Modeling geomagnetic induced currents in Australian power networks

Abstract

Geomagnetic induced currents (GICs) have been considered an issue for high‐latitude power networks for some decades. More recently, GICs have been observed and studied in power networks located in lower latitude regions. This paper presents the results of a model aimed at predicting and understanding the impact of geomagnetic storms on power networks in Australia, with particular focus on the Queensland and Tasmanian networks. The model incorporates a “geoelectric field” determined using a plane wave magnetic field incident on a uniform conducting Earth, and the network model developed by Lehtinen and Pirjola (1985). Model results for two intense geomagnetic storms of solar cycle 24 are compared with transformer neutral monitors at three locations within the Queensland network and one location within the Tasmanian network. The model is then used to assess the impacts of the superintense geomagnetic storm of 29–31 October 2003 on the flow of GICs within these networks. The model results show good correlation with the observations with coefficients ranging from 0.73 to 0.96 across the observing sites. For Queensland, modeled GIC magnitudes during the superstorm of 29–31 October 2003 exceed 40 A with the larger GICs occurring in the south‐east section of the network. Modeled GICs in Tasmania for the same storm do not exceed 30 A. The larger distance spans and general east‐west alignment of the southern section of the Queensland network, in conjunction with some relatively low branch resistance values, result in larger modeled GICs despite Queensland being a lower latitude network than Tasmania.

Modeling Geomagnetically Induced Currents in Australian Power Networks Using Different Conductivity Models

“…The impedance function for a uniform conducting Earth can be derived from equation and is given in Marshall et al () as …”

“…The assumption that the Earth is a uniform half‐space gives the equation of magnetotellurics (e.g., Cagniard, ; Pirjola, , ): where σ is a scalar conductivity for this case, μ o is the permeability of free space, ω is the angular frequency of the incident wavefield, E y is a function of − B x , E x is a function of B y , and x and y are the north and east directions, respectively. Uniform half‐space conductivity structures have been employed in the study of GIC in power networks in South Africa (Koen & Gaunt, ), Sweden (Pulkkinen et al, ), China (Liu et al, ), Spain (Torta et al, ), Australia (Marshall et al, , ), and New Zealand (Marshall et al, ).…”

“…The modeled 3DM tensors are compared with measured magnetotelluric (MT) tensors obtained from recently released data from the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP). The four different geoelectric field models are applied to a network model of the east Australian power system over four geomagnetic storms of solar cycle 24, the geomagnetic storms of 2 October 2013 and 23 June 2015 reported in Marshall et al () with the addition of 17 March 2013 and 8 September 2017 storms, and compared with observations from up to eight different locations within the network. The four different impedance models are assessed using several statistical performance parameters.…”

“…GIC measurements were recorded by monitoring equipment on the neutral lines of HV transformers at Bowen North, Middle Ridge, and Murarrie in the state of Queensland; Bayswater and Bannaby in the state of New South Wales; Para in the state of South Australia; and Chapel Street Hobart and George Town in the state of Tasmania (Figure ). Modeled GIC were determined using data provided by the Transmission Network Service Providers to extend the models presented in Marshall et al () to include the entire interconnected eastern Australia power system. The next section describes the modeling used in this paper in more detail.…”

Quantifying the Performance of Geomagnetically Induced Current Models

“…In order to provide additional information to the skill score P , we recommend its combined use with the correlation coefficient r as a minimal set of performance metrics. We note that it is still a relatively common practice to use the latter statistic alone to test the effectiveness of the model in predicting the data (e.g., Bailey et al, ; Butala et al, ; Mac Manus et al, ; Marshall et al, ; Matandirotya et al, ; Torta et al, ; Viljanen et al, ). However, r accounts neither for scaling factors nor for shifts between the two series, which is a clear weak point of it.…”