Comparison between various control methods for thermal power plant main control system

Abstract

In thermal power plants, it is important to improve the control accuracy of main steam pressure and temperature and so forth during load up/down. This paper focuses on temperature control, the most difficult aspect of control due to the nonlinearity and long dead time of power plants. We applied 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 represent accurately the power plant dynamics. We confirmed that each method can provide proper control, but long-range predictive control is better than the two other methods. In addition, thermal power plants are so complex that further analysis (e.g., persistently exciting condition, learning method of neural networks) is necessary for the application of theoretical algorithms. © 1999 Scripta Technica, Electr Eng Jpn, 128(3): 72–81, 1999