Controller Design for a 1000 MW Ultra Super Critical Once-Through Boiler Power Plant

A large-scale 1000 MW once-through type ultra super-critical boiler power plant, requires investigation for the development of an analyzable model for use in the development of an intelligent control system. Using data from the power plant, a model is realized using dynamically recurrent neural networks. For proper operation, the plant must be broken into smaller subsystems that are each modeled by a separate neural network. Modified predictive optimal control is then used to drive the plant to desired states. Due to the computational intensity of modified predictive optimal control, it was rendered unviable by the computation time required for each time step of the controller. As an alternative, a reference governor was implemented along with a PID feedback control system that utilizes intelligent gain tuning.