神经网络控制技术在现代电站中的应用
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摘要
现代电站的生产过程中存在过热汽温、再热汽温、锅炉水处理、负荷调节等许多大迟延、大滞后、特性时变的对象，它们中有些还是具有强非线性特性的对象或多变量耦合系统，采用常规的PID控制手段很难取得良好的控制效果。若应用现代控制理论中的自适应控制、最优控制、解耦控制、预测控制等控制手段，则需要建立被控对象的数学模型，而且往往控制系统的计算量大、实时性差。这些缺点使其很难满足实际生产过程的需要从而极大地限制了其在现代电站中的应用。随着发电机组向大容量、高参数方向发展及各种新型发电方式的出现，电站中各生产环节的特性越来越复杂而对其控制品质的要求却越来越高，急需新的控制技术来对其进行有效的控制。
神经网络具有表示任意非线性关系和学习等能力,通过恰当选择网络层次和隐层单元数能够以任意精度逼近任意连续函数及其各阶导数。为时变、非线性对象的动态特性的辩识提供了简单而有效的一般性的方法，解决了时变、非线性对象控制中的瓶颈问题。因此基于神经网络的各种先进控制技术是解决现代电站中控制难题的一条有效途径。
本文紧密结合我国电厂的实际情况，以解决电厂实际运行中存在的控制问题为出发点，抓住火电厂热工控制系统普遍存在的大滞后和特性时变的特点，在已有的神经网络控制算法的基础上，针对这些算法中存在的不足进行进一步研究并提出了基于Elman网络的隐式广义预测控制、基于改进的Elman网络的自适应预测函数控制、基于混合神经网络的非线性自适应预测函数控制、基于神经网络的多变量解耦自适应预测函数控制及基于模糊神经网络的模型参考自适应预测控制等多种改进方案。这些改进方案既具有自适应控制和预测控制等现代控制理论的优点，又无需被控对象的数学模型，而且控制效果良好、适应性广，满足了非线性对象及多变量耦合系统等特性不同的生产环节对控制系统的要求。使用MATLAB语言对采用上述控制方式的过热汽温、再热汽温、给水系统、锅炉水处理、负荷调节等不同对象的控制系统进行仿真实验，结果表明了这些控制技术的有效性。
There are many plants that have the character of time-varying,large delay,large inertia in the process of production of modern power station such as the superheated steam temperature,the reheated steam temperature,the water treatment of boiler and the load regulation.Some of them have the strong character of nonlinear and some of them are multivariable coupling systems It is very difficult to obtain good effect of control by conventional PID control.It is necessary to set up the mathematics models of controlled plants if applying control means of modern control theory such as self-adaptive control,optimal control，decoupling control and predictive control.And these control systems have large calculation and bad character of real time in general.These disadvantages make them be not able to meet the need of real production process and limit the application of them in modern power station.Now the generator units are developing towards large capacity and high parameters.Many new kinds of generating electricity means are coming into being.The characers of production units of power station become more and more complex but the demand to quality of control becomes more and more strict.The new control means are in bad need to control them effectively.
Neural network has the ability of learning and expressing any nonlinear relation.It can approximate any continuous function and their any order derivatives with any precision if it has the correct layers of networks and the correct number of hidden units.So the identification of dynamic character of time-varying,nonlinear plants has a kind of simple and effective means.The critical problem of control of time-varying,nonlinear plants is resolved.So all kinds of advanced control means based on neural network is an effective way that resolve the control problem of modern power station.
This dissertation is dedicated to solve the practical control problems in power station under the existence of thermal control system characterized by large delay and time varying.Aiming at the present disadvantages of the neural network control algorithm,some improved means are researched and put forward.They are implicit generalized predictive control based on Elman network,self-adaptive predictive function control based on improved Elman network,nonlinear self-adaptive predictive function
control based no hybrid neural network,multivariable decoupling self-adaptive predictive function control based on neural network and model reference self-adaptive predictive control based on fuzzy neural network.These means do not need the mathematic model of controlled plants but own the merits of self-adaptive control and predictive control of modern control theory .And the control effects are good and the adaptive character of them are extensive.They can meet the demand of production units with different characters of nonlinear plants and multivariable coupling systems.The simulation experiments of different plants such as superheated steam temperature, reheated steam temperature,water-supplying system,water treatment of boiler,load regulation are done in MATLAB.The results show that these control means are effective.
神经网络具有表示任意非线性关系和学习等能力,通过恰当选择网络层次和隐层单元数能够以任意精度逼近任意连续函数及其各阶导数。为时变、非线性对象的动态特性的辩识提供了简单而有效的一般性的方法，解决了时变、非线性对象控制中的瓶颈问题。因此基于神经网络的各种先进控制技术是解决现代电站中控制难题的一条有效途径。
本文紧密结合我国电厂的实际情况，以解决电厂实际运行中存在的控制问题为出发点，抓住火电厂热工控制系统普遍存在的大滞后和特性时变的特点，在已有的神经网络控制算法的基础上，针对这些算法中存在的不足进行进一步研究并提出了基于Elman网络的隐式广义预测控制、基于改进的Elman网络的自适应预测函数控制、基于混合神经网络的非线性自适应预测函数控制、基于神经网络的多变量解耦自适应预测函数控制及基于模糊神经网络的模型参考自适应预测控制等多种改进方案。这些改进方案既具有自适应控制和预测控制等现代控制理论的优点，又无需被控对象的数学模型，而且控制效果良好、适应性广，满足了非线性对象及多变量耦合系统等特性不同的生产环节对控制系统的要求。使用MATLAB语言对采用上述控制方式的过热汽温、再热汽温、给水系统、锅炉水处理、负荷调节等不同对象的控制系统进行仿真实验，结果表明了这些控制技术的有效性。
There are many plants that have the character of time-varying,large delay,large inertia in the process of production of modern power station such as the superheated steam temperature,the reheated steam temperature,the water treatment of boiler and the load regulation.Some of them have the strong character of nonlinear and some of them are multivariable coupling systems It is very difficult to obtain good effect of control by conventional PID control.It is necessary to set up the mathematics models of controlled plants if applying control means of modern control theory such as self-adaptive control,optimal control，decoupling control and predictive control.And these control systems have large calculation and bad character of real time in general.These disadvantages make them be not able to meet the need of real production process and limit the application of them in modern power station.Now the generator units are developing towards large capacity and high parameters.Many new kinds of generating electricity means are coming into being.The characers of production units of power station become more and more complex but the demand to quality of control becomes more and more strict.The new control means are in bad need to control them effectively.
Neural network has the ability of learning and expressing any nonlinear relation.It can approximate any continuous function and their any order derivatives with any precision if it has the correct layers of networks and the correct number of hidden units.So the identification of dynamic character of time-varying,nonlinear plants has a kind of simple and effective means.The critical problem of control of time-varying,nonlinear plants is resolved.So all kinds of advanced control means based on neural network is an effective way that resolve the control problem of modern power station.
This dissertation is dedicated to solve the practical control problems in power station under the existence of thermal control system characterized by large delay and time varying.Aiming at the present disadvantages of the neural network control algorithm,some improved means are researched and put forward.They are implicit generalized predictive control based on Elman network,self-adaptive predictive function control based on improved Elman network,nonlinear self-adaptive predictive function
control based no hybrid neural network,multivariable decoupling self-adaptive predictive function control based on neural network and model reference self-adaptive predictive control based on fuzzy neural network.These means do not need the mathematic model of controlled plants but own the merits of self-adaptive control and predictive control of modern control theory .And the control effects are good and the adaptive character of them are extensive.They can meet the demand of production units with different characters of nonlinear plants and multivariable coupling systems.The simulation experiments of different plants such as superheated steam temperature, reheated steam temperature,water-supplying system,water treatment of boiler,load regulation are done in MATLAB.The results show that these control means are effective.
引文
[1] 袁曾任.人工神经元网络及其应用.北京:清华大学出版社,1999.10
[2] 易继锴,侯媛杉.智能控制技术.北京:北京工业大学出版社,1999.9
[3] 韦巍.智能控制技术.北京：机械工业出版社，1999.5
[4] Jose R.N. Hong Wang. A Direct Adaptive Neural-Network Control for Unknown Nonliner Systems and its Application.IEEE Trans,Neural Networks, 1998,9(1):27—33
[5] 赵林明,胡浩云,魏德华,王树谦.多层前向人工神经网络.河南:黄河水利出版社,1999
[6] 王永骥,涂健.神经元网络控制.北京:机械工业出版社,1998.2
[7] 顾晓栋，徐耀文.电厂热工过程自动调节.北京：电力工业出版社，1981
[8] 华东六省一市电机工程（电力）学会.热工自动化.北京:中国电力出版社,2000.3
[9] 高伟.计算机控制系统.北京：中国电力工业出版社，2000.8
[10] 熊淑燕，王兴叶，田建艳，张伟.火力发电厂集散控制系统.北京：科学出版社，2000，3
[11] Keigo Watanabe. A Fuzzy-Gaussian Neural Network and Its Application to Mobile Robot Control.IEEE Trans.Control Systems Technology,1996,4(2):193-199
[12] 杨献勇.热工过程自动控制.北京:清华大学出版社,2000.10
[13] 王树青.先进控制技术及应用.北京:化学工业出版社,2001.7
[14] 张法文.电厂多变量控制系统的设计与应用.电力技术，1989，（3）：6-10
[15] J.Q.Hu,E.Rose.Generalized Predictive Control Using A Neural Fuzzy Model.Int.J. of System Science,1999,30(1):117-122.
[16] 王士同.神经模糊系统及其应用.北京:北京航空航天大学出版社,1998
[17] Chen C.L.P.et al..An Integration of Neural Network and Rule-Based System for Design and Planning of Mechanical Assemblies.IEEE Trans. On System·Man and Cybernetics, 1993,23(5):237-254
[18] 章卫国.先进控制理论与方法导论.西安:西北工业大学出版社,2000.4
[19] 闻新，周露，王丹力等.MATLAB神经网络应用设计.北京：科学出版社，2001，5
[20] Leonard J,Kramer M A.Improvement of The Back propagation Algorithm for Training Neural Networks.Computers Chem. Eng.,1990,14(3):337-341
[21] 余志文，白雪峰.继电保护与稳定控制的一体化.电力自动化设备，2002，22（7）：71-77
[22] 张泉灵,王树青.基于ARMAX模型自适应预测函数控制.信息与控制，2000,29（5）：431-436.
[23] 周洪煜，张坚.基于Elman网络的隐式广义预测控制.江西电力职业技术学院学报，2003.16
（1）：4-9
[24] 刘向杰，柴天佑.锅炉汽包水位系统的仿人智能控制策略.信息与控制，1998，27（2）：129-134
[25] 李培元.火力发电厂水处理及水质控制.北京：中国电力出版社，2000.2
[26] 张泉灵,王树青.基于Hammerstein模型的非线性预测函数控制.浙江大学学报，2002，36（2）：119-122.
[27] Li Shihua ,Li Qi,Li Jie.Identification of Hammerstein Model Using Hybid Neural Networks.Journal of Southeast University ,2001,17(1):26-30
[28] Zhu Xuefeng and Dale E Sebong.Nonlinear Predictive Control Based on Hammerstein Models.Control and Application,1994,11(5):564-575
[29] Kolians S,Anastassion D. An Adaptive Least Squares Algorithm for The Efficient Training of Artificial Neural Networks.IEEE Trans. Circuits Syst.,1989,36:1092-1101
[30] 欧阳黎明.MATLAB控制系统设计.北京：国防工业出版社，2001.1
[31] 刘增良.模糊技术与应用选编（3）.北京:北京航空航天大学出版社,1998
[32] 杨辉,王金章.模糊控制技术及其应用.南昌:江西科学技术出版社,1997
[33] Mu-Chun Su et al. .Neural-Network-Based Fuzzy Model and Its Application to Transient Stability Prediction in Power System.IEEE Trans. On System·Man and Cybernetics,
1999,29(1): 149—163
[34] Masum I Ishikawa.Prediction of Time Series by A Structure Learning of Neural Network.
Fuzzy Sets and System,1996,82:167-176
[35] Kosko B. Fuzzy Knowledge Conbination.International Journal for Intelligent Systems,1986,1:293-320
[36] 达飞鹏，宋文忠.基于模糊神经网络的大时滞系统的预测控制.东南大学学报，2000，30（2）：67-70
[37] Camacho E.F. and Bordons.C.Model Predictive Control in the Process Industry.Springer London Berlin Heidelberg·New York·1995
[38] 刘建江，倪维斗，张法文.基于理论的状态变量控制在再热汽温控制中的应用.动力工程，2000，20（5）：867-871
[39] Richalet J.Industrial application of model based predictive control.Automatica,1993.29:
1251-1247
[2] 易继锴,侯媛杉.智能控制技术.北京:北京工业大学出版社,1999.9
[3] 韦巍.智能控制技术.北京：机械工业出版社，1999.5
[4] Jose R.N. Hong Wang. A Direct Adaptive Neural-Network Control for Unknown Nonliner Systems and its Application.IEEE Trans,Neural Networks, 1998,9(1):27—33
[5] 赵林明,胡浩云,魏德华,王树谦.多层前向人工神经网络.河南:黄河水利出版社,1999
[6] 王永骥,涂健.神经元网络控制.北京:机械工业出版社,1998.2
[7] 顾晓栋，徐耀文.电厂热工过程自动调节.北京：电力工业出版社，1981
[8] 华东六省一市电机工程（电力）学会.热工自动化.北京:中国电力出版社,2000.3
[9] 高伟.计算机控制系统.北京：中国电力工业出版社，2000.8
[10] 熊淑燕，王兴叶，田建艳，张伟.火力发电厂集散控制系统.北京：科学出版社，2000，3
[11] Keigo Watanabe. A Fuzzy-Gaussian Neural Network and Its Application to Mobile Robot Control.IEEE Trans.Control Systems Technology,1996,4(2):193-199
[12] 杨献勇.热工过程自动控制.北京:清华大学出版社,2000.10
[13] 王树青.先进控制技术及应用.北京:化学工业出版社,2001.7
[14] 张法文.电厂多变量控制系统的设计与应用.电力技术，1989，（3）：6-10
[15] J.Q.Hu,E.Rose.Generalized Predictive Control Using A Neural Fuzzy Model.Int.J. of System Science,1999,30(1):117-122.
[16] 王士同.神经模糊系统及其应用.北京:北京航空航天大学出版社,1998
[17] Chen C.L.P.et al..An Integration of Neural Network and Rule-Based System for Design and Planning of Mechanical Assemblies.IEEE Trans. On System·Man and Cybernetics, 1993,23(5):237-254
[18] 章卫国.先进控制理论与方法导论.西安:西北工业大学出版社,2000.4
[19] 闻新，周露，王丹力等.MATLAB神经网络应用设计.北京：科学出版社，2001，5
[20] Leonard J,Kramer M A.Improvement of The Back propagation Algorithm for Training Neural Networks.Computers Chem. Eng.,1990,14(3):337-341
[21] 余志文，白雪峰.继电保护与稳定控制的一体化.电力自动化设备，2002，22（7）：71-77
[22] 张泉灵,王树青.基于ARMAX模型自适应预测函数控制.信息与控制，2000,29（5）：431-436.
[23] 周洪煜，张坚.基于Elman网络的隐式广义预测控制.江西电力职业技术学院学报，2003.16
（1）：4-9
[24] 刘向杰，柴天佑.锅炉汽包水位系统的仿人智能控制策略.信息与控制，1998，27（2）：129-134
[25] 李培元.火力发电厂水处理及水质控制.北京：中国电力出版社，2000.2
[26] 张泉灵,王树青.基于Hammerstein模型的非线性预测函数控制.浙江大学学报，2002，36（2）：119-122.
[27] Li Shihua ,Li Qi,Li Jie.Identification of Hammerstein Model Using Hybid Neural Networks.Journal of Southeast University ,2001,17(1):26-30
[28] Zhu Xuefeng and Dale E Sebong.Nonlinear Predictive Control Based on Hammerstein Models.Control and Application,1994,11(5):564-575
[29] Kolians S,Anastassion D. An Adaptive Least Squares Algorithm for The Efficient Training of Artificial Neural Networks.IEEE Trans. Circuits Syst.,1989,36:1092-1101
[30] 欧阳黎明.MATLAB控制系统设计.北京：国防工业出版社，2001.1
[31] 刘增良.模糊技术与应用选编（3）.北京:北京航空航天大学出版社,1998
[32] 杨辉,王金章.模糊控制技术及其应用.南昌:江西科学技术出版社,1997
[33] Mu-Chun Su et al. .Neural-Network-Based Fuzzy Model and Its Application to Transient Stability Prediction in Power System.IEEE Trans. On System·Man and Cybernetics,
1999,29(1): 149—163
[34] Masum I Ishikawa.Prediction of Time Series by A Structure Learning of Neural Network.
Fuzzy Sets and System,1996,82:167-176
[35] Kosko B. Fuzzy Knowledge Conbination.International Journal for Intelligent Systems,1986,1:293-320
[36] 达飞鹏，宋文忠.基于模糊神经网络的大时滞系统的预测控制.东南大学学报，2000，30（2）：67-70
[37] Camacho E.F. and Bordons.C.Model Predictive Control in the Process Industry.Springer London Berlin Heidelberg·New York·1995
[38] 刘建江，倪维斗，张法文.基于理论的状态变量控制在再热汽温控制中的应用.动力工程，2000，20（5）：867-871
[39] Richalet J.Industrial application of model based predictive control.Automatica,1993.29:
1251-1247