Two-Level Decentralized-Centralized Control of Distributed Energy Resources in Grid-Interactive Efficient Buildings
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Huo, Xiang↗
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Neural node network and model, and method of teaching same
The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.
Parlos, Alexander G.↗
Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees
We present differentiable predictive control (DPC), a method for offline learning of constrained neural control policies for nonlinear dynamical systems with performance guarantees. We show that the sensitivities of the parametric optimal control problem can be used to obtain direct policy gradients. Specifically, we employ automatic differentiation (AD) to efficiently compute the sensitivities of the model predictive control (MPC) objective function and constraints penalties. To guarantee safety upon deployment, we derive probabilistic guarantees on closed-loop stability and constraint satisfaction based on indicator functions and Hoeffding’s inequality. We empirically demonstrate that the proposed method can learn neural control policies for various parametric optimal control tasks. In particular, we show that the proposed DPC method can stabilize systems with unstable dynamics, track time-varying references, and satisfy nonlinear state and input constraints. Our DPC method has practical time savings compared to alternative approaches for fast and memory-efficient controller design. Specifically, DPC does not depend on a supervisory controller as opposed to approximate MPC based on imitation learning. We demonstrate that, without losing performance, DPC is scalable with greatly reduced demands on memory and computation compared to implicit and explicit MPC while being more sample efficient than model-free reinforcement learning (RL) algorithms.
97 MATHEMATICS AND COMPUTING↗
System risk quantification and decision making support using functional modeling and dynamic Bayesian network
Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. Furthermore, the MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.
42 ENGINEERING↗
F-15 IFCS: Intelligent Flight Control System
This viewgraph presentation describes the F-15 Intelligent Flight Control System (IFCS). The goals of this project include: 1) Demonstrate revolutionary control approaches that can efficiently optimize aircraft performance in both normal and failure conditions; and 2) Demonstrate advance neural network-based flight control technology for new aerospace systems designs.
Bosworth, John↗
Lightning location system supervising Swedish power transmission network
For electric utilities, the ability to prevent or minimize lightning damage on personnel and power systems is of great importance. Therefore, the Swedish State Power Board, has been using data since 1983 from a nationwide lightning location system (LLS) for accurately locating lightning ground strikes. Lightning data is distributed and presented on color graphic displays at regional power network control centers as well as at the national power system control center for optimal data use. The main objectives for use of LLS data are: supervising the power system for optimal and safe use of the transmission and generating capacity during periods of thunderstorms; warning service to maintenance and service crews at power line and substations to end operations hazardous when lightning; rapid positioning of emergency crews to locate network damage at areas of detected lightning; and post analysis of power outages and transmission faults in relation to lightning, using archived lightning data for determination of appropriate design and insulation levels of equipment. Staff have found LLS data useful and economically justified since the availability of power system has increased as well as level of personnel safety.
Melin, Stefan A.↗
Heterogeneous network topology management and control
In one aspect, the present disclosure may provide devices or systems configured to receive indications of geographic areas, each corresponding to distribution networks, and determine whether the distribution networks overlap. The processor(s) may also be configured to receive parameter value(s) obtained by a device within one distribution network at a location within an overlapping area, and cause device(s) within another distribution network to modify operation based on the parameter value(s). In another aspect, the present disclosure may provide devices that include a voltage sensor to measure a voltage of a power distribution network at a power consumption location that corresponds to the device. The device may be configured to provide at least one cable service to the power consumption location via a cable television (CATV) distribution network and output an indication the measured voltage value.
Cruickshank, III, Robert Fraser↗
Enabling Realistic Communications Evaluations for ADMS
Increasing penetration levels of inverter-based distributed energy resources (DERs) in distribution systems are creating challenges in system operation. Currently, wired or proprietary wireless communications are used by advanced distribution management systems (ADMS) to detect the state of the system and control the DERs for optimal operation. Wireless communications are an alternative solution that can enable connectivity between the dispersed assets used in distribution system applications. As demand for the seamless integration of distributed energy resources (DERs) continues to increase, proper networking and communications systems are becoming important to support the DER infrastructure. This report summarizes validation of a private wireless LTE network in a laboratory environment. The purpose of this work is to 1) demonstrate the effectiveness of private wireless LTE communication for grid applications and 2) provide a means of evaluating latency and overall performance of wireless communication in grid applications. First, private wireless LTE communication was validated in a direct transfer trip relaying scenario as an example of one-way communications using grid equipment. This study provided a baseline understanding of the variation in latency for seven different kinds of wireless scenarios shown in Figure A below. As expected, weak (attenuated) wireless signal-based communication yielded the widest ranges in latency. When higher priority was set for the trip signal communication, this resulted in lower latency in communication timing. In the final phase of this project, the private wireless LTE network was validated using two-way communications in an advanced distribution management system (ADMS) testbed which was developed in the ESIF at NREL. The purpose of this was to study the impact of wireless communication on grid performance.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Visibility-enhanced model-free deep reinforcement learning algorithm for voltage control in realistic distribution systems using smart inverters
Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Here, this paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.
14 SOLAR ENERGY↗
Delivery Ring Spill Characterization and Impulse Study
High-intensity particle physics experiments require uniform beam extraction to prevent instantaneous rate spikes from overwhelming detector systems. By analyzing accelerator parameters and extracted beam dynamics, we directly inform spill regulation systems that make real-time adjustments to minimize non-uniformity. This Department of Energy Visiting Faculty Program project transitioned from characterizing Main Injector half-integer slow extraction for SpinQuest to Delivery Ring third-integer slow extraction for Mu2e. Working alongside the Fast Adaptive Neural Control (FANC) group, we developed an automated pipeline that aligns asynchronous instrument channels, embeds quality metrics, and isolates clean spill populations. Analyzing baseline spills alongside a dedicated quadrupole impulse study allowed us to quantify noise structures while mapping time-varying beam response and transit-delay dynamics. These empirical measurements directly ground digital twin models, supporting FANC’s deployment of real-time, FPGA-based neural network controllers in the Mu2e Spill Regulation System.
Dolen, James William [Purdue U., West Lafayette] (↗
Deploying Software-Defined Networking in Operational Technology Environments
Software Defined Networking for Operational Technologies, referred to as OT-SDN, is a leading technology to secure critical infrastructure and command and control (C2) systems. As the name implies, OT-SDN networks are programmable, which allows system owners to utilize the characteristics of their physical process to inform the security of their network. There are best practices for deploying OT-SDN into an environment, whether it is all at once or over time (hybrid) that the network is converted to SDN technologies. Through the development of data mining tools and standardized process control, OT-SDN can be deployed reliably. These tools will minimize or eliminate any communication failures during the transition and provide the network owner with complete documentation of their environment. This documentation could enable or facilitate the network owner to pass any audits or policy checks (Authority to Operate) before being allowed to utilize the OT-SDN infrastructure.
Software Defined Networking, Operational Technolog↗
Tracking and data relay satellite system - NASA's new spacecraft data acquisition system
This paper describes NASA's new spacecraft acquisition system provided by the Tracking and Data Relay Satellite System (TDRSS). Four satellites in geostationary orbit and a ground terminal will provide complete tracking, telemetry, and command service for all of NASA's orbital satellites below a 12,000 km altitude. Western Union will lease the system, operate the ground terminal and provide operational satellite control. NASA's network control center will be the focal point for scheduling user services and controlling the interface between TDRSS and the NASA communications network, project control centers, and data processing. TDRSS single access user spacecraft data systems will be designed for time shared data relay support, and reimbursement policy and rate structure for non-NASA users are being developed.
Schneider, W. C.↗
F-15 837 IFCS Intelligent Flight Control System Project
This viewgraph presentation reviews the use of Intelligent Flight Control System (IFCS) for the F-15. The goals of the project are: (1) Demonstrate Revolutionary Control Approaches that can Efficiently Optimize Aircraft Performance in both Normal and Failure Conditions (2) Advance Neural Network-Based Flight Control Technology for New Aerospace Systems Designs. The motivation for the development are to reduce the chance and skill required for survival.
Bosworth, John T.↗
A new taxonomy for distributed computer systems based upon operating system structure
Characteristics of the resource structure found in the operating system are considered as a mechanism for classifying distributed computer systems. Since the operating system resources, themselves, are too diversified to provide a consistent classification, the structure upon which resources are built and shared are examined. The location and control character of this indivisibility provides the taxonomy for separating uniprocessors, computer networks, network computers (fully distributed processing systems or decentralized computers) and algorithm and/or data control multiprocessors. The taxonomy is important because it divides machines into a classification that is relevant or important to the client and not the hardware architect. It also defines the character of the kernel O/S structure needed for future computer systems. What constitutes an operating system for a fully distributed processor is discussed in detail.
Foudriat, E. C.↗
Problems and concepts of space station guidance, navigation, and control
The Space Station System is defined as a network of space and ground assets which work together to support a variety of missions including commercial missions, science and applications missions, and technology development missions. The elements of the Space Station System include a Space Station Base, Space Platforms, Free Flyers, a Teleoperator Manuevering System (TMS), Orbital Transfer Vehicles (OTV), Orbiter Berthing Equipment, and Ground Support Equipment and Facilities. Guidance, navigation, and control (GNC) subsystem requirements are considered along with configuration trades.
Guha, A. K.↗
Study of a Navigation and Traffic Control Technique Employing Satellites. Volume 2 - System Analysis Interim Report
Systems analyses of satellite constellation and ground station networks for navigation and traffic control techniques, and tracking accuracy and error analyses
Conrad, D. A.↗
Modernization of B-2 Data, Video, and Control Systems Infrastructure
The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) Plum Brook Station (PBS) Spacecraft Propulsion Research Facility, commonly referred to as B-2, is NASA s third largest thermal-vacuum facility with propellant systems capability. B-2 has completed a modernization effort of its facility legacy data, video and control systems infrastructure to accommodate modern integrated testing and Information Technology (IT) Security requirements. Integrated systems tests have been conducted to demonstrate the new data, video and control systems functionality and capability. Discrete analog signal conditioners have been replaced by new programmable, signal processing hardware that is integrated with the data system. This integration supports automated calibration and verification of the analog subsystem. Modern measurement systems analysis (MSA) tools are being developed to help verify system health and measurement integrity. Legacy hard wired digital data systems have been replaced by distributed Fibre Channel (FC) network connected digitizers where high speed sampling rates have increased to 256,000 samples per second. Several analog video cameras have been replaced by digital image and storage systems. Hard-wired analog control systems have been replaced by Programmable Logic Controllers (PLC), fiber optic networks (FON) infrastructure and human machine interface (HMI) operator screens. New modern IT Security procedures and schemes have been employed to control data access and process control flows. Due to the nature of testing possible at B-2, flexibility and configurability of systems has been central to the architecture during modernization.
Cmar, Mark D.↗
Modernization of B-2 Data, Video, and Control Systems Infrastructure
The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) Plum Brook Station (PBS) Spacecraft Propulsion Research Facility, commonly referred to as B-2, is NASA's third largest thermal-vacuum facility with propellant systems capability. B-2 has completed a modernization effort of its facility legacy data, video and control systems infrastructure to accommodate modern integrated testing and Information Technology (IT) Security requirements. Integrated systems tests have been conducted to demonstrate the new data, video and control systems functionality and capability. Discrete analog signal conditioners have been replaced by new programmable, signal processing hardware that is integrated with the data system. This integration supports automated calibration and verification of the analog subsystem. Modern measurement systems analysis (MSA) tools are being developed to help verify system health and measurement integrity. Legacy hard wired digital data systems have been replaced by distributed Fibre Channel (FC) network connected digitizers where high speed sampling rates have increased to 256,000 samples per second. Several analog video cameras have been replaced by digital image and storage systems. Hard-wired analog control systems have been replaced by Programmable Logic Controllers (PLC), fiber optic networks (FON) infrastructure and human machine interface (HMI) operator screens. New modern IT Security procedures and schemes have been employed to control data access and process control flows. Due to the nature of testing possible at B-2, flexibility and configurability of systems has been central to the architecture during modernization.
Cmar, Mark D.↗