Advancing Space Weather Hazard Research in Australia: A Journey of Discovery from AWAGS to AusLAMP

A geomagnetic storm, also known as a geomagnetic disturbance (GMD), is a major disturbance of the Earth's magnetic field caused by solar activity. A geomagnetic storm induces electric currents in the Earth that feed into power lines through substation neutral earthing, causing instabilities and even blackouts in electricity transmission systems. The strength of geomagnetically induced currents (GICs) in the ground is directly related to the electrical conductivity of the surrounding geology. GICs experienced within power transmission lines are also influenced by the orientations and configuration of the power lines with respect to the electric fields. We installed a geoelectric field monitoring system at the Canberra geomagnetic observatory (CNB) to directly measure geomagnetically induced electric fields. This data enhances the capability in modelling and forecasting geoelectric hazards and can be used to validate the modelling approach through convolving magnetotelluric (MT) tensors with geomagnetic fields. In this presentation, we modelled the induced electric fields for the 1989 Quebec geomagnetic storm, using MT data collected at survey sites from the Australian Lithospheric Architecture Magnetotelluric Project. These results give us insight into the potential magnitude of space weather hazards to Australia's modern-day power grids. We extended this approach to a `Carrington-class' geomagnetic storm to evaluate geoelectric fields in the Australian region, allowing GICs flow in the power lines to be estimated. As an example, geomagnetically induced voltages in transmission lines from Queensland for a `Carrington-class' geomagnetic storm are presented.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-11
DOI
https://doi.org/10.5281/zenodo.13918212
Primary Topic
Machine Learning in Healthcare
Type
article
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article

Advancing Space Weather Hazard Research in Australia: A Journey of Discovery from AWAGS to AusLAMP

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Healthcare
article

Advancing Space Weather Hazard Research in Australia: A Journey of Discovery from AWAGS to AusLAMP

article en

Abstract

A geomagnetic storm, also known as a geomagnetic disturbance (GMD), is a major disturbance of the Earth's magnetic field caused by solar activity. A geomagnetic storm induces electric currents in the Earth that feed into power lines through substation neutral earthing, causing instabilities and even blackouts in electricity transmission systems. The strength of geomagnetically induced currents (GICs) in the ground is directly related to the electrical conductivity of the surrounding geology. GICs experienced within power transmission lines are also influenced by the orientations and configuration of the power lines with respect to the electric fields. We installed a geoelectric field monitoring system at the Canberra geomagnetic observatory (CNB) to directly measure geomagnetically induced electric fields. This data enhances the capability in modelling and forecasting geoelectric hazards and can be used to validate the modelling approach through convolving magnetotelluric (MT) tensors with geomagnetic fields. In this presentation, we modelled the induced electric fields for the 1989 Quebec geomagnetic storm, using MT data collected at survey sites from the Australian Lithospheric Architecture Magnetotelluric Project. These results give us insight into the potential magnitude of space weather hazards to Australia's modern-day power grids. We extended this approach to a `Carrington-class' geomagnetic storm to evaluate geoelectric fields in the Australian region, allowing GICs flow in the power lines to be estimated. As an example, geomagnetically induced voltages in transmission lines from Queensland for a `Carrington-class' geomagnetic storm are presented.

Zenodo (CERN European Organization for Nuclear Research)
Geoscience Australia (AU), Nagoya University (JP)
Openalex Percentile: Top 61%
Machine Learning in Healthcare
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