Estimation of 1 in 100-year values of the magnitude of the E-field | E | based on magnetic field measurements from the INTERMAGNET station at Hermanus over the period 2000 to 2012 and three different sets of surface impedance values derived from 1D conductivity models for Wolmaranstad (high resistivity), Grassridge (intermediate), and Loxton (low resistivity) in South Africa. 4.3 Estimation of 1 in 100-year E-field Levelsįig. The Planning Guide of the Geomagnetic Disturbance Task Force of the National Energy Regulator Commission (NERC) in the United Kingdom proposed an extreme E-field value of 8 V/km with a scaling factor for latitude and ground conductivity to be used as the extreme value for planning for mitigation of such events. extrapolated 30 years of digital data for Europe and estimated the maximum rate of change of the geomagnetic field in Europe to be in the range of 1500–4000 nT/min for 1 in 100 year returns and 2000–6000 nT/min for 1 in 200 year returns. Using Extreme Value Theory Thomson et al. The scaling was based on the only nonsaturated magnetic record of the storm in the form of 1-hour resolution data from the Colaba Magnetic Observatory in India. used a spectral analysis technique and a 1D conductivity profile in the United Kingdom and estimated the maximum electric field during the Carrington event to be on the order of 9 V/km. The most extreme geomagnetic storm on record, known as the 1859 Carrington event, was so intense that ground-based magnetometers were saturated at Greenwich and Kew in the United Kingdom. Hence, extreme events are usually characterized in terms of the expected extreme values of the geo-electric field (E-field). Measured GIC data is generally proprietary and location-dependent. Power utilities are interested in the levels and likelihood of extreme GIC events, to plan for the mitigation of extreme geomagnetic storms. Lotz and Cilliers developed a NN model to predict a geomagnetic field-derived proxy for GIC, namely the sum of the variation in the northward and eastward components of the geomagnetic field at Hermanus over a ten-minute period, using as inputs the IMF magnitude, solar wind speed, and fluctuations in solar wind particle density.
Cilliers, in Classical and Recent Aspects of Power System Optimization, 2018 4 Modeling Geomagnetic Storm Intensity From Solar Wind ParametersĪ combination of solar and interplanetary parameters has been used as inputs in Neural Network (NN) models to predict the occurrence and strength of GMDs with an occurrence probability of 87%. This will be the so-called IoT, leading an evolution of the grid and all devices connected to the grid to be more digital.ĭavid T.O. Examples include air conditioning, toasters, the inverter of rooftop solar PV systems, street lighting, EV battery chargers, etc. In the near future, we will see that every electrical device, both domestic and commercial, that is connected to the electricity network will have an Internet address. Power utilities will be able to solve these challenges by applying big data, cloud computing, and machine learning. Power utilities are facing many challenges, including increasing grid resilience, improving customer engagement, managing the operation of advanced metering infrastructure (AMI), preventing meter malfunctions, optimizing the maintenance of network assets, etc. They have aging infrastructure, continuous growth in demand, shifting load patterns, and supply more power using intermittent sources. Ahmad Zahedi, in Application of Smart Grid Technologies, 2018 2.3 Electricity networks worldwide face a number of challenges
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