arXiv subjects

C. T. Gaunt

Publications and source records attributed to C. T. Gaunt.

7 recordsLinked to original sources

arXiv2026

GIC--Related Observations During the May 2024 Geomagnetic Storm in the United States

The May 2024 geomagnetic storm was one of the most severe in the past 20~years. Understanding how large geomagnetic disturbances (GMDs) impact geomagnetically induced currents (GICs) within electrical power grid networks is key to ensuring their resilience. We have assembled and synthesized a large and unique set of GMD-related data, compared model predictions with measurements, and identified empirical relationships for GICs in the contiguous United States for this storm. Measurement data include GIC data from $47$ sites and magnetometer data from $17$ sites. Model data include GIC computed by the Tennessee Valley Authority (TVA) power system operators at $4$ sites, GIC computed using a reference model at $47$ sites, and the difference in the surface magnetic field from a baseline ($Δ\mathbf{B}$) computed at $12$ magnetometer sites from three global magnetospheric models -- the Multiscale Atmosphere-Geospace Environment Model (MAGE), Space Weather Modeling Framework (SWMF), and Open Geospace General Circulation Model (OpenGGCM). GIC measured and computed by TVA had a correlation coefficient $\text{r}>0.8$ and a prediction efficiency between 0.4 and 0.7. The horizontal magnetic field perturbation from a baseline, $ΔB_H$, computed by MAGE, SWMF, and OpenGGCM had a correlation r from $0.21$ to $0.65$. Two empirical relationships were considered: (1) how the correlation between measured GIC site pairs depended on differences in site separation distance, $β$ scaling factor (related to ground conductivity), and geomagnetic latitude; and (2) a regression model for the maximum $\mbox{GIC}$ magnitude at each site given the product of $α$ (related to magnetic latitude) and $β$.

physics.space-ph↗

arXiv2022

Adaptations to a geomagnetic field interpolation method in Southern Africa

Space weather and its impact on infrastructure presents a clear risk in the modern era, as evidenced by the adverse effects of geomagnetically induced currents (GICs) in power networks. To model GICs, ground-based geomagnetic field (B-field) measurements are critical and need to be available in the region of interest. A challenge globally lies in the sparse distribution of magnetometer arrays, which are seldom located near critical power network nodes. Interpolation of the geomagnetic field (B-field) is often needed, with the spherical elementary current system (SECS) approach developed for high-latitude regions favoured. We adapt this interpolation scheme to include low-cost variometers to interpolate dB/dt directly and increase interpolation accuracy. A further adaptation to the scheme is to physically represent the mid-latitude context where most power networks and pipelines lie. The driving current systems in these regions differ from their high-latitude counterparts. Using a physics-consistent mid-latitude version of SECS, we show why previous implementations in Southern Africa are incorrect but still result in useful interpolation. The scope of these adaptations not only has direct application to research in general, but also to utilities, where effective low-cost instrumentation can be used to improve GIC modelling accuracy.

physics.space-ph↗

arXiv2020

Data Driven Transfer Functions and Transmission Network Parameters for GIC Modelling

Typical geomagnetically induced current (GIC) modelling assumes the induced quasi-DC current at a node in the transmission network is linearly related to the local geoelectric field by a pair of network parameters. Given a limited time-series of measured geomagnetic and GIC data, an empirical method is presented that results in a statistically significant generalised ensemble of parameter estimates with the error in the estimates identified. The method is showcased for different transmission networks and geomagnetic storms and, where prior modelling exists, shows improved GIC estimation. Furthermore, modelled networks can be locally characterised and probed without any further network knowledge. Insights include network parameter variation, effective network directionality and response. Merging the network parameters and geoelectric field estimation, a transfer function is derived which offers an alternative approach to assessing transformer exposure to GICs.

physics.space-ph↗

arXiv2020

Probabilistic Analysis of Power Network Susceptibility to GICs

As reliance on power networks has increased over the last century, the risk of damage from geomagnetically induced currents (GICs) has become a concern to utilities. The current state of the art in GIC modelling requires significant geophysical modelling and a theoretically derived network response, but has limited empirical validation. In this work, we introduce a probabilistic engineering step between the measured geomagnetic field and GICs, without needing data about the power system topology or the ground conductivity profiles. The resulting empirical ensembles are used to analyse the TVA network (south-eastern USA) in terms of peak and cumulative exposure to 5 moderate to intense geomagnetic storms. Multiple nodes are ranked according to susceptibility and the measured response of the total TVA network is further calibrated to existing extreme value models. The probabilistic engineering step presented can complement present approaches, being particularly useful for risk assessment of existing transformers and power systems.

physics.space-ph↗

arXiv2019

Frequency Components of Geomagnetically Induced Currents for Power System Modelling

Geomagnetically induced currents (GICs) in power systems are conventionally modelled as direct currents, based on their commonly described quasi-DC nature. For more representative power system dynamic response modelling, it is necessary to model GICs using low-frequency AC. After analysing spectra of geomagnetic field measurements and GICs in several power systems, preliminary results indicate that the GIC spectra depend on the magnitude and frequency spectrum of the geomagnetic disturbance, the earth conductivity and the electrical characteristics of the network. In this paper, analysis based on measured GIC power spectra suggests that the dominant frequencies to use in GIC modelling and simulation are typically below 50 mHz. The associated error introduced by only using these frequencies is also quantified. The results have implications for modelling the geoelectric fields inducing GICs, dynamic models of power system response and the need for GICs and their associated driving fields to be measured at intervals not exceeding 10 s.

physics.space-ph↗

arXiv2019

Ensemble Estimation of Network Parameters: A Tool to Improve the Real-time Estimation of GICs in the South African Power Network

It has long been known that large grounded conducting networks on the surface of Earth are affected by solar activity and geomagnetic storms. Power networks are such extensive grounded conductors and are susceptible to geomagnetically induced currents (GICs). GICs at any specific node in a power network are assumed to be linearly related to the horizontal vector components of an induced plane-wave geoelectric field by a pair of network parameters. These network parameters are not easily measured in the network, but may be estimated empirically. In this work, we present a new approach of using an ensemble of network parameters estimates. The ensembles include a huge number of parameter pair estimates calculated from simultaneously solving pairs of time instances of the governing GIC equation. Each individual estimate is not the true state of the system, but a possible state. Taking the ensemble as a whole though gives the most probable parameter estimate. The most probable parameter estimate for both network parameters, as defined by their respective ensembles, is used directly in the modelling of GICs. The ensembles themselves however allow for further analysis into the nature of GICs. An improvement is seen when comparing the out-of-sample performance of the ensemble estimates with previous GIC modelling in the South African power network during the Halloween Storm of 2003. For the first time, it is shown that errors in the GIC modelling chain are absorbed into the network parameter estimates. Using a range of estimates from the ensemble, a GIC prediction band is produced. This band corresponds to an error estimate for predicted GIC. Furthermore, it has been explicitly shown that estimated network parameters vary with GIC magnitude during an event. This behaviour is then used to refine the parameter estimation further and allow for real time dynamic network parameter estimation.

physics.space-ph↗

arXiv2019

dSECS: Including Variometers in Geomagnetic Field Interpolation

One of the best tools we have in geophysical modelling is the ability to interpolate the horizontal geomagnetic field at the surface of the Earth. This is especially useful in regions, such as southern Africa, where there is a sparse array of absolute magnetometers available for geomagnetic field measurements. In terms of geomagnetic field interpolation, the spherical elementary current systems (SECS) spatial interpolation scheme has shown to be very successful, and the planar approximation of this method adequate for modelling at mid-latitudes. The SECS interpolation scheme is physics based, making use of the Biot-Savart law and equivalent ionospheric currents to interpolate measured geomagnetic field data. As with most interpolation methods, more data points result in lower error. Therefore, we adapt the SECS method to work with variometers. These instruments measure variations in magnetic field and are more abundant in southern Africa. Merging the two resulting interpolated datasets, the initial absolute geomagnetic field interpolation can be significantly improved. This improved interpolation scheme is not only incredibly useful locally, where a sparse magnetometer array is a challenge, but can also be applied just as effectively in other cases across the globe where there are numerous magnetometers and variometers available.

physics.space-ph↗