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Geomagnetically-induced current (GIC) due to space weather can flow in the power grid causing undesirable effects such as transformer overheating, misoperation of protection devices, and potential blackouts. It is therefore important to monitor GIC in the power grid to improve online situational awareness and decision-making of system operators during a geomagnetic disturbance. To avoid the costly installation of GIC monitors at transformers’ neutrals, it is desirable to find correlations between GIC and already-monitored parameters. Hence, this work proposed the use of a convolutional neural network (CNN) to compute GIC amplitudes from learned patterns in the time-series data of transformer even harmonic currents. Using an electromagnetic transient program, GIC injection simulations were performed for a modeled Dominion Energy Virginia (DEV) substation with two 504 MVA, 500/230 kV transformers. Data collected from these offline simulations were used to train the CNN to provide online GIC monitoring. Testing the CNN performance involved using real GIC measurements from published literature and from a physical GIC monitor in the DEV area. The results showed that the proposed method was able to provide GIC readings with a root mean squared error of 1.56 A/phase (equivalent to an average accuracy of 94%) for these real GIC waveforms.

The electrical power grid is a critical infrastructure, with disruptions in transmission having severe repercussions on daily activities, across multiple sectors. To identify, prevent, and mitigate such events, power grids are being refurbished as ‘smart’ systems that include the widespread deployment of GPS-enabled phasor measurement units (PMUs). PMUs provide fast, precise, and time-synchronized measurements of voltage and current, enabling real-time wide-area monitoring and control. However, the potential benefits of PMUs, for analyzing grid events like abnormal power oscillations and load fluctuations, are hindered by the fact that these sensors produce large, concurrent volumes of noisy data. In this paper, we describe working with power grid engineers to investigate how this problem can be addressed from a visual analytics perspective. As a result, we have developed PMU Tracker, an event localization tool that supports power grid operators in visually analyzing and identifying power grid events and tracking their propagation through the power grid's network. As a part of the PMU Tracker interface, we develop a novel visualization technique which we term an epicentric cluster dendrogram, which allows operators to analyze the effects of an event as it propagates outwards from a source location. We robustly validate PMU Tracker with: (1) a usage scenario demonstrating how PMU Tracker can be used to analyze anomalous grid events, and (2) case studies with power grid operators using a real-world interconnection dataset. Our results indicate that PMU Tracker effectively supports the analysis of power grid events; we also demonstrate and discuss how PMU Tracker's visual analytics approach can be generalized to other domains composed of time-varying networks with epicentric event characteristics.

Electric transmission power grids are being revamped with the widespread deployment of GPS-enabled phasor measurement units (PMUs) for real-time wide-area monitoring and control via precise, time-synchronized measurements of voltage and current. Large, concurrently produced volumes of noisy data hinder PMU usability, particularly for the analysis of power oscillation and load fluctuation events in the grid. We examine visualization challenges for events in the electric power grid and develop PMUVis, a visualization platform that supports scalable analysis of grid network topology and anomalous events in near time. PMUVis incorporates a novel FFT-based approach over raw and temporally aggregated data to examine oscillation event propagation through the grid network. We validate PMUVis with expert reviews and a case study and discuss how visualization can be leveraged to enhance real-time spatiotemporal grid analysis by advancing operator capabilities.

Loss of circulating 17β-estradiol (E2) that occurs during menopause can have detrimental effects on cognitive function. The efficacy of hormone replacement therapy declines as women become farther removed from the menopausal transition, yet the molecular mechanisms underlying this age-related switch in E2 efficacy are unknown. We hypothesized that aging and varying lengths of E2 deprivation alters the ratio of alternatively spliced estrogen receptor (ER)β isoforms in the brain of female rats. Further, we tested whether changes in global transcriptional activity and splicing kinetics regulate the alternative splicing of ERβ. Our results revealed brain region-specific changes in ERβ alternative splicing in both aging and E2-deprivation paradigms and showed that ERβ could mediate E2-induced alternative splicing. Global transcriptional activity, as measured by phosphorylated RNA polymerase II, was also regulated by age and E2 in specific brain regions. Finally, we show that inhibition of topoisomerase I resulted in increased ERβ2 splice variant expression.

Recent clinical evidence suggests that the neuroprotective and beneficial effects of hormone therapy may be limited by factors related to age and reproductive status. The patient's age and length of time without circulating ovarian hormones are likely to be key factors in the specific neurological outcomes of hormone therapy. However, the mechanisms underlying age-related changes in hormone efficacy have not been determined. We hypothesized that there are intrinsic changes in estrogen receptor β (ERβ) function that determine its ability to mediate the actions of 17β-estradiol (E2) in brain regions such as the ventral hippocampus. In this study, we identified and quantified a subset of ERβ protein interactions in the ventral hippocampus that were significantly altered by E2 replacement in young and aged animals, using two-dimensional differential gel electrophoresis coupled with liquid chromatography–electrospray ionization–tandem mass spectrometry. This study demonstrates quantitative changes in ERβ protein–protein interactions with E2 replacement that are dependent upon age in the ventral hippocampus and how these changes could alter processes such as transcriptional regulation. Thus, our data provide evidence that changes in ERβ protein interactions are a potential mechanism for age-related changes in E2 responsiveness in the brain after menopause.

The effect of thermal plant response on system frequency control is investigated in the paper using the results of computer simulation studies. Particularly, when islanding conditions for loss of generation or loss of heavy import are involved, the frequency deviation is largely influenced by the prime mover energy supply system as well as by the governing characteristics. As the response time frame of large steam units is on the order of minutes, the problem of the evaluation of grid frequency behaviour is cast in the so-called mid-term period. Consequently, interactions between the electrical grid and boiler turbine units are here evaluated on the basis of a mid-term modelling of the overall system. The time response of the system frequency and of some typical thermal variables are simulated for different operating modes of boiler turbine controls; the trends of such variables are correlated and compared in order to give an insight of the effects of the thermal dynamics on the frequency behaviour of an isolated power system.

The stability of a generator following a sudden power imbalance, caused either by a short circuit or a load rejection, may be significantly increased adopting particular techniques of control of the turbine valves called "fast valving" (also "early valve actuation"). The control law is normally pre-defined on the bases of simulation studies of the power system dynamics, and may sometimes not match the precise system requirements in the real faulted conditions. A new control logic is proposed in the paper, based only on on-line measured variables, and probably better matching the system needs; this logic is called "Closed-Loop Fast Valving" (CLFV).The effectiveness of the CLFV is tested by means of computer simulations making use of the EMTP code.