Quantifying the Performance of Geomagnetically Induced Current Models

Marsal, Santiago; Torta, J. M.

doi:10.1029/2019sw002208

Cited by 10 publications

(15 citation statements)

References 34 publications

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“…Note also that the 400 kV circuit line through which the greatest current flows (connecting Mudarra substation to the North-West), in fact, includes three power lines. the performance parameter, P′ (see Marsal & Torta, 2019), the former goes from 0.83 to an almost perfect correlation of 0.97; while the latter goes from 0.44 to 0.70.…”

Section: New Gic Model Resultsmentioning

confidence: 99%

New Detailed Modeling of GICs in the Spanish Power Transmission Grid

et al. 2021

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 Improvements are achieved by using a 3D inversion of MT data to derive the electric field and adding the 220 kV level to the network model  More accurate GIC estimates for all power transmission lines, nodes and transformers are provided  The system experiences a reduction in GICs flowing to ground, but at the transformer winding level they exhibit, in general, an augmentation Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

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Section: New Gic Model Resultsmentioning

confidence: 99%

New Detailed Modeling of GICs in the Spanish Power Transmission Grid

et al. 2021

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“…To evaluate the agreement between the DMM‐derived and modeled GIC, a combination of the linear correlation coefficient,

ρ

, and the performance parameter,

P'

, between both time series has been used (see Marsal & Torta, 2019).

P'

is defined as

P^{'} = 1 - \frac{σ_{o - m}}{σ_{o}},

where

σ_{o}

is the standard deviation of the observation series (i.e., the GIC derived following the exposed methodology), and

σ_{o - m}

is the standard deviation of the differences between the observations and the model.…”

Section: Resultsmentioning

confidence: 99%

“…To evaluate the agreement between the DMM-derived and modeled GIC, a combination of the linear correlation coefficient,  E , and the performance parameter,  E P , between both time series has been used (see Marsal & Torta, 2019).  E P is defined as…”

Section: Resultsmentioning

confidence: 99%

Validating GIC Modeling in the Spanish Power Grid by Differential Magnetometry

et al. 2021

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Disturbed conditions in the Sun are potential sources of events of strongly enhanced dissipation of solar wind energy in the near-Earth space environment, also known as Space Weather events, which manifest themselves as varying electric currents flowing in the coupled magnetosphere -ionosphere system. These events trigger numerous and diverse phenomena both in the surface and in the space environment of our planet. One of the consequences of such variable currents is a varying electromagnetic field on the ground that, in the presence of the conductive earth, induces an electric current in the subsurface. This, in turn, produces a secondary electromagnetic field that is superposed to the incident one, giving rise to the observed geoelectric and geomagnetic fields. Geomagnetically induced currents (GIC) are electrical currents induced in earthed conductors in response to geoelectric fields ultimately resulting from space weather events. Electric-power transmission grids and pipelines are commonly affected negatively by GIC, potentially causing problems such as damaged power transformers and

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“…In order to numerically quantify the similarity between two ECF patterns, we have used the performance parameter defined in Marsal and Torta (2019) (see also Bailey et al, 2018; Blake et al, 2018; Ingham & Rodger, 2018; Marsal, 2015; Torta et al, 2017):

P = 1 - \frac{{italicRMSE}_{ECFm, ECFo}}{σ_{italicECFm}} = 1 - \sqrt{\frac{\overset{{((), {ECF}_{m} - {ECF}_{o})}^{2}}{true}}{\overset{true¯}{{italicECF}_{m}^{2}} - {\overset{{italicECF}_{m}}{true}}^{2}}},

where ECF m and ECF o denote the two spatial functions to be compared at a given time, playing ECF m the role of model and ECF o the role of objective ; RMSE ECFm , ECFo stands for the root mean square error between them; the bar on top of a variable indicates its mean; and σ ECFm represents the standard deviation of ECF m . The RMSE is thus contextualized by the intrinsic spatial variability of the model ECF, whose role is assumed either by the pattern of step 1 or that of step 2.…”

Section: Resultsmentioning

confidence: 99%

Including the Temporal Dimension in the SECS Technique

et al. 2020

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The equivalent source method of Spherical Elementary Current Systems (SECS) has contributed valuable results for spatial magnetic interpolation purposes where no observations are available, as well as for modeling equivalent currents both in the ionosphere and in the subsurface, thus providing a separation between external and internal sources. It has been successfully applied to numerous Space Weather (SW) events, whereas some advantages have been reported over other techniques such as Fourier or Spherical (Cap) Harmonic Analysis. Although different modalities of SECS exist (either 1-D, 2-D, or 3-D) depending on the number of space dimensions involved, the method provides a sequence of instantaneous pictures of the source current. We present an extension of SECS consisting in the introduction of a temporal dependence in the formulation based on a cubic B-splines expansion. The technique thus adds one dimension, becoming 4-D in general (e.g., 3-D + t), and its application is envisaged for, though not restricted to, the analysis of past events including heterogeneous geomagnetic data sets, such as those containing gaps, different sampling rates or diverse data sources. A synthetic model based on the SW Modeling Framework is used to show the efficacy of the extended scheme. We apply this method to characterize the current systems of past and significant SW events producing geomagnetically induced currents, which we exemplify with an outstanding geomagnetic sudden commencement occurred on 24 March 1991. Plain Language Summary Spherical Elementary Current Systems is a mathematical technique that allocates an electrical current system as the source of the geomagnetic perturbations typically recorded by ground magnetometers. Such a current system is assumed to flow on a sheet at a certain height/depth from the surface. Traditionally, Spherical Elementary Current Systems is a purely spatial technique, which is appropriate if we rely on homogeneous time series at each observation site. Otherwise, the information is typically concentrated at certain timestamps and sparse at others, resulting in uneven source currents being modeled. We have extended the traditional technique by including a time expansion, thus allowing spatial information to be shared across time. This produces a smoother, more realistic equivalent source current, as shown by comparing both techniques with the results of a synthetic model. We have also applied the extended method to an outstanding geomagnetic sudden commencement occurred on 24 March 1991.

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Quantifying the Performance of Geomagnetically Induced Current Models

Cited by 10 publications

References 34 publications

New Detailed Modeling of GICs in the Spanish Power Transmission Grid

New Detailed Modeling of GICs in the Spanish Power Transmission Grid

Validating GIC Modeling in the Spanish Power Grid by Differential Magnetometry

Including the Temporal Dimension in the SECS Technique

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