Bayesian Surrogates for Modeling Natural Gas Price Uncertainty in a Realistic California Power System Test Case
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GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.
Abstract not provided.
A simulation system for use in testing a radar system comprises a coarse delay module, a fine delay module, and a doppler shift module. The coarse delay module is configured to receive a first stream of digital data samples that are sampled from a radar signal at a sample time period or a second stream of digital data samples that are processed by another simulation system component and delay the digital data samples by a selectable first delay time that is greater than or equal to the sample time period. The fine delay module is configured to receive the digital data samples and filter the digital data samples to represent delay by a selectable second delay time that is less than the sample time period. The doppler shift module is configured to receive the digital data samples and adjust a value of a frequency content of the fine delayed samples.
A testing system for evaluating the performance of an electrical/electronic UUT under dynamic operating conditions. The testing system includes a dynamic testing component (e.g., a centrifuge) for applying a stimulus to the UUT, and an iDAQ system configured to perform in situ data acquisition and real-time data analysis. The iDAQ system may also be subject to the stimulus. The iDAQ system includes a processor (e.g., an SoC) component, a power supply, a CR/I component, an IR component, and a single enclosure. The processor component may control the dynamic testing component, including varying in real-time the stimulus applied to the UUT. The processor component may include multiple input channels, and a high current/voltage subcomponent of the power supply may be configured to supply up to five hundred volts.
This paper presents a networked hardware-in-the-loop (HIL) simulation system for modeling large-scale power systems. Researchers have developed many HIL test systems for power systems in recent years. Those test systems can model both microsecond-level dynamic responses of power electronic systems and millisecond-level transients of transmission and distribution grids. By integrating individual HIL test systems into a network of HIL test systems, we can create large-scale power grid digital twins with flexible structures at required modeling resolution that fits for a wide range of system operating conditions. This will not only significantly reduce the need for field tests when developing new technologies but also greatly shorten the model development cycle. In this paper, we present a networked OPAL-RT based HIL test system for developing transmission-distribution coordinative Volt-VAr regulation technologies as an example to illustrate system setups, communication requirements among different HIL simulation systems, and system connection mechanisms. Impacts of communication delays, information exchange cycles, and computing delays are illustrated. Simulation results show that the performance of a networked HIL test system is satisfactory.
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This paper presents a network hardware-in-the-loop (HIL) simulation system for modeling large-scale power systems. Researchers have developed many HIL test systems for power systems in recent years. Those test systems can model both microsecond-level dynamic responses of power electronic systems and millisecond-level transients of transmission and distribution grids. By integrating individual HIL test systems into a network of HIL test systems, we can create large-scale power grid digital twins with flexible structures at required modeling resolution that fits for a wide range of system operating conditions. This will not only significantly reduce the need for field tests when developing new technologies but also greatly shorten the model development cycle. In this paper, we present a networked OPAL-RT based HIL test system for developing transmission-distribution coordinative Volt-VAR regulation technologies as an example to illustrate system setups, communication requirements among different HIL test systems, and connection mechanisms. Impacts of communication delays, information exchange cycles, and computing delays are illustrated. Simulation results show that the performance of a networked HIL test system is satisfactory.
A testing system for testing a missile component having a sense axis includes a centrifuge, a support arm, an orientation assembly, and a controller. The centrifuge rotates the orientation assembly about a vertical axis in a substantially horizontal plane. The orientation assembly includes a first motor, a first gimbal, and a gimballed support. The first motor has a first rotatable shaft defining a first gimbal axis. The first gimbal is coupled with the first rotatable shaft to rotate about the first gimbal axis while the centrifuge rotates the orientation assembly about the vertical axis such that missile component is simultaneously rotated about both the vertical axis and the first gimbal axis to simulate a missile launch of the missile component. The gimballed support is coupled with the first gimbal for supporting the missile component such that the sense axis of the missile component is not parallel to the substantially horizontal plane. The orientation assembly may also include a second gimbal that is rotated about a second gimbals axis by a second motor.
A testing system for testing a missile component having a sense axis includes a centrifuge, a support arm, an orientation assembly, and a controller. The centrifuge rotates the orientation assembly about a vertical axis in a substantially horizontal plane. The orientation assembly includes a first motor, a first gimbal, and a gimballed support. The first motor has a first rotatable shaft defining a first gimbal axis. The first gimbal is coupled with the first rotatable shaft to rotate about the first gimbal axis while the centrifuge rotates the orientation assembly about the vertical axis such that missile component is simultaneously rotated about both the vertical axis and the first gimbal axis to simulate a missile launch of the missile component. The gimballed support is coupled with the first gimbal for supporting the missile component such that the sense axis of the missile component is not parallel to the substantially horizontal plane. The orientation assembly may also include a second gimbal that is rotated about a second gimbals axis by a second motor.
Disclosed herein are systems, devices and methods for creep testing selected materials within a high-temperature molten salt environment. Exemplary creep testing systems include a load train for holding a test specimen under a load within a heated inert gas vessel. An extensometry system can be included to measure elongation of the test specimen while under load. The extensometry system can include fixed members and axially translating member that move along with the elongation of the test specimen, and the system can include a sensor to measure the relative axial motion between such components to measure elongation of the test specimen over time. The test specimen can include a cylindrical gage portion having an internal void filled with a molten salt during creep testing to simulate the corrosive effect of the molten salt on the specimen material during testing.
A two-terminator RF adapter for background noise measurement in a test environment comprises a system test port comprising a system test port termination and a system test port connector to connect to a system under test; and a data acquisition port, comprising a data acquisition port termination and a data acquisition port connector to connect to a data acquisition system.
This presentation provides an overview of the National Renewable Energy Laboratory's advanced distribution management system test bed overview.
LLNL produced two lots of lyophilized LAMP assay containing cartridges, LNL022818 and LNL071818, for testing on Gen3 BioID systems. Thirty-nine cartridges were tested for lot LNL022818. All thirty-nine cartridges performed as expected regarding correct positive control (IPC) and negative control (NTC) reactions. Five different templates were tested on these 39 cartridges. The BioID system detected 79.1% true positives and 9.3% false positives along with 98.9% true negatives and 2.9% false negatives for cartridge lot LNL022818. Thirty cartridges were tested for lot LNL071818. It was noted early that due to a manufacturing issue with the cartridges related to incomplete sonic welding of the cartridge, a large fraction of lot LNL071818 cartridges leaked. Insufficient funds were available to replace this lot of cartridges. Twenty three of 30 cartridges performed as expected regarding correct positive control (IPC) and negative control (NTC) reactions. Four different templates were tested on these 30 cartridges. The BioID system detected 77% true positives and 23% false positives along with 96.9% true negatives and 3.1% false negatives for cartridge lot LNL071818. For all tested cartridges using DNA template, DNA was added at a final concentration of 25 pg/ul. All reactions contained positive control DNA at 2.5 pg/ul.
Tests were performed to evaluate a sample conditioning unit for stack monitoring at Hanford Tank Waste Treatment and Immobilization Plant (WTP) exhaust stacks with elevated air temperatures. The LV-S2, LV-S3, HV-S3A and HV-S3B exhaust stacks are expected to have elevated air temperature and dew point. At these emission points, exhaust temperatures are too high to deliver the air sample directly to the required stack monitoring equipment. As a result, a sample conditioning system is considered to cool and dry the air prior to its delivery to the stack monitoring system. The method proposed for the sample conditioning is a dilution system that will introduce cooler, dry air to the air sample stream. This method of sample conditioning is meant to reduce the sample temperature while avoiding condensation of moisture in the sample stream. An additional constraint is that the ANSI/HPS N13.1-1999 standard states that at least 50% of the 10 µm aerodynamic diameter (AD) particles present in the stack free stream must be delivered to the sample collector. In other words, depositional loss of particles should be limited to 50% in the sampling, transport, and conditioning systems. Based on estimates of particle penetration through the LV-S3 sampling system, the diluter should perform with about 80% penetration or better to ensure that the total sampling system passes the 50% or greater penetration criterion.
In this paper, we address the problem of the optimal power dispatch of Distributed Generators (DGs) in Alternating Current (AC) networks, better known as the Optimal Power Flow (OPF) problem. We used, as the objective function, the minimization of power losses (P loss ) associated with energy transport, which are subject to the set of constraints that compose AC networks in an environment of distributed generation. To validate the effectiveness of the proposed methodology in solving the OPF problem in any network topology, we employed one 10-node mesh test system and three radial text systems: 10, 33, and 69 nodes. In each test system, DGs were allowed to inject 20%, 40%, and 60% of the power supplied by the slack generator in the base case. To solve the OPF problem, we used a master–slave methodology that integrates the optimization method Salps Swarm Algorithm (SSA) and the load flow technique based on the Successive Approximation (SA) method. Moreover, for comparison purposes, we employed some of the algorithms reported in the specialized literature to solve the OPF problem (the continuous genetic algorithm, the particle swarm optimization algorithm, the black hole algorithm, the antlion optimization algorithm, and the Multi-Verse Optimizer algorithm), which were selected because of their excellent results in solving such problems. The results obtained by the proposed solution methodology demonstrate its superiority and convergence capacity in terms of minimization of P loss in both radial and mesh systems. It provided the best reduction in minimum P loss in short processing times and showed excellent repeatability in each test system and scenario under analysis.
This document serves to provide information on all aspects of a system level thermal qualification test including a description of the setup and conduct of a full system fuel fire test in the Thermal Test Complex (TTC) Crosswind Test Facility (XTF). This might be referred to as a “handbook” for future tests. This information is intended to assist technologists and test directors in performing these types of tests in the future.