This project investigates an identification procedure for a hydrogenerator plant using an adaptive technique. The procedure operates on field data consisting of sampled gates position and electrical frequency. The procedure adapts to ongoing plant changes by continuously updating the identification results. It is shown that the adaptive technique, in this case genetic algorithm based, was capable of identifying the hydrogenerator system and following plant parameter changes while the plant operated under conditions of sufficient frequency excursions. These conditions applied to off-line and isolated network operation where effective control is critical.
Many techniques exist for developing optimal controllers.
This work investigates genetic algorithms as a means of finding optimal solutions over a parameter space. In particular, the genetic algorithm is applied to optimal tuning of a governor for a hydrogenerator plant. Analog and digital simulation methods are compared for use in conjunction with the genetic algorithm optimization process. It is shown that analog plant simulation provides advantages in speed over digital plant simulation. This speed advantage makes application of the genetic algorithm in an actual plant environment feasible.