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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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Optimal False Data Injection Attack Against Load-Frequency Control in Power Systems
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Multi-Loop Transient Stability Control via Power Modulation From Energy Storage Devices
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Direct Damping Feedback Control Using Power Electronics-Interfaced Resources
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Real-Time Asynchronous Information Processing in Distributed Power Systems Control
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Multi-Loop Transient Stability Control via Power Modulation From Energy Storage Devices.
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Power and Control Electronics (PACE) Update
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Single-stage, single-phase dual active bridge solid state transformer for increased efficiency and power flow control
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Power system control rooms of the future: integrated big data analytics for security and resilience
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Model, Design, and Control for Power Conversion in Wave Energy Converter System
Wave energy, if all been harvested along the U.S. coastline, can power around 65% of the energy consumption in U.S.. Comparing to other renewable energy sources like solar or wind, ocean wave can provide up to 90% of steady uptime. With the high energy density (2-3 kW/m 2 ), it can produce more energy with the same amount of installation area comparing to the energy density of wind turbine (0.6 kW/m 2 ) and solar panel(0.2 kW/m 2 ). The predictability of wave provides advantages like planning installation, power dispatching, and maintenance activities. Although with all these advantages, wave energy converter system is still in the research stage due to its high system cost per electricity production. One of the challenges that need to be solved is the irregularity from the wave motion that leads to high instantaneous peak power into the wave energy converter, which usually reaches up to 10 - 20 times of the average power. The high peak power will not only bring high mechanical/electrical stress but also result in an overrating design of the components in the system. Another obstacle that prevents the wave energy system from moving forward is the high testing cost from the validations in wave-energy-test sites or tank-test sites. A high-fidelity multi-disciplinary system model, including hydrodynamics, mechanical dynamics, electromagnetics, and power electronics, is needed to predict the behavior of the system and reduce the cost of design validation.
A Computationally Improved Heuristic Algorithm for Transmission Switching Using Line Flow Thresholds for Load Shed Reduction
We present a computationally improved heuristic algorithm for transmission switching (TS) to recover load shed. Research from the past showed that changing power system topology may control power flows and remove line congestion. Hence, TS may reduce the required load shed. One of the main challenges is to find a potential TS candidate in a suitable time. Here, we propose a novel heuristic method that is capable of finding the potential TS candidate faster than existing algorithms in literature. The proposed method is compatible with both the AC and DC optimal power flows (OPF). Three metrics are used to compare the proposed algorithm with the state-of-the-art from literature to show the speedup and accuracy achieved. The proposed method is implemented on the IEEE 30-bus system, PEGASE 89-bus system, IEEE 118-bus system, and Polish 2383- bus system. The results on the large-scale Polish 2383-bus system shows that the proposed algorithm is scalable to large real-world systems. Parallel computing is implemented to further improve the computational performance of the proposed algorithm.
INTEGRATING COMBINED HEAT AND POWER INTO CONTROLLED ENVIRONMENT AGRICULTURE
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Analysis of Slow Spill Data for the Mu2e Experiment
The execution of the Mu2e experiment requires a stable, low-intensity proton beam from the Delivery Ring to produce clean data and protect equipment. This is done by performing a “slow extraction,” which is the gradual contraction of the stable region within the accelerator’s beam pipe. The Delivery Ring is currently unable to perform slow extraction with the stability required by Mu2e. To resolve this, the FAN-C team is training machine learning models with the purpose of replacing the Delivery Ring’s current PID controllers with AI-powered controllers. Training these models requires clean, processed data from slow spills. Over the course of this project, data from previous slow spills were processed and analyzed, and the clean data, graphs, and insights gained from the process were provided to the FAN-C team to assist them in their efforts.
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Power system emergency control is generally regarded as the final safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived “worst” case scenarios or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, for the first time, this paper proposes a novel adaptive emergency control scheme using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction and non-linear generalization capabilities DRL has for complex systems with high-dimensional variations. Furthermore, an open-source platform named DeepGrid has been designed for the first time to assist the DRL development and benchmarking processes in power system emergency control. Details of the platform, DRL, and emergency control schemes that use dynamic braking or under-voltage load shedding are presented. Extensive case studies performed in both two-area four-machine system and IEEE 39-Bus system have demonstrated the excellent performance and robustness of the proposed schemes.
Novel Rotated Virtual Synchronous Generator Control for Power-Sharing in Microgrids with Complex Line Impedance
Virtual synchronous generator (VSG) control is based on the fact that the line impedance in a microgrid is highly inductive. This assumption was made due to the emulation of the stator winding of an electrical machine. This concept can affect the controllability of a microgrid with complex line impedance, generating deviations in the chosen operation point. To overcome this issue, additional techniques must be implemented. This paper describes a novel mathematical approach that uses the power line characteristics in a microgrid to rotate the power control reference frame and proposes a new control method called a “Rotated Virtual Synchronous Generator” (RVSG). This RVSG control approach integrates the effect of complex impedance on the microgrid’s operation and adjusts the reference frame accordingly to improve the system’s stability and performance. The use of this proposed mathematical approach in microgrids allows the further emulation of virtual inertia in microgrids that lack inertia. Finally, a comparison between RVSG control and the classical virtual synchronous generator method is carried out to show that it allows the improvement of the transient power response, power quality, stability, and performance, mainly in microgrids with complex line impedance.