Decentralized PID controllers of steam-turbine generator set based on probabilistic robust method

Based on probabilistic robust and multi-objective optimization algorithm, a method of controller parameters optimization is proposed to meet the requirements of power plants with uncertainties. The general frame of multi-objective genetic algorithm NSGA-II is used, combined with Monte-Carlo experiment evaluation, to solve the probabilistic robust optimization problem. The method has good feasibility, without any limitations on the style of process and controller. The decentralized PID controllers of steam-turbine generator set are optimized using this method. The simulation results show that the proposed method has better probabilistic robustness, compared with those based on nominal model optimization, thus has good feasibility in robust controller optimization.