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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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A fuel control system responsive to a power controller and controlling a fuel delivery system. The fuel control system includes a control arm connected to both the power controller and the fuel delivery system, a position sensor connected to the control arm, and a trim controller connected to the control arm at a pivot point and connected to the position sensor.
Volt-VAR control (VVC) is crucial in active distribution networks for optimizing voltage profiles and minimizing network losses. While traditional deep reinforcement learning (DRL) algorithms exhibit promise for VVC, they often require extensive computational resources to handle such a high-dimensional problem. As a potential solution, quantum reinforcement learning (QRL) algorithms integrate the computational capabilities of quantum computing into the DRL framework. However, existing QRL algorithms struggle with complex VVC problems due to the limitations of current quantum hardware. To bridge this gap, this paper proposes an innovative QRL algorithm featuring an end-to-end architecture that integrates a classical autoencoder, variational quantum circuits (VQCs), and classical post-processing layers. This design efficiently compresses high-dimensional grid states, enabling VQCs to leverage quantum advantages while producing multiple control device outputs tailored for VVC tasks. Numerical studies on three representative distribution systems verify the effectiveness and scalability of the proposed QRL algorithm, and demonstrate its enhanced performance over classical approaches with only approximately 1% of the parameters. Additionally, the robustness of our developed algorithm is validated through noisy quantum environments.
Jeffrey Joe has been invited to give a presentation entitled, “Human Factors Challenges in Modernizing Nuclear Power Plant Control Rooms,” at the 2024 Human Systems Symposium. Experts conducting human factors and human systems research will gather and share research at this conference. This conference is a good opportunity to develop new business for INL via research collaborations, as attendees exchange knowledge and explore the latest trends, advancements, and challenges in the field of human systems research across the DOE national laboratories.
Various examples of power converters including Integrated Capacitor Blocked Transistor (ICBT) cells and methods of control of power converters having ICBT cells are described. In one example, a power converter includes an upper arm including a plurality of upper ICBT cells connected in series to form a series connection path and a lower arm including a plurality of lower ICBT cells connected in series in the series connection path. A controller can be configured to provide a control signal pair to each of the upper ICBT cells and a complementary control signal pair to each of the lower ICBT cells to control the converter output. A capacitor voltage controller can be configured to balance a voltage potential among ICBT capacitors in at least one of the upper arm and the lower arm.
This public data set contains openly-documented, machine readable digital research data corresponding to figures published in M.W. Bongard et al., ‘Digital Control and Power Systems for the Pegasus–III Experiment,’ IEEE Transactions on Plasma Science 50, 4021 (2022).
Methods, apparatus, and systems are disclosed herein for power modulation control of power grids using end-use loads. At the beginning of a coordination period typically lasting a few minutes, a population of end-use loads (water heaters, air conditioners, plug-in electric vehicles, etc.) is interrogated for their willingness to participate in power modulation by undergoing state transitions from on to off, or off to on. Distribution system flexibilities are calculated from the sums of device powers within distribution systems. Area flexibilities are calculated from the sums of device powers within areas. Thresholds for various devices to change state in response to inter-area oscillation damping or frequency regulation are derived from these flexibilities. Two power functions are monitored in real-time and compared with the device thresholds to trigger state changes which then remain locked throughout the remainder of the coordination period, then the devices are released from the lockout state.
Sudden changes in induced emf sensed. In refinement of soft starting three phase power-factor controller, additional components enable circuit to respond quickly to sudden increase in motor load. Rapid-load-response addition senses induced motor emf to detect sudden load increase and generates command signal to increase applied power in response.
The INL TADA GETs Cohort Session 2, held on November 7, 2025, conducted in collaboration with ScottMadden, focused on two core Advanced Transmission Technologies (ATTs): Advanced Power Flow Control (APFC) and Transmission Topology Optimization (TTO). These technologies are pivotal in enhancing grid flexibility, reliability, and cybersecurity resilience. APFC, particularly through modular FACTS devices like Modular Static Synchronous Series Compensators (M-SSSCs), enables dynamic voltage injection to reroute power flows. The session highlighted the deployment benefits of APFC, such as rapid installation, minimal civil works, and re-deployability. Regulatory drivers like FERC Order 2023 mandate the inclusion of Grid-Enhancing Technologies (GETs) in interconnection studies. Case studies from Central Hudson, CAISO, and National Grid (UK) demonstrated APFC’s effectiveness in congestion relief and cost savings. The session also addressed cybersecurity concerns, including firmware vulnerabilities, SCADA integration risks, and supply chain dependencies. Participants engaged in interactive exercises to rank cybersecurity and supply chain risks, emphasizing the need for robust digital assurance strategies. TTO involves software-based reconfiguration of transmission networks to optimize power flow without new infrastructure. The session showcased its operational value, with examples from SPP, PJM, and MISO showing significant congestion cost reductions. Cybersecurity vulnerabilities were discussed, particularly in API security and software supply chains, referencing incidents like SolarWinds and attacks on Danish utilities. Digital assurance exercises explored worst-case scenarios, attack paths, and mitigation responsibilities between vendors and utilities. Reliability challenges such as algorithm stability, vendor dependency, and operator trust were also examined. Cross-cutting themes emphasized the importance of digital assurance tools, including Software Bills of Materials (SBOMs) and hardware-in-loop testing. Human performance, training, and operational confidence were identified as critical enablers of technology adoption. The session concluded with a preview of Session 3, which will focus on High Performance Conductors (HPCs) and risk-based cybersecurity tools. Session 2 of 3.
Large-scale integration of renewables in power systems gives rise to new challenges for keeping synchronization and frequency stability in volatile and uncertain power flow states. To ensure the safety of operation, the system must maintain adequate disturbance rejection capability at the time scales of both rotor angle and system frequency dynamics. This calls for flexibility to be exploited on both the generation and demand sides, compensating volatility and ensuring stability at the two separate time scales. This article proposes a hierarchical power flow control architecture that involves both transmission and distribution networks as well as individual buildings to enhance both small-signal rotor angle stability and frequency stability of the transmission network. The proposed architecture consists of a transmission-level optimizer enhancing system damping ratios, a distribution-level controller following transmission commands and providing frequency support, and a building-level scheduler accounting for quality of service and following the distribution-level targets. Furthermore, we validate the feasibility and performance of the whole control architecture through real-time hardware-in-loop tests involving real-world transmission and distribution network models along with real devices at the Stone Edge Farm Microgrid.
With the advent of a new Precision Time Protocol specification, new opportunities abound for clock synchronization possibilities within power grid control systems. The third iteration of the Institute of Electrical and Electronics Engineers Standard 1588 specification provides several new features specifically aimed at complex, wide-area deployments in which situational awareness and control require precise time agreement. This paper describes the challenges faced by existing technology, introduces the new time distribution specification, and provides examples to explain how it represents a game-changing innovation.
The application of NASTRAN to seismic analysis by considering the example of a nuclear power plant control panel was considered. A modal analysis of a three-dimensional model of the panel, consisting of beam and quadri-lateral membrane elements, is performed. Using the results of this analysis and a typical response spectrum of an earthquake, the seismic response of the structure is obtained. ALTERs required to the program in order to compute the maximum modal responses as well as the resultant response are given. The results are compared with those obtained by using the SAP IV computer program.
Slides for the INL AI/ML Symposium 11.0 focused on AI/ML in instrumentation, control, and automation. This presentation was on how we can use AI/ML for control of nuclear power plants. A recording of the presentation and other INL AI/ML symposiums can be found at https://inl.gov/artificial-intelligence/.
This paper presents an adaptive reinforcement learning (ARL)-based control framework for efficient power distribution in a multi-output resonant converter for UAV applications. The proposed system is based on a high-frequency isolated resonant architecture, where a single energy source supplies multiple propulsion loads through independently controlled output rectifiers, addressing the need for coordinated multi-motor power management. The ARL framework dynamically allocates output power by learning optimal phase-shift control actions under varying load demands and operating conditions. The agent autonomously determines control parameters that maximize conversion efficiency while ensuring accurate power sharing among multiple outputs. In addition, the proposed approach enables adaptive operation without requiring detailed system modeling or manual tuning. Experimental results demonstrate stable and efficient performance over a wide range of operating conditions, confirming the effectiveness and robustness of the learning-based control strategy for multi-output resonant converter system.
This presentation provides an overview of the Advanced Modular Power Systems (AMPS) Project, along with sections to describe the three technical focus areas - Modular Power Electronics & Standards, Autonomous Power Control, and the Modular Power Testbed.
This paper explores the control, operation, and stability characteristics of grid-forming Type III wind turbines. The paper shows that the grid-forming operation mode requires redesign of only the slower control loops for active and reactive power control, whereas the faster current control implementation can stay the same as grid-following turbines. The paper also shows that grid-forming wind turbines face higher mechanical stress than grid-following wind turbines because of the slower speed of the active power control. This stress, however, can be mitigated using advanced pitch control. The paper compares the stability characteristics of the grid-forming and grid-following operation modes of Type III turbines by comparing sequence impedance responses for both operation modes. It is found that, unlike standard Type III wind turbines, the grid-forming mode substantially reduces the risks of subsynchronous oscillations during operation with series-compensated transmission lines. Moreover, the grid-forming Type III turbines can operate stably with extremely weak grids. The findings of this paper are validated using EMT-PSCAD simulations of a 2.5-MW turbine operating in both grid-forming and grid-following modes.
Theoretical analysts of lateral dynamic motion of tailless and conventional airplanes was made for fighter and heavy transport. Their reactions to a lateral gust and control power required by each for simple maneuvers were determined and compared. Both types of airplanes require almost identical aileron control power to perform a given maneuver; tailless airplane requires about 1-2 to 1-3 directional control power of conventional airplane. Tailless airplane also shows greatest displacement for a given disturbance and has least damping in oscillatory mode.
Power grid operation is facing increased penetration of renewables and distributed energy resources at both transmission and distribution levels. This presents an increased level of randomness and uncertainty for its operational quality, where the challenges are mainly how controls and optimizations can be performed so that the operational quality of the power grid can be maintained. An interesting feature is also that we need to make a good use of uncertainties embedded in the swing equation from the renewable contributions rather than simply trying to minimize the impact of uncertainties. Furthermore, this requires effective solutions on system estimation, control, and optimization. Indeed, in past decades, work has been carried out on the development of advanced controls and optimization for such complicated dynamic systems. Examples are optimal operational control for both performance and stability enhancement considering random renewables penetration, frequency, and active power control for microgrids and parameter estimation for various parts of the power grid.
Incorporating physics information into the deep reinforcement learning (DRL) process is a promising approach for addressing the challenges faced in learning-based control design problems for physical systems. Power grid dynamics, being a physical system, adheres to specific physical laws, constraints, as well as operational and control rules. Therefore, consideration of such physics-based law improves the learning process drastically. In general, traditional grid control schemes rely on rule-based mechanisms that cannot adapt to changing operating conditions. To improve the adaptability and computation time, recent research has seen a surge of DRL-based applications in power grid control. A generic DRL-based control design imposes the system performance requirements through the design of reward functions. In some cases, some of the important physics information is injected through this reward function. However, due to the complex dynamics and large state-action space, learning an optimal DRL policy often becomes challenging. Inspired by the latest developments in general machine learning (ML) research, power system researchers have been investigating more direct ways of incorporating physics knowledge into DRL training. This chapter specifically focuses on these aspects of physics-informed DRL designs in grid control. It discusses the significance, applications, research gaps, and open problems that need to be addressed in future research.