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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Review of Machine Learning Applications in Power System Resilience

The integration of power electronics enabled devices and the high penetration of renewable energy drastically increase the complexity of power system operation and control. Power systems are still vulnerable to large-scale blackouts caused by extreme natural events or man-made attacks. With the recent development in artificial intelligence technique, machine learning has shown a processing ability in computational, perceptual and cognitive intelligence. It is an urgent challenge to integrate the advanced machine learning technology and large amount of real-time data from wide area measurement systems and intelligent electronic devices, in order to effectively enhance power system resilience and ensure the reliable and secure operation of power systems. Therefore, this paper aims to systematically review the existing application of machine learning methods on power system resilience enhancement, to expand the interest of researchers and scholars in this topic, and to jointly promote the application of artificial intelligence in the field of power systems.

Deep Learning↗
DOE OSTI2021

Cyber-Physical Event Emulation-Based Transmission-and-Distribution Co-Simulation for Situational Awareness of Grid Anomalies (SAGA)

Energy management of transmission and distribution networks (T&D) is becoming more challenging with the accelerated adoption of distributed energy resources (DERs)-such as distributed photovoltaic generation and battery energy storage systems (BESS)-on the electric grid. To better analyze the impacts of DERs on both transmission and distribution systems, a comprehensive T&D co-simulation platform is developed. Further, with DERs more actively participating in system operation-e.g., by providing real-time grid services-their cyber vulnerability needs to be better understood to maintain system reliability. This paper discusses a cyber-physical events emulation-based T&D co-simulation platform to perform comprehensive cyber events emulations, physical simulation, and analysis of interdependent impacts. Results from the case studies-which show how cyber events on a synthetic distribution network can impact operations on the transmission and distribution network-validate that the proposed T&D cosimulation platform can perform cyber-physical events emulation and produce response in near realtime; therefore, with extensive simulation using the proposed co-simulation platform, the system operators can accumulate adequate training data for system situational awareness of grid anomalies.

anomalies detection↗
DOE OSTI2024

ByzSec — A Multi-layered Byzantine Resilient Architecture for Bulk Power System Protective Relays

Reliability, selectivity, and sensitivity are the fundamental attributes of any protection system, acting as the main drivers in the selection of schemes, and equipment. In high-voltage systems, microprocessor-based relays represent the industry’s preferred solution, providing engineers with a vast array of benefits. However, they remain vulnerable to cybersecurity events that may compromise their functionality. To help mitigate against potential cybersecurity risks, this paper presents a fault-tolerant, Byzantine Resilient (BR) architecture that significantly increases the cybersecurity attributes of a protection system while minimizing the amount of performance impacts and integration overheads introduced. The solution relies on an array of independent relays that utilize robust consensus methods (based on Spire [1], [2]) to ensure correct system behavior is achieved even when a relay has been compromised. Furthermore, the solution has been complemented with a custom-built Situational Awareness engine that can be used to detect and identify potential threats. The implemented solution has been developed in consultation with three hardware vendors and has been tested to comply with the performance requirements of a 345kV differential protection scheme (87T). The results indicate that the proposed architecture is a comprehensive solution that: supports the strict correctness and performance requirements of the bulk power grid while providing a cost-effective alternative that offers a seamless, long-term solution.

byzantine security, Fault Tolerant Application Sof↗
DOE OSTI2023

Towards Smart Grids Enhanced Situation Awareness: A Bi-Level Quasi-Static State Estimation Model

Smart Grid situational awareness is provided by Energy Management Systems. A core process of these systems is State Estimation. The great majority of state estimators model the Smart Grid through a set of nonlinear algebraic equations, named the measurement model. Problem formulation considers the Gauss solution. Several model improvements have been presented regarding the Gauss solution, aiming between others to provide measurement noise robustness to the state estimation process. While considerable effort has been focused on such developments, state estimation is still constrained by the implicit modelling error, and thus inevitably vulnerable to cyber-threats. In this work, a state estimation bi-level formal model is presented towards Smart Grids enhanced situational awareness considering the concepts of synthetic measurements and innovation. Comparative test results with the state-of-the-art on the IEEE 14-bus system are presented highlighting improved situational awareness to bad data. Easy-to-implement model, without hard-to-derive parameters, built-on the classic weighted least squares solution, highlight potential aspects for real-life implementation.

cyber security↗
DOE OSTI2023

Wildfire Risk Evaluation Framework for Grid Operations and Planning

The United States (US) environmental protection agency's (EPA's) climate change indicators for wildfires show a long-term trend of increased annual wildfire activity, larger wildfire size, and more variable dynamics in wildfire behavior. This has caused more frequent preemptive public safety power shutoff (PSPS) events in the regions with recognized high wildfire risk. These preemptive power shutoffs attempt to prevent the ignition of a wildfire but it nonetheless renders the transmission line non-operational, which often sheds load of downstream communities. As wildfires and PSPS events become more frequent, it is crucial to find the communities most at risk of load shedding. To that end, this paper proposes the wildfire risk evaluation of the system (WiRES) framework, which is a performance-based framework that translates extreme weather-related and PSPS event probabilities into a cumulative probability of a non-operational transmission line. This study also provides a geospatial visualization tool that breaks down the entire western electrical coordinating council (WECC) region into 50 km grid cells which can be used to 1) filter out transmission lines with higher than a threshold outage probability, and 2) graphically discover the affected regions and their biophysical and socioeconomic metrics. %such as the social vulnerability index, population density, gross domestic product, etc. Lastly, an impact assessment study is conducted which connects the results of the proposed framework to python and powerworld-based contingency analysis to highlight the applicability of the framework.

Wildfire, Resilience, Extreme Events, Grid Reslien↗
DOE OSTI2026

SDN-Based Smart Cyber Switching (SCS) for Cyber Restoration of a Digital Substation

In recent years, critical infrastructure and power grids have increasingly been targets of cyber-attacks, causing widespread and extended blackouts. Digital substations are particularly vulnerable to such cyber incursions, jeopardizing grid stability. This paper addresses these risks by proposing a cybersecurity framework that leverages software-defined networking (SDN) to bolster the resilience of substations based on the IEC- 61850 standard. The research introduces a strategy involving smart cyber switching (SCS) for mitigation and concurrent intelligent electronic device (CIED) for restoration, ensuring ongoing operational integrity and cybersecurity within a substation. The SCS framework improves the physical network’s behavior (i.e., leveraging commercial SDN capabilities) by incorporating an adaptive port controller (APC) module for dynamic port management and an intrusion detection system (IDS) to detect and counteract malicious IEC-61850-based sampled value (SV) and generic object-oriented system event (GOOSE) messages within the substation’s communication network. The framework’s effectiveness is validated through comprehensive simulations and a hardware-in-the-loop (HIL) testbed, demonstrating its ability to sustain substation operations during cyber-attacks and significantly improve the overall resilience of the power grid.

Liu, Chen-Ching (ORCID:0000000289417958)↗
DOE OSTI2021

BACKFLIP: Identification of Materials and Changes Upon Aging of Emerging Fluoropolymer-Free and Industry-Benchmark PV Backsheets

Extending lifetimes of photovoltaic (PV) modules and decreasing manufacturing and decommissioning costs are motivating research of novel PV module backsheet materials. Backsheet integrity is critical to the safety and performance of a PV module, however, the polymeric materials are often vulnerable to environmental degradation. The long-term durability of emerging polyolefin (PO) materials is being examined to inform regarding the possible replacement of laminated backsheets containing polyethylene terephthalate (PET) cores and fluoropolymer protective inner/outer layers. This study compares traditional PET-based backsheets with novel, co-extruded, and recyclable PO-based backsheets after being subjected to hygrometric and UV photolytic accelerated weathering. A large set of characterization methods will compare physical, chemical, structural, mechanical, and insulating properties of these backsheets and the resulting mini-module (MiMo) PV performances. The study of chemical composition, glass- and phase-transition temperatures, degree of crystallinity, and crystalline structure are described here.

mini-module↗
DOE OSTI2020

Impact of Aggregated PV on Subsynchronous Torsional Interaction: Preprint

This paper investigates induced oscillation (specifically subsynchronous torsional interactions) in power systems arising from photovoltaic (PV) power modulation. Time domain simulations of a modified IEEE first-benchmark system are presented and analyzed. In the IEEE benchmark system, a PV generator was added at the same bus where the synchronous generator is connected. The impact of different parameters of the PV inverters volt-volt-ampere reactive (volt-var) curve on SSTI is examined. The most severe impact found arises from the time response of the inverters reactive power change. A second-order time response with natural frequency matching the rotor speed deviation oscillation frequency and low-damping ratio quickly drives the system to an unstable region. Although it is highly unlikely that conditions necessary for distributed energy resource-induced oscillations would occur in the field, with knowledge of this vulnerability in mind, inverters' control parameters can be designed to avoid it.

aggregated↗
DOE OSTI2020

Quantitative Prediction of Perovskite Stability Using Accelerated Testing and Machine Learning

PV technologies based on hybrid perovskites offer the potential for reducing solar cell costs, but they are particularly vulnerable to degradation by environmental factors such as moisture, oxygen, and illumination. Commercialization will require not only stable materials and device architectures but also accelerated testing protocols and models that can predict degradation from the accelerated testing data. Here, we report results from in situ photoluminescence (PL) and photoconductivity (PC) measurements during perovskite degradation with simultaneous optical transmittance (Tr) measurements or reflected dark field (DF) imaging. From PL, PC, Tr, and DF, we determine (respectively) the steady-state quasi-Fermi level splitting, the mean effective carrier diffusion length, the extent of conversion of perovskite to higher bandgap degradation products, and the extent of scattering from domains with different orientation or composition, all as a function of time during degradation. Simultaneous measurement of PL-PC-Tr or PL-PC-DF on perovskite absorbers in an environmental chamber over a wide range of humidity, oxygen, temperature, and illumination levels yields a rich data set. We use machine learning to develop a model that accurately predicts (within 10%) the time for the diffusion length to decrease to 85% of its initial value. The model takes the environmental conditions and the first few measurements from the PL-PC-Tr or PL-PC-DF experiment as input. Thus, the model provides a framework to interpret the results of accelerated testing of absorber materials. One of the dominant features in the model of degradation for CH 3 NH 3 PbI 3 is the initial rate at which transmittance increase. For devices with opaque contacts, the pixel-averaged rate of change of the intensity in dark-field images can be used in place of transmittance. Results are also presented on full PV devices with in-situ current-voltage (JV) measurements. Here, the data from simultaneous PL-DF-JV under environmental stresses reveal intimate connection between degradation and shunts and provide a framework to extend accelerated testing to devices. Further, the presentation reveals underlying universal behavior in degradation pathways over a broad range of environmental stresses and perovskite compositions.

14 SOLAR ENERGY↗
DOE OSTI2019

Development of a Host-Based Intrusion Detection and Control Device for Industrial Field Control Devices

Industrial control systems contain a multitude of devices deployed into the field to observe and control a physical process. Currently, most of these devices lack common host-based cybersecurity features, which can lead them to be seen as vulnerable targets. The Cymbiote device was developed to address this problem by examining multiple streams of data to determine what might indicate a potential threat, and to evaluate and execute appropriate response actions. This paper discusses the evolution of the Cymbiote device in response to evaluation and testing done on real-world hardware. Experimental results are discussed, as are the new developments that have been made to the device in response to the findings. In addition, we describe future research directions for this approach along with the experimental setup changes in response to the requirements for statistical rigor.

Rice, Theora R.↗
DOE OSTI2019

Demonstration of the Cybersecurity Framework through Real-world Cyber Attack

The Cybersecurity Framework (CSF) developed by the National Institute of Standards and Technology Cybersecurity, provides five concurrent and continuous functions to Identify, Protect, Detect, Respond, and Recover from cyber threats and vulnerabilities to buildings. Based on the core architecture and security best practices defined in the CSF guidelines, we developed the CSF webtools. The CSF webtools provide actionable functions that can be easily adopted by a facility operator to enhance their critical infrastructure security. The essence is captured in a set of “how-to” instructions for the facility operators to adopt, adapt, and apply to their critical infrastructure facilities. When considered together, these functions provide a high-level, strategic view of the lifecycle of an organization’s cybersecurity risk management. The CSF webtools provide an easy to follow set of cybersecurity best practices, policies, and procedures to improve the cybersecurity posture of our nation’s buildings and other critical infrastructures. The CSF webtools are also designed to facilitate communication of cybersecurity activities and outcomes across the organization from the executive to operations levels. Finally, the CSF webtools allow the critical infrastructure operators to assess the overall cybersecurity maturity and posture. The goal of this paper is to demonstrate the capabilities of the CSF webtools through an illustrative cyber-attack based on a real-world scenario.

cybersecurity framework, maturity model, critical ↗
DOE OSTI2020

Recommended Functionalities for Improving Cybersecurity of Distributed Energy Resources

The current electric grid is transitioning through increasing penetration of distributed energy resources (DERs), which include intermittent renewable generation resources on the distribution side. Both the monitoring and control of DERs require extensive data-exchange and communication networks. These networks lead to cyber vulnerabilities and risks of new kinds of cyberattacks that may be extremely destructive for power system operations. Although current standards, such as IEEE Std. 1547-2018, do not discuss cybersecurity measures for DERs, cybersecurity controls should be developed for securing DER systems at the device level, communications level, and applications level. This paper discusses the current industry's best practices related to DER cybersecurity and proposes recommended functionalities for improving the cybersecurity posture of DERs, specifically at the device/distribution level. These practical recommendations have been discussed and verified with the industry through a DER cybersecurity working group.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

On the Limits of EM Based Detection of Control Logic Injection Attacks In Noisy Environments

The difficulty in applying traditional security mechanisms in Industrial Control System (ICS) environments makes a large portion of these mission-critical assets vulnerable to cyber attacks. Therefore, there is a dire need for the development of novel security mechanisms specifically designed to protect such critical systems. Recently a lot of attention has been given to mechanisms that exploit the EM emanations of devices for defense purposes. Such practices may lead to the development of robust external and non-intrusive anomaly detection systems. Nevertheless, the majority of current work in the area neglects to consider the implications of real-life environments, particularly environmental noise. In this work, we explore the limits of EM-based anomaly detection towards identifying injection attacks in control logic software in noisy environments. Our study conducted upon both synthetically generated and real signals identified that indeed environmental noise might significantly degrade the accuracy of the anomaly detection process. Experiments done upon synthetic data indicated that assuming that signals are captured with high sampling rates, even minor code injections can be detected with above-90% accuracy in noisy environments where SNR is up to -2dB. This is true even if naive detection methods are considered. Moreover, experiments done using a real-life testbed attest that even single-instruction injections can be detected with near-perfect accuracy in relatively clean environments. Finally, noise-elimination techniques can drastically improve the reliability of the detection mechanism even in noisy environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Supply Chain Risk Management: Data Structuring

Supply chain risk management (SCRM) is an area of research that addresses both logistics concepts to maximize efficiency, reliability, and revenue as well as risk features, such as potential weak points, break points, and vulnerabilities within the supply chain. SCRM is used to find risks introduced at each node in a supply chain and how these risks can impact a company’s products, individuals, customers, and reputation. SCRM is a relatively new field, so standardized processes including data structuring are not fully documented. This paper explains the importance of a standard data structuring methodology and how it can enhance current SCRM efforts. Data ingest, structuring, and analysis are predominantly managed by humans. Automating some of the less complex steps can positively impact SCRM by allowing human analysts to focus on more strategic analyses. Types of data to be collected and structured are collected via publicly available information related to hardware, software, and corporate entities. After the data has been collected, the information is formatted in a specific manner, conforming to a schema, to allow for more effective and efficient ingest for further analysis. This paper outlines data structures used by Pacific Northwest National Laboratory for SCRM research and analysis purposes. These structures have been used for hundreds of analyses and have been successful in developing a common baseline. Data structuring is one of the first steps in data standardization, which will further mature and enhance the SCRM research area.

supply chain risk management, data structuring, re↗
DOE OSTI2024

Architecture Design for Remote Operation of Microreactors: Poster

The nuclear sector is pursuing a number of advanced reactor concepts, one of which is the microreactor, an advanced reactor characterized by a power output of less than 20 MWth. These reactors are intended for use in applications where traditional power solutions are economically or logistically impractical. One key feature required for the successful deployment of microreactors is a remote operation capability, which can dramatically cut staffing expenses by eliminating the need for licensed operators to be present at the site of each reactor and instead concentrate in a centrally located operations center. In moving to a remote operations framework for nuclear reactors, the number of potential attack surfaces for a cyber adversary looking to cause harm or disruption increases. Therefore, a robust cybersecurity architecture is required to mitigate these potential vulnerabilities. This paper explores the functional infrastructure, security, and communication requirements to adapt remote operations to nuclear applications. It then presents a reference architecture for remote operations of microreactors that applies best practices in cybersecurity and remote communications in the context of a digital twin remote operation system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Estimating Critical Customer Outages Resulting from Extreme Hurricanes

US power outage data has been collected by organizations such as Oak Ridge National Laboratory (ORNL) through Environment for Analysis Geo-Located Energy Infrastructure (EAGLE-I: freely available) and poweroutage.us (commercial data: available to purchase). However, these sources do not provide information specific to outages of critical customers. Critical customers include entities, facilities, and individuals whose continuous access to electricity is essential for public safety, emergency response, disaster recovery, the well-being of vulnerable populations, public safety and order, and public utilities such as natural gas, communications, water and sanitation. Identification and geolocation of critical customers is crucial for understanding and addressing the effects of power outages on essential services and ensuring that necessary measures are taken to maintain their operations during power disruptions. This work is a first step towards estimating the occurrences of critical customer outages and developing a critical customer power outage data repository. This work estimates outage incidents of critical customers through spatiotemporal mapping of power outage data, weather data, building data, and critical infrastructure network data. Our results show that critical customer effects vary across different counties. We provide appropriate mathematical explanations and simplifications to define and systematize the proposed approach.

Bhusal, Narayan [ORNL] (ORCID:0000000222752145)↗
DOE OSTI2024

Lamellar: A Rust-based Asynchronous Tasking and PGAS Runtime for High Performance Computing

Cybersecurity is one of the largest concerns in modern computing, impacting and dictating how governments, private corporations, and individuals interact with and live in an increasingly digital world. The NSA has recently released a memo [ 1] on “Software Memory Safety” where they highlight that both Microsoft and Google have stated around 70% of software vulnerabilities were due to memory safety issues. Although languages such as C and C++ provide freedom and flexibility with memory management, guaran- teeing safety falls mostly on the developer. The NSA recommends using “memory safe” languages whenever possible. In this paper we introduce Lamellar, an asynchronous tasking and PGAS HPC runtime written in Rust, one such "memory safe" language. We describe the entire Lamellar stack, from network interfaces to high- level abstractions such as distributed LamellarArrays and Active Messages. We conclude by showing comparable performance to legacy PGAS runtimes (e.g. OpenSHMEM) on a subset of the BALE kernel suite while maintaining strong memory safety principles.

HPC Software Systems, Rust Programming Language, P↗
DOE OSTI2024

Beyond the Bridge: Contention-Based Covert and Side Channel Attacks on Multi-GPU Interconnect

Recently, high-speed interconnects like NVLink have become a standard integration in modern multi-GPU systems, serving as a crucial bridge between CPUs and GPUs. This study highlights the vulnerability of multi-GPU systems to covert and side-channel attacks arising from congestion on these interconnects. An adversary, operating without special permissions, can extract private information about a victim's activities by monitoring NVLink congestion. Specifically, we leverage this insight to develop a covert channel attack across two GPUs, achieving a respectable bandwidth of 33 Kbps. Additionally, we introduce a side-channel attack that allows attackers to fingerprint applications on a remote GPU by probing the shared NVLink interconnect. We underscore the urgent nature of the threats posed by these attacks and advocate for heightened awareness within both industry and academia to implement multi-GPU interconnects with enhanced security measures.

Zhang, Yicheng↗