Finite volume methods for transient modeling of gas pipelines

Malley, Conor O'; Kourounis, Drosos; Hug, Gabriela; Schenk, Olaf

doi:10.1109/energycon.2018.8398787

Cited by 2 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [11] and [12], the natural gas temperature was taken as measurements to estimate the influence of temperature change on the overall flow of natural gas in the pipeline. Furthermore, a dissipative finite volume discretization scheme was presented to cope with the isothermal flow equation of natural gas [13]. All the above methods mainly concerned the impact of temperature on natural gas flow, whereas compared with pipeline pressure and flow, the influence of temperature on natural gas network is somewhat negligible.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven State Estimation Framework for Natural Gas Networks With Measurement Noise

2023

View full text Add to dashboard Cite

The large-scale coverage of natural gas makes the composition structure and operation mode of natural gas network more complex, higher requirements are put forward for the effectiveness and accuracy of state estimation. The existing methods for state estimation of natural gas network with noise are all modeled after processing the data with noise, leading to the real data being distorted to a certain extent. With that in mind, a data-driven method is presented in this paper. While solving the problem of state estimation for natural gas network with measurement noise in the input data, filtering and denoising are unnecessary during state estimation, retaining the complete information of real data. It avoids destruction of real data induced by separating noise from measured data owing to different methods and intensities of noise processing. According to the gas flow characteristic equation of natural gas system, the original problem is converted into a weighted low-rank approximation problem, the search space is shrunk to an orthogonal complement space. The selection of initial values is not merely unrestricted but there will be no accumulation and transmission of iteration error. The effectiveness of the proposed method is demonstrated through simulating 10-node natural gas network. Compared with the Newton's method, the data-driven method has superior performance, the RMSE achieves 0.2268 and the MAPE achieves 1.63%.INDEX TERMS Data-driven, natural gas network, measurement noise.

show abstract

Section: Introductionmentioning

confidence: 99%