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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 design procedure for control systems that use distributed sensing and processing is summarized. The procedure is applied to the problem of damping vibrations of a simply supported beam.
This presentation provides an overview of the laboratory evaluation of federated, hierarchical controls for distribution power system management.
The model reference control of lumped linear systems and the model reference control of the distributed parameter system (DPS) are presented with their theory and Spacecraft Control Laboratory Experiment (SCOLE) applications.
Improved feedback control of distributed parameter systems for lunar landing vehicle simulator
An approximate method is presented for the synthesis of an optimal control of distributed parameter systems, based on a combination of the ideas of Lyapunov's direct method and those of Bellman's successive approximation in policy space. The method is such that the approximate equation is improved after each iteration in the sense of the performance index being used.
This slide presentation reviews the use of Electrical Power Distribution and Control (EPD&C) Modeling and how modeling can support analysis. The presentation discusses using the EASY5 model to simulate and analyze the Space Shuttle Electric Auxiliary Power Unit. Diagrams of the model schematics are included, as well as graphs of the battery cell impedance, hydraulic load dynamics, and EPD&C response to hydraulic load variations.
The increasing integration of distributed energy resources, including demand-side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data-driven network optimisation approach is proposed to coordinate the control of distributed PVs and smart buildings in distribution networks considering the uncertainties of solar power, outdoor temperature and heat gain associated with building thermal dynamics. These uncertain parameters have a significant impact on the operation and control of distributed PVs and smart buildings, bringing challenges to the distribution system operation. In the proposed data-driven distributionally robust optimisation (DRO) approach, the Wasserstein ball is used to construct an ambiguity set for the uncertain parameters, which does not require the probability distributions to be known. Furthermore, a conditional value-at-risk is incorporated into the Wasserstein-based DRO model and converted into a computationally tractable mixed-integer convex optimisation problem. Benchmarked with robust optimisation and chance-constrained programming, the proposed data-driven model can give a less conservative robust solution.
The control of high-dimensional distributed parameter systems (DPS) remains a challenge when explicit coarse-grained equations are unavailable. Classical equation-free (EF) approaches rely on fine-scale simulators treated as black-box timesteppers. However, repeated simulations for steady-state computation, linearization, and control design are often computationally prohibitive, or the microscopic timestepper may not even be available, leaving us with data as the only resource. We propose a data-driven alternative that uses local neural operators, trained on spatiotemporal microscopic/mesoscopic data, to obtain efficient short-time solution operators. These surrogates are employed within Krylov subspace methods to compute coarse steady and unsteady-states, while also providing Jacobian information in a matrix-free manner. Krylov-Arnoldi iterations then approximate the dominant eigenspectrum, yielding reduced models that capture the open-loop slow dynamics without explicit Jacobian assembly. Both discrete-time Linear Quadratic Regulator (dLQR) and pole-placement (PP) controllers are based on this reduced system and lifted back to the full nonlinear dynamics, thereby closing the feedback loop.
This work presents a numerical framework to investigate distributed aerodynamic control devices for application in large wind turbines. Tool capabilities were extended to facilitate multiple aerodynamic polar tables. The airfoil aerodynamics characteristics were automatically determined, and blade-pitch, generator-torque, and trailing-edge-flap controllers were tuned in-the-loop according to a specific blade design. This automated workflow allows analysis and optimization of trailing-edge flaps, enabling codesign studies. Results targeted reductions of root-flap-bending moment derivatives. The applied trailing-edge-flap control reduced the standard deviation of root-flap-bending moments by more than 6% and benefit related parameters, e.g., reduce blade-tip deflections, by up to 8%. Because of varying thrust distributions along the blade span, different flap designs have nonlinear characteristics in terms of the control objective and show best performance when located at the radial position with maximum thrust. In general, larger flaps provide a greater influence to reduce the target control objective.
In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.
The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. This report documents the independent analysis results corresponding to the Orbiter Electrical Power Distribution and Control (EPD and C) hardware. The EPD and C hardware performs the functions of distributing, sensing, and controlling 28 volt DC power and of inverting, distributing, sensing, and controlling 117 volt 400 Hz AC power to all Orbiter subsystems from the three fuel cells in the Electrical Power Generation (EPG) subsystem. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode. Of the 1671 failure modes analyzed, 9 single failures were determined to result in loss of crew or vehicle. Three single failures unique to intact abort were determined to result in possible loss of the crew or vehicle. A possible loss of mission could result if any of 136 single failures occurred. Six of the criticality 1/1 failures are in two rotary and two pushbutton switches that control External Tank and Solid Rocket Booster separation. The other 6 criticality 1/1 failures are fuses, one each per Aft Power Control Assembly (APCA) 4, 5, and 6 and one each per Forward Power Control Assembly (FPCA) 1, 2, and 3, that supply power to certain Main Propulsion System (MPS) valves and Forward Reaction Control System (RCS) circuits.
The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. This report documents the independent analysis results corresponding to the Orbiter Electrical Power Distribution and Control (EPD and C) hardware. The EPD and C hardware performs the functions of distributing, sensing, and controlling 28 volt DC power and of inverting, distributing, sensing, and controlling 117 volt 400 Hz AC power to all Orbiter subsystems from the three fuel cells in the Electrical Power Generation (EPG) subsystem. Volume 2 continues the presentation of IOA analysis worksheets and contains the potential critical items list.
We present a network initialization algorithmfor wireless networks with distributed intelligence. Each node (agent) has only local, incomplete knowledge and it must make local decisions to meet a predefined global objective. Our objective is to use power control to establish a topology based onthe relative neighborhood graph which has good overall performance in terms of power usage, low interference, and reliability.
This paper considers the problem of the stabilization and control of distributed systems with time-dependent spatial domains. The evolution of the spatial domains with time is described by a finite-dimensional system of ordinary differential equations, while the distributed systems are described by first-order or second-order linear evolution equations defined on appropriate Hilbert spaces. First, results pertaining to the existence and uniqueness of solutions of the system equations are presented. Then, various optimal control and stabilization problems are considered. The paper concludes with some examples which illustrate the application of the main results.
In this paper, we present a new control technique for sustaining dynamic voltage stability by effective reactive power control and coordination of distributed energy resources (DERs) in microgrids. The proposed control technique is based on model-free control (MFC), which has shown successful operation and improved performance in different domains and applications. This paper presents its first use in the voltage stability of a microgrid setting employing multiple synchronous generator (SG)-based and power electronic (PE)-based DERs. MFC is a computationally efficient, data-driven control technique that does not require modelling of the different components and disturbances in the power system. It is utilized as an online controller to achieve the dynamic voltage stability of a microgrid system under different disturbances and fault conditions. A 21-bus microgrid system fed by multiple DERs is considered as a case study and the overall dynamic voltage stability is investigated using time-domain dynamic simulations. Numerical results show that the proposed MFC provides improvements on the dynamic load bus voltage profiles and requires less computational time as compared to the traditional enhanced microgrid voltage stabilizer (EMGVS) scheme. Due to its simplicity and low computational requirement, MFC can be easily implemented in resource-constrained computing devices such as smart inverters.
A general description of the Space Station is given with the basic requirements of the power distribution and controls system presented. The dual bus and branch circuit concepts are discussed and a computer control method presented.
Turbine engines are highly complex mechanical systems that are becoming increasingly dependent on control technologies to achieve system performance and safety metrics. However, the contribution of controls to these measurable system objectives is difficult to quantify due to a lack of tools capable of informing the decision makers. This shortcoming hinders technology insertion in the engine design process. NASA Glenn Research Center is developing a Hardware-inthe- Loop (HIL) platform and analysis tool set that will serve as a focal point for new control technologies, especially those related to the hardware development and integration of distributed engine control. The HIL platform is intended to enable rapid and detailed evaluation of new engine control applications, from conceptual design through hardware development, in order to quantify their impact on engine systems. This paper discusses the complex interactions of the control system, within the context of the larger engine system, and how new control technologies are changing that paradigm. The conceptual design of the new HIL platform is then described as a primary tool to address those interactions and how it will help feed the insertion of new technologies into future engine systems.
Traditionally, distribution system control approaches have been model-based. The deployment of advanced metering infrastructure has provided electric utilities with the capability of data-driven control with real-time measurements. The shift from model-based to data-driven control represents a significant advancement in the management of distribution systems, offering a more adaptive approach to system control because of the ability to dynamically adapt to changing conditions without the need for system modeling. Here, in this paper, a behavioral data-driven control method is developed to provide voltage regulation to an actual distribution system by controlling the legacy devices and distributed energy resource (DER) assets. The studied distribution system has a load tap changer and three capacitor banks as the legacy devices and photovoltaic systems as the DERs. The performance of the proposed control algorithm is validated using a laboratory test bed setup considering multiple scenarios. The results show that the proposed control achieved 99% voltage regulation.