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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

UAS Contingency Management: The Effect of Different Procedures on ATC in Civil Airspace Operations

Current study examined the impact of four, currently-employed, unmanned aerial system (UAS) contingency procedures on air traffic controller (ATC) workload and performance. Controllers managed a Terminal Radar Approach Control (TRACON) sector while a single UAS passed through their sector. In four of the five scenarios, the UAS was scripted to experience an emergency (e.g., loss of command and control link, sudden drop in oil pressure) and then execute one of four possible contingency maneuvers. The fifth scenario was a baseline and contained no emergency. Results showed no significant differences in performance or workload among the four the study's design discussed.

Contingency Management↗
NASA NTRSDate not supplied

Effect of Carbon Dioxide Exposure on Physical and Cognitive Performance in a Simulated Spaceflight Contingency Scenario

Introduction: Carbon dioxide (CO2) produced by astronauts inside space suits can accumulate to levels that affect health and performance. The human health and performance risks associated with different levels of CO2 exposure in the flight environment remain an area of debate. The purpose of this study is to characterize the limit of acceptable performance (cognitive and physical) decrements and symptom severity for mission operations when subjected to elevated inspired CO2 levels in the spacesuit during contingency EVA scenarios. Methods: This study will create a simulation of a 1-hour contingency EVA walk back to the habitat, incorporating a passive treadmill and a fully immersive virtual reality (VR) simulation of a lunar EVA. Subjects will be asked to walk on a treadmill while breathing partial pressures of CO2 of 0, 5, 10, 15, 20, 25, 30mmHg for 1 hour at a time. Cognitive performance will be quantified using validated cognitive tests and measures of functional task performance embedded within the VR environment. Test subject symptoms and self-assessment of performance will be evaluated via survey. Results: This study currently has approval from NASA’s Institutional Review Board and has completed the Test Readiness Review process. Next steps for this research study include test subject recruitment, data collection, and analysis. Data collection is planned for calendar year 2022 and 2023. Discussion: This study will provide valuable information regarding how various partial pressures of CO2 exposure impact acute health, as well as cognitive and physical performance during simulated lunar EVA. This information will be vital in the assessment of overall risk associated with current hardware (vehicle and suit) design for upcoming exploration missions. It will also be invaluable for informing future standards and requirements.

carbon dioxide↗
DOE OSTI2020

An Improved Transmission Switching Algorithm for Managing Post-(N-1) Contingencies in Electricity Networks

In this presentation, we detail the shortcomings of existing transmission switching (TS) methodologies for preventing post-contingency loss of load in wide area electric power networks. Following that, we present a bi-level algorithm as an improvement to the state of the art. We show proof-of-concept level results that indicate computational viability, fast solutions, and the ability to pick the same best candidate as non computationally viable methods. Further, our method also minimizes load shedding after the contingency, thus enhancing the reliability and security of electricity supply. We also include a futuristic scenario of increased renewables and decreased natural gas sourced generators to show promising results. We grant permission for our presentation to be recorded and uploaded by the conference organizers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Generic Advanced Computing Framework for Executing Windows-based Dynamic Contingency Analysis Tool in Parallel on Cluster Machines

Dynamic contingency analysis tool (DCAT) has been developed to assess the impact and likelihood of extreme contingencies and potential cascading events across their systems and interconnections. By including more customized protection models and corrective actions into the Windows-based commercial tools, DCAT can help operators understand the cascading behavior and find mitigation approaches to reduce the risk of cascading outages in a more realistic manner. In order to further enhance the capability of DCAT, this paper presents a design of an advanced computing framework that enables DCAT to run on a cluster machine to improve its computational performance. This framework is generic and can be applied to other Windows-based simulation tools to fill the technical gap of applying advanced computing technology to vendors' Windows-based tools. The preliminary tests using medium to large power systems have shown the effectiveness of this framework and its potential for accelerating the adoption of advanced computing in utilities.

Advanced computing, dynamic contingency analysis, ↗
DOE OSTI2020

Probabilistic Look-ahead Contingency Analysis Integration with Commercial Tool and Practical Data

This paper presents an initial effort of integrating a smart sampling-based probabilistic look-ahead contingency analysis algorithm with General Electric (GE) Grid Solutions’ commercial energy management system (EMS) tool as a proof-of-concept for a seamless research tool integration using real world large-scale grid data. With the increasing impact of random forces such as variable generation and load, their stochastic behaviors cannot be ignored. However, the current practices are still dominated by deterministic tools. They are becoming increasingly inadequate for the future grid. The developed look-ahead contingency analysis algorithm incorporates forecast errors of variable energy and load to address the challenges brought by the increasing uncertainty of power system. The algorithm can reveal the potential violations caused by the variance of variable energy and load that are not normally detected by traditional deterministic approaches. To test its performance under practical environments ( real data with real commercial tool), significant efforts have been made to prepare test cases, modify GE EMS tool, and adapt an extreme value distribution algorithm to analyze the GE EMS’s violation-only outputs. The test results clearly demonstrate the effectiveness of the developed algorithm as new transformer violations that were not previously detected have been identified. This performance provides better situational awareness to engineers for their decision-making process under uncertainty. Moreover, with the discussion of computational performance and future work, this paper has shown a clear path for integrating the probabilistic algorithm with commercial tools to make us better equipped for the changing power system.

Modeling and simulation of power systems, constrai↗
DOE OSTI2019

Contingency rules for pathogen competition and antagonism in a genetically based, plant defense hierarchy

Abstract Plant defense against pathogens includes a range of mechanisms, including, but not limited to, genetic resistance, pathogen‐antagonizing endophytes, and pathogen competitors. The relative importance of each mechanism can be expressed in a hierarchical view of defense. Several recent studies have shown that pathogen antagonism is inconsistently expressed within the plant defense hierarchy. Our hypothesis is that the hierarchy is governed by contingency rules that determine when and where antagonists reduce plant disease severity. Here, we investigated whether pathogen competition influences pathogen antagonism using Populus as a model system. In three independent field experiments, we asked whether competition for leaf mesophyll cells between a Melampsora rust pathogen and a microscopic, eriophyid mite affects rust pathogen antagonism by fungal leaf endophytes. The rust pathogen has an annual, phenological disadvantage in competition with the mite because the rust pathogen must infect its secondary host in spring before infecting Populus . We varied mite–rust competition by utilizing Populus genotypes characterized by differential genetic resistance to the two organisms. We inoculated plants with endophytes and allowed mites and rust to infect plants naturally. Two contingency rules emerged from the three field experiments: (a) Pathogen antagonism by endophytes can be preempted by host genes for resistance that suppress pathogen development, and (b) pathogen antagonism by endophytes can secondarily be preempted by competitive exclusion of the rust by the mite. Synthesis : Our results point to a Populus defense hierarchy with resistance genes on top, followed by pathogen competition, and finally pathogen antagonism by endophytes. We expect these rules will help to explain the variation in pathogen antagonism that is currently attributed to context dependency.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Techniques for Improving Filters in Power Grid Contingency Analysis

In large-scale power transmission systems, predicting faults and preemptively taking corrective action to avoid them is essential to preventing rolling blackouts. The computational study of the constantly-shifting state of the power grid and its weaknesses is called contingency analysis. Multiple-contingency planning in the electrical grid is one example of a complex monitoring system where a full computational solution is operationally infeasible. We present a general framework for building and evaluating resource-aware models of filtering techniques for this type of monitoring.

Adolf, Robert D.↗
DOE OSTI2015

Integrated State Estimation and Contingency Analysis Software Implementation using High Performance Computing Techniques

Power system simulation tools are traditionally developed in sequential mode and codes are optimized for single core computing only. However, the increasing complexity in the power grid models requires more intensive computation. The traditional simulation tools will soon not be able to meet the grid operation requirements. Therefore, power system simulation tools need to evolve accordingly to provide faster and better results for grid operations. This paper presents an integrated state estimation and contingency analysis software implementation using high performance computing techniques. The software is able to solve large size state estimation problems within one second and achieve a near-linear speedup of 9,800 with 10,000 cores for contingency analysis application. The performance evaluation is presented to show its effectiveness.

Chen, Yousu↗
DOE OSTI2018

Fuel properties and steam reactivity of solid waste streams from contingency bases

Highlights: • Composite waste fuels were prepared for characterizing fuel property and reactivity. • Characterization of the composite fuels can be predicted by individual components. • A simple methodology for characterizing multi-component solid wastes was validated. • Plastic components increase carbon content and HHV of solid waste fuels. • Effects of fuel composition on syngas production and composition were explored. - Abstract: The solid waste generated from military contingency bases (CBs) is a potential fuel for appropriately-scaled, waste to energy (WTE) systems. To inform WTE system design, fuel properties of seventeen components of synthetic contingency-base waste recipes from the literature were characterized for ultimate analysis, heating values, proximate analysis and reactivity in a steam atmosphere at three temperature typical of gasification conditions. A simple mathematical approach was applied to predict the properties and reactive behavior of composite mixtures using the weighted sum of the corresponding components, assuming negligible interaction between components. The method was validated using prepared composite mixture samples. In addition, chemical equilibrium calculations for steam gasification of the composite samples were conducted to explore the effects of waste composition variability on syngas production and composition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Modeling Zone-3 Protection with Generic Relay Models for Dynamic Contingency Analysis

This paper presents a cohesive approach for calculating and coordinating the settings of multiple zone-3 protections for dynamic contingency analysis. The zone-3 protections are represented by generic distance relay models. A two-step approach for determining zone-3 relay settings is proposed. The first step is to calculate settings, particularly, the reach, of each zone-3 relay individually by iteratively running line open-end fault short circuit analysis; the blinder is also employed and properly set to meet the industry standard under extreme loading conditions. The second step is to systematically coordinate the protection settings of the zone-3 relays. The main objective of this coordination step is to address the over-reaching issues. We have developed a tool to automate the proposed approach and generate the settings of all distance relays in a PSS/E dyr format file. The calculated zone-3 settings have been tested on a modified IEEE 300 system using a dynamic contingency analysis tool (DCAT).

computational modeling↗
DOE OSTI2017

A Study of the Impact of Peak Demand on Increasing Vulnerability of Cascading Failures to Extreme Contingency Events

The vulnerability of large power systems to cascading failures and major blackouts has become evident since the Northeast blackout in 1965. Based on analyses of the series of cascading blackouts in the past decade, the research community realized the urgent need to develop better methods, tools, and practices for performing cascading-outage analysis and for evaluating mitigations that are easily accessible by utility planning engineers. PNNL has developed the Dynamic Contingency Analysis Tool (DCAT) as an open-platform and publicly available methodology to help develop applications that aim to improve the capabilities of power planning engineers to assess the impact and likelihood of extreme contingencies and potential cascading events across their systems and interconnections. DCAT analysis will help identify potential vulnerabilities and allow study of mitigation solutions to reduce the risk of cascading outages in technically sound and effective ways. Using the DCAT capability, we examined the impacts of various load conditions to identify situations in which the power grid may encounter cascading outages that could lead to potential blackouts. This paper describes the usefulness of the DCAT tool and how it helps to understand potential impacts of load demand on cascading failures on the power system.

indexes↗
DOE OSTI2024

Multi-Agent Graph-Attention Deep Reinforcement Learning for Post-Contingency Grid Emergency Voltage Control

Grid emergency voltage control (GEVC) is paramount in electric power systems to improve voltage stability and prevent cascading outages and blackouts in case of contingencies. While most deep reinforcement learning (DRL)-based paradigms perform single agents in a static environment, real-world agents for GEVC are expected to cooperate in a dynamically shifting grid. Moreover, due to high uncertainties from combinatory natures of various contingencies and load consumption, along with the complexity of dynamic grid operation, the data efficiency and control performance of the existing DRL-based methods are challenged. To address these limitations, we propose a multi-agent graph-attention (GATT)-based DRL algorithm for GEVC in multi-area power systems. Here, we develop graph convolutional network (GCN)-based agents for feature representation of the graph-structured voltages to improve the decision accuracy in a data-efficient manner. Furthermore, a cutting-edge attention mechanism concentrates on effective information sharing among multiple agents, synergizing different-sized subnetworks in the grid for cooperative learning. We address several key challenges in the existing DRL-based GEVC approaches, including low scalability and poor stability against high uncertainties. Test results in the IEEE benchmark system verify the advantages of the proposed method over several recent multi-agent DRL-based algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Distributionally Robust Resilience Enhancement Strategy for Distribution Networks Considering Decision-Dependent Contingencies

When performing the resilience enhancement for distribution networks, there are two obstacles to reliably model the uncertain contingencies: 1) decision-dependent uncertainty (DDU) due to various line hardening decisions, and 2) distributional ambiguity due to limited outage information during extreme weather events (EWEs). Here, to address these two challenges, this paper develops scenario-wise decision-dependent ambiguity sets (SWDD-ASs), where the DDU and distributional ambiguity inherent in EWE-induced contingencies are simultaneously captured for each possible EWE scenario. Then, a two-stage tri-level decision-dependent distributionally robust resilient enhancement (DD-DRRE) model is formulated, whose outputs include the optimal line hardening, distributed generation (DG) allocation, and proactive network reconfiguration strategy under the worst-case distributions in SWDD-ASs. Subsequently, the DD-DRRE model is equivalently recast to a mixed-integer linear programming (MILP)-based master problem and multiple scenario-wise subproblems, facilitating the adoption of a customized column-and-constraint generation (C&CG) algorithm. Finally, case studies demonstrate a remarkable improvement in the out-of-sample performance of our model, compared to its prevailing stochastic and robust counterparts. Moreover, the potential values of incorporating the ambiguity and distributional information are quantitatively estimated, providing a useful reference for planners with different budgets and risk-aversion levels.

decision-dependent uncertainty↗
DOE OSTI2016

Dynamic Contingency Analysis Tool

The Dynamic Contingency Analysis Tool (DCAT) is an open-platform and publicly available methodology to help develop applications that aim to improve the capabilities of power system planning engineers to assess the impact and likelihood of extreme contingencies and potential cascading events across their systems and interconnections. Outputs from the DCAT will help find mitigation solutions to reduce the risk of cascading outages in technically sound and effective ways. The current prototype DCAT implementation has been developed as a Python code that accesses the simulation functions of the Siemens PSS/E planning tool (PSS/E). It has the following features: It uses a hybrid dynamic and steady-state approach to simulating the cascading outage sequences that includes fast dynamic and slower steady-state events. It integrates dynamic models with protection scheme models for generation, transmission, and load. It models special protection systems (SPSs)/remedial action schemes (RASs) and automatic and manual corrective actions. Overall, the DCAT attempts to bridge multiple gaps in cascading-outage analysis in a single, unique prototype tool capable of automatically simulating and analyzing cascading sequences in real systems using multiprocessor computers. While the DCAT has been implemented using PSS/E in Phase I of the study, other commercial software packages with similar capabilities can be used within the DCAT framework.

Source record↗
DOE OSTI2016

Contingency Base Energy Management System

CB-EMS is the latest implementation of DSOM (Decision Support for Operations and Maintenance), which was previously patented by PNNL. CB-EMS WAS specifically designed for contingency bases for the US Army. It is a software package that is designed to monitor energy consumption at an Army contingency base to alert the camp manager when the systems are wasting energy. It's main feature that separates it from DSOM is it's ability to add systems using a plug and play menu system.

Source record↗
DOE OSTI2025

Seismic Contingency Auto Generator

This code takes in premade earthquake scenario XML files from USGS, power grid data, and converts them into a contingency file (.con file) that can be used by power grid solvers. Within the .con file are a number (Specified by the user) of contingencies that have randomly failed power transformers based on their likelihood of failure and peak ground acceleration (PGA) value around the transformer. The transformers' likelihood of failure was calculated based on a variety of finite element modeling on various transformer designed for specific transformer voltage classes. Parameters from these FEM were used to create generic fragility curves for transformers within a specific voltage class, which correspond with earthquake PGA values to produced a probability of failure for a given earthquake scenario. More refined versions of this process, such as specifying specific transformer design categories within a voltage class, could also be applied in future iterations of the software.

Vaagensmith, Bjorn [Idaho National Laboratory (INL↗
DOE OSTI2012

Contingency contractor optimization. Phase 2, requirements document

This document represents the results of Task 1 (Requirements Elicitation) in Phase 2 of the Contingency Contractor Optimization project. The aim of the overall effort is to create a tool for the contingency contractor element of total force planning during the Support for Strategic Analysis (SSA). The goal of Phase 2 is to develop an electronic storyboard prototype of a tool for use in conjunction with the strategic planning framework of the SSA that can be used for communication with senior decision makers and other strategic contract support stakeholders.

Jones, Katherine A.↗