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Enhancing Geomagnetic Disturbance Modeling for Power Grids: Investigating the Effects of Spatial Variation and Exploring Advanced Data Techniques
Abstract
Solar activity can cause geomagnetic disturbances (GMDs) that give rise to geomagnetically induced currents (GICs) which can compromise the reliability of the electric grid. The primary goal of this thesis is to investigate how models can be improved to better represent the behavior of GMDs and their interaction with the power grid.
In the first chapter, the impact of incorporating spatially varying magnetic fields into surface electric field models on GMD risk metrics are examined, comparing these against spatially independent magnetic field models. To perform this analysis, the earth’s magnetic field disturbances are transformed into surface electric fields using the one-dimensional earth conductivity model. Then, the modeling impact of these electric fields is studied using a 2,000-bus grid for Texas and a 25,000-bus grid for the northeast and mid-Atlantic regions of the United States. Simulation results reveal that the inclusion of spatially varying magnetic fields results in considerable differences in GMD risk metrics, highlighting the importance of accounting for spatial variability when assessing GMD risks in the power system.
In the second chapter, the use of machine learning for generating "synthetic" geomagnetic field data are explored. Specifically, the application and preliminary results of a modified form of the generative adversarial network to create time-series synthetic geomagnetic field data of three different severities are described. A key challenge to GMD studies is the scarcity of severe geomagnetic field data available to researchers due to the low frequency of such events. This study is motivated by the need for more data that captures the behavior of geomagnetic field fluctuations caused by severe GMDs. The scope of this study is based on recent, more mild, GMD events. However, future studies should seek to expand this work to generate data that better captures the behavior of more severe GMD storms.
Subject
power systemgeomagnetic disturbances
geomagnetically induced currents
machine learning
neural networks
generative adversarial networks
Citation
Zhang, Anna (2023). Enhancing Geomagnetic Disturbance Modeling for Power Grids: Investigating the Effects of Spatial Variation and Exploring Advanced Data Techniques. Master's thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /199873.