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Current efforts to assess risk to the power grid from geomagnetic disturbances (GMDs) that result in geomagnetically induced currents (GICs) seek to identify potential "hotspots," based on statistical models of GMD storm scenarios and power distribution grounding models that assume that the electrical conductivity of the Earth's crust and mantle varies only with depth. The NSF-supported EarthScope Magnetotelluric (MT) Program operated by Oregon State University has mapped 3-D ground electrical conductivity structure across more than half of the continental US. MT data, the naturally occurring time variations in the Earth’s vector electric and magnetic fields at ground level, are used to determine the MT impedance tensor for each site (the ratio of horizontal vector electric and magnetic fields at ground level expressed as a complex-valued frequency domain quantity). The impedance provides information on the 3-D electrical conductivity structure of the Earth’s crust and mantle. We demonstrate that use of 3-D ground conductivity information significantly improves the fidelity of GIC predictions over existing 1-D approaches. We project real-time magnetic field data streams from US Geological Survey magnetic observatories into a set of linear filters that employ the impedance data and that generate estimates of ground level electric fields at the locations of MT stations. The resulting ground electric fields are projected to and integrated along the path of power transmission lines. This serves as inputs to power flow models that represent the power transmission grid, yielding a time-varying set of quasi-real-time estimates of reactive power loss at the power transformers that are critical infrastructure for power distribution. We demonstrate that peak reactive power loss and hence peak risk for transformer damage from GICs does not necessarily occur during peak GMD storm times, but rather depends on the time-evolution of the polarization of the GMD’s inducing fields and the complex ground (3-D) electric field response, and the resulting alignment of the ground electric fields with the power transmission line paths. This is informing our efforts to provide a set of real-time tools for power grid operators to use in mitigating damage from space weather events.
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Fusing Model-Driven and Data-Driven Approaches for GMD Mitigation
The operation of the electric power grid is foundational to the health, safety, and economic well-being of the nation, yet it is increasingly fragile and exposed to risk from exogenous factors. When power disruptions are widespread, prolonged, or impact critical services, the consequences can be grave. GMDs) can impact electric power transmission grids through premature ageing and transformer failure, which can lead to cascading failures and extended power disruptions. Geomagnetically induced currents (GICs) arising from geomagnetic disturbances (GMD mitigation poses a challenging problem to grid operators due to the nature of its impact. Space weather events arising from solar coronal mass ejections (CMEs) that intersect Earth's orbit occur on a continuum of timescales and levels of severity. Moderately sized CMEs, such as the 1989 event that lead to the failure of the HydroQuebec system, illustrate the risk to the power grid. Even larger space weather events that have the potential for profound impacts and prolonged power disruptions on a continental scale are thought to happen approximately storm has occurred during the existence of electric infrastructure. At the other end of the severity spectrum, recent evidence has shown that GICs flow at low levels continuously on the grid even in the every one hundred years; however, no such absence of a solar storm. This behavior may cause eventual, but slow to manifest breakdown of grid assets misattributed to non-GMD causes. In either case, it is difficult for utilities to justify the costly installation of sensors and telemetry to monitor this phenomenon. This paper details a system to augment human operators with two new abilities—(1) the real-time prediction of GICs flowing on their system and (2) the real-time monitoring of GMD grid manifestations—without installing new sensors. The system will do this by fusing a “top-down” approach using physics-based modeling driven by detailed 3-D Earth conductivity measurements and real-time magnetic observatory data with a “bottom-up” approach using artificial intelligence techniques driven by synchrophasor data. This hybrid methodology will enable utility operators to identify the best strategies to modify grid voltages and topology to mitigate damage and deal with a changing federal regulatory framework that requires GMD monitoring and mitigation efforts.
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- Award ID(s):
- 1720175
- PAR ID:
- 10513341
- Publisher / Repository:
- https://cigre-usnc.org/wp-content/uploads/2017/10/C4_Murphy.pdf
- Date Published:
- Journal Name:
- CIGRE US National Committee 2017 Grid of the Future Symposium
- Format(s):
- Medium: X
- Location:
- Paris, France.
- Sponsoring Org:
- National Science Foundation
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