新西兰天然气管道地磁暴引起的管道到土壤电位建模

IF 3.7 2区 地球科学
Space Weather Pub Date : 2023-12-19 DOI:10.1029/2023sw003601
Tim Divett, Malcolm Ingham, Gemma Richardson, Mark Sigley, Craig J. Rodger
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引用次数: 0

摘要

在地磁扰动期间,由于感应地电场的作用,天然气管道会出现管道与土壤电位(PSP)升高的现象。天然气管道运营商使用阴极保护将 PSP 保持在 -0.85 至 -1.2 V 之间,以防止钢管腐蚀和管道保护层脱落。我们为新西兰北岛的天然气管道开发了一个模型,以确定这些管道是否存在危险以及这种危险有多大。我们使用输电线表示法对管道进行建模,并使用节点导纳矩阵法计算管道沿线最多相距 5 千米的节点处的 PSP。我们使用该模型计算了由理想化的 100 mVkm-1 电场产生的 PSP,该电场最初向北和向东。计算得出的最大 PSP 值位于外加电场矢量方向的管道两端。计算得出的 PSP 沿管道长度方向呈现一条与理论相符的特征曲线,但由于分支线和管道方向的变化而出现偏差。建模结果表明,PSP 幅值对支线涂层电导率很敏感,涂层电导率越高,大多数位置的 PSP 就越小。增强的 PSP 会产生最高的脱粘和腐蚀风险,因此该建模可帮助我们深入了解风险最高的管网位置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling Pipe to Soil Potentials From Geomagnetic Storms in Gas Pipelines in New Zealand
Gas pipelines can experience elevated pipe to soil potentials (PSPs) during geomagnetic disturbances due to the induced geoelectric field. Gas pipeline operators use cathodic protection to keep PSPs between −0.85 and −1.2 V to prevent corrosion of the steel pipes and disbondment of the protective coating from the pipes. We have developed a model of the gas pipelines in the North Island of New Zealand to identify whether a hazard exists to these pipelines and how big this hazard is. We used a transmission line representation to model the pipelines and a nodal admittance matrix method to calculate the PSPs at nodes up to 5 km apart along the pipelines. We used this model to calculate PSPs resulting from an idealized 100 mVkm−1 electric field, initially to the north and east. The calculated PSPs are highest are at the ends of the pipelines in the direction of the applied electric field vector. The calculated PSP follows a characteristic curve along the length of the pipelines that matches theory, with deviations due to branchlines and changes in pipeline direction. The modeling shows that the PSP magnitudes are sensitive to the branchline coating conductance with higher coating conductances decreasing the PSPs at most locations. Enhanced PSPs produce the highest risk of disbondment and corrosion occurring, and hence this modeling provides insights into the network locations most at risk.
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