天然气管网稳动态仿真及其负荷预测系统的研究
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摘要
本文主要研究了天然气管网系统稳动态仿真和天然气短期负荷预测两个问题。
天然气管网系统稳动态仿真及其负荷预测是天然气管理系统中的一项重要工作。精确的稳动态仿真及负荷预测对天然气管网系统的生产计划、优化调度和安全分析都起着十分重要的作用，直接影响着天然气管网系统的经济效益。本文依据陕西省天然气管网系统的历史数据，对天然气管网系统稳动态仿真和天然气短期负荷预测进行了研究。
本文研究了天然气在简单管线中稳定流态下的水力计算问题，在此基础上针对并行、环行等复杂管线的情形，改进了其算法。通过实例仿真表明利用这种算法得出的计算结果具有较高的精度。
西安理工大学硕士学位论文
针对天然气管网系统动态仿真的问题，本文在静态仿真的基础上采用特征
线法对大然气在管道中的瞬态特性进行了仿真计算:对实际管网系统中出现的
卜况进行理论计算分析，对算法做了改进，并通过计算仿真验证改进后的算法
满足实际}一程中的需要。
针对大然气短期负荷预测的问题，在分析了国内外技术现状的基础上，综
合考虑影响负荷变化的各种因素，提出了基于白织织竞争网络和多层感知机网
络棍合的大然气短期负荷预测方法。为使预测值不受负荷逐年变化这一趋势的
影响，把负荷数据变换为特征、均值和方差的形式，利用白组织竞争网络预测
负荷的特征，然后利用多层感知器网络预测负荷的日均值和方差，最终实现对
一大24小时负荷的预测。通过实例计算，验证了该模型的有效性。
This thesis is concerned with two problems: static and dynamic simulation for natural gas pipeline network and short-time natural gas load forecasting.
Static and dynamic simulation for natural gas pipe network and natural gas load forecasting is an important work of natural gas management system. Precise static and dynamic simulation and load forecasting will have an significant effect on production planning, optimizing attemper and safety analysis of natural gas network system, and will directly affects its economic benefits. Based on historical data of Shaanxi natural gas network system, static and dynamic simulation and load forecasting of natural gas system has been researched in this thesis.
The thesis firstly made a research on waterpower calculation arithmetic of simple pipeline, and research results were used in other complicated pipeline system such as parallel pipeline, annular pipeline. By practical simulation, the results show that the arithmetic has a higher precise in static simulation.
For natural gas pipe network dynamic simulation, the thesis adopts characteristic method to discuss. Based on theoretic calculation, analyzing practical working phenomena of the network, the thesis improved the arithmetic. Tested by waterpower calculation simulation, the simulation results show that the arithmetic meet the requirement of practical project.
For short-time natural gas load forecasting. Based on analyzing tech situation at home and abroad, considering all kinds of factors which will have influence on load changes, a hybrid approach combined the Self-organizing Feature Map (SOFM) neural network with Multilayer Perceptron (MLP) is presented, and short-time load forecasting model is established. To make the prediction values with independence of the general trend, which is changed from year to year, the load data are transformed by profiles, mean value, and variance. SOFM is used for the prediction of profiles and MLP networks for prediction of daily mean and daily variance. At a result, load forecasting for 24 hours in a day can be gotten. It shows the validity of model by practiced simulations.
天然气管网系统稳动态仿真及其负荷预测是天然气管理系统中的一项重要工作。精确的稳动态仿真及负荷预测对天然气管网系统的生产计划、优化调度和安全分析都起着十分重要的作用，直接影响着天然气管网系统的经济效益。本文依据陕西省天然气管网系统的历史数据，对天然气管网系统稳动态仿真和天然气短期负荷预测进行了研究。
本文研究了天然气在简单管线中稳定流态下的水力计算问题，在此基础上针对并行、环行等复杂管线的情形，改进了其算法。通过实例仿真表明利用这种算法得出的计算结果具有较高的精度。
西安理工大学硕士学位论文
针对天然气管网系统动态仿真的问题，本文在静态仿真的基础上采用特征
线法对大然气在管道中的瞬态特性进行了仿真计算:对实际管网系统中出现的
卜况进行理论计算分析，对算法做了改进，并通过计算仿真验证改进后的算法
满足实际}一程中的需要。
针对大然气短期负荷预测的问题，在分析了国内外技术现状的基础上，综
合考虑影响负荷变化的各种因素，提出了基于白织织竞争网络和多层感知机网
络棍合的大然气短期负荷预测方法。为使预测值不受负荷逐年变化这一趋势的
影响，把负荷数据变换为特征、均值和方差的形式，利用白组织竞争网络预测
负荷的特征，然后利用多层感知器网络预测负荷的日均值和方差，最终实现对
一大24小时负荷的预测。通过实例计算，验证了该模型的有效性。
This thesis is concerned with two problems: static and dynamic simulation for natural gas pipeline network and short-time natural gas load forecasting.
Static and dynamic simulation for natural gas pipe network and natural gas load forecasting is an important work of natural gas management system. Precise static and dynamic simulation and load forecasting will have an significant effect on production planning, optimizing attemper and safety analysis of natural gas network system, and will directly affects its economic benefits. Based on historical data of Shaanxi natural gas network system, static and dynamic simulation and load forecasting of natural gas system has been researched in this thesis.
The thesis firstly made a research on waterpower calculation arithmetic of simple pipeline, and research results were used in other complicated pipeline system such as parallel pipeline, annular pipeline. By practical simulation, the results show that the arithmetic has a higher precise in static simulation.
For natural gas pipe network dynamic simulation, the thesis adopts characteristic method to discuss. Based on theoretic calculation, analyzing practical working phenomena of the network, the thesis improved the arithmetic. Tested by waterpower calculation simulation, the simulation results show that the arithmetic meet the requirement of practical project.
For short-time natural gas load forecasting. Based on analyzing tech situation at home and abroad, considering all kinds of factors which will have influence on load changes, a hybrid approach combined the Self-organizing Feature Map (SOFM) neural network with Multilayer Perceptron (MLP) is presented, and short-time load forecasting model is established. To make the prediction values with independence of the general trend, which is changed from year to year, the load data are transformed by profiles, mean value, and variance. SOFM is used for the prediction of profiles and MLP networks for prediction of daily mean and daily variance. At a result, load forecasting for 24 hours in a day can be gotten. It shows the validity of model by practiced simulations.
引文
【1】Boetius A, Worldwide Pipelining Shows Jump in Construction Activity, Pipeline & Gas Industry, Mar. 1997, Vol. 80, No. 11, PP. 223.
【2】Clarke M A, Westacott R E, Zhang W, Pipeline Conpanies Plan to lay 22200 miles in 1998, Pipeline & Gas Industry, Apr. 1998, Vol. 10, No. 21,PP. 201.
【3】Dcuglas J.F, Gasiorek J.M, & Swaffield, Fluid Mechanics 2~(ed) edition, Pitman Publishing LTD, 1985.
【4】Deng JingLiu, Sun haiping Liqibai, The lnflience of Bulk Modulus of Actuating on The Pressure Transients in a Hydraulic System, Proceedings of International Conferences on Mechanical Dynamic, Apr. 1997, Vol. 12,No. 121, PP. 101,
【5】E.B.Wylie and V.L.Streeter, Fluid Transients in System, Prentice Hall NJ,Jan, 1993.
【6】Farmer E.D, Potton M.J. Development of On-line Load Prediction Techniques with Results From Trials in the South-West Region of the CEGB, Proceedings of the IEEE, Jan, 1995, Vol. 115 No. 10, PP. 21.
【7】Fox R.W, & Mc Donald A.T, Introduction to Fluid Mechanics, John Wiley & Sor, Feb, 1978, Vol. 15 No. 10, PP. 22.
【8】G.Z.Watters, Analysis and Contral of Unsteady Flow in Pipelines, Butterworth Publishers, Feb, 1985.
【9】Henrique Steinherz Hippert, Carlos Eduardo Pedreira, and Reinaido Castro Souza, Nerural Networks for Short-Term Load Forecasting: A Review and Evaluation, IEEE Transactions on Power Systems, Feb, 2001,Vol. 16, No.1, PP. 802-805.
【10】Hubele N.F, Cheng C.S. Identification of Seasonal Short-Term Load
Forecasting Models Using Statistical Decision Functions, IEEE Transaction on Power System, Mar, 1998, Vol. 10, No. 20, PP. 80-82.
【11】Keller G. R, Hydraulic System Analysis, H & P 1995, Vol. 11,No. 110, PP.10-12.
【12】K. Kohonen, Self-organizing maps. Springer Verlag, Berlin, 1995.
【13】K.Y.Lee, J.H.Park, Short-Term Load Forecasting Using an Artificial Neural Network, IEEE Transaction on Power System, Mar, 1996, Vol. 20,No. 10, PP. 110-112.
【14】Kun-Long Ho, Short-Term Load Forecasting Using a Multilayer Neural Network With an Adaptive Learning Algorithm, IEEE Transaction on Power System, Mar, 1997, Vol. 2, No. 1, PP. 84-86.
【15】Magbram I, Rahman S, Analysis and Evaluation of Five Short-Term Load Forecasting Techniques, IEEE Transaction on Power System, Apr, 1997, Vol. 1, No. 12, PP. 65-68.
【16】M. Cottrell, Daily electrical power curve: classification and forecasting using a Kohonen map, in From Neural to Artificial Neural Computation, Apr, 1999, Vol. 11, No. 1, PP. 1165-1168.
【17】Mohammed, Practical Experiences with an Adaptive Neural Network Short-Term Load Forecasting System, IEEE Transaction on Power System, Apr, 1998, Vol. 12, No. 21, PP. 165-168.
【18】O.Hyde, P.F.Hodnett, An Adaptable Automated Procedure for Short-Term Electricity Load Forecasting, IEEE Transaction on Power System, Feb, 1997, Vol. 12, No. 1, PP. 165-168.
【19】O. Mohammed, Practical Experiences with an Adaptive Neural Network Short-Term Load Forecasting System, IEEE Transaction on Power System,Feb, 1995, Vol. 10, No. 1, PP. 101-105.
【20】Park J.H, Park Y.M, Lee K.Y, Composeite Modeling for Adaptive Short Term Load Forecasting, IEEE Trans on Power System, Feb, 1992, Vol. 1,No. 11, PP. 34-37.
【21】Paul Lamb, Dan Logue, Implementation of a Gas Load At Williams Gas Pipeline, 2001 Pip- eline Simulation lnterest Group Meeting, Salt Lake City, Utah, October 2001, Vol. 1, No. 11, PP. 12-15.
【22】Peizheng Zhou, Erbar J H, Maddox R N , Analyzing Unsteady-state Flow in Gas Pipelines, Pitman Publishing LTD, 1983.
【23】Peter J. Brockwell Richard A. Davis, Time Series:Theory and Methods,高等教育出版社，施普林格出版社，1990.
【24】Rahman S, Bhalnagar R, An Expert System Based Algorithm for Short-Term Load Forecasting, IEEE Transaction on Power System, Feb,1998, Vol. 3, No. 2, PP. 23-25.
【25】Shin-Tzo Chen, Weather Sensitive Short-Term Load Forecasting Using Nonfully Connected Artificial Neural Network, IEEE Transaction on Power System, August 1992, Vol. 7, No. 3, PP. 14-17.
【26】S. Osowski, K. Siwek, The self-organizing neural network approach to load forecasting in the power system, 1999 IEEE Conf. on Neural Networks, Washington, Feb, 1999, Vol. 1, No. 11, PP. 1120-1123.
【27】Steward G. W, The Theory of the Herschel-Quincke Tube, Physical Review, April 1998, Vol. 21, No. 2, PP. 11-13.
【28】Steeeter V.L, & Wylie E.B, Fluid Mechanics, Mc Graw-Hill. Jun, 1979.
【29】T.M.Peng, N.F.Hubele, G.G.Karady, Advancement in the Application of Neural Networks for Short-Term Load Forecasting, IEEE Transaction on Power System, February 1992, Vol. 7, No. 1, PP. 110-114.
【30】V．L．Streeter and E．B．Wylie，Fluid Mechanics, MeGraw-Hill Book Co．Mar,1990．
【31】蔡亦刚，盛敬超，《流体管道瞬态响应特性大型综合实验指导书》，浙江人学流体传动及控制教研室，1985。
【32】李长俊，曾自强，江茂泽，天然气在管网系统中不稳定流动的分析，天然气工业，2000，Vol．7，No．1，PP．110-114．
【33】陆金甫，关治，偏微分方程数值解法，清华大学出版社，1990。
【34】盛敬超，工程流体力学，机械工业出版社，1983。
【35】王寿喜，曾自强，瞬变分析特征线法，天然气工业，1999，Vol．10，No．1，PP．101-105．
【36】王寿喜，曾自强，天然气管网动静态仿真，天然气工业，1995，Vol．7，No．11．PP．110-114．
【37】黎洪珍，天然气集输管网稳动态仿真模拟基本原理及应用探索，天然气与石油，2001，Vol．4，No．1，PP．23-26．
【38】刘德有，索丽生，复杂给水管网恒定流计算新方法—特征线法，中国给水排水，1995，Vol．14，No．3，PP．15-19．
【39】焦文玲，金佳宾等，时间序列分析在城市天然气短期负荷预测中的应用，哈尔滨建筑大学学报，2001，Vol．1，No．10，PP．120-124．
【40】谭羽非，陈家新等，基于人工神经网络的城市煤气短期负荷预测，煤气与热力，2001，Vol．2，No．1，PP．230-233．
【41】周恩泽，供暖热水锅炉房的热负荷预测，哈尔滨建筑大学学报，2000，Vol．5．No．12．PP．1150-1154．
【42】张乃尧，阎平凡。神经网络与模糊控制。清华大学出版社，1989。
【43】孙增圻。智能控制理论与技术。清华大学出版社，广西科技技术出版社，1997。
【44】郑岗，周勇，刘丁等。基于神经元网络的短期电力负荷预测。西安理工大学学报，Journal of Xi'an University of Technology，2002，Vol．18，No．2，pp．126．
【2】Clarke M A, Westacott R E, Zhang W, Pipeline Conpanies Plan to lay 22200 miles in 1998, Pipeline & Gas Industry, Apr. 1998, Vol. 10, No. 21,PP. 201.
【3】Dcuglas J.F, Gasiorek J.M, & Swaffield, Fluid Mechanics 2~(ed) edition, Pitman Publishing LTD, 1985.
【4】Deng JingLiu, Sun haiping Liqibai, The lnflience of Bulk Modulus of Actuating on The Pressure Transients in a Hydraulic System, Proceedings of International Conferences on Mechanical Dynamic, Apr. 1997, Vol. 12,No. 121, PP. 101,
【5】E.B.Wylie and V.L.Streeter, Fluid Transients in System, Prentice Hall NJ,Jan, 1993.
【6】Farmer E.D, Potton M.J. Development of On-line Load Prediction Techniques with Results From Trials in the South-West Region of the CEGB, Proceedings of the IEEE, Jan, 1995, Vol. 115 No. 10, PP. 21.
【7】Fox R.W, & Mc Donald A.T, Introduction to Fluid Mechanics, John Wiley & Sor, Feb, 1978, Vol. 15 No. 10, PP. 22.
【8】G.Z.Watters, Analysis and Contral of Unsteady Flow in Pipelines, Butterworth Publishers, Feb, 1985.
【9】Henrique Steinherz Hippert, Carlos Eduardo Pedreira, and Reinaido Castro Souza, Nerural Networks for Short-Term Load Forecasting: A Review and Evaluation, IEEE Transactions on Power Systems, Feb, 2001,Vol. 16, No.1, PP. 802-805.
【10】Hubele N.F, Cheng C.S. Identification of Seasonal Short-Term Load
Forecasting Models Using Statistical Decision Functions, IEEE Transaction on Power System, Mar, 1998, Vol. 10, No. 20, PP. 80-82.
【11】Keller G. R, Hydraulic System Analysis, H & P 1995, Vol. 11,No. 110, PP.10-12.
【12】K. Kohonen, Self-organizing maps. Springer Verlag, Berlin, 1995.
【13】K.Y.Lee, J.H.Park, Short-Term Load Forecasting Using an Artificial Neural Network, IEEE Transaction on Power System, Mar, 1996, Vol. 20,No. 10, PP. 110-112.
【14】Kun-Long Ho, Short-Term Load Forecasting Using a Multilayer Neural Network With an Adaptive Learning Algorithm, IEEE Transaction on Power System, Mar, 1997, Vol. 2, No. 1, PP. 84-86.
【15】Magbram I, Rahman S, Analysis and Evaluation of Five Short-Term Load Forecasting Techniques, IEEE Transaction on Power System, Apr, 1997, Vol. 1, No. 12, PP. 65-68.
【16】M. Cottrell, Daily electrical power curve: classification and forecasting using a Kohonen map, in From Neural to Artificial Neural Computation, Apr, 1999, Vol. 11, No. 1, PP. 1165-1168.
【17】Mohammed, Practical Experiences with an Adaptive Neural Network Short-Term Load Forecasting System, IEEE Transaction on Power System, Apr, 1998, Vol. 12, No. 21, PP. 165-168.
【18】O.Hyde, P.F.Hodnett, An Adaptable Automated Procedure for Short-Term Electricity Load Forecasting, IEEE Transaction on Power System, Feb, 1997, Vol. 12, No. 1, PP. 165-168.
【19】O. Mohammed, Practical Experiences with an Adaptive Neural Network Short-Term Load Forecasting System, IEEE Transaction on Power System,Feb, 1995, Vol. 10, No. 1, PP. 101-105.
【20】Park J.H, Park Y.M, Lee K.Y, Composeite Modeling for Adaptive Short Term Load Forecasting, IEEE Trans on Power System, Feb, 1992, Vol. 1,No. 11, PP. 34-37.
【21】Paul Lamb, Dan Logue, Implementation of a Gas Load At Williams Gas Pipeline, 2001 Pip- eline Simulation lnterest Group Meeting, Salt Lake City, Utah, October 2001, Vol. 1, No. 11, PP. 12-15.
【22】Peizheng Zhou, Erbar J H, Maddox R N , Analyzing Unsteady-state Flow in Gas Pipelines, Pitman Publishing LTD, 1983.
【23】Peter J. Brockwell Richard A. Davis, Time Series:Theory and Methods,高等教育出版社，施普林格出版社，1990.
【24】Rahman S, Bhalnagar R, An Expert System Based Algorithm for Short-Term Load Forecasting, IEEE Transaction on Power System, Feb,1998, Vol. 3, No. 2, PP. 23-25.
【25】Shin-Tzo Chen, Weather Sensitive Short-Term Load Forecasting Using Nonfully Connected Artificial Neural Network, IEEE Transaction on Power System, August 1992, Vol. 7, No. 3, PP. 14-17.
【26】S. Osowski, K. Siwek, The self-organizing neural network approach to load forecasting in the power system, 1999 IEEE Conf. on Neural Networks, Washington, Feb, 1999, Vol. 1, No. 11, PP. 1120-1123.
【27】Steward G. W, The Theory of the Herschel-Quincke Tube, Physical Review, April 1998, Vol. 21, No. 2, PP. 11-13.
【28】Steeeter V.L, & Wylie E.B, Fluid Mechanics, Mc Graw-Hill. Jun, 1979.
【29】T.M.Peng, N.F.Hubele, G.G.Karady, Advancement in the Application of Neural Networks for Short-Term Load Forecasting, IEEE Transaction on Power System, February 1992, Vol. 7, No. 1, PP. 110-114.
【30】V．L．Streeter and E．B．Wylie，Fluid Mechanics, MeGraw-Hill Book Co．Mar,1990．
【31】蔡亦刚，盛敬超，《流体管道瞬态响应特性大型综合实验指导书》，浙江人学流体传动及控制教研室，1985。
【32】李长俊，曾自强，江茂泽，天然气在管网系统中不稳定流动的分析，天然气工业，2000，Vol．7，No．1，PP．110-114．
【33】陆金甫，关治，偏微分方程数值解法，清华大学出版社，1990。
【34】盛敬超，工程流体力学，机械工业出版社，1983。
【35】王寿喜，曾自强，瞬变分析特征线法，天然气工业，1999，Vol．10，No．1，PP．101-105．
【36】王寿喜，曾自强，天然气管网动静态仿真，天然气工业，1995，Vol．7，No．11．PP．110-114．
【37】黎洪珍，天然气集输管网稳动态仿真模拟基本原理及应用探索，天然气与石油，2001，Vol．4，No．1，PP．23-26．
【38】刘德有，索丽生，复杂给水管网恒定流计算新方法—特征线法，中国给水排水，1995，Vol．14，No．3，PP．15-19．
【39】焦文玲，金佳宾等，时间序列分析在城市天然气短期负荷预测中的应用，哈尔滨建筑大学学报，2001，Vol．1，No．10，PP．120-124．
【40】谭羽非，陈家新等，基于人工神经网络的城市煤气短期负荷预测，煤气与热力，2001，Vol．2，No．1，PP．230-233．
【41】周恩泽，供暖热水锅炉房的热负荷预测，哈尔滨建筑大学学报，2000，Vol．5．No．12．PP．1150-1154．
【42】张乃尧，阎平凡。神经网络与模糊控制。清华大学出版社，1989。
【43】孙增圻。智能控制理论与技术。清华大学出版社，广西科技技术出版社，1997。
【44】郑岗，周勇，刘丁等。基于神经元网络的短期电力负荷预测。西安理工大学学报，Journal of Xi'an University of Technology，2002，Vol．18，No．2，pp．126．