Building an Analytical Framework to Measure Offshore Infrastructure Integrity, Identify Risk, and Strategize Future Use for Oil and Gas
Presented by LRST at the ESRI User Virtual Conference, July 13-15, 2020, 7/13
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Presented by LRST at the ESRI User Virtual Conference, July 13-15, 2020, 7/13
Presented by LRST at GIS Week, 10/6
Abstract not provided.
Presented by LRST at the Offshore Project Review Meeting, 10/26
Abstract not provided.
This presentation highlights the key developments of the ARAP-e NODES project, including the innovative real-time optimal power flow (RT-OPF) algorithm for distributed energy resource (DER) management, trip planning for T+D coordination, and extensive validation of the RT-OPF algorithm in NREL ESIF laboratory with 100+ physical hardware devices, controller-hardware-in-the-loop test at Southern California Edison (SCE), and two field demonstrations.
In this edition of Journal of Information Warfare, we explore a spectrum of concepts at the macro cyber scale. The papers in this special edition are organized into three main categories: Foundations, Tools & Technologies, and Policy & Strategy.
AGU Fall Meeting 2020 (Conference), Virtual, December 1- 17, 2020
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The network of more than 2.5 million miles of natural gas pipelines the U.S. are exposed to a range of vulnerabilities, ranging from extreme weather, to human behavior, and increasingly, to cyber threats. Advanced, data-driven analytics, including the use of machine learning and artificial intelligence, afford an opportunity to identify potential vulnerabilities, better understand risks, and help mitigate vulnerabilities in the network. At NETL, machine learning is being used to explore pipeline vulnerabilities and identify significant clusters of failure events, including failures due to weather, human behavior, and materials. These findings are being used to support the development of new technologies, including sensors, but can also be used to evaluate and inform mitigation strategies to reduce risks and vulnerabilities throughout the network.