Dynamic Modeling of Power Outages Caused by Thunderstorms

Thunderstorms are complex weather phenomena that cause substantial power outages in a short period. This makes thunderstorm outage prediction challenging using eventwise outage prediction models (OPMs), which summarize the storm dynamics over the entire course of the storm into a limited number of parameters. We developed a new, temporally sensitive outage prediction framework designed for models to learn the hourly dynamics of thunderstorm-caused outages directly from weather forecasts. Validation of several models built on this hour-by-hour prediction framework and comparison with a baseline model show abilities to accurately report temporal and storm-wide outage characteristics, which are vital for planning utility responses to storm-caused power grid damage

Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas

Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) can monitor power outages in disaster-affected areas through the identification of missing city lights. When combined with locally-relevant geospatial information, these observations can be used to estimate power outages, defined as geographic locations requiring manual intervention to restore power. In this study, we produced a power outage product based on Suomi-NPP VIIRS DNB observations to estimate power outages following Hurricane Sandy in 2012. This product, combined with known power outage data and ambient population estimates, was then used to predict power outages in a layered, feedforward neural network model. We believe this is the first attempt to synergistically combine such data sources to quantitatively estimate power outages. The VIIRS DNB power outage product was able to identify initial loss of light following Hurricane Sandy, as well as the gradual restoration of electrical power. The neural network model predicted power outages with reasonable spatial accuracy, achieving Pearson coefficients (r) between 0.48 and 0.58 across all folds. Our results show promise for producing a continental United States (CONUS)- or global-scale power outage monitoring network using satellite imagery and locally-relevant geospatial data

Evaluation of a semi-automated approach for FDG PET image analysis for routine clinical application in patients with multiple myeloma

Background: FDG PET/CT is a tool for assessing response to therapy in various cancers, and may provide an earlier biomarker of clinical response. We developed a novel semi-automated approach for analyzing FDG PET/CT images in patients with multiple myeloma (MM) to standardize FDG PET application. Methods: Patients (n = 8) with relapsed/refractory MM from the Phase 2 study (NCT02899052) of venetoclax plus carfilzomib and dexamethasone underwent FDG PET/CT at baseline and up to two timepoints during treatment. Images were processed using an established automated segmentation algorithm, with the modification that a red marrow region in an unaffected lumbar vertebra was used to define background standardized uptake value normalized to lean body mass (SUL) threshold above which uptake was considered disease-specific uptake. This approach was compared to lesion segmentation, and to International Myeloma Working Group (IMWG) response criteria, including minimal residual disease (MRD). Results: The two FDG PET analysis techniques agreed on evaluation of patient-level SULpeak for 67% of scans. In the metabolic response assessment per PET Response Criteria in Solid Tumors (PERCIST), the two techniques agreed in 75% of patients. Differences between techniques occurred in low-uptake lesions due to greater reader sensitivity to lesions with uptake marginally above background. PERCIST outcomes were generally in agreement with IMWC and MRD. Conclusions: This semi-automated analysis was in high agreement with standard approaches for detecting response to MM therapy. This proof-of-concept study suggests that larger studies should be conducted to confirm how FDG PET analysis may aid early response detection in MM

A comprehensive review of reactive power control strategies for three phase grid connected photovoltaic systems with low voltage ride through capability

Photovoltaic (PV) sources have recently become one of the most mature technologies. With the increasing penetration level and the integration of a PV system into the grid, the stability and reliability of the power networks have been serious concerns. Thus, grid codes are being released by grid operators for low voltage networks under grid faults. Consequently, the Low Voltage Ride Through (LVRT) capability of the grid connected PV system became the most important issue related to grid codes, i.e., more reactive power is injected into the grid during voltage disturbances. In this paper, a comprehensive review of reactive power control strategies for the three-phase PV system has been analyzed to support the grid during voltage sags by providing LVRT capability. The control techniques have been classified into three main categories: Fixed power factor, constant active power control, and constant reactive power control. The results illustrate that the stability of the system is improved when two control techniques are simultaneously implemented. This paper concludes that further research must be carried on LVRT control techniques for the reactive power injection during unbalanced voltage sags