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DOE OSTI2021

Optimized Dispatch of Distributed Energy Resources for Resiliency and Power Quality Improvements at the Grid-Edge

Distributed Energy Resources (DERs) installed on low-voltage distribution systems, both utility-owned and nonutility owned may be employed to make improvements to system resiliency and power quality, in addition to simply serving load demand. By selectively dispatching active and reactive power from these existing DERs, improvements in voltage profiles and service availability during adverse events may be achieved. Further benefits can be gained by optimally choosing which DERs to dispatch power from, and the distribution of power from those DERs. For a given operating condition, using Linear Programming methods, optimal power dispatch values for individual DERs can be determined, while considering variables such as voltage gains, losses in the lines and the DERs, and DER reserve capacities. One such optimization technique is developed in this paper, and results from a real-time simulation study on a modified IEEE 13-bus distribution system with three DERs, are being presented. The operation of the optimized DER selection and dispatch algorithm is shown during normal operation of the transmission system when the DERs operate in grid-connected mode. Similar analyses can drive decision making during transmission system failure when the DERs operate in grid-forming or islanded mode.

Chowdhury, Prithwiraj Roy↗

DOE OSTI2025

The Nature of Standing Plasma Waves Following Fast Breakdown in Thunderstorms

Narrow bipolar events (NBEs) are produced by large-scale systems of streamer corona discharges that occur inside thunderstorms and radiate intensely at very-high frequencies. Here we examine 14 high-power NBEs recorded in Florida which are followed by a lasting oscillatory signal. We isolate the dominant frequencies of the oscillations using empirical mode decomposition methods, and cross-reference this information with the radiator size inferred from broadband interferometry. We conclude that the oscillations are evidence of a standing electromagnetic wave in the plasma cavity created by the preceding streamer system, that is, the cavity's fundamental mode with wavelength equal to twice the radiator length. Contrasting the dispersive and attenuation properties of these waves across several different events allows us to conclude that the cavity is filled with ions resulting in an average conductivity of 0.01–0.1 mS/m, which are left behind after the streamers decay via electron attachment to oxygen molecules.

58 GEOSCIENCES↗

DOE OSTI2022

New data-driven approach to bridging power system protection gaps with deep learning

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this paper, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A combined convolutional neural network and long short-term memory (CNN-LSTM) network is implemented to achieve data translation invariance and capture the temporal correlation of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN-LSTM--based method avoids the complicated, manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on two kinds of protection gaps: high-impedance faults and transformer inter-turn faults. Lastly, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

42 ENGINEERING↗

DOE OSTI2022

Smoke from 2020 United States wildfires responsible for substantial solar energy forecast errors

Abstract The 2020 wildfire season (May through December) in the United States was exceptionally active, with the National Interagency Fire Center reporting over 10 million acres ( > 40 000 km 2 ) burned. During the September 2020 wildfire events, large concentrations of smoke particulates were emitted into the atmosphere. As a result, smoke was responsible for ∼10%–30% reduction in solar power production during peak hours as recorded by the California Independent System Operator (CAISO) sites. In this study, we focus on a 9 d period in September when wildfire smoke had a profound impact on solar energy production. During the smoke episodes, hour-ahead forecasts utilized by CAISO did not include the effects of smoke and therefore overestimated the expected power production by ∼10%–50%. Here we use multiple observational networks and a numerical weather prediction (NWP) model to show that the wildfire events of 2020 had a significantly detrimental influence on solar energy production due to high aerosol loading. We find that including the contribution of biomass burning particles greatly improves the day-ahead solar energy bias forecast of both global horizontal irradiance and direct normal irradiance by nearly ∼50%. Our results suggest that a more comprehensive treatment of aerosols, including biomass burning aerosols, in NWP models may be an important consideration for energy grid balancing, in addition to solar resource assessment, as solar power reliance increases.

14 SOLAR ENERGY↗

DOE OSTI2021

Bridging Power System Protection Gaps with Data-driven Approaches

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this research, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A convolutional neural network (CNN) based fault detection approach is implemented to achieve data translation invariance of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN–based method avoids the complicated manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on four kinds of protection gaps: high impedance faults, transformer/generator inter-turn faults, distribution system PV circuit faults, and the mis-operation situations of Zone 3 line protection relays operating under system stress. Finally, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Burst-mode dual-comb spectroscopy

We introduce a new modality of dual-comb spectroscopy that enables a simplified and powerful new approach for time-resolved measurements with increased acquisition rates. This "burst mode" form of dual-comb spectroscopy (BM-DCS) relies on the multiplexing of each probe pulse into a short train of pulses. With this approach we demonstrate a time-resolved series of absorption-based spectroscopic measurements of a laser-induced plasma using only a single laser ablation shot and identify 22 Nd lines not previously reported in the literature. The transmission spectra spanned 3.1 THz and were acquired at an effective acquisition rate of 25 kHz with 40 ms time-resolution. This simple modification to 100 MHz level dual-comb systems provides a flexible approach for studying transient and low duty-cycle events such as laser-induced plasmas, combustion, and explosive reactions.

(280.5395) Plasma Diagnostics, broadband laser spe↗

NASA NTRS2006

Non Radiation Hardened Microprocessors in Spaced Based Remote Sensing Systems

The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) mission is a comprehensive suite of active and passive sensors including a 20Hz 230mj Nd:YAG lidar, a visible wavelength Earth-looking camera and an imaging infrared radiometer. CALIPSO flies in formation with the Earth Observing System Post-Meridian (EOS PM) train, provides continuous, near-simultaneous measurements and is a planned 3 year mission. CALIPSO was launched into a 98 degree sun synchronous Earth orbit in April of 2006 to study clouds and aerosols and acquires over 5 gigabytes of data every 24 hours. The ground track of one CALIPSO orbit as well as high and low intensity South Atlantic Anomaly outlines is shown. CALIPSO passes through the SAA several times each day. Spaced based remote sensing systems that include multiple instruments and/or instruments such as lidar generate large volumes of data and require robust real-time hardware and software mechanisms and high throughput processors. Due to onboard storage restrictions and telemetry downlink limitations these systems must pre-process and reduce the data before sending it to the ground. This onboard processing and realtime requirement load may mean that newer more powerful processors are needed even though acceptable radiation-hardened versions have not yet been released. CALIPSO's single board computer payload controller processor is actually a set of four (4) voting non-radiation hardened COTS Power PC 603r's built on a single width VME card by General Dynamics Advanced Information Systems (GDAIS). Significant radiation concerns for CALIPSO and other Low Earth Orbit (LEO) satellites include the South Atlantic Anomaly (SAA), the north and south poles and strong solar events. Over much of South America and extending into the South Atlantic Ocean the Van Allen radiation belts dip to just 200-800km and spacecraft entering this area are subjected to high energy protons and experience higher than normal Single Event Upset (SEU) and Single Event Latch-up (SEL) rates. Although less significant, spacecraft flying in the area around the poles experience similar upsets. Finally, powerful solar proton events in the range of 10MeV/10pfu to 100MeV/1pfu as are forecasted and tracked by NOAA's Space Environment Center in Colorado can result in Single Event Upset (SEU), Single Event Latch-up (SEL) and permanent failures such as Single Event Gate Rupture (SEGR) in some technologies. (Galactic Cosmic Rays (GCRs) are another source, especially for gate rupture) CALIPSO mitigates common radiation concerns in its data handling through the use of redundant processors, radiation-hardened Application Specific Integrated Circuits (ASIC), hardware-based Error Detection and Correction (EDAC), processor and memory scrubbing, redundant boot code and mirrored files. After presenting a system overview this paper will expand on each of these strategies. Where applicable, related on-orbit data collected since the CALIPSO initial boot on May 4, 2006 will be noted.

Decoursey, Robert J.↗

DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

Storm-DEPART (Damage Estimate Prediction and Restoration Tool): Each year hurricanes and tropical storms in the United States damage critical infrastructure assets, disrupt the services they provide, and cause millions to billions of dollars in economic impacts due to extended recovery times. The Storm-DEPART tool and analytical output enable more impactful data-driven decision-making capabilities and strengthen national-level disaster preparedness, response, and recovery. Storm-DEPART, built through multi-month collaboration between Entergy and INL, combines Entergy’s critical infrastructure inventory data with weather forecasts to predict damages to Electric utility’s assets due to natural disasters and the estimated recovery support needed, including time, materials, and resource allocation. In the event of an approaching hurricane, this innovative solution can assess potential damage to power generation capacity, transmission grids, distribution networks, and communications assets from wind bands, storm surge, and flooding. With more effective predictions, Entergy can more efficiently allocate resources to mitigate impacts and optimize recovery for customers. Storm-DEPART also allows Electric utilities the ability to apply a planning scenario and model expected damage to better inform infrastructure restoration needs leading to enhance system resiliency. The technology is fully transferrable to other electric utilities with the same damage estimating challenges. The INL team is working on the evolution of Storm-DEPART to include ice event damage prediction framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Dynamic Model Reduction for Large-Scale Power Systems Using Wide-Area Measurements

To perform faster than the real-time dynamic simulation of large-scale power systems, it is necessary to reduce the simulated system size by using equivalents for surrounding areas of the study area, and existing dynamic model reduction approach could provide the needed structure of the reduced area. However, further parameter optimization is required to achieve the desired accuracy. In this paper, a particle swarm optimization (PSO) based approach is used to solve the above problem. Parameters for the individual dynamic elements in the reduced system are calibrated repeatedly until the wide-area measurements of the reduced model and the original model are very similar to each other with satisfactory accuracy. Results indicate that after optimization, the dynamic response of the reduced model matches better with that of the original one than using existing methods. Under both the generator-trip event and the bus-fault event, the reduced model has a higher frequency match and less power mismatch.

42 ENGINEERING↗

DOE OSTI2022

Data-Driven PMU Noise Emulation Framework using Gradient-Penalty-Based Wasserstein GAN

Availability of phasor measurement unit (PMUs) data has led to research on data-driven algorithms for event monitoring, control and ensuring stability of the grid. Unavailability of infrequent critical event field PMU data with component failures is driving the need to generate realistic synthetic PMU data for research. The synthetic data from power system simulation software often neglect noise profiles of received phasors, thus creating some discrepancies between real PMU data and synthetic ones. To address this issue, this work presents an initial study on the noise characteristics of PMUs, as well as presenting models for recreating their unique noise signatures. The proposed method, utilizing the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) architecture, provides an excellent benchmark for matching the noise distribution. One can use a well-learned GAN model to draw noise signatures from a distribution that seemingly mirrors the real PMU noise distribution, while also being able to be detached from the PMU data once the training is done. Based on the observed results and employed data-driven methodology, it is expected that the proposed methods can be adapted to replicate the behavior of other sensors, providing research and other applications with a tool for data synthesis and sensor characterization.

PMU↗

DOE OSTI2020

Adaptable Monitoring Package Development and Deployment: Lessons Learned for Integrated Instrumentation at Marine Energy Sites

Integrated instrumentation packages are an attractive option for environmental and ecological monitoring at marine energy sites, as they can support a range of sensors in a form factor compact enough for the operational constraints posed by energetic waves and currents. Here we present details of the architecture and performance for one such system—the Adaptable Monitoring Package—which supports active acoustic, passive acoustic, and optical sensing to quantify the physical environment and animal presence at marine energy sites. we describe cabled and autonomous deployments and contrast the relatively limited system capabilities in an autonomous operating mode with more expansive capabilities, including real-time data processing, afforded by shore power or in situ power harvesting from waves. Across these deployments, we describe sensor performance, outcomes for biological target classification algorithms using data from multibeam sonars and optical cameras, and the effectiveness of measures to limit biofouling and corrosion. On the basis of these experiences, we discuss the demonstrated requirements for integrated instrumentation, possible operational concepts for monitoring the environmental and ecological effects of marine energy converters using such systems, and the engineering trade-offs inherent in their development. Overall, we find that integrated instrumentation can provide powerful capabilities for observing rare events, managing the volume of data collected, and mitigating potential bias to marine animal behavior. These capabilities may be as relevant to the broader oceanographic community as they are to the emerging marine energy sector.

16 TIDAL AND WAVE POWER↗

NASA NTRS1994

Spectral representation of high-frequency Space Shuttle data

High frequency Space Shuttle liftoff data are treated by autoregressive (AR) and autoregressive-moving-average (ARMA) digital algorithms. These algorithms provide useful information on the spectral densities of the data. Further, they yield spectral models, which lend themeselves to incorporation into the concept of the random response spectrum. This concept yields a reasonably smooth power spectrum for the design of structural and mechanical systems when the available data bank is limited. Due to the nonstationary of the liftoff event, the pertinent data are split into three slices. Each of the slices is associated with a rather distinguished phase of the liftoff event, in which stationarity can be expected. The presented results are preliminary in nature; they aim to call attention to the availability of the discussed concepts and to the need to augment the Space Shuttle data bank as more flights are completed.

Spanos, P. D.↗

NASA NTRSDate not supplied

Utilization of Machine Learning Techniques for Managing the Tracking and Data Relay Satellite Constellation

National Aeronautics and Space Administration’s (NASA) Goddard Space Flight Center (GSFC) operates a constellation of ten geosynchronous Tracking and Data Relay Satellites (TDRS). The TDRS constellation consists of multiple geosynchronous communication relay satellites located around the equator so they can provide continual coverage of any mission in low earth orbit. The TDRS are located primarily in three oceanic regions around the earth. NASA’s White Sands Complex provides the ground communication support for TDRS located over the Atlantic and Pacific Oceans. Another TDRS ground station in Guam supports the TDRS over the Indian Ocean. With these satellites the TDRS network can provide continuous coverage of satellites in low-earth orbit. The NASA Space Network (SN) project office at GSFC manages the constellation of spacecraft. Major customers of the TDRS constellation include, but are not limited to, the International Space Station and the Hubble Space Telescope. The TDRS constellation has three generations of satellites and has been active for over 30 years providing reliable communication links between customer satellites and corresponding ground stations. However, one of the major concerns for TDRS, and in any space mission, is to ensure the health and safety of the spacecraft. Generally, engineers use telemetry data to monitor and analyze the performance and state of health of the spacecraft. Telemetry data contains hundreds of parameters that monitor each important component in the spacecraft, which can be utilized to recognize and characterize the behavior of the spacecraft. Each parameter contains considerable information to represent time-dependent properties of each spacecraft subsystem and component. During the entire life of a TDRS spacecraft, thousands of gigabytes of telemetry data are transmitted in real-time from the spacecraft to the ground station at the White Sands Complex in Las Cruces, New Mexico, and recorded as historical data sets for engineers to process and analyze the events that occurred on-orbit. These parameters contain the function of multiple spacecraft subsystems, such as the attitude control system (ACS), Thermal, Electrical Power Subsystem (EPS), etc. . The first and second generations have exceeded their required lifetime and NASA is keen to manage these spacecrafts carefully in order to maximize the remaining life using the spacecraft telemetry. The challenge is to know when the risk of losing a spacecraft in geosynchronous orbit exceeds the benefit of continued operations for customer support. In the TDRS fleet, the EPS is the most critical subsystem related to spacecraft operations. Failure of the EPS would strand a spacecraft in geosynchronous orbit. Since EPS provides power to the spacecraft, component failures ultimately lead to the inability to support the spacecraft loads and the communications payload. For instance, TDRS-8 has several anomalies in EPS including the Bus Voltage Limiter (BVL) shunt current, solar array loss of circuits, and failed battery cells. Any of these anomalies can cause critical issues to the spacecraft. Therefore, developing a system to analyze and perform early detection of a potential anomaly is an important issue in telemetry data analysis. In recent years, Telemetry Mining (TM) has been proposed to process telemetry data by using Data Mining (DM) techniques such as classification, clustering, regression and anomaly detection. Anomaly detection, also known as outlier detection, has been widely used in many data mining areas such as remote sensing, medical data processing and digital image processing. The goal of anomaly detection is to detect abnormal data, which contains a relatively low probability of occurrence among the entire data set. Early detection of anomalies is one of the most significant issues in managing the spacecraft configuration. If anomalies can be detected early enough, then the redundant resources can be used to extend the life of the operational spacecraft. We present an unsupervised anomaly detection method to process the EPS data extracted from TDRS-8. This is different from traditional analytical methods, which use telemetry data to illustrate behavior and physical meaning of each spacecraft component. TM connects multiple parameters as a vector and then conducts data analysis on this high dimension telemetry vector. This method is looking at the properties of a high dimensional vector that is able to consider the relationship between different parameters in the anomaly detection problem. This kind of method performs much better than the traditional limit checking method. In addition, we propose a new approach of real-time anomaly detection to process telemetry data in real-time, which can then be applied to spacecraft monitoring with high reliability, low cost and high accuracy.

Machine Learning (ML)↗

DOE OSTI1992

Nuclear Safety [Vol. 33, No. 1, January-March 1992]

Nuclear Safety is a review journal that covers significant developments in the field of nuclear safety. Its scope includes the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, the safety aspects of the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 1 Technical Note: A New Approach to Fission Reactor Safety, Yu. V. Petrov; 5 Erratum; ACCIDENT ANALYSIS: 6 Analysis and Modeling of Fission Product Release from Various Uranium-Aluminum Plate-Type Reactor Fuels, R. P. Taleyarkhan; CONTROL AND INSTRUMENTATION: 23 Applications of a Surveillance and Diagnostics Methodology Using Neutron Noise From a Pressurized-Water Reactor, R. T. Wood, L. F. Miller, and R. B. Perez; DESIGN FEATURES: 36 Westinghouse Advanced Passive 600 Plant, B. A. McIntyre and R. K. Beck; 47 System 80+™ PWR Safety Design, C. W. Bagnal, R. A. Matzie, and R. S. Turk; ENVIRONMENTAL EFFECTS: 58 The MATS Experiments—Mesoscale Atmospheric Transport Studies at the Savannah River Site, A. H. Weber, S. Berman, R. J. Kurzeja, and R. P. Addis; 75 Book Review: Environmental Radioactivity in the European Community 1984-1985-1986, C. A. Little; WASTE AND SPENT FUEL MANAGEMENT: 76 Activities Related to Waste and Spent Fuel Management, M. D. Muhlheim and E. G. Silver; OPERATING EXPERIENCES: 87 Aging Assessment of BWR Control Rod Drive Systems, R. H. Greene; 100 Reactor Shutdown Experience, Compiled by J. W. Cletcher; 103 Selected Safety-Related Events, Compiled by G. A. Murphy; 110 Operating U.S. Power Reactors, Compiled by M. D. Muhlheim and E. G. Silver; RECENT DEVELOPMENTS: 129 General Administrative Activities, Compiled by M. D. Muhlheim and E. G. Silver; 139 Reports, Standards, and Safety Guides, D. S. Queener; 143 Proposed Rule Changes as of Sept. 30 1991; ANNOUNCEMENTS: 128 Harvard School of Public Health Announces Short Courses; 128 16th Biennial ANS Topical Meeting on Reactor Operating Experience: Present and Future Technologies—Applying Lessons Learned (Call for Papers); 147 The Authors; 150 Indexes to Nuclear Safety, Volume 32; 154 Errata.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Autonomous Intelligence Measurements and Sensor Systems (AIMS): Gaussian Processes in Remote Sensing: Literature Review

Power grid resilience and reliability is crucial to supporting critical infrastructure. Service interruptions can have devastating consequences to communication, emergency, and transportation services, just to name a few. In addition to infrastructure, power services are crucial to everyday life. Nearly every element of modern-day living relies on a stable and functioning power grid - heating and cooling systems to lighting, refrigeration, telecommunications, and internet access. Interruptions in the power supply can range from minor inconveniences, such as the temporary loss of internet or television services, to more significant problems, like the inability to access medical equipment or emergency services during critical situations. As climate conditions worsen, the reliability of the power grid becomes even more significant. Extreme weather events, such as hurricanes, wildfires, or severe storms, can cause serious damage to power infrastructure, leading to prolonged power outages. Rising temperatures and changing weather patterns can strain the grid, resulting in increased demand for electricity, stressing transmission and distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Physically Realistic Estimates of Electric Water Heater Demand Response Resource

Multiple grid integration studies have examined the value of demand response, including from water heaters, to bulk power systems. Common shortcomings of these studies include physically unrealistic assumptions. For example, studies often assume that all electric water heater load can be shed for up to an hour to provide capacity or contingency service, that all electric water heater load is shiftable, and that round-trip efficiencies for shifting service are 100%. Another issue is that simply summing up estimates of individual water heaters' flexibility bounds, an attractive idea for constructing the MW-scale resources necessary for direct inclusion in grid investment and operational models, can result in significant overestimates of actual resource, because, e.g., a water heater with the ability to reduce load may not be able sustain the reduction for as long as the aggregate model suggests is possible. This presentation demonstrates the impact of estimating the flexibility of electric water heaters with physically realistic models suitable for grid service analysis. Electric water heater flexibility representations are constructed based on ResStock simulations of New England. Specifically, we examine contingency resource (related to capacity, shed, and contingency reserve services) and energy shifting resource from electric resistance and heat pump water heaters. Different estimation methods are compared; and the surrogate models' event responses are validated against events directly simulated in EnergyPlus. We also demonstrate the impact of using electric water heaters for contingency reserves and energy shifting in detailed models of possible future ISO New England power systems.

building energy modeling↗

DOE OSTI2022

Cybersecurity of DER Systems: Cybersecurity Training for State Commissions

NARUC regularly hosts multi-day cybersecurity training events for commissioners and their staff to gain knowledge of IT/OT technologies, understand the spectrum of state and federal approaches to cybersecurity, and explore cybersecurity cost recovery challenges and innovative solutions to address them. About 300 commissioners and staffers typically register for these virtual events.

cybersecurity cost recovery↗

DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

24 POWER TRANSMISSION AND DISTRIBUTION↗