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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

CP‐SyNet: A tool for generating customised cyber‐power synthetic network for distribution systems with distributed energy resources

Abstract The integration of distributed energy resources and advancement in information technology has enabled the transition of traditional power distribution systems to active cyber‐physical distribution systems. A growing amount of research has been done on the modelling, analysis, and optimisation of power distribution system behaviour. However, existing publicly available distribution test feeders are limited in numbers and have minimal features. Furthermore, these test feeders do not include cyber models and are not customisable. To bridge this gap, we propose and develop Cyber‐physical synthetic distribution system network (CP‐SyNet), a tool for generating customisable cyber‐physical synthetic distribution test feeders. CP‐SyNet generates three‐phase unbalanced test feeders according to users' requirements, while simultaneously considering both the cyber side and the physical side of the network for cyber‐physical analysis. The physical test network is developed using a graph‐theoretical approach that employs information from existing test feeders. The cyber side considers an equivalent communication network by transforming the physical topology into possible and feasible simulated network. Two examples are presented to demonstrate the feasibility of the proposed framework to generate cyber‐physical test feeders.

Wang, Lusha↗
DOE OSTI2021

Design and evaluation of a cyber‐physical testbed for improving attack resilience of power systems

Abstract A power system is a complex cyber‐physical system whose security is critical to its function. A major challenge is to model, analyse and visualise the communication backbone of the power systems concerning cyber threats. To achieve this, the design and evaluation of a cyber‐physical power system (CPPS) testbed called Resilient Energy Systems Lab (RESLab) are presented to capture realistic cyber, physical, and protection system features. RESLab is architected to be a fundamental platform for studying and improving the resilience of complex CPPS to cyber threats. The cyber network is emulated using Common Open Research Emulator (CORE), which acts as a gateway for the physical and protection devices to communicate. The physical grid is simulated in the dynamic time frame using Power World Dynamic Studio (PWDS). The protection components are modelled with both PWDS and physical devices including the SEL Real‐Time Automation Controller (RTAC). Distributed Network Protocol 3 (DNP3) is used to monitor and control the grid. Then, the design is exemplified and the tools are validated. This work presents four case studies on cyberattack and defence using RESLab, where we demonstrate false data and command injection using Man‐in‐the‐Middle and Denial of Service attacks and validate them on a large‐scale synthetic electric grid.

Sahu, Abhijeet↗
DOE OSTI2021

Strategy for distributed controller defence: Leveraging controller roles and control support groups to maintain or regain control in cyber-adversarial power systems

Distributed controllers play a prominent role in electric power grid operation. The coordinated failure or malfunction of these controllers is a serious threat, where the resulting mechanisms and consequences are not yet well-known and planned against. If certain controllers are maliciously compromised by an adversary, they can be manipulated to drive the system to an unsafe state. The authors present a strategy for distributed controller defence (SDCD) for improved grid tolerance under conditions of distributed controller compromise. The work of the authors’ first formalises the roles that distributed controllers play and their control support groups using controllability analysis techniques. With these formally defined roles and groups, the authors then present defence strategies for maintaining or regaining system control during such an attack. A general control response framework is presented here for the compromise or failure of distributed controllers using the remaining, operational set. The SDCD approach is successfully demonstrated with a 7-bus system and the IEEE 118-bus system for single and coordinated distributed controller compromise; the results indicate that SDCD is able to significantly reduce system stress and mitigate compromise consequences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

SQL and NoSQL Databases for Cyber Physical Production Systems in Internet of Things for Manufacturing (IoTfM)

Abstract In this paper, the design and performance differences between Relational Database Management Systems (RDBMS) and NoSQL Database Systems are examined, with attention to their applicability for real-world Internet of Things for manufacturing (IoTfM) data. While previous work has extensively compared SQL and NoSQL for both generalized and IoT uses, this work specifically examines the tradeoffs and performance differences for manufacturing applications by using a high-fidelity data set collected from a large US manufacturing firm. Growing an IoT system beyond the pilot stage requires scalable data storage; this work seeks to determine the impact of selected database systems on data write performance at scale. Payload size and message frequency were used as the primary characteristics to maintain model fidelity in simulated clients. As the number of simulated asset clients grow, the data write latency was calculated to determine how both database systems’ performance were affected. To isolate the RDBMS and NoSQL differences, a cloud environment was created using Amazon Web Services (AWS) with two identical data ingestion pipelines: writing data to an RDMBS (1) using AWS Aurora MySQL, and (2) using AWS DynamoDB NoSQL. The findings may provide guidance for further experimentation in large-scale manufacturing IoT implementations.

Gamero, David↗
NASA NTRSDate not supplied

Uncertainty Quantification using Deep Ensembles for Decision Making in Cyber-Physical-Human Systems

In this paper and its companion, Differential Equation Approximation Using Gradient-Boosted Quantile Regression, Robison et al., we examine an approach to quantifying model uncertainty with the aim of increasing the trustworthiness of computational models in human-machine interactions. In Differential Equation Approximation Using Gradient-Boosted Quantile Regression, we focus on gradient-boosted decision trees, while in this one, we give more details about deep ensembles. Uncertainty quantification is crucial for building trustworthy autonomous decision-making agents in human-machine teams. There are two types of uncertainties: aleatoric and epistemic. The former is related to the inherent stochasticity (noise) of the process, whereas the latter is associated with the lack of knowledge or representation capability of models, such as neural networks. By lack of knowledge, we mean the model’s inability to accurately predict outputs for all possible inputs. The aleatory uncertainty can be estimated fairly easily with, for example, filters, whereas epistemic uncertainty is challenging to compute. This paper uses deep ensembles to quantify both aleatory and epistemic uncertainty. It can act as an uncertainty-aware surrogate transition model for decision-making frameworks. "Uncertainty-aware" means that the surrogate transition model should make predictions along with confidence in those predictions. In the context of decision-making, the transition models are ordinary differential equations (ODEs). Since ODEs can be simulated to make one-step or multi-step predictions, a good surrogate model for them should perform reasonably well in both modes. In a multi-step approach, the trajectory sampling method TS∞ was used to propagate uncertainty over multiple steps. The cartpole dynamical system was selected to demonstrate the ability of deep ensembles as good surrogate transition models for decision-making frameworks. The deep ensembles modeled the dynamics of cartpole ODEs and made uncertainty-aware predictions in single-step and multi-step transition modes.

CPH systems↗
DOE OSTI2020

Autonomous reconfigurable virtual sensing system for cyber-attack neutralization

An industrial asset may be associated with a plurality of monitoring nodes, each monitoring node generating a series of monitoring node values over time representing current operation of the industrial asset. An abnormality detection computer may determine that at least one abnormal monitoring node is currently being attacked or experiencing a fault. A virtual sensing estimator may continuously execute an adaptive learning process to create or update virtual sensor models for the monitoring nodes. Responsive to an indication that a monitoring node is currently being attacked or experiencing a fault, the virtual sensing estimator may be dynamically reconfigured to estimate a series of virtual node values for the abnormal monitoring node or nodes based on information from normal monitoring nodes and appropriate virtual sensor models. The series of monitoring node values from the abnormal monitoring node or nodes may then be replaced with the virtual node values.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Adaptive, self-tuning virtual sensing system for cyber-attack neutralization

An industrial asset may have a plurality of monitoring nodes, each monitoring node generating a series of monitoring node values over time representing current operation of the industrial asset. An abnormality detection computer may determine that an abnormal monitoring node is currently being attacked or experiencing a fault. An autonomous, resilient estimator may continuously execute an adaptive learning process to create or update virtual sensor models for that monitoring node. Responsive to an indication that a monitoring node is currently being attacked or experiencing a fault, a level of neutralization may be automatically determined. The autonomous, resilient estimator may then be dynamically reconfigured to estimate a series of virtual node values based on information from normal monitoring nodes, appropriate virtual sensor models, and the determined level of neutralization. The series of monitoring node values from the abnormal monitoring node or nodes may then be replaced with the virtual node values.

Abbaszadeh, Masoud↗
NASA NTRS1973

Implementation experiences of NASTRAN on CDC CYBER 74 SCOPE 3.4 operating system

The implementation of the NASTRAN system on the CDC CYBER 74 SCOPE 3.4 Operating System is described. The flexibility of the NASTRAN system made it possible to accomplish the change with no major problems. Various sizes of benchmark and test problems, ranging from two hours to less than one minute CP time were run on the CDC CYBER SCOPE 3.3, Univac EXEC-8, and CDC CYBER SCOPE 3.4. The NASTRAN installation deck is provided.

Go, J. C.↗