大型泵站优化调度的研究与仿真实现
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摘要
大型泵站的优化调度是达到整个泵站节能降耗的一种重要方法。近年来,由于在油田注水泵站中广泛引入变频调速技术,以及往复泵和离心泵多种泵型并联注水现象的普及,增加了大型注水泵站优化调度的复杂性;而当前自动化技术水平的提高又使得人们对生产效率和经济效益提出了更高的要求。为了解决以上矛盾,本文对带有变频调速往复泵和离心泵并联注水的大型泵站进行了深入的研究,并提出了通用性的仿真平台。
首先,本文建立了适合大型注水泵站特点的优化调度数学模型,给出了满足外界流量变化需求和优化运行的约束条件,以同时达到泵站高效运行和恒压控制的目的。该数学模型是一个含有连续变量和离散变量的复杂非线性规划问题。本文在综合分析和讨论了目前各种智能优化算法的基础上,设计了一种改进遗传算法求解该问题,并通过算例证实了模型的正确性和算法的有效性。
本文还设计并实现了大型泵站优化调度决策仿真系统软件,为将本文的研究成果应用于工程实际奠定了基础。作为泵站自动化系统的一部分,本系统软件在实际应用中考虑了同西门子等工控机的兼容性以及和数据采集系统的交互性,同时该软件具有应用于同类大型泵站的灵活性和可扩展性。
因此,本文的研究对油田自动化水平的提高和节能降耗都具有重要的现实意义和广阔的应用前景。
Optimal scheduling of large-scale pumping stations is an important method to realize the energy-saving demanding of the whole pumping stations. In recent years, reciprocating plunger pumps and centrifugal multilevel pumps were connected in parallel and frequency converter technology was widely used in the water injection stations in the oilfields of our country. Therefore, it became more and more complicated to implement optimal scheduling and control for large-scale water injection stations. Nowadays, people demands much more for the efficiency and cost-effective production due to the improvement of the present automatization level. To solve these conflicts, this dissertation is proposed to study and simulation on such large-scale pumping stations in the oilfields.
In this paper, an appropriate mathematical model was firstly presented for the efficiency optimization of the large-scale water injection stations. The model is used to describe the characteristics of the stations and to achieve the goal of high efficiency and constant-pressure control. This is of a complex nonlinear programming with the effects of the continuous variables and discrete variables, which made its solving relatively trouble. Hence, an improved genetic algorithm was designed to get the optimal objective solution. Numerical examples show that the proposed model is correct and the method is efficient.
Following the above research, a decision simulation system on optimal scheduling for the large-scale pumping stations is designed and implemented in this paper. The simulation software presents one of the applications of the proposed method. As one part of the automatization system for pumping stations, this software has many excellent qualities, such as compatibility with industry computers such as SIEMENS, interaction with data acquisition systems, and flexibility and extensibility to apply for the similar industry problems.
Therefore, it is of great practical significance and has promising prospect to do research work in this thesis. It will contribute more to improvement of the automatization level of oilfields and more to reduce energy consume.
首先,本文建立了适合大型注水泵站特点的优化调度数学模型,给出了满足外界流量变化需求和优化运行的约束条件,以同时达到泵站高效运行和恒压控制的目的。该数学模型是一个含有连续变量和离散变量的复杂非线性规划问题。本文在综合分析和讨论了目前各种智能优化算法的基础上,设计了一种改进遗传算法求解该问题,并通过算例证实了模型的正确性和算法的有效性。
本文还设计并实现了大型泵站优化调度决策仿真系统软件,为将本文的研究成果应用于工程实际奠定了基础。作为泵站自动化系统的一部分,本系统软件在实际应用中考虑了同西门子等工控机的兼容性以及和数据采集系统的交互性,同时该软件具有应用于同类大型泵站的灵活性和可扩展性。
因此,本文的研究对油田自动化水平的提高和节能降耗都具有重要的现实意义和广阔的应用前景。
Optimal scheduling of large-scale pumping stations is an important method to realize the energy-saving demanding of the whole pumping stations. In recent years, reciprocating plunger pumps and centrifugal multilevel pumps were connected in parallel and frequency converter technology was widely used in the water injection stations in the oilfields of our country. Therefore, it became more and more complicated to implement optimal scheduling and control for large-scale water injection stations. Nowadays, people demands much more for the efficiency and cost-effective production due to the improvement of the present automatization level. To solve these conflicts, this dissertation is proposed to study and simulation on such large-scale pumping stations in the oilfields.
In this paper, an appropriate mathematical model was firstly presented for the efficiency optimization of the large-scale water injection stations. The model is used to describe the characteristics of the stations and to achieve the goal of high efficiency and constant-pressure control. This is of a complex nonlinear programming with the effects of the continuous variables and discrete variables, which made its solving relatively trouble. Hence, an improved genetic algorithm was designed to get the optimal objective solution. Numerical examples show that the proposed model is correct and the method is efficient.
Following the above research, a decision simulation system on optimal scheduling for the large-scale pumping stations is designed and implemented in this paper. The simulation software presents one of the applications of the proposed method. As one part of the automatization system for pumping stations, this software has many excellent qualities, such as compatibility with industry computers such as SIEMENS, interaction with data acquisition systems, and flexibility and extensibility to apply for the similar industry problems.
Therefore, it is of great practical significance and has promising prospect to do research work in this thesis. It will contribute more to improvement of the automatization level of oilfields and more to reduce energy consume.
引文
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[2]林永茂.油田注水系统的模拟控制优化及效率分析[硕士学位论文].保存地点:西南石油大学,2005,5.
[3] Igor J.Karassik. Pump Handbook[D].北京:中国石化出版社,2002:23-45.
[4]李洪斌.泵站效率优化控制理论与应用[硕士学位论文].保存地点:山东大学,2001:45-47.
[5]王淑超.文南油田注水泵站节能优化监控系统研究与设计[硕士学位论文].保存地点:华中科技大学,2004,4.
[6]郭瑞,曹兴滨等.变频器节能技术在油田输油泵机组上的应用[J].石油矿场机械,2006,35(2):76~78.
[7]何根木,朱荣生,李维斌等.泵站优化运行算法[J].农机化研究,2004,3:97~99.
[8]邵旻.基于混合遗传算法的油田注水泵站优化控制[硕士学位论文].保存地点:华中科技大学,2006,4
[9]王芳.基于优化调度的大型泵站综合自动化系统研究[硕士学位论文].保存地点:湖南大学,2005,6.
[10]中国石油天然气总公司科技情报所.注水系统国内外状况.国外油田节能技术调查报告,1998.
[11]陈森鑫,李从信,司光宇,樊万鹏.大型注水系统的运行优化[J].大庆石油学院学报,1996,20(6):41~45.
[12]陈森鑫,刘翠玲,陈晓听,李天歇.大型注水系统的最优控制[J].黑龙江自动化技术与应用,1996,18(6).
[13]陈森鑫,刘铁男,司光宇.大型注水系统的建模、优化和控制[J].石油规划设计,1995,3:34 ~36.
[14]陈森鑫,李艳辉,刘翠玲.注水站最优控制[J].黑龙江自动化技术与应用,1997,16(4):14~16.
[15]李从信,陈森鑫,郭福田,周丽佩.注水系统的计算方法[J].石油学报,1998,19(3):120~124.
[16]陈森鑫,尚福华,赵恩艳,樊万鹅.注水系统的通用模型和通用算法[J].大庆石油学院学报,1998,22(3):35~37.
[17]陈森鑫,司光宇.大型注水系统的运行、计算和控制[J].大庆石油学院学报,1995,19( 3):71~75.
[18]刘炜光.油田注水系统运行优化研究[硕士学位论文].保存地点:北京航空航天大学,2002,11.
[19]徐小力,郭莹,胡海.油田注水机组节能优化的指标及数学模型[J].石油矿场机械,2002,31(5):4~6.
[20]刘炜光.油田离心注水泵站系统效率分析与节能对策[J].石油机械,2005,33(5):73~75.
[21]杜晓雷.基于遗传算法和神经网络的泵站经济运行研究[硕士学位论文].保存地点:扬州大学,2005.
[22]赵光亮.泵站监控及优化调度研究[硕士学位论文].保存地点:河海大学,2004.
[23]关晓晶,魏立新,杨建军.基于混合遗传算法的油田注水系统运行方案优化模型[J].石油学报,2005,26(3):114~117.
[24]刘扬,袁振中,魏立新.大型油田注水系统节能降耗与运行方案优化[J].大庆石油学院学报,2006,30(3):43~46.
[25]张承慧,李洪斌,廖莉,程兆林.变频调速给水泵站效率最优控制策略[J].控制理论与应用,2004,21(3):470~474
[26] YING LI,YUAN-CHUN LI.Hybrid Nonlinear Homotopy BP Neural Network and Genetic Algorithm for Oil-Pumping Control System[C].Proceedings of the Fourth International Conference on Machine Learning and Cybernetics,Guangzhou,2005,8.
[27]蔡增基,龙天渝.流体力学泵与风机(第4版)[M].北京:中国建筑工业出版社,1999,13~56.
[28] Bodenheimer B, Bendotti P.Optimal Linear Parameter-Varying Control Design for a Pressurized Water Reactor[J].Decision and Control,1995,1:182~187.
[29] Coulbeck B,Orr C.Optimal Control of Water Supply[J].Control and Optimization Techniques for the Water Industry,1990,1:1~4.
[30]马永庆,张承慧,程兆林.变频驱动油田往复式注水泵站最小轴功率优化控制[J].系统工程理论与实践,2003,8:136-140.
[31]王鉴光.泵站最优节能控制[J].石油钻采工艺,1995,17(3):80-84.
[32]袁晓辉,袁艳斌,张勇传.电力系统中机组组合的现代智能优化方法综述[J].电力自动化设备,2003,23(2).
[33]韦凌云,柴跃廷,赵玫.不等式约束的非线性规划混合遗传算法[J].计算机工程与应用,2006,22:46~49.
[34]刘琼荪,周声华.基于自适应惩罚函数法的混合遗传算法[J].重庆大学学报(自然科学版),2006,29(6):78~81.
[35]刘淳安.解非线性规划的多目标遗传算法及其收敛性[J].计算机工程与应用.2006,25:27~29
[36] SENJYU T,YAMASHIRO H,UEZATO K.A unit commitment problem by using genetic algorithm based on unit characteristic classification[A].IEEE Power Engineering Society Winter Meeting[C].New York:IEEE,2002,58~63.
[37]代颖,马祖军,刘飞.基于混合遗传算法的制造/再制造集成物流网络优化设计[J].计算机集成制造系统,2006,12(11):1853~1859.
[38]张贵军,吴惕华.三级泵优化配置问题算法设计及求解[J].上海交通大学学报,2005,39(12):1984~1987.
[39] Lu Ping,Khan M.Nonsmooth trajectory optimization:An approach using continuous simulated annealing[J].Journal of Guidance,Control and Dynamics,1994,17(4):685– 691.
[40]罗亚中,唐国金,田蕾.基于模拟退火算法的最优控制问题全局优化[J].南京理工大学学报,2005,29(2):144~148.
[41] MANTAWY A H,ABDEL-MAGID Y L,SELIM S Z.Integrating genetic algorithms,tabu search,and simulated annealing for the unit commitment problem[J].IEEE Trans. on Power Systems,1999,14(3):829~836.
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