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英文翻译部分

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Advanced control algorithms for steam temperature

regulation of thermal power plants

A.Sanchez-Lopez,G.Arroyo-Figueroa*,A.Villavicencio-Ramirez

Instituto de Investigaciones Electricas, Division de Sistemas de Control, Reforma No.

113, Colonia Palmira,Cuernavaca, Morelos 62490, Mexico

Received 5 February 2003; revised 6 April 2004; accepted 8 July 2004

Abstract

A model-based controller (Dynamic Matrix Control) and an intelligent controller

(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 generation 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 better 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 controllers can achieve good result for complex nonlinear processes with

dynamic variation or with long delay times.

Keywords: Thermal power plants; Power plant control; Steam temperature

regulation; Predictive control; Fuzzy logic control

1. Introduction

Current economic and environment factors put a stringer requirement on thermal

power plants to be operated at a high level of efficiency and safety at minimum 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 [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 playing 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 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

containing 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 controllers, 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

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 deviation 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 interactions between

steam temperature control 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 implement PID

conventional controllers motivate to research the use of model predictive

controllerssuch as the DMC or intelligent control techniques such as the Fuzzy Logic

Controller (FLC) as a solution for controlling systems in which time delays, and non-

linear behavior need to be addressed [3,4]. The paper is organized as follows. A brief

description of the DMC is presented in Section 2. The FLC design is described in

Section 3. Section 4 presents the implementation of both controllers DMC and FLC to

regulate the superheated and reheated steam temperature of a thermal power plant.

The performance of the FLC controller was evaluated against two other controllers,

the conventional PID controller and the predictive DMC controller. Results are

presented in Section 5. Finally, the main set of conclusions according to the analysis

and results derived from the performance of controllers is presented in Section 6.

2. Dynamic matrix control

The DMC is a kind of model-based predictive control (Fig. 1). This controller

was developed to improve control of oil refinement processes [5]. The DMC and

other predictive control techniques such as the Generalized Predictive Control [6] or

Smith predictor [6] algorithms are based on past and present information of controlled

and manipulated variables to predict the future state of the process.

The DMC is based on a time domain model. This model is utilized to predict the

future behavior of the process in a defined time horizon (Fig. 2). Based on this precept

the control algorithm provides a way to define the process behavior in the time,

predicting the controlled variables trajectory in function of previous control actions

and current values of the process [7]. Controlled behavior can be obtained calculating

the suitable future control actions. To obtain the process model, the system is

allows designers the use of time domain information to create a process model. The

mathematical method for prediction matches 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 recalculations that can lead

to 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 is adjusted continuously based

on deviations of the predicted and real behavior while the model remains static.

3. Fuzzy logic control

Fuzzy control is used when the process follows some general operating

characteristic and 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 phenomena and the capability to model 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 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, 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 five 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 of

various manipulated variables (Input). Then, a numerical fuzzy 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 order to derive fuzzy values of the control signal

from the input facts of the controller. Finally, a symbolic-numerical interface known

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