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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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Resiliency Metrics for Monitoring and Analysis of Cyber-Power Distribution System With IoTs
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Cyber-Physical Data Fusion & Threat Detection with LSTM-Based Autoencoders in the Grid
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Towards the Design of Grid Cyber-Physical Integrated Security Operations Center Visualizations
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Agentic AI and the Cyber Arms Race
Here, in this article, we examine the implications for cyberwarfare and global politics as agentic artificial intelligence becomes more powerful and enables the broad proliferation of capabilities only available to the most well-resourced actors today.
Design of Next-Generation Cyber-Physical Energy Management Systems: Monitoring to Mitigation
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Unsupervised Online Anomaly Detection to Identify Cyber-Attacks on Internet Connected Photovoltaic System Inverters.
Abstract not provided.
Deep Learning Architecture for Processing Cyber-Physical Data in the Electric Grid.
Abstract not provided.
Towards Cyber-Physical Special Protection Schemes: Design and Development of a Co-Simulation Testbed Leveraging SCEPTRE.
Abstract not provided.
Towards the Characterization of Cyber-Physical System Interdependencies in the Electric Grid
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Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to inject sufficient resiliency into the operations of the network of microgrids. We consider adversarial actions to the voltage and power control loop reference signals at the grid forming (GFM) inverters in the microgrids which are essential to integrate renewable resources. Therefore, we formulate a resilient reinforcement learning training setup that uses these adversarial injections to generate episodic trajectories and train the RL agents to alleviate their impact on performance. To circumvent the concerns about data-sharing and privacy for different owners of the microgrids in the networked setting, we bring in the aspects of the federated operation to propose novel Fed-RL algorithms. As the dynamics of each microgrid are coupled due to electrical interlinks, the conventional federated RL approaches using decoupled independent environments are not applicable, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC). We have performed numerical simulations on an IEEE 123-bus benchmark test feeder with three microgrids by creating a customized simulation setup by encapsulating the microgrid dynamic simulations in GridLAB-D/HELICS co-simulation platform with the OpenAI Gym environment and validated the proposed resilient and secured learning methodology.
Adaptive, Cyber-Physical Special Protection Schemes to Defend the Electric Grid Against Predictable and Unpredictable Disturbances.
Abstract not provided.
A Moving Target Defense for Securing Cyber-Physical Systems
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Cyberattack Defense With Cyber-Physical Alert and Control Logic in Industrial Controllers
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Encrypted Decentralized Multi-Agent Optimization for Privacy Preservation in Cyber-Physical Systems
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An Interactive and Adaptive Learning Cyber Physical Human System for Manufacturing With a Case Study in Worker Machine Interactions
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