Multiobjective control of power plants using particle swarm optimization techniques

Multiobjective optimal power plant operation requires an optimal mapping between unit load demand and pressure set point in a fossil fuel power unit (FFPU). In general, the optimization problem with varying unit load demand cannot be solved using a fixed nonlinear mapping. This paper presents a modern heuristic method, particle swarm optimization (PSO), to realize the optimal mapping by searching for the best solution to the multiobjective optimization problem, where the objective functions are given with preferences. This optimization procedure is used to design the reference governor for the control system. This approach provides the means to specify optimal set points for controllers under a diversity of operating scenarios. Variations of the PSO technique, hybrid PSO, evolutionary PSO, and constriction factor approach are applied to the FFPU, and the comparison is made among the PSO techniques and genetic algorithm.