nerc.ac.uk

Modeling Pipe to Soil Potentials From Geomagnetic Storms in Gas Pipelines in New Zealand

Divett, Tim; Ingham, Malcolm; Richardson, Gemma; Sigley, Mark; Rodger, Craig J.. 2023 Modeling Pipe to Soil Potentials From Geomagnetic Storms in Gas Pipelines in New Zealand. Space Weather, 21 (12), e2023SW003601. 10.1029/2023SW003601

Before downloading, please read NORA policies.
[thumbnail of Open Access Paper]
Preview
Text (Open Access Paper)
Space Weather - 2023 - Divett - Modeling Pipe to Soil Potentials From Geomagnetic Storms in Gas Pipelines in New Zealand.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (771kB) | Preview

Abstract/Summary

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.

Item Type: Publication - Article
Digital Object Identifier (DOI): 10.1029/2023SW003601
ISSN: 1542-7390
Date made live: 30 Jan 2024 14:26 +0 (UTC)
URI: https://nora.nerc.ac.uk/id/eprint/536811

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...