Identification of Main Steam Temperature of Power Plant Using Fractional-order Transfer Function Based on Lévy Flights-Artificial Bee Colony Algorithm
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- 英文论文题名：Identification of Main Steam Temperature of Power Plant Using Fractional-order Transfer Function Based on Lévy Flights-Artificial Bee Colony Algorithm
- 论文作者：Ming Sun ; Ze Dong ; Qin Zhang
- 英文论文作者：Ming Sun ; Ze Dong ; Qin Zhang ; Department of Automation ; North China Electric Power University
- 年：2017
- 作者机构：Department of Automation,North China Electric Power University;
- 英文论文关键词：Fractional-order transfer function ; Artificial bee colony algorithm ; Main steam temperature object ; Identification
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TM621;TP18
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：258k
- 原文格式：D
- 会议级别：全国
摘要
Fractional-order calculus with special memorability could describe dynamic characteristics of the object more accurately, and the establishment of fractional-order mathematical model can provide more convenience for tuning the parameters of PIλDμ controller. Fractional-order transfer functions can completely represent characteristics of thermal process compared with the integral-order ones applied in coal-fired power plant, such as strong coupling, disturbance factors, huge difference between response speed, etc. Aiming at the characteristics of multiple parameters for fractional-order transfer function model, two promising approaches are proposed. Firstly, an improved artificial bee colony algorithm, based on Lévy flights strategy, is introduced in optimizing the parameters of nominal model. Secondly, objective(fitness) function with penalty factor is developed to obtain the static gain of model. Finally, it is attempted to apply the proposed identification method, based on fractional-order transfer function model, to a circulating fluidized bed(CFB) superheater in an in-service power unit. The field data identification well demonstrates the virtues of improved identification method and indicates promising prospects for fractional-order transfer function model in thermal process of power plant.
Fractional-order calculus with special memorability could describe dynamic characteristics of the object more accurately, and the establishment of fractional-order mathematical model can provide more convenience for tuning the parameters of PIλDμ controller. Fractional-order transfer functions can completely represent characteristics of thermal process compared with the integral-order ones applied in coal-fired power plant, such as strong coupling, disturbance factors, huge difference between response speed, etc. Aiming at the characteristics of multiple parameters for fractional-order transfer function model, two promising approaches are proposed. Firstly, an improved artificial bee colony algorithm, based on Lévy flights strategy, is introduced in optimizing the parameters of nominal model. Secondly, objective(fitness) function with penalty factor is developed to obtain the static gain of model. Finally, it is attempted to apply the proposed identification method, based on fractional-order transfer function model, to a circulating fluidized bed(CFB) superheater in an in-service power unit. The field data identification well demonstrates the virtues of improved identification method and indicates promising prospects for fractional-order transfer function model in thermal process of power plant.
Fractional-order calculus with special memorability could describe dynamic characteristics of the object more accurately, and the establishment of fractional-order mathematical model can provide more convenience for tuning the parameters of PIλDμ controller. Fractional-order transfer functions can completely represent characteristics of thermal process compared with the integral-order ones applied in coal-fired power plant, such as strong coupling, disturbance factors, huge difference between response speed, etc. Aiming at the characteristics of multiple parameters for fractional-order transfer function model, two promising approaches are proposed. Firstly, an improved artificial bee colony algorithm, based on Lévy flights strategy, is introduced in optimizing the parameters of nominal model. Secondly, objective(fitness) function with penalty factor is developed to obtain the static gain of model. Finally, it is attempted to apply the proposed identification method, based on fractional-order transfer function model, to a circulating fluidized bed(CFB) superheater in an in-service power unit. The field data identification well demonstrates the virtues of improved identification method and indicates promising prospects for fractional-order transfer function model in thermal process of power plant.
引文
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[2]S.Canat,Contributionàla modélisation dynamique d’ordre non entier de la machine asynchroneàcage.Thèse de Doctorat INP de Toulouse,2005.
[3]X.Y.Wang,Research on fractional order Control systems and its application(in Chinese),Baoding:North China Electric Power University,2011.
[4]D.Karaboga,B.Gorkemli,C.Ozturk,et al.A comprehensive survey:artificial bee colony(ABC)algorithm and applications,Artificial Intelligence Review,42:21–57,2014.
[5]H.Schiessel,C.Friedrich,and A.Blumen,Applications to problems in polymer physics and rheology,In:Hilfer R,ed.Applications of fractional calculus in physics[C].Singapore:World Scientific Publishing Co.Pte.Ltd,2000:331-376.
[6]D.Y.Xue,Computer Aided Control Systems Design-using MATLAB Language(3rd Edition)(in Chinese).Beijing:Tsinghua University Press,2012:444–450.
[7]D.Karaboga,An idea based on honey bee swarm for numerical optimization,Technical Report TR06,Erciyes University,Engineering Faculty,Computer Engineering Department,2005.
[8]D.Karaboga,B.Akay,A modified Artificial Bee Colony(ABC)algorithm for constrained optimization problems,Applied Soft Computing,11:3021–3031,2011.
[9]D.Karaboga,B.Akay,A modified Artificial Bee Colony algorithm for real-parameter optimization,Information Sciences,192:120–142,2012.
[10]H.T.Jadhav,P.D.Bamane,Temperature dependent optimal power flow using g-best guided artificial bee colony algorithm,Electrical Power and Energy Systems,77:77-10,2016.
[11]M.Ghaemi,Z.Zabihipour,and Y.Asgari,Computer simulation study of the Lévy flights process,Physica A Statistical Mechanics and its Applications,388(8):1509–1514,2009.
[12]X.S.Yang,S.Deb,Cuckoo search via Lévy flights,in IEEE World Congress on Nature and Biologically Inspired Computing,2009:210–214.
[13]X.S.Yang,S.Deb.Engineering optimization by cuckoo search,International Journal of Mathematical Modelling and Numerical Optimisation,1(4):330–343,2010.
[14]X.S.Yang,S.Deb,et al.Cuckoo search for business optimization applications,National Conference on Computing and Communication Systems,2012:1–5.
[15]R.N.Mantegna,Fast,accurate algorithm for numerical simulation of Lévy stable stochastic processes,Physical Review E,49(5):4677–4683,1994.
[16]C.L.Liu,X.N.Yu,W.Y.Yao,et al,Model identification of power plant thermal process based on genetic algorithm(in Chinese),Proceedings of the CSEE,23(3):170–174,2003.
[17]S.M.Jiao,P.Han,Y.Huang,et al,Fuzzy quantum genetic algorithm and its application research in thermal process identification(in Chinese),Proceedings of the CSEE,27(5):87–92,2007.
[18]P.Han,Theory and Applications of Intelligent Control(in Chinese).Beijing:China Electric Power Press,2013,chapter4.