing. Numerical results show that all optimization techniques are more or less equally very effective in yielding optimal transient responses of area frequency and tie-line power flow deviations. The gains obtained by particle swarm optimization are more optimal than those obtained by GA/hybrid GA-simulated annealing. Particle swarm optimizations take the least time to achieve the same optimal gains. These gains are for nominal system parameters. For varying off-nominal on-line system parameters, fast acting Sugeno fuzzy logic manipulates the nominal gains adaptively to determine transient responses." @default.
- W2063474911 created "2016-06-24" @default.
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- W2063474911 date "2004-12-01" @default.
- W2063474911 modified "2023-10-11" @default.
- W2063474911 title "Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control" @default.
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- W2063474911 doi "https://doi.org/10.1016/j.epsr.2004.04.004" @default.
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