【作者】 张笑；

【导师】 刘丁； 刘涵；

【作者基本信息】 西安理工大学 ， 控制理论与控制工程， 2004， 硕士

【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。更多还原

【Abstract】 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.更多还原