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At least 127 records · Page 7

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DOE OSTI2023

Interpretable Data-Driven Probabilistic Power System Load Margin Assessment with Uncertain Renewable Energy and Loads

The increasing uncertainties caused by the high-penetration of stochastic renewable generation resources poses a significant threat to the power system voltage stability. To address this issue, this paper proposes a probabilistic deep kernel learning enabled surrogate model to extract the hidden relationship between uncertain sources, i.e., wind power and loads, and load margin for probabilistic load margin assessment (PLMA). Unlike other deep learning approaches, a kernel SHAP provides the sensitivity analysis as well as interpretability of the inputs to outputs influences. This allows identifying the critical factors that affect load margin so that corrective control can be initiated for stability enhancement. Numerical results carried out on the IEEE 118-bus power system demonstrate the accuracy and efficiency of the proposed data-driven PLMA scheme.

deep kernel learning↗

DOE OSTI2023

A Modified Sequence-to-point HVAC Load Disaggregation Algorithm

This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption. The original S2P model is convolutional neural network (CNN) based, which uses load profiles as inputs. We propose three modifications. First, the input convolution layer is changed from 1D to 2D so that normalized temperature profiles are also used inputs to the S2P model. Second, a drop-out layer is added to improve adaptability and generalizability so that the model trained in one area can be transferred to other geographical areas without labelled HVAC data. Third, a fine-tuning process is proposed for areas with a small amount of labelled HVAC data so that the pre-trained S2P model can be fine-tuned to achieve higher disaggregation accuracy (i.e., better transferability) in other areas. The model is first trained and tested using smart meter and sub-metered HVAC data collected in Austin, Texas. Then, the trained model is tested on two other areas: Boulder, Colorado and San Diego, California. Simulation results show that the proposed modified S2P algorithm outperforms the original S2P model and the support-vector machine based approach in accuracy, adaptability, and transferability.

Ye, Kai↗

DOE OSTI2019

DGs for Service Restoration to Critical Loads in a Secondary Network

During a major outage in a secondary network distribution system, distributed generators (DGs) connected to the primary feeders as well as the secondary network can be used to serve critical loads. This paper proposed a resilience-oriented method to determine restoration strategies for secondary network distribution systems after a major disaster. Technical issues associated with the restoration process are analyzed, including the operation of network protectors, inrush currents caused by the energization of network transformers, synchronization of DGs to the network, and circulating currents among DGs. A look-ahead load restoration framework is proposed, incorporating technical issues associated with secondary networks, limits on DG capacity and generation resources, dynamic constraints, and operational limits. The entire outage duration is divided into a sequence of periods. Restoration strategies can be adjusted at the beginning of each period using the latest information. Numerical simulation of the modified IEEE 342-node low voltage networked test system is performed to validate the effectiveness of the proposed method.

Xu, Yin (ORCID:0000000209459024)↗

DOE OSTI2021

Operating a commercial building HVAC load as a virtual battery through airflow control

Virtual battery (VB) is an innovative method to model flexibility of building loads and effectively coordinate them with other resources at a system level. Unlike a real battery with a dedicated power conversion system for charging control, methods are required for operating building loads to deviate from the baseline to respond to grid signals. This paper presents a VB control for a commercial heating, ventilation, and air conditioning (HVAC) system to follow the desired power consumption in real-time by adjusting zonal airflow rates. The proposed method consists of two parts. At the system level, a mixed feedforward and feedback control is used to estimate the desired total airflow rate. At the zone level, two priority-based algorithms are then proposed to distribute the total airflow rate to individual zones. In particular, a zonal airflow limit estimation method is proposed using machine-learning techniques, in contrast to physics-based thermal models in existing studies, to more accurately capture zonal thermal dynamics and improve temperature control performance. An office building on the Pacific Northwest National Laboratory campus is implemented in EnergyPlus, and used to illustrate and validate the proposed control.

Wang, Jiyu↗

DOE OSTI2018

Methodology for Stress-Controlled Fatigue Test Under In-Air and Pressurized Water Reactor Coolant Water Condition and to Evaluate the Effect of Pressurized Water Reactor Water and Loading Rate on Ratcheting [Methodology for Stress-Controlled Fatigue Test Under In-Air and PWR Coolant Water Condition and to Evaluate the Effect of PWR Water and Loading Rate on Ratcheting]

Here, this work investigates the behavior of 316 stainless steel (SS) under stress-controlled low cycle fatigue loading. Several fatigue experiments are conducted under different environment such as in air at 300°C and primary loop water conditions for a pressurized water reactor (PWR). Two different loading conditions are also employed to examine the effect of stress rate on material hardening and ratcheting. During PWR water test, actuator position measurements are used to determine the strain of the specimen. Under PWR environment, 316 SS is found to ratchet to a significantly greater degree compared with in air. At slow stress rate, higher amount of cyclic hardening is observed in 316 SS, and slow stress rate increases the rate of ratcheting. Results also indicate that 316 SS exhibits asymptotic strain response at higher stress loading which can cause material to behave very differently under same stress cyclic loading.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Gaining Insights in Loading Events for Wind Turbine Drivetrain Prognostics

Wind energy is one of the largest sources of renewable energy in the world. To further reduce the operations and maintenance (O&M) costs of wind farms, it is essential to be able to accurately pinpoint the root causes of different failure modes of interest. An example of such a failure mode that is not yet fully understood is white etching cracks (WEC). This can cause the bearing lifetime to be reduced to 5–10% of its design value. Multiple hypotheses are available in literature concerning its cause. To be able to validate or disprove these hypotheses, it is essential to have historic high-frequency measurement data (e.g., load and vibration levels) available. In time, this will allow linking to the history of the turbine operating data with failure data. This paper discusses the dynamic loading on the turbine during certain events (e.g., emergency stops, run-ups, and during normal operating conditions). By combining the number of specific events that each turbine has seen with the severity of each event, it becomes possible to assess which turbines are most likely to show signs of damage.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2017

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response: Preprint

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility and reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1978

Load and dynamic assessment of B-52B-008 carrier aircraft for finned configuration 1 space shuttle solid rocket booster decelerator subsystem drop test vehicle. Volume 2: Airplane flutter and load analysis results

The airplane flutter and maneuver-gust load analysis results obtained during B-52B drop test vehicle configuration (with fins) evaluation are presented. These data are presented as supplementary data to that given in Volume 1 of this document. A brief mathematical description of airspeed notation and gust load factor criteria are provided as a help to the user. References are defined which provide mathematical description of the airplane flutter and load analysis techniques. Air-speed-load factor diagrams are provided for the airplane weight configurations reanalyzed for finned drop test vehicle configuration.

Quade, D. A.↗

NASA NTRS1978

Load and dynamic assessment of B-52B-008 carrier aircraft for finned configuration 1 space shuttle solid rocket booster decelerator subsystem drop test vehicle. Volume 3: Pylon load data method 1

The pylon loading at the drop test vehicle and wing interface attach points is presented. The loads shown are determined using a stiffness method, which assumes the side stiffness of the foreward hook guide to be one-fourth of the fore and aft stiffness of each drag pin. The net effect of this assumption is that the forward hook guide reacts approximately 85% of the drop test vehicle yawing moment. For a comparison of these loads to previous X-15 analysis design loadings, see Volume 1 of this document.

Quade, D. A.↗

NASA NTRS1978

Load and dynamic assessment of B-52B-008 carrier aircraft for finned configuration 1 space shuttle solid rocket booster deceleration subsystem drop test vehicle. Volume 4: Pylon load data

The pylon loading at the drop test vehicle and wing interface attack points is presented. The loads shown are determined using a stiffness method, which assumes the side stiffness of the forward hook guide and the fore and aft stiffness of each drag pin to be equal. The net effect of this assumption is that the forward hook guide reacts approximately 96% of the drop test vehicle yawing moment. For a comparison of these loads to previous X-15 analysis design loadings, see Volume 1 of this document.

Quade, D. A.↗

NASA NTRS1984

Hole interaction and load introduction effects for compression-loaded laminates with holes

The effects of hole interaction and load introduction for (+ or - 30/90) sub 8 (s) and (+ or - 45) sub 12 (s) laminates with holes was conducted. A hole interaction region was identified in some specimens, and this region was ineffective for carrying load. The membrane stiffness of a specimen with a hole interaction region is less than the membrane stiffness of a similar specimen without a hole interaction region. A load introduction effect that occurs on the sublaminate level was also observed. This effect can cause a nonuniform load diffusion into the interior of the specimen.

Shuart, M. J.↗

NASA NTRS1987

Probabilistic load model development and validation for composite load spectra for select space propulsion engines

A major task of the program to develop an expert system to predict the loads on selected components of a generic space propulsion engine is the design development and application of a probabilitic loads model. This model is being developed in order to account for the random nature of the loads and assess the variable load ranges' effect on the engine performance. A probabilistic model has been developed. The model is based primarily on simulation methods, but also has a Gaussian algebra method (if all variables are near normal), a fast probability integrator routine (for the calculation of low probability events), and a separate, stand alone program for performing barrier crossing calculations. Each of these probabilistic methods has been verified with theoretical calculations using assumed distributional forms.

Kurth, R.↗

NASA NTRS1986

Effects of winglets on a first-generation jet transport wing. 7: Sideslip effects on winglet loads and selected wing loads at subsonic speeds for a full-span model

The effect of sideslip on winglet loads and selected wing loads was investigated at high and low subsonic Mach numbers. The investigation was conducted in two separate wind tunnel facilities, using two slightly different 0.035-scale full-span models. Results are presented which indicate that, in general, winglet loads as a result of sideslip are analogous to wing loads caused by angle of attack. The center-of-pressure locations on the winglets are somewhat different than might be expected for an analogous wing. The spanwise center of pressure for a winglet tends to be more inboard than for a wing. The most notable chordwise location is a forward center-of-pressure location on the winglet at high sideslip angles. The noted differences between a winglet and an analogous wing are the result of the influence of the wing on the winglet.

Meyer, Robert R., Jr.↗

NASA NTRS1950

Time histories of horizontal-tail loads, elevator loads, and deformations on a jet-powered bomber airplane during abrupt pitching maneuvers at approximately 20,000 feet

Time histories are presented of horizontal-tail loads, elevator loads, and deformations on a jet-powered bomber during abrupt pitching maneuvers at a pressure altitude of approximately 20,000 feet. The normal and pitching accelerations measured varied from -0.90b to 3.41g and from -0.73 to 0.80 radian per second per second (sic), respectively, with a Mach number variation of from 0.40 to o.75. The maximum horizontal-tail load measured was 17,250 pounds down. The maximum elevator load was 1900 pounds up. The stabilizer twisted a maximum of 0.76 degrees leading edge down at the tip. The greatest fuselage deflection at the tail was about 1.7 inches down.

Wiener, Bernard↗

NASA NTRS1940

Effect of wing loading, aspect ratio, and span loading of flight performances

An investigation is made of the possible improvements in maximum, cruising, and climbing speeds attainable through increase in the wing loading. The decrease in wing area was considered for the two cases of constant aspect ratio and constant span loading. For a definite flight condition, an investigation is made to determine what loss in flight performance must be sustained if, for given reasons, certain wing loadings are not to be exceeded. With the aid of these general investigations, the trend with respect to wing loading is indicated and the requirements to be imposed on the landing aids are discussed

Gothert, B↗

NASA NTRS2008

E-2C Loads Calibration in DFRC Flight Loads Lab

Objectives: a) Safely and efficiently perform structural load tests on NAVAIR E-2C aircraft to calibrate strain gage instrumentation installed by NAVAIR; b) Collect load test data and derive loads equations for use in NAVAIR flight tests; and c) Assist flight test team with use of loads equations measurements at PAX River.

Schuster, Lawrence S.↗

DOE OSTI2017

Load power device and system for real-time execution of hierarchical load identification algorithms

A load power device includes a power input; at least one power output for at least one load; and a plurality of sensors structured to sense voltage and current at the at least one power output. A processor is structured to provide real-time execution of: (a) a plurality of load identification algorithms, and (b) event detection and operating mode detection for the at least one load.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Plug load management system with load identification

The present disclosure relates to a plug load management system having automatic and dynamic load detection, meaning it has the ability to identify devices that are plugged into outlets of a building and determine the location of the plug load down to the specific outlet. When a device is moved, the plug load management system can determine this change and update accordingly.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗