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At least 523 records · Page 29

Atmospheric pressure loading parameters from very long baseline interferometry observations

Atmospheric mass loading produces a primarily vertical displacement of the Earth's crust. This displacement is correlated with surface pressure and is large enough to be detected by very long baseline interferometry (VLBI) measurements. Using the measured surface pressure at VLBI stations, we have estimated the atmospheric loading term for each station location directly from VLBI data acquired from 1979 to 1992. Our estimates of the vertical sensitivity to change in pressure range from 0 to -0.6 mm/mbar depending on the station. These estimates agree with inverted barometer model calculations (Manabe et al., 1991; vanDam and Herring, 1994) of the vertical displacement sensitivity computed by convolving actual pressure distributions with loading Green's functions. The pressure sensitivity tends to be smaller for stations near the coast, which is consistent with the inverted barometer hypothesis. Applying this estimated pressure loading correction in standard VLBI geodetic analysis improves the repeatability of estimated lengths of 25 out of 37 baselines that were measured at least 50 times. In a root-sum-square (rss) sense, the improvement generally increases with baseline length at a rate of about 0.3 to 0.6 ppb depending on whether the baseline stations are close to the coast. For the 5998-km baseline from Westford, Massachusetts, to Wettzell, Germany, the rss improvement is about 3.6 mm out of 11.0 mm. The average rss reduction of the vertical scatter for inland stations ranges from 2.7 to 5.4 mm.

Macmillan, D. S.↗

A Physics-Based Digital Twin for Wave Elevation and Seabed Moment Estimation of Offshore Monopiles: Preprint

In this work, we present a proof of concept of a physics-based digital twin for a monopile structure (with overhead inertia) subjected to wave loading. The digital twin is formulated using reduced-order models derived from first principles and combined with a Kalman filter for state estimation. The proposed framework estimates the monopile top motion, the wave elevation, and the section forces and moments along the pile using primarily acceleration measurements at the monopile top. Key innovations include the use of a hydrodynamic shape function to represent distributed wave loading in a compact and computationally efficient manner, and the introduction of a shaping filter to augment the state-space with wave kinematics. Synthetic measurement data are generated using OpenFAST and used as a reference to assess the performance of the digital twin. Results demonstrate that the wave elevation can be accurately reconstructed without direct sea-state measurements as long as the wave regime is inertia-dominated. Under the ideal tested conditions, the total hydrodynamic force and sea-bed bending moment are estimated with relative errors on the order of 1% and correlation coefficients exceeding 96%. Future work will evaluate the estimator's performance under operational uncertainties and more complex loading conditions.

17 WIND ENERGY↗

On the Response of a Nonlinear Structure to High Kurtosis Non-Gaussian Random Loadings

This paper is a follow-on to recent work by the authors in which the response and high-cycle fatigue of a nonlinear structure subject to non-Gaussian loadings was found to vary markedly depending on the nature of the loading. There it was found that a non-Gaussian loading having a steady rate of short-duration, high-excursion peaks produced essentially the same response as would have been incurred by a Gaussian loading. In contrast, a non-Gaussian loading having the same kurtosis, but with bursts of high-excursion peaks was found to elicit a much greater response. This work is meant to answer the question of when consideration of a loading probability distribution other than Gaussian is important. The approach entailed nonlinear numerical simulation of a beam structure under Gaussian and non-Gaussian random excitations. Whether the structure responded in a Gaussian or non-Gaussian manner was determined by adherence to, or violations of, the Central Limit Theorem. Over a practical range of damping, it was found that the linear response to a non-Gaussian loading was Gaussian when the period of the system impulse response is much greater than the rate of peaks in the loading. Lower damping reduced the kurtosis, but only when the linear response was non-Gaussian. In the nonlinear regime, the response was found to be non-Gaussian for all loadings. The effect of a spring-hardening type of nonlinearity was found to limit extreme values and thereby lower the kurtosis relative to the linear response regime. In this case, lower damping gave rise to greater nonlinearity, resulting in lower kurtosis than a higher level of damping.

Rizzi, Stephen A.↗

Distributed Outage Detection in Power Distribution Networks

Real time topology knowledge is essential for situational awareness of power distribution networks. Line outages change the topology of a distribution network. Hence, outage detection is an important task. Most of the existing outage detection algorithms are centralized, in which sensors communicate their data to a control center which performs outage detection using the received data. However, with the increasing size of the distribution network and with different areas of the network being monitored by different operators, communication is a bottleneck and scalability is a major concern. To address these issues, we propose a novel outage detection algorithm using a divide and conquer approach. First, we divide a distribution network into sub-networks, such that outage detection can be run in parallel in each sub-network independently ensuring scalability to large networks. Further, to reduce the latency, bandwidth and attenuation challenges associated with communications in a large network, we divide each sub-network into multiple control areas which communicate only with their neighbors. We employ a distributed iterative load estimation across the control areas of each sub-network and then use the load estimate for local outage detection in each control area. Here, the performance of our algorithm is evaluated for multiple feeder models and compared against traditional centralized outage detection algorithms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

SSV Launch Monitoring Strategies: HGDS Design Implementation Through System Maturity

With over 500,000 gallons of liquid hydrogen and liquid oxygen, it is of vital importance to monitor the space shuttle vehicle (SSV) from external tank (ET) load through launch. The Hazardous Gas Detection System (HGDS) was installed as the primary system responsible for monitoring fuel leaks within the orbiter and ET. The HGDS was designed to obtain the lowest possible detection limits with the best resolution while monitoring the SSV for any hydrogen, helium, oxygen, or argon as the main requirement. The HGDS is a redundant mass spectrometer used for real-time monitoring during Power Reactant Storage and Distribution (PRSD) load and ET load through launch or scrub. This system also performs SSV processing leak checks of the Tail Service Mast (TSM) umbilical quick disconnects (QD's), Ground Umbilical Carrier Plate (GUCP) QD's and supports auxiliary power unit (APU) system tests. From design to initial implementation and operations, the HGDS has evolved into a mature and reliable launch support system. This paper will discuss the operational challenges and lessons learned from facing design deficiencies, validation and maintenance efforts, life cycle issues, and evolving requirements

Shoemaker, Marc D.↗

Impacts of Control Set-Points on Battery Energy Storage Performance for Peak Shaving Application

This article presents the impact of battery energy storage system’s (BESS) control triggers on its performance during peak shaving application. With the increasing deployment of BESS for load leveling and grid capacity relief, the sensitivity of BESS performance to its set-points is yet to be investigated. Most utilities have defined thresholds for activating BESS control during peak demand leveling and baseload BESS charging. However, this study shows that without using optimal setpoints for control applications, the BESS can be under-utilized. Simulations results of the peak shaving algorithm implementation in 30-minute time step over a period of 10 years show the impact of different set-points on distribution transformer loading, line and system losses.

battery energy storage system↗

Load balancing and closed chain multiple arm control

The authors give the general dynamical equations for several rigid link manipulators rigidly grasping a commonly held rigid object. It is shown that the number of arm-configuration degrees of freedom lost due to imposing the closed-loop kinematic constraints is the same as the number of degrees of freedom gained for controlling the internal forces of the closed-chain system. This number is equal to the dimension of the kernel of the Jacobian operator which transforms contact forces to the net forces acting on the held object, and it is shown that this kernel can be identified with the subspace of controllable internal forces of the closed-chain system. Control of these forces makes it possible to regulate the grasping forces imparted to the held object or to control the load taken by each arm. It is shown that the internal forces can be influenced without affecting the control of the configuration degrees of freedom. Control laws of the feedback linearization type are shown to be useful for controlling the location and attitude of a frame fixed with respect to the held object, while simultaneously controlling the internal forces of the closed-chain system. Force feedback can be used to linearize and control the system even when the held object has unknown mass properties. If saturation effects are ignored, an unconstrained quadratic optimization can be performed to distribute the load optimally among the joint actuators.

Kreutz, Kenneth↗

An Evaluation of the Economic and Resilience Benefits of a Microgrid in Northampton, Massachusetts

Recent developments and advances in distributed energy resource (DER) technologies make them valuable assets in microgrids. This paper presents an innovative evaluation framework for microgrid assets to capture economic benefits from various grid and behind-the-meter services in grid-connecting mode and resilience benefits in islanding mode. In particular, a linear programming formulation is used to model different services and DER operational constraints to determine the optimal DER dispatch to maximize economic benefits. For the resiliency analysis, a stochastic evaluation procedure is proposed to explicitly quantify the microgrid survivability against a random outage, considering uncertainties associated with photovoltaic (PV) generation, system load, and distributed generator failures. Optimal coordination strategies are developed to minimize unserved energy and improve system survivability, considering different levels of system connectedness. The proposed framework has been applied to evaluate a proposed microgrid in Northampton, Massachusetts that would link the Northampton Department of Public Works, Cooley Dickenson Hospital, and Smith Vocational Area High School. The findings of this analysis indicate that over a 20-year economic life, a 441 kW/441 kWh battery energy storage system, and 386 kW PV solar array can generate $2.5 million in present value benefits, yielding a 1.16 return on investment ratio. Results of this study also show that forming a microgrid generally improves system survivability, but the resilience performance of individual facilities varies depending on power-sharing strategies.

25 ENERGY STORAGE↗

Post-Disaster Microgrid Formation for Enhanced Distribution System Resilience

This paper proposes a deep reinforcement learning (DRL) based approach for post-disaster critical load restoration in active distribution systems to form microgrids through network reconfiguration to minimize critical load curtailments. Distribution networks are represented as graph networks, and optimal network configurations with microgrids are obtained by searching for the optimal spanning forest. The constraints to the research question being explored are the radial topology and power balance. Unlike existing analytical and population-based approaches, which necessitate the repetition of entire analyses and computation for each outage scenario to find the optimal spanning forest, the proposed approach, once properly trained, can quickly determine the optimal, or near-optimal, spanning forest even when outage scenarios change. When multiple lines fail in the system, the proposed approach forms microgrids with distributed energy resources in active distribution systems to reduce critical load curtailment. The proposed DRL-based model learns the action-value function using the REINFORCE algorithm, which is a model-free reinforcement learning technique based on stochastic policy gradients. A case study was conducted on a 33-node distribution test system, demonstrating the effectiveness of the proposed approach for post-disaster critical load restoration.

active distribution systems↗

Model-Free Probabilistic Forecasting of Nodal Voltages in Distribution Systems

As the penetration of distributed energy resources (DERs) into distribution systems increases, so does the interest in forecasting relevant system variables to help mitigate the associated challenges. One such challenge is the more frequent occurrence of excessive voltages in distribution systems with higher shares of DERs. Accurate and reliable estimates together with forecasts of system states (i.e., nodal voltages) will therefore play a key role in improving the utilization of these variable and uncertain sources while mitigating potential operational risks. Whilst recent literature has explored machine learning (ML) methods for voltage estimation and their extrapolation for a short-time period into the future, few have taken uncertainty quantification into account, and these methods have not yet been translated into operations. This paper discusses the advantages offered by probabilistic voltage forecasts and proposes a non-parametric Bayesian method suitable for forecasting nodal voltages at short-term time horizons while accounting for uncertainties in load and distributed photovoltaic (PV) generation. We demonstrate the value of the proposed Gaussian process (GP) model for a case study using historical forecasts and observation data.

distribution system↗

A Power Application Developer’s Guide to the Common Information Model: An Introduction for Power Systems Engineers and Application Developers – CIM17v40

A key issue in creating the next generation of energy management system (EMS) and advanced distribution management system (ADMS) platforms will be the ability to represent and exchange power system network model data in a consistent manner. To this end, the Common Information Model (CIM) stands out as the only standardized vocabulary (or ontology) for defining power system network models and asset data in a comprehensive, consistent manner across the generation-transmission-distribution boundary. The CIM is freely available to use and extend. The CIM is maintained by the UCAiug (informally known as the CIM User’s Group) under an Apache 2.0 license. The CIM Users Group collaborates with the IEC and other standards communities for the development of technical and informative specifications. Although portions of the information model are referred to by the corresponding IEC standards naming, it is not necessary to purchase any of the IEC standards to use the CIM information model. This document provides a roadmap for power system engineers and application developers not familiar with semantic modeling to start using the CIM for modeling, simulation, optimization, and development of advanced power applications. The key classes needed for defining power system topology and equipment are explained systematically. Key focus areas include modeling of lines, transformers, generators, switching equipment, loads, and distributed energy resources (DERs).

97 MATHEMATICS AND COMPUTING↗

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗

Categorizing distributed wind energy installations in the United States to inform research and stakeholder priorities

Abstract Background Distributed wind energy adoption in the United States can contribute to the diverse portfolio of energy technologies needed to achieve ambitious decarbonization goals. However, with limited deployment to date, the current distributed wind market must be better understood; these efforts will support the range of stakeholders who will drive successful deployment. This article first distinguishes three categories of distributed wind from existing literature: (1) behind the meter, (2) intended for explicit local load, and (3) physically distributed. A novel methodology to classify individual wind installations into each of these categories is then presented and applied to two data sets of wind installations in the United States to categorize and illuminate distinct segments in the distributed wind market. Results Physically distributed installations, constituted by small to moderately sized projects serving local loads on distribution systems solely because of their proximity to them, account for the highest amount of capacity but the lowest number of installations out of the three categories. The inverse is true for behind-the-meter installations, which are used to serve on-site loads. Installations intended for explicit local load, which are interconnected on the utility side of the distribution system and intentionally built to provide energy to loads on the same distribution system, rank in the middle for both installed capacity and number of installations. Conclusions Distributed wind energy deployment in the United States is geographically widespread, but the extent to which a single category is developed in each state varies. Policies, wind resources, and broad energy technology trends contribute to these deployment patterns. By identifying the extent to which each category of installations exists, decision-makers are empowered with data necessary to tailor research and development programs and address stakeholder priorities through policy and other means, ultimately supporting future deployment.

17 WIND ENERGY↗

Comparison Made of Operating Characteristics of Spiral Bevel Gears Manufactured Using Different Methods

Spiral bevel gears are important components on all current rotorcraft drive systems. These components are required to operate at high speeds, high loads, and for an extremely large number of load cycles. In this application, spiral bevel gears are used to redirect the shaft from the horizontal gas turbine engine to the vertical rotor. Because of the high expense of manufacturing these gears, methods that can achieve the same level of performance at reduced cost are highly desirable to aerospace gear manufacturers. Gears manufactured for aerospace applications use high-quality materials and are manufactured to tight tolerances. Special manufacturing machine tools and computer numerically controlled coordinate measurement systems have enabled rotorcraft drive system manufacturers to produce extremely high-quality gears during their normal production. Because of low production rates for rotorcraft, these gears are manufactured in small batches, and thus are unable to benefit from the economics of high production numbers as in other industries. In this investigation, two different manufacturing methods, face-milled and face-hobbed, were used to fabricate spiral bevel gears. For face-milled spiral bevel gears, grinding of the contacting surfaces is the final manufacturing step. At least two different specialty machines are needed to generate the teeth for face-milled spiral bevel gears. For face-hobbed gears, hard cutting is the final manufacturing process. The same machine is used to rough cut and finish cut the gears. This study compared the operational behavior of face-milled spiral bevel gears with that of face-hobbed spiral bevel gears. Test hardware was manufactured to fit within NASA Glenn Research Center's Spiral Bevel Test Facility and to aerospace quality standards. Tests were conducted for stress, vibration, and noise. A comparison of the results attained indicated that the face-hobbed gears had a lower alternating stress level with a more even distribution of loading across the teeth, and slightly reduced levels of vibration and noise. Results of this study show that the face-hobbed method is a viable and lower-cost alternative for producing aerospace-quality spiral-bevel gears.

Handschuh, Robert F.↗

Scalable Solution-Processed Electrolyte Membranes with Optimized Microstructure for High-Performance Protonic Ceramic Electrochemical Cells

Proton-conducting electrochemical cells (PCECs) are promising for efficient hydrogen production, but achieving dense, uniform, thin electrolyte layers remains a key challenge, particularly for scalable fabrication. Here, we present a solution-processed deposition approach with a mechanistically optimized slurry for uniform electrolyte formation. By tailoring particle size distribution, solid loading, and solvent/additive balance, we regulated wetting behavior and evaporation kinetics of the electrolyte slurry to promote homogeneous electrolyte particle packing. These features facilitate tight grain boundary contact and early stage neck growth during sintering, eliminating residual porosity, and improving mechanical integrity. The resulting ∼15 μm thick electrolyte shows high density, strong electrode adhesion, and stable interfaces outperforming the previously reported spray-based fabricated electrolyte by about 31% at 600 °C in FC mode. Single cells deliver 0.962 W cm –2 at 600 °C in fuel cell mode and 1.31 A cm –2 at 1.3 V in electrolysis mode, maintaining robust performance over 100 h with negligible degradation (≤0.02% h –1 ) in each mode. Scale-up to 2.5 cm diameter substrates confirmed reproducible densification and geometric stability. This work demonstrates a cost-effective, scalable route where control over particle-fluid interactions and drying dynamics enables a superior electrolyte microstructure and high PCEC performance.

dense microstructure↗

DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models

In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. One common pattern emerging in such applications is frequent checkpointing of the state of the learning model during training, needed in a variety of scenarios: analysis of intermediate states to explain features and correlations with training data, exploration strategies involving alternative models that share a common ancestor, knowledge transfer, resilience, etc. However, with increasing size of the learning models and popularity of distributed data-parallel training approaches, simple checkpointing techniques used so far face several limitations: low serialization performance, blocking I/O, stragglers due to the fact that only a single process is involved in checkpointing. This paper proposes a checkpointing technique specifically designed to address the aforementioned limitations, introducing efficient asynchronous techniques to hide the overhead of serialization and I/O, and distribute the load over all participating processes. Experiments with two deep learning applications (CANDLE and ResNet) on a pre-Exascale HPC platform (Theta) shows significant improvement over state-of-art, both in terms of checkpointing duration and runtime overhead.

checkpointing↗

Bulk Grid Frequency Support Using Electrolyzers

Increasing renewable penetration in the bulk power systems has resulted in declining inertial response of the generation during system disturbances. This results in larger frequency deviations and oscillations during such events. This paper proposes a strategy for controlled dispatch of electrolyzers as loads to improve the system’s frequency response. A droop based controller with constraints based on electrolyzer operating limits has been developed to regulate its load. Consecutively, a localized frequency based control strategy has been developed to support the bulk system during disturbances. The impact of the electrolyzer support has been demonstrated using the 23-bus SAVNW system, and the 240-bus WECC system for baseline, 25% and 50% renewable penetration. The impact of changing droop settings and the available controllable electrolyzer capacities on the system’s frequency response has also been presented. Overall, the dispatchable electrolyzers helps improve (reduce) both the maximum frequency deviation and the settling times during common disturbances like loss of load/generation, and line faults. Systems with lower inertia were seen to benefit more from the controlled electrolyzer dispatch.

39 EE - Hydrogen and Fuel Cell Technologies (EE-3F↗