Application of adaptive Grey predictor based algorithm to boiler drums level control

Application of adaptive Grey predictor based

algorithm to boiler drum level control

Yu Nanhua *,Ma Wentong,Su Ming

Key Laboratory for Power Machinery and Engineering of Ministry of Education,Shanghai Jiao Tong University,

Shanghai 200030,PR China

Received 4February 2005;received in revised form 3September 2005;accepted 22March 2006

Available online 22May 2006

Abstract

To improve the boiler drum level control system of a power plant,the three challenging issues encountered include (1)effect of ‘‘false water level’’,(2)controller parameter mismatches due to variant working conditions and (3)signal noise caused by uncertainties of drum level.In this paper,based on analyses of the drum level signal,an adaptive derivative action is presented to monitor steam flow,and thus,the effect of ‘‘false water level’’is weakened.The uncertainties of parameter mismatches and noise are predicted by developing a Grey predictor based algorithm (GPBA).In order to resolve the three problems and further control performance,an adaptive technique is combined with the GPBA to develop an adaptive Grey predictor based method.Finally,concrete simulations give that the proposed method has obvious supe-riority over conventional methods.

Ó2006Elsevier Ltd.All rights reserved.

Keywords:Drum level;False water level;PID control;Grey predictor;Median filter

1.Introduction

Since many of emergency shutdowns are caused by poor drum level control in power plants,stable drum level control is critical to economic operation of any power plant steam generator system.Generally,there are uncertain disturbances for the water level process [1].These disturbances offer us an interesting challenge for better control performance [2,3].

The PI controller,a sub-class of generic proportional–integral–derivative (PID)controllers,is widely used in drum level regulation.PID controllers can handle most problems except under certain operation conditions such as the effects of ‘‘false water level’’[10]and wave action (wild oscillation of water surface).Many methods aiming at the above problems are readily found in the open literature.Adaptive optimal control (AOC)is an important approach for dealing with nonlinearity problem [4],similar to other approaches such as model

0196-8904/$-see front matter Ó2006Elsevier Ltd.All rights reserved.doi:10.1016/j.enconman.2006.03.035

*

Corresponding author.Tel.:+862164477748;fax:+862164078095.E-mail address:ynhzy2@ (N.

Yu).

3000N.Yu et al./Energy Conversion and Management47(2006)2999–3007

predictive control(MPC)[5],fuzzy control(FC)[6]and robust control[7]that are implemented successfully in thermal power plants.In most cases,the effect of‘‘false water level’’is of interest to researchers,however,it is noted that wave action is often ignored because of an insufficient assumption that multiple sensor measure-ment can eliminate the errors originating from water surface waves.In addition,the severe oscillation of sen-sor readings forces us to try to avoid the derivative(D)action of the PID controller,whereas the D action represents a significant prediction ability to improve control.Moreover,the drum level shows nonlinear dynamics under varying operation condition.Therefore,‘‘parameter mismatch’’of the PID controller should be a consideration for us.

In this paper,we denote the drum level signal’s errors caused by wave action as noise and develop an adap-tive Grey predictor based method to deal with the discussed problems including(1)the effect of‘‘false water level’’,(2)parameter mismatches and(3)noise.This paper is organized as follows:Section2focuses on the problem descriptions;in Section3,the proposed approach is illustrated;and comparisons and conclusions are presented in Sections4and5,respectively.

2.Problem descriptions

Our work is specifically motivated by an ongoing research project associated with a boiler of the power plant in Qingdao of China.A cascade three element drum level control system is applied in the boiler(see Fig.1).The master controller of part A in Fig.1is designed with afixed parameter PI algorithm,and the sub-master controller of part B is realized by afixed parameter P algorithm.Note that part B is used to deal with the disturbances from steamflow and feedwater,and part A is the key control part to affect control per-formance.With this control system serving the boiler,a set offield data is collected and shown in Fig.2(sam-pling period is1s).As indicated by the A in Fig.2,the known nonlinear effect of‘‘false water level’’is described as a reverse shift of the drum level when the steamflow rises abruptly.Given that the D action