A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning

We present a novel algorithm that predicts the probability that the time derivative of the horizontal component of the ground magnetic field $dB/dt$ exceeds a specified threshold at a given location. This quantity provides important information that is physically relevant to Geomagnetically Induced Currents (GIC), which are electric currents { associated to} sudden changes in the Earth's magnetic field due to Space Weather events. The model follows a 'gray-box' approach by combining the output of a physics-based model with machine learning. Specifically, we combine the University of Michigan's Geospace model that is operational at the NOAA Space Weather Prediction Center, with a boosted ensemble of classification trees. We discuss the problem of re-calibrating the output of the decision tree to obtain reliable probabilities. The performance of the model is assessed by typical metrics for probabilistic forecasts: Probability of Detection and False Detection, True Skill Statistic, Heidke Skill Score, and Receiver Operating Characteristic curve. We show that the ML enhanced algorithm consistently improves all the metrics considered.Comment: under revie

On the regional variability of dB/dt and its significance to GIC

Faraday's law of induction is responsible for setting up a geoelectric field due to the variations in the geomagnetic field caused by ionospheric currents. This drives geomagnetically induced currents (GICs) which flow in large ground‐based technological infrastructure such as high‐voltage power lines. The geoelectric field is often a localized phenomenon exhibiting significant variations over spatial scales of only hundreds of kilometers. This is due to the complex spatiotemporal behavior of electrical currents flowing in the ionosphere and/or large gradients in the ground conductivity due to highly structured local geological properties. Over some regions, and during large storms, both of these effects become significant. In this study, we quantify the regional variability of dB/dt using closely placed IMAGE stations in northern Fennoscandia. The dependency between regional variability, solar wind conditions, and geomagnetic indices are also investigated. Finally, we assess the significance of spatial geomagnetic variations to modeling GICs across a transmission line. Key results from this study are as follows: (1) Regional geomagnetic disturbances are important in modeling GIC during strong storms; (2) dB/dt can vary by several times up to a factor of three compared to the spatial average; (3) dB/dt and its regional variation is coupled to the energy deposited into the magnetosphere; and (4) regional variability can be more accurately captured and predicted from a local index as opposed to a global one. These results demonstrate the need for denser magnetometer networks at high latitudes where transmission lines extending hundreds of kilometers are present

Progress in space weather modeling in an operational environment

This paper aims at providing an overview of latest advances in space weather modeling in an operational environment in Europe, including both the introduction of new models and improvements to existing codes and algorithms that address the broad range of space weather’s prediction requirements from the Sun to the Earth. For each case, we consider the model’s input data, the output parameters, products or services, its operational status, and whether it is supported by validation results, in order to build a solid basis for future developments. This work is the output of the Sub Group 1.3 ‘‘Improvement of operational models’’ of the European Cooperation in Science and Technology (COST) Action ES0803 ‘‘Developing Space Weather Products and services in Europe’’ and therefore this review focuses on the progress achieved by European research teams involved in the action

High-performance computing for smart grid analysis and optimization

The smart grid leverages a variety of advanced technologies, including smart metering, smart equipment, communication and control technologies, renewable energy sources, and machine learning, to improve the efficiency and reliability of existing electric power systems. The efficiency and reliability of power systems are of considerably importance to economic and environmental health in this new era. However, there are significant challenges for modernizing the power grids and accomplishing the vision of the smart grid. This dissertation presents a variety of optimization techniques that solve several key challenges in the smart grid and improve its efficiency and reliability. Optimal power flow (OPF) plays an important role in power system operation. The emerging smart grid aims to create an automated energy delivery system that enables two-way flows of electricity and information. As a result, it will be desirable if OPF can be solved in real time in order to allow the implementation of the time-sensitive applications such as real-time pricing. We develop a novel method, the fast OPF algorithm, to accelerate the computation of alternating current optimal power flow (ACOPF). We first construct and solve an equivalent OPF problem for an equivalent reduced system. Then, a distributed algorithm is developed to retrieve the optimal solution for the original power system. Experimental results show that for a large power system, our method achieves 7.01X speedup over ACOPF with only 1.72% error, and is 75.7% more accurate than the DCOPF solution. The experimental results demonstrate the unique strength of the proposed technique for fast, scalable, and accurate OPF computation. With the integration of intermittent renewable energy sources and demand response in the smart grid, there is increasing uncertainty involved in the traditional OPF problem. Therefore, probabilistic optimal power flow (POPF) analysis is required to accomplish the electrical and economic operational goals. We propose a novel method, the ClusRed algorithm, to accelerate the computation of POPF for large-scale smart grid through clustering and network reduction (NR). A cumulant-based method and Gram-Charlier expansion theory are used to efficiently obtain the statistics of system states. We also develop a more accurate linear mapping method to compute the unknown cumulants. ClusRed can speed up the computation by up to 4.57X and can improve accuracy by about 30% when Hessian matrix is ill-conditioned compared to the previous approach. Aside from improving the efficiency and reliability of power grids through addressing OPF related problems, we also study geomagnetic disturbances (GMDs) and how to mitigate their threat to the reliability of power grids. Geomagnetically induced currents (GICs) introduced by GMDs can damage transformers, increase reactive power losses and cause reliability issues in power systems. Finding an optimal strategy to place blocking devices (BDs) at transformer neutrals is essential to mitigating the negative impact of GICs. We develop a branch and cut (BC) based method and demonstrate that the BC method can provide optimal solutions to OBP problems. Furthermore, to practically solve the OBP problem, it is also important to account for the potential impact of BD placement on neighboring interconnected systems, solve the case where per-transformer GIC constraint exists and take the time-varying nature of the geoelectric field into consideration. In addition, the combined complexity of solving the OBP problem on a large-scale system poses a big computational challenge. However, together with other existing methods, the BC method cannot address the above issues well due to its algorithmic limitations. We then develop a simulated annealing (SA) based algorithm that, for the first time, can achieve near-optimal solutions for OBP problems for the above scenarios at a reduced computational complexity. More importantly, the SA method provides a comprehensive framework that can be used to solve various OBP problems, with different objective functions and constraints. We demonstrate the effectiveness and efficiency of our BC and SA methods using power systems of various sizes. In addition to natural disasters, in the era of internet of things, cybersecurity is of growing concern to power industries. Malicious cyberbehaviors and technologies that used to challenge security in areas unrelated to power systems, such as information integrity or privacy, have suddenly started to endanger the safety of large-scale smart grids. In particular, short-term load forecasting (STLF) is one of many aspects that are subject to these attacks. STLF systems have demonstrated high accuracy and have been widely employed for commercial use. However, classic load forecasting systems, which are based on statistical methods, are vulnerable to training data poisoning. We build and implement a first-of-its-kind data poisoning strategy that is effective at corrupting the forecasting model even in the presence of outlier detection. Our method applies to several forecasting models, including the most widely adapted and best-performing ones, such as multiple linear regression (MLR) and neural network (NN) models. Starting with the MLR model, we develop a novel closed-form solution that enables us to quickly estimate the new MLR model after a round of data poisoning without retraining. We then employ line search and simulated annealing to find the poisoning attack solution. Furthermore, we use the MLR attacking solution to generate a numerical solution for other models, such as NN. The effectiveness of our algorithm has been demonstrated on the Global Energy Forecasting Competition (GEFCom2012) data set with the presence of outlier detection

Progress in space weather modeling in an operational environment

YesThis paper aims at providing an overview of latest advances in space weather modeling in an operational environment in Europe, including both the introduction of new models and improvements to existing codes and algorithms that address the broad range of space weather's prediction requirements from the Sun to the Earth. For each case, we consider the model's input data, the output parameters, products or services, its operational status, and whether it is supported by validation results, in order to build a solid basis for future developments. This work is the output of the Sub Group 1.3 "Improvement of operational models'' of the European Cooperation in Science and Technology (COST) Action ES0803 "Developing Space Weather Products and services in Europe'' and therefore this review focuses on the progress achieved by European research teams involved in the action

Findings on the October Effect

Very Low Frequency (VLF) radio signals provide a unique possibility of continuously monitoring the lower ionosphere and their dynamics since these signals are reflected at the ionospheric D region between 60-90 km. Recent investigations have shown a very sharp decrease in signal amplitude at the beginning of October which deviates from the actual symmetric course of solar zenith angle variation over the year. The effect is developed differently depending on latitude, longitude and frequency, as we will present. In investigation for the cause of this phenomenon, first comparisons suggest a close correlation with the sudden reversal from easterly to westerly zonal flow, the asymmetric peak in semidiurnal solar tide S2, and the progression of the lower mesospheric temperature. Independent of the solar zenith angle mostly in high latitudes, a strong warming of the lower mesosphere during fall can be observed, confirming dominating atmospheric inner dynamics. Further studies are ongoing

Development of a finite element matrix (fem)three-phase three-limb transformer model for Geomagnetically Induced Currents (GIC) experiments

Geomagnetically Induced Currents (GIC) have been a growing concern within power system operators and researchers as they have been widely reported to lead to power system related issues and material damage to system components like power transformers. In power transformers, GIC impacts are evidenced by part-wave saturation, resulting in transformers experiencing increased presence of odd and even harmonics. The three-phase three-limb (3p3L) transformer has been found to be the most tolerant to high dc values compared to other core types. The research was based on a hypothesis which reads “transformer laboratory testing results can be used as a guide towards developing suitable Finite Element Matrix (FEM) models to be used for conducting GIC/DC experiments”. This study thus investigates the response of a 15 kVA 3p3L laboratory transformer to dc current, emulating the effects of GICs. GIC and dc current are the same under steady state conditions, and hence mentioned interchangeably. Laboratory tests conducted identified two critical saturation points when the transformer is exposed to dc. The early saturation point was identified to be at around 1.8 A/phase of dc (18% of rated current), while the deep saturation point was at around 15 to 20 A/phase of dc (about 72% of rated current). Further analysis showed that holes drilled on the transformer can lower the transformer knee-point by about 26%, depending on the size and location of the holes. The holes hence end up affecting the operating point of the transformer due to losses occurring around the holes. A transformer FEM model was developed following the laboratory exercise, where it was concluded that a 2D model leads to grossly erroneous results, distorting the magnetizing current by about 60% compared to the laboratory results. A solid 3D model improved performance by about 30% as it took the transformer's topological structure into consideration. The 3D model was then refined further to include joints and laminations. It was discovered that laminations on the transformer need to be introduced as stacks of the core, with each core step split into two, allocating a 4% air gap space between stacks. Refinement of the T-joints proved that the joints have a relatively high influence on the transformer behaviour, with their detailed refinement improving the transformer behaviour by about 60%. The final FEM model was used for dc experiments. The results of such experiments showed close resemblance to the laboratory results, with saturation points identified in FEM lying within 10% of the laboratory identified saturation points. Overall, the various investigation methods explored showed that the hypothesis was satisfactorily proven true. Laboratory results functioned as a guide in developing the model, offering a reference case

Considerations for Real Time Data Analysis Using Multiple Magnetometer Sources for GIC Studies to Improve the Situational Awareness of an Electric Grid Model

This thesis aims to effectively utilize actual historical and real-time data to enhance electric grid model knowledge by reconstructing GMD scenarios. Magnetic and electric field data used for the analysis are generated and calculated from Texas A&M Magnetometer Network (TAMUMN). Days of GMD activity (G2, G1) during the past year are selected to reconstruct events with similar data on a synthetically developed version of the Texas electric grid with 7000 bus network. Upon integrating the data to a simulated power system model, the impact of geomagnetically induced currents (GIC) can be determined. By performing certain power system analysis techniques, the most affected regions, magnitudes of maximum current at substations and the transmission lines with the highest activity can be obtained. This is greatly useful for planning purposes and studies as it directs the user to focus on a specific section of the grid model and works toward strengthening it

Risk Management for the Impacts of Coronal Mass Ejections, Electromagnetic Pulse Threats and Climate-Related Weather Events on Power Cables Supporting the Potential of Offshore Wind Energy Off of the Northern East Coast of the United States

The offshore wind industry is in the midst of its technology breakthrough phase, as humankind has experienced recently in other technology breakthroughs such as the internet and the smart phone. Wind turbine capability is growing considerably every few years, and infrastructure is in rapid advancement in hopes to barely keep up. As the offshore wind industry has announced the technology of a 15 MW turbine, a 525 KV HVDC subsea transmission cable has also made its debut, reflecting a capability to support 2 GW, more than 130 of these new turbines. Offshore Wind power has very positive risk benefits. It has been in a steady decline in price possibly supporting economic feasibility, while also growing in potential for abundance, where it could provide as a solid improvement in CO2 emissions from our power grid. However, there are negative risks that the supportive infrastructure will face. The coastal areas are more susceptible to Earth’s weather disasters, mainly hurricanes and tropical storms, and findings also suggest a higher susceptibility to solar weather caused by our sun. When it comes to addressing these risks and assuring a reliable, resilient power source to the public, there are politics involved. Offshore Wind Energy is new to the energy mix of the United States. As a result, it may face challenges if policy does not maintain an accurate evaluation of these risks into policy. This study investigates how Offshore Wind Energy in the North Atlantic offshore region of the United States will be affected by recent policy mitigating geomagnetic storm impacts, by analyzing its resiliency to the risk within thresholds presented to and enacted by congress. Findings suggest that future Offshore Wind Energy farms and grid resiliency efforts will benefit from early planning and collaboration efforts, especially in the use of larger cable sizes and HVDC power infrastructure. Conclusive results also suggest key future partnering opportunities in policy to address risks from solar weather events and climate-related events