Search NASA⌕ Search

SEARCH · Search NASA

Results for “reactivity 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.

At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Making real-time reactive systems reliable

A reactive system is characterized by a control program that interacts with an environment (or controlled program). The control program monitors the environment and reacts to significant events by sending commands to the environment. This structure is quite general. Not only are most embedded real time systems reactive systems, but so are monitoring and debugging systems and distributed application management systems. Since reactive systems are usually long running and may control physical equipment, fault tolerance is vital. The research tries to understand the principal issues of fault tolerance in real time reactive systems and to build tools that allow a programmer to design reliable, real time reactive systems. In order to make real time reactive systems reliable, several issues must be addressed: (1) How can a control program be built to tolerate failures of sensors and actuators. To achieve this, a methodology was developed for transforming a control program that references physical value into one that tolerates sensors that can fail and can return inaccurate values; (2) How can the real time reactive system be built to tolerate failures of the control program. Towards this goal, whether the techniques presented can be extended to real time reactive systems is investigated; and (3) How can the environment be specified in a way that is useful for writing a control program. Towards this goal, whether a system with real time constraints can be expressed as an equivalent system without such constraints is also investigated.

Marzullo, Keith↗
DOE OSTI2021

A comparative study of machine learning models for predicting the state of reactive mixing

Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Here, to investigate the evolution of mixing dynamics under different scenarios (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. The outputs comprised concentration profiles of reactants and products. The inputs to and outputs from these simulations were concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) models intended to emulate system behavior. These 20 ML emulators, based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptrons (MLPs), were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay (i.e., average concentration, square of average concentration), and degree of mixing (i.e., variances of species concentration). Unsurprisingly, linear classifiers and regressors failed to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP model accurately classified the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predicted the QoIs. At run time, trained ML emulators produced results times faster than the finite-element simulations. Due to their low computational expense and high accuracy, ensemble and MLP models are excellent emulators for these numerical simulations and great utilities in uncertainty quantification exercises, which can require 1,000s of forward model runs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Late Effects of Heavy Ion Irradiation on Ex Vivo Osteoblastogenesis and Cancellous Bone Microarchitecture

Prolonged spaceflight causes degeneration of skeletal tissue with incomplete recovery even after return to Earth. We hypothesize that heavy ion irradiation, a component of Galactic Cosmic Radiation, damages osteoblast progenitors and may contribute to bone loss during long duration space travel beyond the protection of the Earth's magnetosphere. Male, 16 week old C57BL6/J mice were exposed to high LET (56 Fe, 600MeV) radiation using either low (5 or 10cGy) or high (50 or 200cGy) doses at the NASA Space Radiation Lab and were euthanized 3 - 4, 7, or 35 days later. Bone structure was quantified by microcomputed tomography (6.8 micron pixel size) and marrow cell redox assessed using membrane permeable, free radical sensitive fluorogenic dyes. To assess osteoblastogenesis, adherent marrow cells were cultured ex vivo, then mineralized nodule formation quantified by imaging and gene expression analyzed by RT PCR. Interestingly, 3 - 4 days post exposure, fluorogenic dyes that reflect cytoplasmic generation of reactive nitrogen/oxygen species (DAF FM Diacetate or CM H2DCFDA) revealed irradiation (50cGy) reduced free radical generation (20-45%) compared to sham irradiated controls. Alternatively, use of a dye showing relative specificity for mitochondrial superoxide generation (MitoSOX) revealed an 88% increase compared to controls. One week after exposure, reactive oxygen/nitrogen levels remained lower(24%) relative to sham irradiated controls. After one month, high dose irradiation (200 cGy) caused an 86% decrement in ex vivo nodule formation and a 16-31% decrement in bone volume to total volume and trabecular number (50, 200cGy) compared to controls. High dose irradiation (200cGy) up regulated expression of a late osteoblast marker (BGLAP) and select genes related to oxidative metabolism (Catalase) and DNA damage repair (Gadd45). In contrast, lower doses (5, 10cGy) did not affect bone structure or ex vivo nodule formation, but did down regulate iNOS by 0.54 - 0.58 fold. Thus, both low and high doses of heavy ion irradiation cause time dependent, adaptive changes in redox state within marrow cells but only high doses (50, 200cGy) inhibit osteoblastogenesis and cause cancellous bone loss. We conclude space radiation has the potential to cause persistent damage to bone marrow derived stem and progenitor cells for osteoblasts despite adaptive changes in cellular redox state.

Tran, Luan Hoang↗
DOE OSTI2021

Computational design of microarchitected porous electrodes for redox flow batteries

Porous electrodes are used as the core reactive component across electrochemical technologies. In flowing systems, controlling the fluid distribution, species transport, and reactive environment is critical to attaining high performance. However, conventional electrode materials like felts and papers provide few opportunities for precise engineering of the electrode and its microstructure. To address these limitations, architected electrodes composed of unit cells with spatially varying geometry determined via computational optimization are proposed. Resolved simulation is employed to develop a homogenized description of the constituent unit cells. These effective properties serve as inputs to a continuum model for the electrode when used in the negative half-cell of a vanadium redox flow battery. Porosity distributions minimizing power loss are then determined via computational design optimization to generate architected porosity electrodes. The architected electrodes are compared to bulk, uniform porosity electrodes and found to lead to increased power efficiency across operating flow rates and currents. The design methodology is further used to generate a scaled-up electrode with comparable power efficiency to the bench-scale systems. Finally, the variable porosity architecture and computational design methodology presented here thus offers a novel pathway for automatically generating spatially engineered electrode structures with improved power performance.

25 ENERGY STORAGE↗
DOE OSTI2025

Topology-Aware Reinforcement Learning for Voltage Control: Centralized and Decentralized Strategies

Volt-VAR control (VVC) methods based on deep reinforcement learning (DRL) can effectively control distribution grid voltage and minimize power loss by implementing corrective and preventive control measures on the reactive power output of inverter-based distributed energy resources (DERs). However, model-free DRL-based VVC approaches usually cannot capture the important topological feature of the power system since they use a fully-connected network (FCN) to deliver the action. Therefore, this paper proposes a graph convolutional network (GCN)-based DRL approach that can employ the topological information of the network to take better control action for regulating the voltage. Our implementation allows for both centralized and decentralized configurations, utilizing a single agent and multiple agents respectively. Although the centralized GCN-based DRL approach has its advantages of minimizing voltage fluctuation and power loss, it is not suitable for large scale power systems due to its challenges in terms of scalability, computation speed and potential single points of failure. Therefore, these problems can be resolved using the decentralized GCN-based DRL approach. Moreover, to ensure the safe operation of the model, our proposed approach incorporates an exponential barrier function while formulating the reward function for each agent. To validate performance of the proposed approaches, the proposed model is tested on modified IEEE test systems and the performances are measured in terms on voltage fluctuation reduction, minimization of power loss and computational speed. Finally, the results show that the proposed topology-aware approach outperforms the FCN-based DRL approach in terms of reducing voltage fluctuation and minimizing power loss of the network. Moreover, it is shown that the decentralized GCN-based DRL has faster computational speed than other approaches.

42 ENGINEERING↗
DOE OSTI2023

Solar PV and BESS Plant-Level Voltage Control and Interactions: Experiments and Analysis

In real world operation, low-frequency oscillations associated with plant-level voltage control of inverter-based resources (IBRs) have been a concern of grid operators. Here, this research aims to demonstrate and analyze the effect of plant-level voltage control on system stability and potential interactions among IBRs. The experiment results of a 13.2-kV system interconnected with two IBRs—a 1-MW battery energy storage system (BESS) and a 430-kW solar photovoltaic (PV) plant—are presented. Both 0.5-Hz and 3-Hz oscillations become visible at certain conditions. The phenomenon is further explained by use of a feedback system consisting of the plant-level droop control and the effect of reactive power or var injection on voltage. The main components of the block diagram are obtained via frequency scan measurements. The analysis offers an understanding of the main contributors and other influencing factors of the 3-Hz and 0.5-Hz modes. It also reveals the role of the volt-var droop gain, the low-pass filter time constant, and the system impedance in stability.

14 SOLAR ENERGY↗
DOE OSTI2024

Select Proliferation Studies on TRISO-fueled, Heat-Pipe-cooled Microreactors

Nuclear microreactors carry the potential to open up new markets for the nuclear industry, as their expected cost competitiveness in non-traditional market segments (e.g., mines, military bases, extraterrestrial surfaces, and remote areas), and their inherent safety features make them deployable when other power sources are unavailable or difficult to exploit. For countries that have not traditionally participated in nuclear power, microreactors represent a clean energy solution [1]. However, their use, especially in non-weapons states, may entail challenges in terms of maintaining international nuclear safeguards [2]. Furthermore, the deployment locations where microreactors may prove most cost competitive would be difficult to access by state and International Atomic Energy Agency (IAEA) inspectors [3]. In addition to the isolated nature of potential deployment sites, the low-power characteristic of microreactors suggests that numerous microreactors would need to be deployed to meet energy demands. That, coupled with the unique physics of many current microreactor designs, opens up a new area of research with respect to nonproliferation and safeguards concerns [2]. Whereas traditional facilities are inspected as isolated cases when looking for signs of diversion or misuse; microreactors may need to be assessed in the context of the entire fleet to which they belong. International safeguards necessitate timely detection of any significant quantities (SQs) of material that are being diverted (e.g., 1 SQ of special nuclear material diverted over the course of a 1-year period) [4]. For low-enriched uranium, the IAEA defines 1 SQ as corresponding to 75 kg of 235U. The purpose of the present paper is to explore the detectability threshold for material diversion in microreactors by relying on critical control drum angles, excess reactivity, and the reactor lifetime as the selected operational parameters. For this assessment, a heat-pipe-cooled microreactor was regarded as the base design. While the conclusions reached in this paper are not readily extendable to the design of actual microreactors, the analysis herein enables conclusions to be drawn regarding the level of accuracy needed for reference calculations in order to detect diversion scenarios by utilizing the selected operational parameters (i.e., mainly control drum angles, critical insertion angle, and the reactor lifetime).

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2018

Late Effects of Heavy-Ion Irradiation on Ex Vivo Osteoblastogenesis and Cancellous Bone Microarchitecture

Prolonged spaceflight causes degeneration of skeletal tissue with incomplete recovery even after return to Earth. We hypothesize that heavy-ion irradiation, a component of Galactic Cosmic Radiation, damages osteoblast progenitors and may contribute to bone loss during long duration space travel beyond the protection of the Earth's magnetosphere. Male, 16 week-old C57BL6/J mice were exposed to high-LET (56-Fe, 600MeV) radiation using either low (5 or 10cGy) or high (50 or 200cGy) doses at the NASA Space Radiation Lab and were euthanized 3-4, 7, or 35 days later. Bone structure was quantified by microcomputed tomography (6.8 μm pixel size) and marrow cell redox assessed using membrane permeable, free radical-sensitive fluorogenic dyes. To assess osteoblastogenesis, adherent marrow cells were cultured ex vivo, then mineralized nodule formation quantified by imaging and gene expression analyzed by RT-PCR. Interestingly, 3-4 days post-exposure, fluorogenic dyes that reflect cytoplasmic generation of reactive nitrogen/oxygen species (DAF-FM Diacetate or CM-H2DCFDA) revealed irradiation (50cGy) reduced free radical generation (20-45%) compared to sham-irradiated controls. Alternatively, use of a dye showing relative specificity for mitochondrial superoxide generation (MitoSOX) revealed an 88% increase compared to controls. One week after exposure, reactive oxygen/nitrogen levels remained lower (24%) relative to sham-irradiated controls. After one month, high dose irradiation (200 cGy) caused an 86% decrement in ex vivo nodule formation and a 16-31% decrement in bone volume to total volume and trabecular number (50, 200cGy) compared to controls. High dose irradiation (200cGy) up-regulated expression of a late osteoblast marker (BGLAP) and select genes related to oxidative metabolism (Catalase) and DNA damage repair (Gadd45). In contrast, lower doses (5, 10cGy) did not affect bone structure or ex vivo nodule formation, but did down-regulate iNOS by 0.54-0.58 fold. Thus, both low- and high-doses of heavy-ion irradiation cause time-dependent, adaptive changes in redox state within marrow cells but only high doses (50, 200cGy) inhibit osteoblastogenesis and cause cancellous bone loss. We conclude space radiation has the potential to cause persistent damage to bone marrow-derived stem and progenitor cells for osteoblasts despite adaptive changes in cellular redox state.

LATE EFFECTS OF HEAVY-ION IRRADIATION↗
DOE OSTI2020

Improving the Performance of Integrated Power-Hardware-in-the-Loop and Quasi-Static Time-Series Simulations

This paper introduces a novel multirate cosimulation architecture that overcomes previous challenges integrating faster real-time, microseconds-scale, power-hardware-in-the-loop (PHIL) with larger scale but slower, real-time, seconds-scale, quasi-static time-series (QSTS) simulation. Specifically, an intermediate reduced-equivalent electromagnetic transient (EMT) model duplicates the key power system topology to capture high-speed dynamics and converge hardware-power system interactions between QSTS updates. Exchanging multidimensional parameter vectors (loads, control status, etc.) between the QSTS and EMT models enables capturing the interactions of the rich, high-node-count (thousands of electrical nodes) QSTS model with the hardware. This architecture offers full spatial resolution from QSTS, including individual load dynamics, actual distribution management system controls, and nodal voltages. Simultaneously, the intermediate EMT model provides more detailed high-speed transients of key distributed energy resource hardware interactions. In addition, we use careful PHIL interface design and the exchange of complex power data, rather than current, to further improve performance. This cosimulation architecture is demonstrated by testing a simulated distribution system with an interconnected 500-kVA advanced photovoltaic (PV) inverter in PHIL. In conclusion, the architecture successfully captured PV local volt-volt ampere reactive (volt-VAR) control interactions with the larger network under time-varying electrical and weather conditions.

42 ENGINEERING↗
DOE OSTI2022

Facile synthesis of epoxide-co-propylene sulphide polymers with compositional and architectural control

Here, we present a facile method to produce propylene sulphide (PS) homopolymers up to 100 kg mol -1 and PS-epoxide statistical, block, and ABA copolymers using inexpensive and versatile thio-aluminium (SAl) based initiators. Homopolymerizations of PS with SAl initiators are living and controlled, with number averaged molecular weights ($\bar M$ n ) up to 100 kg mol -1 while maintaining narrow polydispersity (Ð < 1.4). Statistical and block copolymers of PS and epichlorohydrin (ECH) or propylene oxide (PO) are synthesized and characterized by size-exclusion chromatography (SEC), differential scanning calorimetry (DSC), 1 H and 13 C NMR spectroscopy, diffusion ordered spectroscopy (DOSY), and small-angle X-ray scattering (SAXS). This work represents the first statistical copolymerization of PS and epoxides with similar reactivity ratios, allowing fine control over composition. Block-copolymers of PS and epoxides are synthesized by sequential addition, without intermediate preparative steps. Polymer architecture is controlled through modification of the initiator; we synthesized a di-functional (d-H) SAl initiator to produce ABA tri-block-copolymers. Finally, poly(ethylene glycol) (PEG) was used as a macroinitiator to create PEG-b-PPS block copolymers and characterized by 1 H, 13 C NMR spectroscopy, DOSY, DSC, and SEC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Review of Power System Support Functions for Inverter-Based Distributed Energy Resources - Standards, Control Algorithms, and Trends

Penetration of renewable energy in power systems has been increasing in the past decades in response to increased global electricity demand and concerns for the environment. Distributed energy resources (DERs) based on renewables have experienced rapid growth thanks to the incentive programs and broad-based participation. With the growing prevalence of DERs, the risk of grid instability and vulnerability increases due to the intermittent nature of renewable energy. At the same time, the voltage and frequency deviation problems emerge more often when the reverse power flow occurs under supply-demand imbalance in distributed power systems. Standards and grid codes have been issued for DER inverters to interconnect with the distribution grid. The updated standard and grid codes expect DERs to provide a variety of power system support functions in order to incorporate higher DER penetration and to maximize DER value to the grid. This paper provides an overview of the power system support functions from renewable DER inverters, which are categorized as: voltage regulation by active/reactive power control, frequency regulation by active power control, voltage ride-through, and frequency ride-through. The benefits and drawbacks of each algorithm are presented and compared with its predecessor, manifesting the logic in the evolution of the algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Comprehensive compilations of computation results and validations for neutronics start-up tests at China Experimental Fast Reactor

This paper compiles and analyzes refined results of the coordinated research project (CRP) on Neutronics Benchmark of China Experimental Fast Reactor (CEFR) Start-Up Tests conducted by the International Atomic Energy Agency (IAEA) since 2018. Twenty-eight research organizations participated with various code systems. The China Institute of Atomic Energy (CIAE) provided the benchmark specifications and participants conducted the benchmark through blind and refined phases. This paper presents the benchmark results on six experimental measurements, criticality, control rod worth, temperature reactivity coefficients, sodium void worth, assembly swap reactivities, and foil activations. Except for a few outlier results, the simulation results show good agreement with the measured data within 1-σ experiment uncertainties. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Quantitative Trade-Off in Distributed Secondary Control for Autonomous AC Microgrids

In this paper, we propose to quantify the trade-off between voltage regulation and reactive power sharing in autonomous AC microgrids with distributed secondary control. It is known that voltage regulation and reactive power sharing in droop-controlled autonomous AC microgrids are two conflicting control objectives that present a natural trade-off between voltage regulation towards the voltage magnitude reference and reactive power sharing accuracy. This trade-off is commonly shown qualitatively without sufficient quantification. In this work, to quantify the trade-off between the two objectives, we focus on distributed secondary control and utilize regression and polynomial surface fitting to identify the requisite parameter area to satisfy the predefined error bands for voltage magnitude regulation and reactive power sharing. Extensive case studies are presented to validate the proposed method.

autonomous AC microgrids↗
DOE OSTI2020

Thermally Activated Delayed Photoluminescence: Deterministic Control of Excited-State Decay

Thermally activated photophysical processes are ubiquitous in numerous organic and metal-organic molecules, leading to chromophores with excited state properties that can be considered an equilibrium mixture of the available low-lying states. Relative populations of the equilibrated states are governed by temperature. Such molecules have been devised as high quantum yield emitters in modern organic light-emitting diode technology and for deterministic excited state lifetime control to enhance chemical reactivity in solar energy conversion and photocatalytic schemes. This recent discovery of thermally activated photophysics at CdSe nanocrystal-molecule interfaces enables a new paradigm wherein molecule-quantum dot constructs are used to systematically generate material with predetermined photophysical response and excited state properties. Semiconductor nanomaterials feature size-tunable energy level engineering, which considerably expands the purview of thermally activated photophysics beyond what is possible using only molecules. This Perspective is intended to provide a non-exhaustive overview of the advances that led to the integration of semiconductor quantum dots in thermally activated delayed photoluminescence (TADPL) schemes and to identify important challenges moving into the future. The initial establishment of excited state lifetime extension utilizing triplet-triplet excited-state equilibria is detailed. Next, advances involving the rational design of molecules composed of both metal-containing and organic-based chromophores that produce the desired TADPL are described. Lastly, the recent introduction of semiconductor nanomaterials into hybrid TADPL constructs is discussed, paving the way towards the realization of fine-tuned deterministic control of excited state decay. It is envisioned that libraries of synthetically facile composites will be broadly deployed as photosensitizers and light emitters for numerous synthetic and optoelectronic applications in the near future.

14 SOLAR ENERGY↗
DOE OSTI2023

Automated power-following control for nuclear thermal propulsion startup and shutdown using MOOSE-based applications

This paper describes an investigation of various automated control strategies applied to a full-core multiphysics Griffin/Bison/RELAP-7 model of a prototypical nuclear thermal propulsion system. In all cases, control is achieved by actuating control drums based on the demanded power and predicted quantities from the numerical model. One key finding is that hybrid proportional integral derivative controllers – a novel type of controller that uses both power and reactivity predicted signals – can demonstrate a level of performance rivaling that of period-generated control. The former requires parameter tuning, while the latter mostly necessitates providing reactivity coefficients and temperature rates of change, which could be very challenging to accurately measure in real-time. In addition, decay heat plays an important role in determining cooling requirements during cooldown phases. A decay heat model, accounting for burn time and throttling, was derived, and is incorporated within the model to simulate the steady-state and shutdown phases and satisfyingly follow the power demand. However, temperature overshoots and non-constant specific impulse during throttling will necessitate further improvements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Data-Driven Chance-Constrained Design of Voltage Droop Control for Distribution Networks: Preprint

This paper addresses the design of local control methods for voltage control in distribution networks with high level of distributed energy resources (DERs). The designed control methods adapt the active and reactive power output of distributed energy resources proportional to the deviation of the local measured voltage magnitudes from a reference voltage, which is referred to as droop control. Thus, the design focuses on determining the droop characteristics which satisfy network-wide voltage magnitude constraints. The uncertainty and variability of DERs renders the design of optimal droop controls very challenging. Hence, this paper proposes chance constraints to limit the risk from intermittent DERs, by designing droop control coefficients that guarantee the satisfaction of network operational constraints with a specific probability. In addition, the proposed approach relies entirely on historical data rather than assuming knowledge of the probability distributions that characterize the uncertainty of DERs. The efficacy of the proposed method is demonstrated on a 37-bus distribution feeder.

chance-constrained optimization↗
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↗
DOE OSTI2020

Current Observer Based Predictive Decoupled Power Control Grid-Interactive Inverter

This paper presents a sensor-less current model predictive control (MPC) scheme via a full state observer based current estimator. The grid interactive inverters’ control schemes require current and voltage sensors. Elimination of current sensor enhances the inverter reliability. This paper leverages the inherent characteristics of MPC towards robust current sensor-less grid interactive inverter with LC filter. The observer for inductor current is developed based on the existing capacitor voltage measurement. The estimator dynamic state-space matrices are then obtained through reconstruction of the inverter model with the capacitor voltage and inductor current being the state variables. The controller objectives are to regulate active and reactive power in a decoupled manner. The theoretical expectation, controller performance, and accuracy of the current estimation are verified by conducting a real-time simulation via Typhoon HIL.

Zhang, Zhen↗