Predictive control with adaptive model maintenance: Application to power plants

Abstract

This paper describes a method for model predictive control (MPC) with model maintenance. A supervisor maintains the control model in real time by providing an estimate of disturbances and noise. A switch is triggered when the predictions of the control model deviate from disturbance estimate by more than a pre-determined amount. The predictive control algorithm described in the paper uses the innovation representation of a Markov–Laguerre model. A Monte Carlo study and an experiment show that good models and stable control are obtained. A simulation study based on models of a boiler–turbine unit shows that the algorithm can adapt to time-varying data. The performance is assessed using the area regulation (AR) test criterion currently adopted by PJM Interconnection LLC. The proposed adaptive MPC gives an AR test score of more than 90 with pressure fluctuations less than 3% even when the coal quality changes.