4.4 Article

Calculation of GIC in the North Island of New Zealand Using MT Data and Thin-Sheet Modeling

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1029/2020SW002580

Keywords

GIC; MT; thin‐ sheet modeling; New Zealand

Funding

  1. New Zealand Ministry of Business, Innovation and Employment Hazards and Infrastructure Research Fund [UOOX1502]
  2. Transpower New Zealand
  3. New Zealand Ministry of Business, Innovation & Employment (MBIE) [UOOX1502] Funding Source: New Zealand Ministry of Business, Innovation & Employment (MBIE)

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Geomagnetically induced currents (GICs) in the North Island New Zealand power transmission network during two large magnetic storms are calculated from both magnetotelluric (MT) data and a thin-sheet conductance model of New Zealand previously used to study GIC in the South Island. We focus on the 2015 St. Patrick's Day magnetic storm and the storm of 20 November 2003. Lack of MT data in the northwestern part of the Island means that the transmission network in this region is represented by an equivalent circuit. Lack of GIC observations in the North Island means that results cannot be directly compared with measured GIC. However, our calculation of GIC shows that substations and individual transformers in the lower part of the Island with significant currents are generally the same as those where total harmonic distortion has been observed during periods of enhanced geomagnetic activity. MT data in the period range 2-30 min are used to predict GIC associated with the sudden storm commencement and rapid variations in the magnetic field. In contrast, the thin-sheet modeling approach shows that GIC may be expected to occur in conjunction with longer-period variations. Calculations for the 2003 storm suggest that at some locations GIC in excess of 10 A may persist for long periods of time and may produce significant harmonic distortion which could lead to localized transformer heating. It is concluded that despite its relatively low latitude the North Island power network is potentially at risk from significant GIC during extreme storms.

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