Results for “Control of electric power systems” — Page 12

DOE OSTI2023

Tuning Phase Lock Loop Controller of Grid Following Inverters by Reinforcement Learning to Support Networked Microgrid Operations

The dynamic operation of networked microgrids leads to varying topological configurations and generator commitments and dispatches. These variations correspond to systems with different electrical characteristics. The fixed control gains of high-speed power electronic devices may result in undesirable system performance when the electrical characteristics change significantly. As such, it is necessary to tune the control gains of power electronics devices to adapt to the changing system characteristics. This paper uses observer-based reinforcement learning to automatically tune the proportional-integral (PI) gains of phase lock loop (PLL) controller of grid-following (GFL) inverters to adapt to the changing system strengths, that would be seen in networked microgrid operations. Simulation results using an operational electric distribution system, modeled as networked microgrids, are presented to demonstrate the need and effectiveness of the proposed adaptive controls.

networked microgrids, reinforcement learning, grid↗

NASA NTRS1998

Advanced Electric Distribution, Switching, and Conversion Technology for Power Control

The Electrical Power Control Unit currently under development by Sundstrand Aerospace for use on the Fluids Combustion Facility of the International Space Station is the precursor of modular power distribution and conversion concepts for future spacecraft and aircraft applications. This unit combines modular current-limiting flexible remote power controllers and paralleled power converters into one package. Each unit includes three 1-kW, current-limiting power converter modules designed for a variable-ratio load sharing capability. The flexible remote power controllers can be used in parallel to match load requirements and can be programmed for an initial ON or OFF state on powerup. The unit contains an integral cold plate. The modularity and hybridization of the Electrical Power Control Unit sets the course for future spacecraft electrical power systems, both large and small. In such systems, the basic hybridized converter and flexible remote power controller building blocks could be configured to match power distribution and conversion capabilities to load requirements. In addition, the flexible remote power controllers could be configured in assemblies to feed multiple individual loads and could be used in parallel to meet the specific current requirements of each of those loads. Ultimately, the Electrical Power Control Unit design concept could evolve to a common switch module hybrid, or family of hybrids, for both converter and switchgear applications. By assembling hybrids of a common current rating and voltage class in parallel, researchers could readily adapt these units for multiple applications. The Electrical Power Control Unit concept has the potential to be scaled to larger and smaller ratings for both small and large spacecraft and for aircraft where high-power density, remote power controllers or power converters are required and a common replacement part is desired for multiples of a base current rating.

Soltis, James V.↗

DOE OSTI2024

Multi-Agent Graph-Attention Deep Reinforcement Learning for Post-Contingency Grid Emergency Voltage Control

Grid emergency voltage control (GEVC) is paramount in electric power systems to improve voltage stability and prevent cascading outages and blackouts in case of contingencies. While most deep reinforcement learning (DRL)-based paradigms perform single agents in a static environment, real-world agents for GEVC are expected to cooperate in a dynamically shifting grid. Moreover, due to high uncertainties from combinatory natures of various contingencies and load consumption, along with the complexity of dynamic grid operation, the data efficiency and control performance of the existing DRL-based methods are challenged. To address these limitations, we propose a multi-agent graph-attention (GATT)-based DRL algorithm for GEVC in multi-area power systems. Here, we develop graph convolutional network (GCN)-based agents for feature representation of the graph-structured voltages to improve the decision accuracy in a data-efficient manner. Furthermore, a cutting-edge attention mechanism concentrates on effective information sharing among multiple agents, synergizing different-sized subnetworks in the grid for cooperative learning. We address several key challenges in the existing DRL-based GEVC approaches, including low scalability and poor stability against high uncertainties. Test results in the IEEE benchmark system verify the advantages of the proposed method over several recent multi-agent DRL-based algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1971

Power processing

Processing of electric power has been presented as a discipline that draws on almost every field of electrical engineering, including system and control theory, communications theory, electronic network design, and power component technology. The cost of power processing equipment, which often equals that of expensive, sophisticated, and unconventional sources of electrical energy, such as solar batteries, is a significant consideration in the choice of electric power systems.

Schwarz, F. C.↗

DOE OSTI2022

Controlling a power output of a nuclear reaction without control rods

A nuclear power system includes a reactor vessel that includes a reactor core mounted therein. The reactor core includes nuclear fuel assemblies configured to generate a nuclear fission reaction. The reaction vessel does not include any control rod assemblies therein. The nuclear power system further includes a riser positioned above the reactor core, a primary coolant flow path, a primary coolant that circulates through the primary coolant flow path to receive heat from the nuclear fission reaction and release the received heat to generate electric power in a power generation, and a control system communicably coupled to the power generation system and configured to control a power output of the nuclear fission reaction independent of any control rod assemblies.

Callaway, Allyson↗

NASA NTRS1991

Precise time and time interval applications to electric power systems

There are many applications of precise time and time interval (frequency) in operating modern electric power systems. Many generators and customer loads are operated in parallel. The reliable transfer of electrical power to the consumer partly depends on measuring power system frequency consistently in many locations. The internal oscillators in the widely dispersed frequency measuring units must be syntonized. Elaborate protection and control systems guard the high voltage equipment from short and open circuits. For the highest reliability of electric service, engineers need to study all control system operations. Precise timekeeping networks aid in the analysis of power system operations by synchronizing the clocks on recording instruments. Utility engineers want to reproduce events that caused loss of service to customers. Precise timekeeping networks can synchronize protective relay test-sets. For dependable electrical service, all generators and large motors must remain close to speed synchronism. The stable response of a power system to perturbations is critical to continuity of electrical service. Research shows that measurement of the power system state vector can aid in the monitoring and control of system stability. If power system operators know that a lightning storm is approaching a critical transmission line or transformer, they can modify operating strategies. Knowledge of the location of a short circuit fault can speed the re-energizing of a transmission line. One fault location technique requires clocks synchronized to one microsecond. Current research seeks to find out if one microsecond timekeeping can aid and improve power system control and operation.

Robert E. Wilson↗

NASA NTRSDate not supplied

Electrified Aircraft Propulsion Controls Hardware Testing

Electrified Aircraft Propulsion (EAP) systems hold potential for the reduction of aircraft fuel burn and emissions. To realize this potential for single-aisle aircraft, control technology challenges associated with EAP designs are increasing the demand for Hardware-In-the-Loop (HIL) studies that address the tightly coupled electrical powertrain and turbofan propulsion systems. Reconfigurable HIL testbeds enable the study of integrated supervisory control and control approaches that augment engine shaft torques to improve performance. This paper presents an overview of conceptual EAP controls architecture testing in two HIL testbeds. The NASA Electric Aircraft Testbed provides the ability for megawatt class electric powertrain testing for technology maturation. A 100 kilowatt testbed, the Hybrid Propulsion Emulation Rig, allows for rapid controls technology trade studies. In both testbeds, controls testing is performed by implementing the electrical power system in hardware while turbomachinery is emulated via electric machines that are commanded by a real-time model and controls. A novel scaling algorithm is applied to emulate the inertial loads of the turbomachinery that causes the electric machines to respond in a fashion similar to that of the full-scale propulsion system they represent. Results demonstrate desired control performance at both testbed scales for the conceptual EAP architecture.

Electrified Aircraft Propulsion↗

NASA NTRSDate not supplied

Electrified Aircraft Propulsion Controls Hardware Testing

Electrified Aircraft Propulsion (EAP) systems hold potential for the reduction of aircraft fuel burn and emissions. To realize this potential for single-aisle aircraft, control technology challenges associated with EAP designs are increasing the demand for Hardware-In-the-Loop (HIL) studies that address the tightly coupled electrical powertrain and turbofan propulsion systems. Reconfigurable HIL testbeds enable the study of integrated supervisory control and control approaches that augment engine shaft torques to improve performance. This paper presents an overview of conceptual EAP controls architecture testing in two HIL testbeds. The NASA Electric Aircraft Testbed provides the ability for megawatt class electric powertrain testing for technology maturation. A 100 kilowatt testbed, the Hybrid Propulsion Emulation Rig, allows for rapid controls technology trade studies. In both testbeds, controls testing is performed by implementing the electrical power system in hardware while turbomachinery is emulated via electric machines that are commanded by a real-time model and controls. A novel scaling algorithm is applied to emulate the inertial loads of the turbomachinery that causes the electric machines to respond in a fashion similar to that of the full-scale propulsion system they represent. Results demonstrate desired control performance at both testbed scales for the conceptual EAP architecture.

Electrified Aircraft Propulsion↗

DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources

Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (control-lable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.

DER↗

NASA NTRS1966

Surveyor spacecraft system - Surveyor 1 flight performance, volume 2 Final report

Performance analysis of electric power, data link, signal processing, and flight control systems of Surveyor 1 spacecraft

Beall, K. C.↗

DOE OSTI2023

Off-policy deep reinforcement learning with automatic entropy adjustment for adaptive online grid emergency control

Electric overloading conditions and contingencies put modern power systems at risk of voltage collapse and blackouts. Load shedding is crucial to maintain voltage stability for grid emergency control. However, the rule- or model-based schemes rely on accurate dynamic system models and face considerable challenges in adapting to various operating conditions and uncertain event occurrences. Here, to address these issues, this paper proposes a novel deep reinforcement learning (DRL)-based voltage stability control algorithm with automatic entropy adjustment (AEA) for grid emergency control. Various dynamic network components for complex system operations are modeled to construct the DRL environment. An off-policy soft actor-critic architecture is developed to maximize the expected reward and policy entropy simultaneously. The AEA mechanism is proposed to facilitate the policy maximum entropy procedure, and the proposed method can automatically provide effective discrete and continuous actions against various fault scenarios. Our approach accomplishes high sampling efficiency, scalability, and auto-adaptivity of the control policies under high uncertainties. Comparative studies with the existing DRL-based control methods in IEEE benchmarks indicate salient performance improvement of the proposed method for dynamic system emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources: Preprint

Integration of a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problem requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (controllable DERs) including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperformed other benchmark controllers regarding computation time without compromising operational performance.

DER↗

DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

NASA NTRS1972

Circuit performance of a parasitic-loading digital speed controller for a 400-hertz turbine-driven alternator

Parasitic speed controllers are used in space power electrical generating systems to maintain the speed of the turbine-driven alternators within specified limits. Selected subcircuits for a solid state parasitic-loading speed controller were assembled and tested. The test results as obtained to date consist of the performance evaluation of the frequency reference and the period counter subcircuits. These circuits performed as expected.

Ryan, J. P.↗

NASA NTRS2019

Evaluation of Electrical System Requirements for Implementing Turbine Electrified Energy Management

Turbine Electrified Energy Management (TEEM) is a concept concerned with the management of energy in an electrified propulsion system. The management of energy in the hybrid-electric architecture has potential to benefit the turbomachinery and the aircraft it powers. The concept is particularly useful for improving operability during transient operation and could be leveraged to design a better performing engine. The concept utilizes electric machines coupled to the engine shafts and an electric power distribution system that includes energy storage. A controller is used to decide when and how energy is moved around the electrified propulsion system, particularly when considering energy conversion between mechanical and electrical forms. Prior work has shown that the electric machines can be used to supply/or extract supplemental power to/from the engine shafts to improve their operability and achieve or enable propulsion efficiency and performance benefits. However, the previous studies did not consider the practical constraints of the electrical machines and energy storage devices that are required for implementing the TEEM system architecture concept. This paper presents an integrated engine and electrical system model that is used to evaluate the electrical system requirements. The model captures the physics of the conceptual, Advanced Geared Turbofan 30,000lbf (AGTF30) engine, which features advanced technologies such as a compact gas turbine and a variable area fan nozzle. For this work, the engine is augmented with electrical system components that allow for the implementation of the TEEM concept. The evaluation presented suggests the potential of the TEEM concept to provide performance benefits for a turbofan engine.

electrified↗

NASA NTRS2019

Evaluation of Electrical System Requirements for Implementing Turbine Electrified Energy Management

Turbine Electrified Energy Management (TEEM) is a concept concerned with the management of energy in an electrified propulsion system. The management of energy in the hybrid-electric architecture has potential to benefit the turbomachinery and the aircraft it powers. The concept is particularly useful for improving operability during transient operation and could be leveraged to design a better performing engine. The concept utilizes electric machines coupled to the engine shafts and an electric power distribution system that includes energy storage. A controller is used to decide when and how energy is moved around the electrified propulsion system, particularly when considering energy conversion between mechanical and electrical forms. Prior work has shown that the electric machines can be used to supply/or extract supplemental power to/from the engine shafts to improve their operability and achieve or enable propulsion efficiency and performance benefits. However, the previous studies did not consider the practical constraints of the electrical machines and energy storage devices that are required for implementing the TEEM system architecture concept. This paper presents an integrated engine and electrical system model that is used to evaluate the electrical system requirements. The model captures the physics of the conceptual, Advanced Geared Turbofan 30,000lbf (AGTF30) engine, which features advanced technologies such as a compact gas turbine and a variable area fan nozzle. For this work, the engine is augmented with electrical system components that allow for the implementation of the TEEM concept. The evaluation presented suggests the potential of the TEEM concept to provide performance benefits for a turbofan engine.

hybrid electric propulsion↗

NASA NTRS2015

Advanced Stirling Convertor Dual Convertor Controller Testing at NASA Glenn Research Center in the Radioisotope Power Systems System Integration Laboratory

NASA Glenn Research Center developed a nonnuclear representation of a Radioisotope Power System (RPS) consisting of a pair of Advanced Stirling Convertors (ASCs), Dual Convertor Controller (DCC) EMs (engineering models) 2 and 3, and associated support equipment, which were tested in the Radioisotope Power Systems System Integration Laboratory (RSIL). The DCC was designed by the Johns Hopkins University Applied Physics Laboratory (JHU/APL) to actively control a pair of ASCs. The first phase of testing included a Dual Advanced Stirling Convertor Simulator (DASCS), which was developed by JHU/APL and simulates the operation and electrical behavior of a pair of ASCs in real time via a combination of hardware and software. RSIL provides insight into the electrical interactions between a representative radioisotope power generator, its associated control schemes, and realistic electric system loads. The first phase of integration testing included the following spacecraft bus configurations: capacitive, battery, and super-capacitor. A load profile, created based on data from several missions, tested the RPS's and RSIL's ability to maintain operation during load demands above and below the power provided by the RPS. The integration testing also confirmed the DCC's ability to disconnect from the spacecraft when the bus voltage dipped below 22 volts or exceeded 36 volts. Once operation was verified with the DASCS, the tests were repeated with actual operating ASCs. The goal of this integration testing was to verify operation of the DCC when connected to a spacecraft and to verify the functionality of the newly designed RSIL. The results of these tests are presented in this paper.

Controllers↗

NASA NTRS2015

Advanced Stirling Convertor Dual Convertor Controller Testing at NASA Glenn Research Center in the Radioisotope Power Systems System Integration Laboratory

NASA Glenn Research Center (GRC) developed a non-nuclear representation of a Radioisotope Power System (RPS) consisting of a pair of Advanced Stirling Convertors (ASC), a Dual Convertor Controller (DCC) EM (engineering model) 2 & 3, and associated support equipment, which were tested in the Radioisotope Power Systems System Integration Laboratory (RSIL). The DCC was designed by the Johns Hopkins University/Applied Physics Laboratory (JHU/APL) to actively control a pair of Advanced Stirling Convertors (ASC). The first phase of testing included a Dual Advanced Stirling Convertor Simulator (DASCS) which was developed by JHU/APL and simulates the operation and electrical behavior of a pair of ASC's in real time via a combination of hardware and software. RSIL provides insight into the electrical interactions between a representative radioisotope power generator, its associated control schemes, and realistic electric system loads. The first phase of integration testing included the following spacecraft bus configurations: capacitive, battery, and supercapacitor. A load profile, created based on data from several missions, tested the RPS and RSIL ability to maintain operation during load demands above and below the power provided by the RPS. The integration testing also confirmed the DCC's ability to disconnect from the spacecraft when the bus voltage dipped below 22 V or exceeded 36 V. Once operation was verified with the DASCS, the tests were repeated with actual operating ASC's. The goal of this integration testing was to verify operation of the DCC when connected to a spacecraft and to verify the functionality of the newly designed RSIL. The results of these tests are presented in this paper.

systems integration↗