Engineering topics
Gunda, Thushara
Publications and source records attributed to Gunda, Thushara.
Importance of Plant-Level Characteristics for Assessment of Water-Related Threats to Electric Power Sector.
Abstract not provided.
A Complex Systems Approach to Develop a Multilayer Network Model for High Consequence Facility Security (Slides).
Abstract not provided.
Generating actionable information through the fusion of text and timeseries data: A case study of extreme weather effects at Photovoltaic plants.
Abstract not provided.
Empirical Insights for a Multi-Layered Network Complex Systems Model for Engineering Nuclear Security Systems.
Abstract not provided.
Empirical Insights for a Multi-Layered Network Complex Systems Model for Engineering Nuclear Security Systems-Slides.
Abstract not provided.
Malicious Threat Anticipation using an Adaptive Complex Systems Approach.
Abstract not provided.
An assessment of the value of principal component analysis for photovoltaic IV trace classification of physically-induced failures.
Abstract not provided.
Malicious Threat Anticipation using an Adaptive Complex Systems Approach.
Abstract not provided.
The Grey Zone Test Range Integrated Urban Simulation Environment
Sandia National Laboratories is part of the government test and evaluation team for the Defense Advanced Research Projects Agency Collection and Monitoring via Planning for Active Situational Scenarios program. The program is designed to better understand competition in the area between peace and conventional conflict when adversary actions are subtle and difficult to detect. For the purposes of test and evaluation, Sandia conducted a range of activities for the program: creation of the Grey Zone Test Range; design of the data stream for a user experiment conducted with U.S. Indo-Pacific Command; design, implementation, and execution of the formal evaluation; and analysis and summary of the evaluation results. This report details Sandia's activities and provides additional information on the Grey Zone Test Range urban simulation environment developed to evaluate the performer technologies.
A Complex Systems Approach to Develop a Multilayer Network Model for High Consequence Facility Security.
Abstract not provided.
Predicting Solar Plant Generation with Machine Learning Techniques
Accurately predicting power generation for PV sites is critical for prioritizing relevant operations & maintenance activities, thereby extending the lifetime of a system and improving profit margins. A number of factors influence power generation at PV sites, including local weather, shading and soiling losses, design of modules, DC mismatches, and degradation over time. Other external factors such as curtailment and grid outages can also have a notable impact on power generation. Machine learning techniques can be used to provide more accurate predictions of PV power production by accounting for important weather and climate information neglected by current industry methods. This article will cover the deficiencies of those methods and will show how machine learning can dramatically improve power generation predictions.