Ground Simulator Studies of a Small Side-located Controller in a Power Control System
Explore the source record for details and available documents.
SEARCH · Search NASA
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.
Explore the source record for details and available documents.
Power-flow controls (PFCs) technologies provide a suite of alternatives to more-efficiently direct the flow of power on the grid, improving flexibility and enabling the grid to be more responsive and resilient. PFCs can be used in many cases to mitigate overloads and consequently defer the installation of grid-expansion projects. They can also be suitable options in situations where new transmission lines cannot be built because of environmental constraints, land use, or other types of restrictions. Other uses include mitigation of loop or unintended flows, forcing of contractual or scheduled flows, reduction of congestion, and mitigation solutions during large maintenance or construction projects. While many studies have demonstrated the potential economic benefits of PFC for different types of applications, the implementation implications of using multiple PFCs for system operation and reliability have not been analyzed in detail. The lack of information and practical experience on these aspects hinders adoption of this technology. One use case would be that multiple PFCs are required to coordinate congestion management. In such cases, multiple PFCs are deployed in a meshed power system to control power flows across a wide area. The challenges are associated with PFC interaction—namely, the self and mutual power-flow sensitivity of each PFC, which must be accounted for in the calculation of the respective operating setpoints. This report analyzes implementation and operation that must be considered in evaluating transmission solutions using PFCs. The report first analyzes attributes with which PFC-based solutions should comply, based on the objective and characteristics of each particular use case. It then describes different approaches for coordinated control of multiple PFC devices for different applications, including options to avoid adverse interactions and reduce risks due to inappropriate coordination. Control and operation are illustrated through case studies performed on both generic and actual power system models. This report provides insights for transmission planners and operation engineers about the implementation of PFC-based transmission solutions; consequently, it will help them make informed decisions about the adoption of the technology.
Power supply utilizes small quantities of two-phase fluid of suitable thermodynamic properties for circulation in a capillary-pumped heat transfer loop. Fluid is vaporized in evaporator, passed through fluidic system load, condensed, pumped by multistage capillary pump, and returned to the evaporator.
Electrical circuit control techniques in power systems are disclosed herein. In one embodiment, a supervisory computer in the power system can be configured to fit phasor measurement data from phasor measurement units into a Gaussian distribution with a corresponding Gaussian confidence level. When the Gaussian confidence level of the fitted Gaussian distribution is above a Gaussian confidence threshold, the supervisory computer can be configured to perform an ambient analysis on the received phasor measurement data to determine an operating characteristic of the power system. The supervisory computer can then automatically applying at least one electrical circuit control action to the power system in response to the determined operating characteristic.
A control system for autonomous distribution and control of electrical power during space missions is being developed. This system should free the astronauts from localizing faults and reconfiguring loads if problems with the power distribution and generation components occur. The control system uses an object-oriented simulation model of the power system and first principle knowledge to detect, identify, and isolate faults. Each power system component is represented as a separate object with knowledge of its normal behavior. The reasoning process takes place at three different levels of abstraction: the Physical Component Model (PCM) level, the Electrical Equivalent Model (EEM) level, and the Functional System Model (FSM) level, with the PCM the lowest level of abstraction and the FSM the highest. At the EEM level the power system components are reasoned about as their electrical equivalents, e.g, a resistive load is thought of as a resistor. However, at the PCM level detailed knowledge about the component's specific characteristics is taken into account. The FSM level models the system at the subsystem level, a level appropriate for reconfiguration and scheduling. The control system operates in two modes, a reactive and a proactive mode, simultaneously. In the reactive mode the control system receives measurement data from the power system and compares these values with values determined through simulation to detect the existence of a fault. The nature of the fault is then identified through a model-based reasoning process using mainly the EEM. Compound component models are constructed at the EEM level and used in the fault identification process. In the proactive mode the reasoning takes place at the PCM level. Individual components determine their future health status using a physical model and measured historical data. In case changes in the health status seem imminent the component warns the control system about its impending failure. The fault isolation process uses the FSM level for its reasoning base.
The connection of more loads and distributed energy resources (DERs) to the distribution power system brings both challenges and opportunities to system operators. There are opportunities to aggregate flexible loads and DERs to provide transmission grid services, but the coordinated actions of DERs being managed by independent, third-party DER aggregators to support transmission system operations can present challenges. We developed a federated DER management architecture and control framework that aims to manage heterogeneous DERs to deliver reliable transmission grid services while respecting distribution system constraints. The controls include stochastic day-ahead optimization, model predictive control, and a simple real-time management scheme. We present simulation results obtained from a realistic laboratory test bed of federated controls managing DERs within a substation service area to make the substation net power follow the optimal net power determined by the day-ahead optimization based on cost and limiting reverse power flow.
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.
Systems and methods for simulating and controlling a power system in real time, using a controller, are provided. The controller includes a simulation layer to simulate an operation of the power system, a disturbance generation layer to provide data to the simulation layer to disturb the simulated operation of the power system, an application layer to display the simulated operation of the power system and generate a control signal to control, based on the simulated operation of the power system, at least one element of the power system, and a distributed data streaming server (D2S2) to allow interoperability among the simulation layer, the disturbance layer, and the application layer.
The design, fabrication and flight testing of a powered elevator system for the Augmentor Wing Jet STOL Research Aircraft (AWJSRA or Mod C-8A) are discussed. The system replaces a manual spring tab elevator control system that was unsatisfactory in the STOL flight regime. Pitch control in the AWJSRA is by means of a single elevator control surface. The elevator is used for both maneuver and trim control as the stabilizer is fixed. A fully powered, irreversible flight control system powered by dual hydraulic sources was designed. The existing control columns and single mechanical cable system of the AWJSRA have been retained as has been the basic elevator surface, except that the elevator spring tab is modified into a geared balance tab. The control surface is directly actuated by a dual tandem moving body actuator. Control signals are transmitted from the elevator aft quadrant to the actuator by a linkage system that includes a limited authority series servo actuator.
The Auxiliary Payload Power System (APPS) provides supplementary power and cooling to Space Processing Application (SPA) experiments to be mounted in the APPS and the Spacelab in the Shuttle Payload Bay. SPA experiment operations are planned for early Shuttle flights. This paper presents thermal control study results for preliminary analysis and design definition of the APPS. A 100/sq m, three-wing, pumped-fluid, deployable radiator with separate APPS equipment and SPA experiments coolant loops was selected as the baseline. The system is capable of rejecting the heat (approximately 26 kw) associated with the production and consumption of approximately 16 kw of electrical power produced by the APPS fuel cells for a worst case radiator orientation. For the most favorable orientation, the heat rejection and power capability approach 38 and 24 kw, respectively. Alternate approaches were evaluated, such as heat pipes for the radiator and alternate fluids for the coolant loops. Emphasis was placed on using Shuttle developed hardware: coolant pumps, heat exchangers, fluids, and radiator technology.
Electrical utility substations are wired with intelligent electronic devices (IEDs), such as protective relays, power meters, and communication switches. Substation engineers commission these IEDs to assess the appropriate measurements for monitoring, control, power system protection, and communication applications. Like real electrical utility substations, complex electrical substation grid testbeds (ESGTs) need to be assessed for measuring current and voltage signals in monitoring, power system protection, control (synchro check), and communication applications that are limited by small measurement percentage errors. In the process of setting an ESGT with real-time simulators and IEDs in the loop, protective relays, power meters, and communication devices must be commissioned before running experiments. In this study, an ESGT with IEDs and distributed ledger technology was developed. The ESGT with a real-time simulator and IEDs in the loop was satisfactorily assessed and commissioned. The commissioning and problem-solving tasks of the testbed are described to define a method with flowcharts to assess possible trouble-shooting in ESGTs. This method was based on comparing the simulations versus IED measurements for the phase current and voltage magnitudes, three-phase phasor diagrams, breaker states, protective relay times with selectivity coordination at electrical faults, communication data points, and time-stamp sources.
The advanced control and power system (ACAPS) program is to establish the technology necessary to satisfy space station and related large space structures requirements for efficient, reliable, and cost effective energy storage and attitude control. Technology advances in the area of integrated flywheel systems capable of performing the dual functions of energy storage and attitude control are outlined.
Deep reinforcement learning (DRL) algorithms have successfully solved many challenging problems in various power system control scenarios. However, their decision-making process is usually regarded as black-boxes. Furthermore, how DRL models interact with human intelligence remains an open problem. Thus, this paper proposes a policy extraction framework to extract a complex DRL model into an explainable policy. This framework includes three parts: 1) DRL training and data generation. We train an agent for a specific control task and generate data, which contains the control policy of the agent. 2) Policy extraction. We propose an information gain rate based weighted oblique decision tree (IGR-WODT) for DRL policy extraction. 3) Policy evaluation. We define three metrics to evaluate the performance of the proposed approach. A case study for the under-voltage load shedding problem shows that the IGR-WODT presents a performance enhancement compared with DRL, weighted oblique decision tree, and univariate decision tree. The proposed policy extraction method could provide an intuitive explanation of the neural network decision-making process to the dispatchers when making final decisions on power grid operation. Also, the resulted rule-based controller could replace the deep neural network-based controller in many field edge devices with limited computing resources, providing comparable performance.
The integration of power electronics enabled devices and the high penetration of renewable energy drastically increase the complexity of power system operation and control. Power systems are still vulnerable to large-scale blackouts caused by extreme natural events or man-made attacks. With the recent development in artificial intelligence technique, machine learning has shown a processing ability in computational, perceptual and cognitive intelligence. It is an urgent challenge to integrate the advanced machine learning technology and large amount of real-time data from wide area measurement systems and intelligent electronic devices, in order to effectively enhance power system resilience and ensure the reliable and secure operation of power systems. Therefore, this paper aims to systematically review the existing application of machine learning methods on power system resilience enhancement, to expand the interest of researchers and scholars in this topic, and to jointly promote the application of artificial intelligence in the field of power systems.
In December, 1986, a Center Director's Discretionary Fund (CDDF) proposal was granted to study power system control techniques in large space electrical power systems. Presented are the accomplishments in the area of power system control by power quality load management. In addition, information concerning the distortion problems in a 20 kHz ac power system is presented.
Intelligent or autonomous control of an entire spacecraft is a major technology that must be developed to enable NASA to meet its human exploration goals. NASAs current long term human space platform, the International Space Station, is in low earth orbit with almost continuous communication with the ground based mission control. This permits the near real-time control by the ground of all of the core systems including power. As NASA moves beyond Low Earth Orbit, the issues of communication time-lag and lack of communication bandwidth beyond geosynchronous orbit does not permit this type of operation. This paper presents the work currently ongoing at NASA to develop an architecture for an autonomous power control system as well as the effort to assemble that controller into the framework of the vehicle mission manager and other subsystem controllers to enable autonomous control of the complete spacecraft. Due to the common problems faced in both space power systems and terrestrial power system, the potential for spin-off applications of this technology for use in micro-grids located at the edge or user end of terrestrial power grids for peak power accommodation and reliability are described.
Intelligent or autonomous control of an entire spacecraft is a major technology that must be developed to enable NASA to meet its human exploration goals. NASA's current long term human space platform, the International Space Station, is in low Earth orbit with almost continuous communication with the ground based mission control. This permits the near real-time control by the ground of all of the core systems including power. As NASA moves beyond low Earth orbit, the issues of communication time-lag and lack of communication bandwidth beyond geosynchronous orbit does not permit this type of operation. This paper presents the work currently ongoing at NASA to develop an architecture for an autonomous power control system as well as the effort to assemble that controller into the framework of the vehicle mission manager and other subsystem controllers to enable autonomous control of the complete spacecraft. Due to the common problems faced in both space power systems and terrestrial power system, the potential for spin-off applications of this technology for use in micro-grids located at the edge or user end of terrestrial power grids for peak power accommodation and reliability are described.
The increasing penetration of inverter-based resources (IBRs) into the existing power systems introduces tremendous benefits for enhanced sustainability but also poses inevitable challenges in terms of insufficient inertia, potential instability, and complex network dynamics, among others. However, the additional coupling introduced by the interactions among gridfollowing (GFL) and grid-forming (GFM) IBRs and the other components (i.e., synchronous generators [SGs], loads, and network, etc.) has not been clearly explored. A holistic, scalable, and quantitative stability analysis framework with the control systems and power networks is still missing. Here, in this paper, to fill in the technical gaps, a holistic small-signal model of the entire system with both rotating generation units and IBRs is established. An extended power flow model with operation dynamics from both generator control schemes and power networks is proposed to provide the varying steady-state operating points for small-signal modeling. The proposed method is compared with MATLAB solvers, and the results show that the proposed approach has a minimum calculation time, which can be less than 12 seconds for a large-scale power system with up to 2,000 buses. Furthermore, a quantitative method is developed to identify the impacts of IBRs on system performance with emphases on the potential stability issues with GFL IBRs, additional benefits of employing GFM IBRs, the feasibility of replacing SGs with GFM IBRs, and the impact of penetration level of different kinds of generation units. Finally, a field island power system is used to verify the proposed approach, and hardware-in-the-loop (HIL) tests are provided to further demonstrate the effectiveness of the proposed analysis.