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Continuous-time system identification from discrete-time measurements with application to natural gas pipeline modeling.

机译：从离散时间测量到天然气管道建模的连续时间系统识别。

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This work was motivated by the need to model a network of natural gas pipelines and its corresponding demand pipeline, in order to make predictions of the pressures at critical junctions in the network Development of such a model amounts to a system identification problem with limited information.; In order to solve this problem, we developed a demand model that would provide estimates of the gas usage for the communities serviced by the pipeline network. The parameters of the demand model were estimated using an adaptive genetic algorithm. This new algorithm was first developed and compared with existing genetic algorithms. A discussion of the role played by crossover and mutation operators in the genetic algorithm was also presented.; Based on the theory of gas dynamics and the known pipeline network topology, a resistor-capacitor network analog to the pipeline network was developed. The parameters of the resistor-capacitor model were estimated using ordinary least squares techniques. We first studied and developed a number principles and guidelines for a class of system identification problems.; One of the main areas studied was the development of a generalized framework for least squares “parameter” identification of continuous-time systems from discrete-time measurements of the states of the continuous-time system. Subsequently, we extended our generalized framework to the least squares parameter identification of a class of resistor-capacitor networks. We also studied the effects on the estimated results of the integration scheme used in the process and the noise levels in the measured data. A demonstration of the benefits of the incorporation of the maximum available structural information of the system being modeled was also presented. Finally, we developed a set of guidelines for the required input signal frequencies and sampling frequencies to provide acceptable identification results for both the plant-model-match and reduced-order modeling problems.; Finally, we applied these techniques to the identification of an actual natural gas pipeline network. The results provided significantly better pressure estimates than those previously reported.
机译：这项工作的动机是需要对天然气管道及其相应的需求管道网络进行建模，以便对网络中关键节点的压力进行预测。开发这种模型相当于信息识别有限的系统识别问题。 ;为了解决该问题，我们开发了一个需求模型，该模型将为管道网络所服务的社区提供天然气使用量的估计值。使用自适应遗传算法估计需求模型的参数。最初开发了这种新算法，并将其与现有的遗传算法进行了比较。还讨论了交叉和变异算子在遗传算法中的作用。基于气体动力学理论和已知的管道网络拓扑，开发了类似于管道网络的电阻器-电容器网络。电阻-电容模型的参数是使用普通最小二乘法估算的。我们首先研究和开发了针对一类系统识别问题的一些原则和准则。研究的主要领域之一是为从连续时间系统状态的离散时间测量中确定连续时间系统的最小二乘“ 参数”的通用框架的开发。随后，我们将广义框架扩展到一类电阻-电容器网络的最小二乘参数识别。我们还研究了对过程中使用的集成方案的估计结果的影响以及测量数据中的噪声水平。还展示了合并正在建模的系统的最大可用结构信息的好处。最后，我们针对所需的输入信号频率和采样频率制定了一套准则，以为工厂模型匹配和降阶建模问题提供可接受的识别结果。最后，我们将这些技术应用于实际天然气管道网络的识别。结果提供的压力估算值比以前报告的要好得多。

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