外文翻譯--- 火力發(fā)電廠先進(jìn)的蒸汽溫度調(diào)節(jié)控制算法 （英文）

1、Advanced control algorithms for steam temperature regulation of thermal power plantsA. Sanchez-Lopez, G. Arroyo-Figueroa*, A. Villavicencio-RamirezInstituto de Investigaciones Electricas, Division de Sistemas de Control,

2、 Reforma No. 113, Colonia Palmira, Cuernavaca, Morelos 62490, MexicoReceived 5 February 2003; revised 6 April 2004; accepted 8 July 2004AbstractA model-based controller (Dynamic Matrix Control) and an intelligent control

3、ler (Fuzzy Logic Control) have been designed and implemented for steam temperature regulation of a 300 MW thermal power plant. The temperature regulation is considered the most demanded control loop in the steam generati

4、on process. Both proposed controllers Dynamic Matrix Controller (DMC) and Fuzzy Logic Controller (FLC) were applied to regulate superheated and reheated steam temperature. The results show that the FLC controller has a b

5、etter performance than advanced model-based controller, such as DMC or a conventional PID controller. The main benefits are the reduction of the overshoot and the tighter regulation of the steam temperatures. FLC control

6、lers can achieve good result for complex nonlinear processes with dynamic variation or with long delay times. q 2004 Elsevier Ltd. All rights reserved.Keywords: Thermal power plants; Power plant control; Steam temperatur

7、e regulation; Predictive control; Fuzzy logic control1. IntroductionCurrent economic and environment factors put a stringer requirement on thermal power plants to be operated at a high level of efficiency and safety at m

8、inimum cost. In addition, there are an increment of the age of thermal plants that affected the reliability and performance of the plants. These factors have increased the complexity of power control systems operations [

9、1,2]. Currently, the computer and information technology have been extensively used in thermal plant process operation and control. Distributed control systems (DCS) and management information systems (MIS) have been pla

10、ying an important role to show the plant status. The main function of DCS is to handle normal disturbances and maintain key process parameters in pre-specified local optimal levels. Despite their great success, DCS have

11、little function for abnormal and non-routine operation because the classical proportional-integral-derivative (PID) controlis widely used by the DCS. PID controllers exhibit poor performance when applied to process conta

12、ining unknown non-linearity and time delays. The complexity of these problems and the difficulties in implementing conventional controllers to eliminate variations in PID tuning motivate the use of other kind of controll

13、ers, such as model-based controllers and intelligent controllers. This paper proposes a model-based controller such as Dynamic Matrix Controller (DMC) and an intelligent controller based on fuzzy logic as an alternative

14、control strategy applied to regulate the steam temperature of the thermal power plant. The temperature regulation is considered the most demanded control loop in the steam generation process. The steam temperature deviat

15、ion must be kept within a tight variation rank in order to assure safe operation, improve efficiency and increase the life span of the equipment. Moreover, there are many mutual inter- actions between steam temperature c

16、ontrol loops that have been considered. Other important factor is the time delay. It is well know that the time delay makes the temperature loops hard to tune. The complexity of these problems and difficulties to impleme

17、nt PID conventional controllers motivate to research the use of model predictive controllers0142-0615/$ - see front matter q 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijepes.2004.08.003Electrical Power and En

18、ergy Systems 26 (2004) 779–785www.elsevier.com/locate/ijepes* Corresponding author. Tel.: C52777 3623820; fax: C52777 328985. E-mail addresses: jasl@iie.org.mx (A. Sanchez-Lopez), garroyo@ iie.org.mx (G. Arroyo-Figueroa)

19、.a vector. This vector is based on previous control actions and current values of the process. (d) Calculation of control movements. Control movements are obtained using the future vector of error and the dynamic matrix

20、of the process. The equation developed to obtain the control movements is shown below:Dþ ¼ ATA þ f2I ? ?K1ATXþ (2)where A represents the dynamic matrix, AT the transpose matrix of A X the vector of fu

21、ture states of the process, f a weighting factor, I the image matrix and? D the future control actions. Further details about this equation are found in Ref. [5]. (e) Control movements’ implementation. In this step the f

22、irst element of the control movements’ vector is applied to manipulated variables.A DMC controller allows designers the use of time domain information to create a process model. The mathematical method for prediction mat

23、ches the predicted behavior and the actual behavior of the process to predict the next state of the process. However, the process model is not continuously updated because this involves recalcula- tions that can lead to

24、an overload of processors and performance degradation. Discrepancies in the real behavior of the process and the predicted state are considered only in the current calculation of control movements. Thus, the controller i

25、s adjusted continuously based on deviations of the predicted and real behavior while the model remains static.3. Fuzzy logic controlFuzzy control is used when the process follows some general operating characteristic and

26、 a detailed process understanding is unknown or process model become overly complex. The capability to qualitatively capture the attributes of a control system based on observable phenom- ena and the capability to model

27、the nonlinearities for the process are the main features of fuzzy control. The ability of Fuzzy Logic to capture system dynamics qualitatively and execute this qualitative schema in a real time situation is an attractive

28、 feature for temperature control systems [8]. The essential part of the FLC is a set of linguistic control rules related to the dual concepts of fuzzy implication and the compositional rule of inference [9]. Essentially,

29、 the fuzzy controller provides an algorithm that can convert the linguistic control strategy, based on expert knowledge, into an automatic control strategy. In general, the basic configuration of a fuzzy controller has f

30、ive main modules as it is shown in Fig. 4. In the first module, a quantization module converts to discrete values and normalizes the universe of discourse ofvarious manipulated variables (Input). Then, a numerical fuzzy

31、converter maps crisp data to fuzzy numbers characterized by a fuzzy set and a linguistic label (Fuzzification). In the next module, the inference engine applies the compositional rule of inference to the rule base in ord

32、er to derive fuzzy values of the control signal from the input facts of the controller. Finally, a symbolic-numerical interface known as defuzzification module provides a numerical value of the control signal or incremen

33、t in the control action. This is integrated by a fuzzy-numerical converter and a dequantization module (output). Thus the necessary steps to build a fuzzy control system are Refs. [10,11]: (a) input and output variables

34、represen- tation in linguistic terms within a discourse universe; (b) definition of membership functions that will convert the process input variables to fuzzy sets; (c) knowledge base configuration; (d) design of the in

35、ference unit that will relate input data to fuzzy rules of the knowledge base; and (e) design of the module that will convert the fuzzy control actions into physical control actions.4. ImplementationThe control of the st

36、eam temperature is performed by two methods. One of them is to spray water in the steam flow, mainly before the super-heater (Fig. 5). The sprayed water must be strictly regulated in order to avoid the steam temperature

37、to exceed the design temperature range of G1% (G5 8C). This guaranties the correct operation of the process, improvement of the efficiency and extension of the lifetime of the equipment. The excess of sprayed water in th

38、e process can result in degradation of the turbine. The water in liquid phase impacts on the turbine’s blades. The other process to control the steam temperature is to change the burner slope in the furnace, mainly in th