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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 OSTI2023

Physics-informed Data-driven Degradation and Prognostic For Hydropower System

The hydropower system is a cyber-physical system, and the industry is experiencing high operation and maintenance (O&M) costs along with increasing unplanned outages, primarily due to aging equipment and new asset loading patterns. Current (O&M) practices in asset management are predominantly reactive rather than predictive and systematic. To address these challenges, a proposed physics-informed data-driven degradation model aims to simulate the health of components in various environments. Its purpose is to accurately characterize wear and fatigue in the pilot components of hydropower systems. Additionally, the model provides informed confidence in prognostic predictions concerning asset degradation and remaining life.

13 HYDRO ENERGY↗
DOE OSTI2021

A Review of Cyber-Physical Security for Photovoltaic Systems

In this paper, the challenges and a future vision of the cyber-physical security of photovoltaic (PV) systems are discussed from a firmware, network, PV converter controls, and grid security perspective. The vulnerabilities of PV systems are investigated under a variety of cyber-attacks, ranging from data integrity attacks to software-based attacks. A success rate metric is designed to evaluate the impact and facilitate decision making. Model-based and data-driven methods for threat detection and mitigation are summarized. In addition, the blockchain technology that addresses cyber-attacks in software and cyber networks is described. Simulation and experimental results that show the impact of cyber-attacks at the converter (device) and grid (system) levels are presented. Finally, potential research opportunities are discussed for next-generation, cyber-secure power electronics systems. These opportunities include multi-scale controllability, self-/event-triggering control, artificial intelligence/machine learning, hot patching, and online security. As of today, this study will be one of the few comprehensive studies in this emerging and fast-growing area.

14 SOLAR ENERGY↗
DOE OSTI2022

Harmonized Automatic Relay Mitigation of Nefarious Intentional Events (HARMONIE) - Special Protection Scheme (SPS)

The harmonized automatic relay mitigation of nefarious intentional events (HARMONIE) special protection scheme (SPS) was developed to provide adaptive, cyber-physical response to unpredictable disturbances in the electric grid. The HARMONIE-SPS methodology includes a machine learning classification framework that analyzes real time cyber-physical data and determines if the system is in normal conditions, cyber disturbance, physical disturbance, or cyber-physical disturbance. This classification then informs response, if needed and/or suitable, and included cyber-physical corrective actions. Beyond standard power system mitigations, a few novel approaches were developed that included a consensus algorithm-based relay voting scheme, an automated power system triggering condition and corrective action pairing algorithm, and a cyber traffic routing optimization algorithm. Both the classification and response techniques were tested within a newly integrated emulation environment composed of a real-time digital simulator (RTDS) and SCEPTRE™. This report details the HARMONIE-SPS methodology, highlighting both the classification and response techniques, and the subsequent testing results from the emulation environment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Model-Based Diagnostics and Mitigation of Cyber Threats

The report summarizes key tasks performed to develop a toolkit for detecting cyber-attack events in instrumentation and control (I&C) systems of nuclear power plants. The toolkit connects the state-of-the-art GPWR Simulator with the RELAP5 code providing best-estimate nuclear steam supply system (NSSS) analyses, via the application programming interface (API), and allows users to introduce potential cyber-attack scenarios into power plant operational simulation. This summary for the project reflects topical reports submitted during the project as well as a journal paper published in 2022. The focus areas of the summary include: (1) modeling I&C systems for the AP1000 Generation III+ nuclear plant and GPWR simulator, (2) simulation and monitoring of plant response to cyber-attack events, (3) API structure for the toolkit interfacing the GPWR simulator and RELAP5 code, and (4) restructuring of the three-loop NSSS software of GPWR to model the two-loop AP1000 structure. Discussed in some details are (a) the attack tree analysis assessing the susceptibility of the AP1000 I&C system, resulting in reactor trips, in terms of the attack possibility and component sensitivity and (b) realistic estimation of the time to steam generator trip due to cyber-intrusions in the GPWR Simulator. Finally, sample demonstrations of the cyber-security tool kit, in the form of the GPWR-RELAP5 API, are summarized.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Decentralised Reinforcement Learning for Dynamic Cyberattack Response in Microgrid Networks

Microgrids rely on communication networks for reliable operation, which makes them inherently vulnerable to cyberattacks. Such attacks can destabilise system dynamics and drive states away from their nominal operating trajectories. Although several physics-informed and machine learning-based strategies have been developed to counter these threats, the rapidly evolving cyber landscape enables adversaries to bypass static defences or rules-based mitigation approaches. This paper proposes a dynamic, online-trained and fully decentralised reinforcement learning (RL)-based cyberattack response framework to protect microgrids from evolving cyberattacks. The proposed framework deploys multiple deep Q-networks (DQNs), each associated with a distributed energy resource (DER), to enable localised and adaptive attack mitigation. In this framework, each DQN processes local voltage and frequency measurements—combined with intrusion detection system (IDS) alerts—as observations and rewards to guide decision-making. Extensive simulation studies demonstrate the robustness of the proposed framework under diverse attack scenarios and varying IDS-induced detection delays. Comparative analysis highlights its superiority over existing static or preexisting rules-based mitigation approaches. Finally, we present an analysis that shows the framework's scalability to real-life microgrids with more interacting agents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Detection of DoS Attacks Using ARFIMA Modeling of GOOSE Communication in IEC 61850 Substations

Integration of Information and Communication Technology (ICT) in modern smart grids (SGs) offers many advantages including the use of renewables and an effective way to protect, control and monitor the energy transmission and distribution. To reach an optimal operation of future energy systems, availability, integrity and confidentiality of data should be guaranteed. Research on the cyber-physical security of electrical substations based on IEC 61850 is still at an early stage. In the present work, we first model the network traffic data in electrical substations, then, we present a statistical Anomaly Detection (AD) method to detect Denial of Service (DoS) attacks against the Generic Object Oriented Substation Event (GOOSE) network communication. According to interpretations on the self-similarity and the Long-Range Dependency (LRD) of the data, an Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model was shown to describe well the GOOSE communication in the substation process network. Based on this ARFIMA-model and in view of cyber-physical security, an effective model-based AD method is developed and analyzed. Two variants of the statistical AD considering statistical hypothesis testing based on the Generalized Likelihood Ratio Test (GLRT) and the cumulative sum (CUSUM) are presented to detect flooding attacks that might affect the availability of the data. Our work presents a novel AD method, with two different variants, tailored to the specific features of the GOOSE traffic in IEC 61850 substations. The statistical AD is capable of detecting anomalies at unknown change times under the realistic assumption of unknown model parameters. The performance of both variants of the AD method is validated and assessed using data collected from a simulation case study. We perform several Monte-Carlo simulations under different noise variances. The detection delay is provided for each detector and it represents the number of discrete time samples after which an anomaly is detected. In fact, our statistical AD method with both variants (CUSUM and GLRT) has around half the false positive rate and a smaller detection delay when compared with two of the closest works found in the literature. Our AD approach based on the GLRT detector has the smallest false positive rate among all considered approaches. Whereas, our AD approach based on the CUSUM test has the lowest false negative rate thus the best detection rate. Depending on the requirements as well as the costs of false alarms or missed anomalies, both variants of our statistical detection method can be used and are further analyzed using composite detection metrics.

IEC 61850 electrical substations↗
DOE OSTI2021

A High-Fidelity Cyber-Physical Testbed-Based Benchmarking Dataset For Testing Operational Technology Specific Intrusion Detection Systems

Quality datasets serve a critical purpose in cyber security research. Data is needed to understand system behavior and develop security controls to protect critical systems. However, for critical infrastructure operational environments there is a lack of available datasets to study because of the high cost and specialized capabilities necessary to generate them. This paper documents the development of a dataset of high fidelity hardware in the loop laboratory simulated models of electric and natural gas distribution systems with real cyber attack test cases. A deep dive discussion for the experimental setup and controls for generating the data is provided along with observations from using the data in evaluating intrusion detection approaches.

Ashok, Aditya↗
DOE OSTI2023

Cyber Physical Subsystems under Adverse Conditions

The United States power grid is built upon complex cyber-physical systems (CPS), including many distributed energy resources (DERs) and microgrid structures. Due to cross-disciplinary domains that govern the interplay between cyber and physical technologies that sustain DERs, it is difficult to confidently predict how these systems will react under adverse conditions such as faults and cyberattacks. This project utilized power hardware in the loop (PHiL) to gain system behavior knowledge and robust understanding through experimentation and observational science. It also produced a large volume of datasets to feed into other efforts to advance high-fidelity model simulations and to support the design of novel control approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Modeling Communication Infrastructures of Cyber Physical Systems

This effort explores modeling cyber infrastructure, including communication devices like switches, routers, sensors, and controllers, and physical media attributes like propagation of radio signals, in coordination with power distribution system model layouts. To do this, the project studies real-world configurations to define axioms of how different communication media is deployed with control equipment. These axioms will be used to develop tools to generate realistic cyber infrastructure models from starting power system models. This effort leverages and builds on the co-simulation platform developed by the other RD2C projects. The axioms and tools will be validated and demonstrated with the NS3 simulation tool as part of a co-simulation to show the behavior/impacts of cyber infrastructure on control operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimization-Based Resiliency Verification in Microgrids via Maximal Adversarial Set Characterization

Critical energy infrastructures are increasingly relying on advanced sensing and control technologies for efficient and optimal utilization of flexible energy resources. Algorithmic procedures are needed to ensure that such systems are designed to be resilient to a wide range of cyber-physical adversarial events. This paper provides a robust optimization framework to quantify the largest adversarial perturbation that a system can accommodate without violating pre-specified resiliency metrics. We formulate the maximal adversarial set characterization as a bi-level optimization problem which is solved via Lagrangian relaxations.We illustrate the proposed algorithm on an islanded microgrid example: a modified IEEE 123-node feeder with distributed energy resources. Simulations are carried out to characterize the tolerable adversarial perturbations for varying levels of available flexibility (energy reserves).

Nazir, Mohammad Nawaf↗
DOE OSTI2022

Hybrid Data-Driven Physics-Based Model Framework Implementation: Towards a Secure Cyber-Physical Operation of the Smart Grid

False data injection cyber-attack detection models on smart grid operation have been much explored recently, considering analytical physics-based and data-driven solutions. Recently, a hybrid data-driven physics-based model framework for monitoring the smart grid is developed. However, the framework has not been implemented in real-time environment yet. In this paper, the framework of the hybrid model is developed within a real-time simulation environment. OPAL-RT real-time simulator is used to enable Hardware-in-the-Loop testing of the framework. IEEE 9-bus system is considered as a testing grid for gaining insight. The process of building the framework and the challenges faced during development are presented. The performance of the framework is investigated under various false data injection attacks.

false data injection attack, machine learning, sta↗
DOE OSTI2020

Communication-efficient certificate revocation management for Advanced Metering Infrastructure and IoT Integration

Advanced Metering Infrastructure forms a communication network for the collection of power data from smart meters in Smart Grid. As the communication between smart meters could be secured utilizing public-key cryptography, however, public-key cryptography still has certain challenges in terms of certificate revocation and management particularly related distribution and storage overhead of revoked certificates. To address this challenge, in this work, we propose a novel revocation management approach by utilizing cryptographic accumulators which reduces the space requirements for revocation information significantly and thus enables efficient distribution of such information to all smart meters. We implemented the proposed approach on both ns-3 network simulator and a testbed. We demonstrated its superior performance with respect to traditional methods for revocation management.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Signal Decomposition for Intrusion Detection in Reliability Assessment in Cyber Resilience (Summary Report)

The complexity of assuring cyber resilience for physical process interactions in connected systems such as energy grids increases dramatically as the coupling between processes becomes more direct and responsive. An example of this growing complexity is provided by Integrated Energy Systems (IES), in which various processes such as nuclear heat generation and commodity production are being directly coupled for increased responsiveness to highly variable signals such as market pricing or electricity demand. As such, the potential attack surface of the coupled processes is larger than the two processes independently. Securing these complex systems requires two-fold monitoring: cybersecure monitoring for potential malicious incursion, and physics monitoring for system tampering. Physics monitoring includes analyzing the behavior of the signals within the system for anomalous behavior. This analysis has been shown to be insufficient if approached by only data-driven machine learning and artificial intelligence (MLAI) techniques or only low-level model comparison. Previous efforts at Purdue University suggested combining high-fidelity models with MLAI algorithms as a basis for a software tool for detecting anomalies in physical processes. This work built on that suggestion, developing an advanced library for signal decomposition and analysis using both MLAI and high-fidelity physics algorithms for greatly improved anomaly detection, especially false data injection. This software can be used as part of a secure imbedded intelligence (SEI) system designed under Consequence-driven Cyber-informed Engineering (CCE) for complex coupled systems. This library established a foundation for online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in digital signals representing physical processes. Demonstrations carried out throughout the development highlight the effective use of characterization algorithms to detect signal perturbations, particularly triangle attack-style perturbations, in three wide-ranging applications: seismic monitoring, nuclear thermal hydraulics system simulation, and custom manufacturing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Opportunities and Challenges in Metal Forming for Lightweighting: Review and Future Work

Abstract The purposes of this review are to summarize the historical progress in the last 60 years of lightweight metal forming, to analyze the state-of-the-art, and to identify future directions in the context of Cyber-physically enabled circular economy. In honoring the 100th anniversary of the establishment of the Manufacturing Engineering Division of ASME, this review paper first provides the impact of the metal forming sector on the economy and historical perspectives of metal forming research work published by the ASME Journal of Manufacturing Science and Engineering, followed by the motivations and trends in lightweighting. To achieve lightweighting, one needs to systematically consider: (1) materials and material characterization; (2) innovative forming processes; and (3) simulation tools for integrated part design and process design. A new approach for process innovation, i.e., the Performance-Constraints-Mechanism-Innovation (PCMI) framework, is proposed to systematically seek new processes. Finally, trends and challenges for the further development in circular economy are presented for future exploration.

Engineering↗
DOE OSTI2022

Implementation Aspects of Smart Grids Cyber-Security Cross-Layered Framework for Critical Infrastructure Operation

Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitates the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks, though, create a roam for cyber-threats. An adversary can launch an attack on the communication network, which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data, or intercepting messages. Using machine learning-based processing on data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. In this paper, a co-simulation of cyber-security for cross-layer strategy is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection as well as identification of anomalies in the operation of the power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three controller software-defined networking (SDN) framework is proposed that utilizes the Open Network Operating System (ONOS) cluster. According to the findings of our suggested architecture, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. This provides for a higher flow of data throughout the network while decreasing congestion caused by a single controller’s processing restrictions. Furthermore, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based techniques in terms of attack classification. The performance of the framework is investigated under various types of communication attacks.

cross-layered↗
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↗
DOE OSTI2023

Virtualizing Industrial Control Networks for Cyber Resilience Experiments

Industrial control systems (ICS) networks are undergoing constant shifts to accommodate new security measures. It is challenging to test varying network configurations and security tools with physical systems as they typically include large, expensive equipment. Not only this, but researchers often do not have access to this type of equipment for development of new security tools and techniques. As a solution to these issues, this work presents a set of tools for utilizing GNS3 and Docker as a virtual ICS network. Additionally, the virtual network can be attached to physical devices including network switches, hardware simulations, and intelligent electronic devices (IEDs). Two case studies showcase a relatively complex automatically generated network and an attack on a simple ICS network with an example mitigation.

42 ENGINEERING↗