Adaptive hydrogenerator governor tuning with a genetic algorithm

An important aspect of a good control algorithm is robustness with respect to changing plant parameters. Adaptive control strategies attempt to address the robustness issue by optimizing control parameters as changes occur in the plant. This paper investigates the genetic algorithm as one possible means of adaptively optimizing the gains of a proportional-plus-integral hydrogenerator governor. Previous work has shown that the genetic algorithm can effectively optimize the control parameters for a fixed plant. An enhanced version of the genetic algorithm, using the notions of diploidy and dominance, can be used to address the robustness issue. Here, changes in the conduit time constant as well as load constant are considered. It is shown that the genetic algorithm can effectively follow changes in the plant parameters, producing optimal control parameters in an adaptive environment