To realize the most economical operation of the plant, requires the controller recognize the interaction between multiple inputs and the constraints imposed by the physical limits of the system. A model-based multivariable predictive controller with real-time constrained optimization has been designed and implemented. Artificial neural networks (ANN) modeling technique has been used for identifying global dynamic models of the plant variables. Results are given to demonstrate the superiority of the controller in controlling the main steam temperature and reheat steam temperature during rapid load changes.
|Title of host publication||Unknown Host Publication|
|Number of pages||4|
|Publication status||Published - Sep 1996|
|Event||Universities Power Engineering Conference 1996 - Iraklio, Greece.|
Duration: 1 Sep 1996 → …
|Conference||Universities Power Engineering Conference 1996|
|Period||1/09/96 → …|