Abstract
In thermal power plants, it is an important operation to improve the control accuracy of main steam pressure and temperature etc. during load up/down. This paper focused on temperature control that is most difficult to control due to the nonlinearity and long dead time of power plants. We applied some control methods such as MRAC, neural network and long range predictive control to the power plant main control system. Each method was evaluated by a simulator using detailed physical models that represents accurately the dynamics of power plants. We confirmed that each method is possible to control properly, but long range predictive control is better than other two methods. It is also stated that the thermal power plant is so complex that some consideration (persistently exiting condition, learning method of neural networks etc.) is necessary for the application of theoretical algorithm.
