自動化專業(yè)外文翻譯---鍋爐蒸汽溫度模糊神經(jīng)網(wǎng)絡(luò)的廣義預(yù)測控制

1、1Neuro-fuzzy generalized predictive controlof boiler steam temperatureXiangjie LIU, Jizhen LIU, Ping GUANAbstract: Power plants are nonlinear and uncertain complex systems. Reliable control of superheated steam temperatu

2、re is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. A nonlinear generalized predictive controller based on neuro-fuzzy network (NFGPC) is proposed in this

3、paper. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the

4、 traditional controller is obtained.Keywords: Neuro-fuzzy networks; Generalized predictive control; Superheated steam temperature1. IntroductionContinuous process in power plant and power station are complex systems char

5、acterized by nonlinearity, uncertainty and load disturbance. The superheater is an important part of the steam generation process in the boiler-turbine system, where steam is superheated before entering the turbine that

6、drives the generator. Controlling superheated steam temperature is not only technically challenging, but also economically important. From Fig.1,the steam generated from the boiler drum passes through the low-temperature

7、 superheater before it enters the radiant-type platen superheater. Water is sprayed onto the steam to control the superheated steam temperature in both the low and high temperature superheaters. Proper control of the sup

8、erheated steam temperature is extremely important to ensure the overall efficiency and safety of the power plant. It is undesirable that the steam temperature is too high, as it can damage the superheater and the high pr

9、essure turbine, or too low, as it will lower the efficiency of the power plant. It is also important to reduce the temperature 3NFN can be devised with the network incorporating all the local generalized predictive contr

10、ollers (GPC) designed using the respective local linear models. Following this approach, the nonlinear generalized predictive controllers based on the NFN, or simply, the neuro-fuzzy generalized predictive controllers (N

11、FG-PCs)are derived here. The proposed controller is then applied to control the superheated steam temperature of the 200MW power unit. Experimental data obtained from the plant are used to train the NFN model, and from w

12、hich local GPC that form part of the NFGPC is then designed. The proposed controller is tested first on the simulation of the process, before applying it to control the power plant.2. Neuro-fuzzy network modellingConside

13、r the following general single-input single-output nonlinear dynamic system:), 1 ( ),..., ( ), ( ),..., 1 ( [ ) ( ' ' ? ? ? ? ? ? ? u y n d t u d t u n t y t y f t y(1) ? ? ? ? / ) ( )] ( ),..., 1 ( ' t e n t

14、 e t e ewhere f[.]is a smooth nonlinear function such that a Taylor series expansion exists, e(t)is a zero mean white noise andΔis the differencing operator, and d are ' ' ' , , e u y n n nrespectively the kn

15、own orders and time delay of the system. Let the local linear model of the nonlinear system (1) at the operating point be given by the following ) (t oControlled Auto-Regressive Integrated Moving Average (CARIMA) model:

16、(2) ) ( ) ( ) ( ) ( ) ( ) ( 1 1 1 t e z C t u z B z t y z A d ? ? ? ? ? ? ?Where are polynomials in , the backward shift ) ( ) ( ), ( ) ( 1 1 1 1 ? ? ? ? ? ? z andC z B z A z A 1 ? zoperator. Note that the coefficients

17、of these polynomials are a function of the operating point .The nonlinear system (1) is partitioned into several operating ) (t oregions, such that each region can be approximated by a local linear model. Since NFN is a

18、 class of associative memory networks with knowledge stored locally, they can be applied to model this class of nonlinear systems. A schematic diagram of the NFN is shown in Fig.2.B-spline functions are used as the membe