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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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Three-Stage Optimization Model to Inform Risk-Averse Investment in Power System Resilience to Winter Storms
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Model-Free Cyber-Resilient Coordinated Inverter Control in a Microgrid
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Blockchain-Assisted Resilient Control for Distributed Energy Resource Management Systems
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DriveSense: A Noise-Resilient Framework for Driving Mode Identification
Accurate drive mode classification is essential for enhancing the reliability and predictive maintenance of heavy-duty electric trucks. This study proposes a novel fuzzy logic-based framework, DriveSense, for real-time drive mode classification, addressing key challenges such as sensor noise, transitional behaviors, and computational efficiency. The proposed approach integrates a two-stage filtering pipeline, combining adaptive outlier removal and a dynamic Kalman filter to enhance data quality. A fuzzy inference system with smoothened trapezoidal membership functions is then applied to classify driving modes into standstill, constant speed, acceleration, and deceleration while mitigating the effects of noise and edge cases. Performance evaluation using real-world and simulated drive cycles demonstrates significant improvements in classification accuracy (up to 97.8%), F1-score (up to 0.97), and robustness against noise, while reducing false positives. Comparative analysis against baseline models, demonstrates DriveSense’s superior accuracy and generalizability across diverse driving patterns. The framework’s lightweight and interpretable fuzzy inference engine operates with low computational latency, ensuring compatibility with real-time embedded systems typical of heavy-duty electric trucks. Moreover, DriveSense models transitional behaviors through overlapping fuzzy sets and adaptive borderline classification logic, enabling smooth identification of subtle shifts such as rolling stops or gradual deceleration. These results highlight DriveSense’s potential to enhance predictive maintenance strategies, reduce downtime, and support scalable, fleet-wide diagnostics.
Integrating process, control-flow, and data resiliency layers using a hybrid Fenix/Kokkos approach.
Abstract not provided.
Region-Based Stability Analysis of Resilient Distribution Systems with Hybrid Grid-Forming and Grid-Following Inverters
In this paper, distribution system stability considering mixed grid-forming (GFM) and grid-following (GFL) inverters is discussed. A holistic small-signal model of the distribution feeder accommodating both GFM and GFL inverters is derived. In contrast to conventional point-by-point stability analysis, a region-based stability assessment approach is proposed and implemented to evaluate small-signal system stability with both GFM and GFL controls. Furthermore, to enhance the computational efficiency, an Artificial Intelligence (AI) assisted Kernel Ridge Regression (KRR) approach is developed to obtain the stability region boundary. Therefore, with the derived stability region, the impact of critical parameters on system stability is intuitively shown, and more importantly, the relationship between the maximum penetration level and the corresponding feasible range of the selected control parameters can be quantified. The proposed approach has been validated using MATLAB/Simulink.
Increasing Battery Management System Resilience Following Identification of Sensor Anomalies Using Unknown Input Observer
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Leveraging Resilience Metrics to Support Security System Analysis.
Abstract not provided.
Resilience-Based Performance Measures for Next-Generation Systems Security Engineering .
Abstract not provided.
A Suppression-based STDP Rule Resilient to Jitter Noise in Spike Patterns for Neuromorphic Computing
Multi-spike models of synaptic plasticity, such as the triplet and suppression spike-timing-dependent plasticity (STDP) rules, exhibit better alignment with neurophysiological data in the brain compared to the pair-based STDP rule. Previous studies have empirically shown that the pair-based STDP rule can detect spatiotemporal spike patterns hidden in equally dense distractor spike trains in an unsupervised manner. However, it fails to detect spike patterns influenced by jitter noise. Given that spiking neural networks (SNNs) exhibit variability in generated spike trains in response to the same inputs, it becomes imperative to have learning rules capable of detecting spike patterns even in the presence of jitter noise. In this study, we introduce a simplified suppression-based STDP rule that demonstrates significantly enhanced tolerance to jitter in spike patterns compared to the pair-based STDP rule. Unlike the ideal suppression STDP rule, characterized by an exponential learning window and requiring high-resolution synapses, the simplified rule limits the synaptic efficacy update to a single bit at any given instant. Moreover, it employs 4-bit fixed-point synapses, facilitating straightforward implementation in neuromorphic hardware.
VAC: A Software Approach to Resilient SCADA Automation
To better secure critical infrastructure, especially power systems, this paper introduces a virtual SCADA automation controller. The automation controller is a gateway into a power subsystem, making it a valuable target for cyber-attacks that could cut it off from the control center and cause a loss of view and control. To prevent this, the Virtual Automation Controller (VAC) is a backup device that mirrors the capabilities of the physical controller. It can communicate via Modbus and DNP3 and is containerized so it can be deployed on a variety of platforms. Furthermore, it utilizes software-defined networking to quickly disconnect a failed automation controller and preserve its state for forensics. The VAC gives system operators time to replace the failed controller and prevents dangerous and costly damage to power systems. The VAC is compared against the SEL 3505-3 RTAC and shown to have the necessary features to act as a failover controller.
Black-Start and Service Restoration in Resilient Distribution Systems With Dynamic Microgrids
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Resiliency Metrics for Monitoring and Analysis of Cyber-Power Distribution System With IoTs
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Toward an Electromagnetic Event Resilient Grid
The motivation for writing this article is that the threat of a large-scale electromagnetic event having a negative impact on the electric grid is real. Whether human-caused, via the detonation of a nuclear device, or natural, via a high-intensity burst of solar radiation, our historic experience with these phenomena indicates that as a global community, we should be prepared for such events and know how to mitigate their impacts. Current studies and related discussions provide a wide range of damage assessments for these events. We recommend continuing current technical investigations and research as well as strengthening collaboration between stakeholders and experts. This would ensure future threats are addressed in a timely and effective manner.
Mobile Energy Storage Systems: A Grid-Edge Technology to Enhance Reliability and Resilience
Increase in the number and frequency of widespread outages in recent years has been directly linked to drastic climate change necessitating better preparedness for outage mitigation. Severe weather conditions are experienced more frequently and on larger scales, challenging system operation and recovery time after an outage. In conclusion, the impact is more evident and concerning than before, considering the increased dependency on electricity in all aspects of our lives.
Resilient Adaptive Parallel sImulator for griD (RAPID): An Open Source Power System Simulation Toolbox
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