Use of Statistical Identification for Optimal Control of a Supercritical Thermal Power Plant

Abstract The use of a multivariate autoregressive model for the implementation of a new practical optimal oontrol of a supercritioal thermal power plant is discussed. The control is realized by identifying the system characteristics of the plant under the conventional PlD control by the autoregressive model fitting and then implementing the digital control to Correct the defect of the analog control. The procedure of identification and the controller implementation is described in detail by using the experimental results of a real plant. The results clearly demonstrate the advantage of the new oontroller over the conventional PID controller. The experienoe of the oOl!llleroial operation of the plant confirms that the new controller is extremely robust against the gradual change of the plant oharacteristios, which shows the practical utility of the identification procedure on which the design of the controller is based.