Virtual Oscillator Grid-Forming Inverters: State of the Art, Modeling, and Stability
Not provided.
Engineering↗
SEARCH · Search NASA
Results for “Decentralized Autonomous Control”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Autonomous Energy Systems: Building Reliable, Resilient, and Secure Electrified Communities
Technological changes across energy systems are forcing utilities and operators to reconsider their methods for managing power delivery, but few operators have adopted advanced controls and operational software. Their challenge is that every system has peculiar requirements, and the available solutions are relatively new, untested, and difficult to integrate into an operational environment. Through extensive collaboration with utilities and cooperatives, the National Renewable Energy Laboratory has realized the need for autonomous and optimized management of energy resources, leading to the development of Autonomous Energy Systems, a packaged set of controls that is ready to be integrated into existing control rooms.
automation↗
Enhancing Grid Resilience with HIVE: Decentralized V2G Coordination for Black Starts
This paper proposes the HIVE (Harmonized Integration of Vehicle Energy for Grid Support) model, a novel game-theoretic framework for decentralized coordination of electrified vehicles to enable black start and load restoration during grid outages. In the absence of a central controller, HIVE employs a cooperative game to model vehicle interactions, allowing autonomous decision-making while admitting to a Nash equilibrium for grid restoration. The framework addresses the heterogeneity of vehicles and their operational constraints, selecting a lead vehicle for grid-forming and coordinating grid-following vehicles to support prioritized loads. Applied to a hospital blackout scenario, HIVE demonstrates robust performance in forming an islanded microgrid and sustaining critical loads under varying vehicle availability, state of charge, and power constraints. Simulation results highlight the model’s effectiveness in ensuring decentralized coordination of energy allocation and prioritizing loads, offering a scalable solution for resilient grid operations.
32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
Development of management technology for large power systems
Autonomous power management has been proposed as a method to perform optimization of power subsystem performance in connection with the management of multikilowatt space platforms. A concept for a 250-kW utility-type power subsystem was developed. A Cassegrain concentrator solar array primary source is conditioned by a solar array switching unit which supplies seventeen 220 +20 Vdc power channels. A power management subsystem provides the monitoring and control of the overall electrical power subsystem. The discussed system concept for autonomous management of high power space platforms utilizes on-board microprocessors in a decentralized data management architecture. A data bus protocol and a data bus contention resolution scheme were selected in conjunction with the dencentralized management architecture.
Decker, D. K.↗
Security Architecture for Microgrid Integration
The purpose of this document is to present cyber security risk considerations and mitigations for community microgrids. The mitigations discussed herein specifically focus on cyber security technical requirements for the De-centralized Autonomous Community Controller (DAC) as part of the SECURE project’s technology development effort. These requirements also consider the associated security implementation challenges involved with the decentralized characteristics of microgrids where upstream communications to utility security management functions are lost. These security recommendations and technical requirements may be used by microgrid implementers and technology vendors to specify the security design of the microgrid systems and their components.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Signal Whisperers: Enhancing Wireless Reception Using DRL-Guided Reflector Arrays
This paper presents a multi-agent reinforcement learning (MARL) approach for controlling adjustable metallic reflector arrays to enhance wireless signal reception in non-line-of-sight (NLOS) scenarios. Unlike conventional reconfigurable intelligent surfaces (RIS) that require complex channel estimation, our system employs a centralized training with decentralized execution (CTDE) paradigm where individual agents corresponding to reflector segments autonomously optimize reflector element orientation in three-dimensional space using spatial intelligence based on user location information. Through extensive ray-tracing simulations with dynamic user mobility, the proposed multi-agent beam-focusing framework demonstrates substantial performance improvements over single-agent reinforcement learning baselines, while maintaining rapid adaptation to user movement within one simulation step. Comprehensive evaluation across varying user densities and reflector configurations validates system scalability and robustness. The results demonstrate the potential of learning-based approaches for adaptive wireless propagation control.
deep reinforcement learning↗
Distributed, Intelligent Edge-Sensing for a Smarter Grid
The electric grid is undergoing major transformations and developments resulting in unprecedented levels of volatility, uncertainty, and stress on grid infrastructure. Smart sensors and methods aiding in advanced visibility and situational awareness are key for tackling these issues. In this work, a decentralized architecture is proposed, where sensing, local computation and control capability are embedded in the edge devices, communicating with a set of trusted 'data mules' in a 'delay-tolerant' manner, while functioning autonomously. This system has been designed and implemented as an overall platform – called Global Asset Monitoring, Management and Analytics (GAMMA) Platform intended to provide the backbone for a global array of sensors and actuators. Further, as a building block for advanced current sensing solutions, a smart, low-cost ‘clip-on’ current sensor based on PCB-embedded Rogowski coil has been developed. The sensor hosts a novel signal conditioning stage allowing an 'auto-tuning' feature, resulting in a universal current sensor design for measuring a wide range of currents, including faults for smart grid applications. Finally, the research proposes a method to instrument and monitor key parameters for the most common electric utility asset – the pole-top distribution transformer. The work done in this research enables scalable, edge-intelligent sensing solutions for monitoring grid infrastructure, allowing utility operators to gain advanced visibility in an economical way.
Kulkarni, Shreyas Bhalchandra↗
DISTRI: Distributed Multi-Facility HPC Simulator (DISTRI) v2.1
DISTRI is an advanced network simulator designed for multi-facility computational infrastructures with agentic behavior. It simulates HPC facilities where computational resources act as autonomous agents, making intelligent decisions about job scheduling, load balancing, and resource allocation. The simulator focuses on developing and testing decentralized algorithms that promote resilience and efficiency in multi-facility environments. Key Features: - Agentic Resource Behavior: Processors and DTNs act as autonomous agents with decision-making capabilities - Pheromone-Based Load Balancing: Decentralized load balancing inspired by ant colony optimization - Dual Topology Support: Mesh (normal operations) and Dumbell (network testing) topologies - Comprehensive TCP Simulation: Realistic TCP implementations with multiple congestion control algorithms - Failure Resilience Testing: Processor failure simulation with automatic job reassignment - Extensive Visualization: Detailed performance analysis and metrics collection - Research-Ready: Designed for algorithm development and benchmarking
Bez, Jean Luca [Lawrence Berkeley National Laborat↗
A Closed-Loop Hardware Simulation of Decentralized Satellite Formation Control
In recent years, there has been significant interest in the use of formation flying spacecraft for a variety of earth and space science missions. Formation flying may provide smaller and cheaper satellites that, working together, have more capability than larger and more expensive satellites. Several decentralized architectures have been proposed for autonomous establishment and maintenance of satellite formations. In such architectures, each satellite cooperatively maintains the shape of the formation without a central supervisor, and processing only local measurement information. The Global Positioning System (GPS) sensors are ideally suited to provide such local position and velocity measurements to the individual satellites. An investigation of the feasibility of a decentralized approach to satellite formation flying was originally presented by Carpenter. He extended a decentralized linear-quadratic-Gaussian (LQG) framework proposed by Speyer in a fashion similar to an extended Kalman filter (EKE) which processed GPS position fix solutions. The new decentralized LQG architecture was demonstrated in a numerical simulation for a realistic scenario that is similar to missions that have been proposed by NASA and the U.S. Air Force. Another decentralized architecture was proposed by Park et al. using carrier differential-phase GPS (CDGPS). Recently, Busse et al demonstrated the decentralized CDGPS architecture in a hardware-in-the-loop simulation on the Formation Flying TestBed (FFTB) at Goddard Space Flight Center (GSFC), which features two Spirent Cox 16 channel GPS signal generator. Although representing a step forward by utilizing GPS signal simulators for a spacecraft formation flying simulation, only an open-loop performance, in which no maneuvers were executed based on the real-time state estimates, was considered. In this research, hardware experimentation has been extended to include closed-loop integrated guidance and navigation of multiple spacecraft formations using GPS receivers and real-time vehicle telemetry. A hardware closed-loop simulation has been performed using the decentralized LQG architecture proposed by Carpenter in the GPS test facility at the Center for Space Research (CSR). This is the first presentation using this type of hardware for demonstration of closed-loop spacecraft formation flying.
Ebimuma, Takuji↗
Sensor fusion IV: Control paradigms and data structures; Proceedings of the Meeting, Boston, MA, Nov. 12-15, 1991
Various papers on control paradigms and data structures in sensor fusion are presented. The general topics addressed include: decision models and computational methods, sensor modeling and data representation, active sensing strategies, geometric planning and visualization, task-driven sensing, motion analysis, models motivated biology and psychology, decentralized detection and distributed decision, data fusion architectures, robust estimation of shapes and features, application and implementation. Some of the individual subjects considered are: the Firefly experiment on neural networks for distributed sensor data fusion, manifold traversing as a model for learning control of autonomous robots, choice of coordinate systems for multiple sensor fusion, continuous motion using task-directed stereo vision, interactive and cooperative sensing and control for advanced teleoperation, knowledge-based imaging for terrain analysis, physical and digital simulations for IVA robotics.
Schenker, Paul S.↗
Precision Formation Keeping at L2 Using the Autonomous Formation Flying Sensor
Recent advances in formation keeping for large numbers of spacecraft using the Autonomous Formation Flying are presented. This sensor, currently under development at JPL, has been identified as a key component in future formation flying spacecraft missions. The sensor provides accurate range and bearing measurements between pairs of spacecraft using GPS technology. Previous theoretical work by the authors has focused on developing a decentralized scheduling algorithm to control the tasking of such a sensor between the relative range and bearing measurements to each node in the formation. The resulting algorithm has been modified to include switching constraints in the sensor. This paper also presents a testbed for real time validation of a sixteen-node formation based on the Stellar Imager mission. Key aspects of the simulation include minimum fuel maneuvers based on free-body dynamics and a three body propagator for simulating the formation at L2.
McLoughlin, Terence H.↗
Autonomous Frequency-Domain Indentification
Test and data-processing system determines plant models and uncertainties. Integrated system of methods, digital signal-processing, and algorithms identifies parametric model of large, flexible structures. Experiments in development of system conducted on laboratory model intended to represent large space antenna or flexible spacecraft. Also applicable to many terrestrial systems, robust control of dynamic plants and processes, robust control of systems about which knowledge uncertain or incomplete, decentralized control, and knowledge-based control systems.
Mettler, Edward↗
Decentralized Low-Rank State Estimation for Power Distribution Systems
This article considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix completion methodology. Matrix completion has been shown to be an effective technique in state estimation that exploits the low dimensionality of the power system measurements to recover missing information. This technique can utilize an approximate (linear) load flow model, or it can be used with no physical models in a network where no information about the topology or line admittance is available. The direct application of matrix completion algorithms requires solving a semi-definite programming (SDP) problem, which becomes computationally challenging for large networks. We therefore develop a decentralized algorithm that capitalizes on the popular proximal alternating direction method of multipliers (proximal ADMM). The method allows us to distribute the computation among different areas of the network, leading to a scalable algorithm. By doing all computations at individual control areas and only communicating with neighboring areas, the algorithm eliminates the need for data to be sent to a central processing unit and thus increases efficiency and contributes to the goal of autonomous control of distribution networks. We illustrate the advantages of the proposed algorithm numerically using standard IEEE test cases.
41 EE - Solar Energy Technologies Office (EE-4S)↗
Autonomous Energy Systems: Empower Distributed Energy Resources With Information and Controls
Sensors, controls, and data can add value to energy systems, but there are millions of touchpoints to consider and harness. It all depends on the right integration strategy, and through the National Renewable Energy Laboratory's (NREL's) research into Autonomous Energy Systems (AES), we are creating options that work for anyone.
autonomous↗
Decentralized Microgrid Protection Through Relative Fault Direction Classification: Preprint
Protection in inverter-based resources (IBRs) dominated microgrids generally face significant challenges due to the low fault current and inconsistent fault behaviors from IBRs. Recently, machine learning-based approaches have attracted considerable attention to address these challenges. This paper introduces a novel decentralized protection strategy for microgrids. The proposed method decomposes the protection challenge into several distributed learning tasks, enabling individual relays to autonomously determine the direction of faults using a binary classification framework based on support vector machine (SVM) algorithms. Following the distributed fault direction estimation, classifier outcomes are shared among neighboring relays, facilitating a local decision-making process to ascertain the presence of faults within the neighborhood. Finally, a tripping signal is generated based on the classifier results of each relay to operate the circuit breaker. To test and validate this approach, a 100% renewable microgrid model is simulated in MATLAB/Simulink. In the numerical analysis, the application of SVM classifiers in our approach yields impressive results: an average relay classification accuracy of 98%, and a 96% accuracy in circuit breaker control. These findings highlight the potential of machine-learning-based approaches in enhancing the efficiency and reliability of microgrid protection systems.
decentralized algorithm↗
Distributed Spacecraft Control Architectures
A fundamental issue for estimation and control of distributed systems such as formation flying spacecraft is the information exchange architecture. In centralized schemes, each subordinate need only share its measurement data with a central hub, and the subordinates depend on the center to direct their actions. In decentralized schemes, all nodes participate in the data exchange, so that each has the same in by formation as the center, and may thereby self-direct the same action that the center would have commanded, assuming all share a common goal. This talk compares and contrasts the centralized and decentralized schemes in the context of autonomously maintaining a distributed satellite formation.
Carpenter, James Russell↗
Facility Monitoring: A Qualitative Theory for Sensor Fusion
Data fusion and sensor management approaches have largely been implemented with centralized and hierarchical architectures. Numerical and statistical methods are the most common data fusion methods found in these systems. Given the proliferation and low cost of processing power, there is now an emphasis on designing distributed and decentralized systems. These systems use analytical/quantitative techniques or qualitative reasoning methods for date fusion.Based on other work by the author, a sensor may be treated as a highly autonomous (decentralized) unit. Each highly autonomous sensor (HAS) is capable of extracting qualitative behaviours from its data. For example, it detects spikes, disturbances, noise levels, off-limit excursions, step changes, drift, and other typical measured trends. In this context, this paper describes a distributed sensor fusion paradigm and theory where each sensor in the system is a HAS. Hence, given the reach qualitative information from each HAS, a paradigm and formal definitions are given so that sensors and processes can reason and make decisions at the qualitative level. This approach to sensor fusion makes it possible the implementation of intuitive (effective) methods to monitor, diagnose, and compensate processes/systems and their sensors. This paradigm facilitates a balanced distribution of intelligence (code and/or hardware) to the sensor level, the process/system level, and a higher controller level. The primary application of interest is in intelligent health management of rocket engine test stands.
Figueroa, Fernando↗
Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency
Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.
24 POWER TRANSMISSION AND DISTRIBUTION↗