Search NASA⌕ Search

SEARCH · Search NASA

Results for “controller tools”

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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A structured data model frame for human factors application - 175

Building a Human Factors analysis and Verification and Validation case for new control rooms and even for control room modifications is a highly labor intensive project. Any optimization that can be made in the process translates into savings in the overall control room project schedule and budget. Several support tools from word, to excel, mind maps and databases have been traditionally used by human factors practitioners to conduct the analysis and document its results. While the use of these tools has had satisfactory results, they don't provide in our experience strong enough means for configuration control management, to avoid duplicities, streamline the analysis processes and allow collaborative team efforts. This paper documents how the established practices (methods, processes and tools) were questioned, which were the sources for comparison and how the results were translated into requirements for the new tool constituting the data frame model for Human Factors Engineering applications. The exercise has allowed us to evaluate the efficiency of our current practices in comparison to previous ones while improving our compliance with the quality assurance standards. (authors)

42 ENGINEERING↗
DOE OSTI2014

Reconfiguring Power Systems to Minimize Cascading Failures: Models and Algorithms

The main goal of this project was to develop new scientific tools, based on optimization techniques, with the purpose of controlling and modeling cascading failures of electrical power transmission systems. We have developed a high-quality tool for simulating cascading failures. The problem of how to control a cascade was addressed, with the aim of stopping the cascade with a minimum of load lost. Yet another aspect of cascade is the investigation of which events would trigger a cascade, or more appropriately the computation of the most harmful initiating event given some constraint on the severity of the event. One common feature of the cascade models described (indeed, of several of the cascade models found in the literature) is that we study thermally-induced line tripping. We have produced a study that accounts for exogenous randomness (e.g. wind and ambient temperature) that could affect the thermal behavior of a line, with a focus on controlling the power flow of the line while maintaining safe probability of line overload. This was done by means of a rigorous analysis of a stochastic version of the heat equation. We incorporated a model of randomness in the behavior of wind power output; again modeling an OPF-like problem that uses chance-constraints to maintain low probability of line overloads; this work has been continued so as to account for generator dynamics as well.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Field demonstration and implementation analysis of model predictive control in an office HVAC system

Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Deep Reinforcement Learning for Automatic Generation Control of Wind Farms

This paper provides a model-free framework for real-time control of wind farms to accurately track a power reference signal. This problem requires tractable dynamical models for capturing the aerodynamic interaction between wind turbines and controllers that can make decisions in realtime given varying atmospheric conditions. In this paper, we propose a deep reinforcement learning framework to provide real-time yaw control of a wind farm. Modifications have been made to FLOw Redirection and Induction in Steady State (FLORIS), a modeling tool that incorporates transient wake behavior. The control problem is formulated to track a synthetic power reference signal based on historical atmospheric (wind speed and direction) information, price signals, and regulation deployment data from U.S. regional transmission operators. Results indicate that a wind farm, with this control paradigm, can achieve good tracking performance when tested with real atmospheric data.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2021

Nonlinear dynamics of proton beams with hollow electron lens in the CERN high-luminosity LHC

The design stored beam energy in the CERN high-luminosity large hadron collider (HL-LHC) upgrade is about 700 MJ, with about 36 MJ in the beam tails, according to estimates based on scaling considerations from measurements at the LHC. Such a large amount of stored energy in the beam tails poses serious challenges on its control and safe disposal. In particular, orbit jitters can cause significant losses on primary collimators, which can lead to accidental beam dumps, magnet quenches, or even permanent damage to collimators and other accelerator elements. Thus, active control of the diffusion speed of halo particles is necessary and the use of hollow electron lenses (HELs) represents the most promising approach to handle overpopulated tails at the HL-LHC. HEL is a very powerful and advanced tool that can be used for controlled depletion of beam tails, thus enhancing the performance of beam halo collimation. For these reasons, HELs have been recently included in the HL-LHC baseline. In this paper, we present detailed beam dynamics calculations performed with the goal of defining HEL specifications and operational scenarios for HL-LHC. The prospects for effective halo control in HL-LHC are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Introduction to ALERT: A User-Interface Tool for Resilient Optimization

As the cyber-physical systems grow in complexity, there is a need for proactive resilience strategies that involve online, adaptive control actions to best prepare for any impending adversarial events. In this technical effort, supported by RD2C LDRD initiative, the project team designed and demonstrated online strategies – referred to as ALERT controls – for proactive and adaptive tuning of existing optimal controls in a microgrid, with quantifiably assured margins of resilience to various cyber-physical adversarial events. This ALERT functionality is made available to the end-users, e.g., the system operators, via an interactive user-interface. The end-users will not only be able to use the interface to visualize the system’s operation under various cyber-physical adversarial scenarios, but also evaluate the amount of tolerance the system has against selected adversarial perturbations of interest (e.g., malfunctioning sensors, suspected attacked measurements) via the adversarial plots. In this technical report, we briefly outline the algorithmic modules of the developed ALERT control technology, and introduce the user-interface tool that allows end-users (e.g., microgrid operators) to enter their system description, specify various operational and resilience requirements, and evaluate the impact of the control decisions via illustrative plots.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Overview of recent progress in 3D field physics in KSTAR

Various 3D field physics challenges of magnetically confined plasmas arise when the driving source comes from either externally applied non-axisymmetric 3D magnetic perturbations or plasma instabilities inside the plasma. Recently, several key outstanding topics of 3D field physics have been extensively studied in the Korean Superconducting Tokamak Advanced Research (KSTAR), such as edge-localized-mode (ELM) control by resonant magnetic perturbation (RMP), error field (EF) control, 3D field effects on rotation and transport, and RMP-induced alteration of divertor heat flux and detachment. KSTAR has a few physically unique features (i.e., high rotation and long-pulse plasmas with a low intrinsic EF) and machine/diagnostic capabilities (i.e., 3-row in-vessel control coil and state-of-the-art 2D/3D imaging diagnostics), which have been taken advantage of until now to address critical 3D field physics issues relevant to ITER and K-DEMO. Among many remarkable achievements are the robust access to and control of n = 1 RMP ELM suppression, along with a development of its physics basis tools, parameter expansion, optimization, and long-pulse control techniques. Nonetheless, a series of unresolved 3D physics themes, as well as limited coverage of 3D field operating regimes, have also been identified as future works for the 3D field research in KSTAR. In this paper, we provide an overview about the recent progress of KSTAR 3D field physics and present future plans of KSTAR 3D research toward a future fusion reactor.

3D field physics↗
DOE OSTI2014

A Visual Analytics Approach to Structured Data Analysis to Enhance Nonproliferation and Arms Control Verification Activities

Analysis activities for Nonproliferation and Arms Control verification require the use of many types of data. Tabular structured data, such as Excel spreadsheets and relational databases, have traditionally been used for data mining activities, where specific queries are issued against data to look for matching results. The application of visual analytics tools to structured data enables further exploration of datasets to promote discovery of previously unknown results. This paper discusses the application of a specific visual analytics tool to datasets related to the field of Arms Control and Nonproliferation to promote the use of visual analytics more broadly in this domain. Visual analytics focuses on analytical reasoning facilitated by interactive visual interfaces (Wong and Thomas 2004). It promotes exploratory analysis of data, and complements data mining technologies where known patterns can be mined for. Also with a human in the loop, they can bring in domain knowledge and subject matter expertise. Visual analytics has not widely been applied to this domain. In this paper, we will focus on one type of data: structured data, and show the results of applying a specific visual analytics tool to answer questions in the Arms Control and Nonproliferation domain. We chose to use the T.Rex tool, a visual analytics tool developed at PNNL, which uses a variety of visual exploration patterns to discover relationships in structured datasets, including a facet view, graph view, matrix view, and timeline view. The facet view enables discovery of relationships between categorical information, such as countries and locations. The graph tool visualizes node-link relationship patterns, such as the flow of materials being shipped between parties. The matrix visualization shows highly correlated categories of information. The timeline view shows temporal patterns in data. In this paper, we will use T.Rex with two different datasets to demonstrate how interactive exploration of the data can aid an analyst with arms control and nonproliferation verification activities. Using a dataset from PIERS (PIERS 2014), we will show how container shipment imports and exports can aid an analyst in understanding the shipping patterns between two countries. We will also use T.Rex to examine a collection of research publications from the IAEA International Nuclear Information System (IAEA 2014) to discover collaborations of concern. We hope this paper will encourage the use of visual analytics structured data analytics in the field of nonproliferation and arms control verification. Our paper outlines some of the challenges that exist before broad adoption of these kinds of tools can occur and offers next steps to overcome these challenges.

arms control↗
DOE OSTI2024

A Transfer Learning Strategy for Improving the Data Efficiency of Deep Reinforcement Learning Control in Smart Buildings

Reinforcement learning (RL) is a powerful tool that has shown promising results in many domains such as robotics and game-playing. Because RL algorithms learn optimal control policies by continuously interacting with their environments, these algorithms require a lot of data to learn, which limits their application to a wide range of domains. For this reason, there is an immense need for improving the training and data efficiency of RL. Towards addressing this research gap, this paper proposes a transfer learning (TL) approach to improve the efficiency of the RL algorithms by reducing data need and, thus, reducing training time. To demonstrate the proposed approach, a knowledge transfer from a set of buildings to another building was conducted. The results show that the proposed TL approach is a promising method that can efficiently harness the information from similar RL tasks and reduce the data needs of RL algorithms.

Amasyali, Kadir↗
DOE OSTI2013

Assessment of Energy Storage Alternatives in the Puget Sound Energy System Volume 2: Energy Storage Evaluation Tool

This volume presents the battery storage evaluation tool developed at Pacific Northwest National Laboratory (PNNL), which is used to evaluate benefits of battery storage for multiple grid applications, including energy arbitrage, balancing service, capacity value, distribution system equipment deferral, and outage mitigation. This tool is based on the optimal control strategies to capture multiple services from a single energy storage device. In this control strategy, at each hour, a look-ahead optimization is first formulated and solved to determine battery base operating point. The minute by minute simulation is then performed to simulate the actual battery operation. This volume provide background and manual for this evaluation tool.

25 ENERGY STORAGE↗
DOE OSTI2021

Feature Classification for Control System Devices

Control systems are used to automate industrial processes, smart grids, and smart cities. Unfortunately, cyber attacks on control systems are on the rise. Additionally, control systems lack the plethora of tools available for commodity systems for forensic investigation. An important step towards the proper forensic investigation is to analyze device memory. To assist in identifying features of device memory, we present a machine learning-based technique that integrates ontology information for feature classification in a control system device’s memory.

ahmed mithu, M Rayhan↗
DOE OSTI2018

Benford’s law and continuous dependent random variables

Many mathematical, man-made and natural systems exhibit a leading-digit bias, where a first digit (base 10) of 1 occurs not 11% of the time, as one would expect if all digits were equally likely, but rather 30%. This phenomenon is known as Benford’s Law. Analyzing which datasets adhere to Benford’s Law and how quickly Benford behavior sets in are the two most important problems in the field. Most previous work studied systems of independent random variables, and relied on the independence in their analyses. Inspired by natural processes such as particle decay, we study the dependent random variables that emerge from models of decomposition of conserved quantities. We prove that in many instances the distribution of lengths of the resulting pieces converges to Benford behavior as the number of divisions grow, and give several conjectures for other fragmentation processes. The main difficulty is that the resulting random variables are dependent. We handle this by using tools from Fourier analysis and irrationality exponents to obtain quantified convergence rates as well as introducing and developing techniques to measure and control the dependencies. The construction of these tools is one of the major motivations of this work, as our approach can be applied to many other dependent systems. As an example, we show that the n! entries in the determinant expansions of n×n matrices with entries independently drawn from nice random variables converges to Benford’s Law.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Investigating Power System Primary and Secondary Reserve Interaction under High Wind Penetration Using Frequency Response Model

Power system frequency needs to be maintained close to its nominal value at all times to avoid machine damage, under-frequency load-shedding and even blackouts. Adequate primary frequency response and secondary frequency response are the primary forces to correct an energy imbalance at the second to minute level. As wind energy becomes a larger portion of the world's energy portfolio, there are greater oppotunities for wind to provide frequency response services. This paper addresses one area of frequency control that has been missing in previous work - the reliabilty impacts and interactions between primary and secondary frequency control. The lack of a commercially available tools to simulate the interaction of these two responses has limited the energy industry's understanding of when the depletion of primary control reserve will impact the performance of secondary conrol response or vice versa. To investigate this issue, in this paper we develop a multi-area frequency response integration model with combined primary and secondary frequency control capabilities.

17 WIND ENERGY↗
DOE OSTI2023

Spatially Adaptive Tunable Lighting Control System with Expanded Wellness and Energy Saving Benefits

Lighting design is becoming increasingly complex, including active dimming for energy savings and spectral tuning for human wellbeing. Modern commercial lighting control systems are already difficult to use, and maintain, and even with so-called Smart Lighting, optimizing light settings is rapidly exceeding the capabilities of direct human control, limiting the adoption of lighting control systems. The growing importance of Occupancy Centric Controls (OCC) for advanced building management systems can be applied to the development of autonomous lighting system controls needed to drive the adoption of advanced lighting controls for improved adaptive “sculpted illumination” for both greater energy savings and broader human wellbeing perspectives. This project, led by Rensselaer Polytechnic Institute, entitled “Spatially adaptive tunable lighting control system with expanded wellness and energy saving benefits” will develop and test an entirely new platform for automated optimized lighting design and control that requires little or no human engagement, yet contours lighting profiles automatically to minimize lighting energy use. We call this approach to lighting control “dynamic light sculpting” since the right amount of illumination is automatically delivered to occupants in real time. Since the system uses new, privacy-preserving occupant position and pose detection technologies developed by Rensselaer for broad OCC building applications, the control system will analyze how to deliver the right amount of the right type of illumination only where and when it is needed based these OCC platforms. To create these powerful autonomous lighting control platforms, the project will integrate evolving augmented reality (AR) and virtual reality (VR) tools with sophisticated lighting and interior design toolkits to create interactive lighting design and control simulators. Using the quickly growing paradigm of digital twins, these tools will integrate light fixture properties, occupancy sensor data, interior design data, and lighting specifications to accurately visualize how various design concepts interact with simulated yet realistic human activities that occur in commercial office buildings of various types. These advanced digital twin design and simulation tools will be combined with a new class of digitally-programmable LED lighting fixtures that can dynamically change the spectral content and direction of light emission. When fully integrated, the new autonomous lighting control system will take all of the guesswork out of optimizing light quality while minimizing energy consumption. Digital twin tools will simplify the design, installation, commissioning, operation, and maintenance of future energy-efficient lighting systems. It should be possible to reduce lighting energy costs from 40% to 70% with OCC based dynamic light sculpting systems. The work will be led by the Center for Lighting Enabled Systems and Applications (LESA) and the Center for Architectural Science and Ecology (CASE), both at Renssealer Polytechnic Institute; Lumileds, a global leader in the development of advanced LED systems; and HKS, a leading global architecture design firm. This interdisciplinary project team’s experience includes all of the design simulation tools, VR/AR digital twin visualization technology, advanced occupancy sensing technology, and complex control system design capabilities that will revolutionize lighting design and control technology to autonomously deliver high-quality lighting that improves human health and wellbeing while simultaneously maximizing lighting energy savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

A Micro-Grid Simulator Tool (SGridSim) using Effective Node-to-Node Complex Impedance (EN2NCI) Models

This paper presents a micro-grid simulator tool useful for implementing and testing multi-agent controllers (SGridSim). As a common engineering practice it is important to have a tool that simplifies the modeling of the salient features of a desired system. In electric micro-grids, these salient features are the voltage and power distributions within the micro-grid. Current simplified electric power grid simulator tools such as PowerWorld, PowerSim, Gridlab, etc, model only the power distribution features of a desired micro-grid. Other power grid simulators such as Simulink, Modelica, etc, use detailed modeling to accommodate the voltage distribution features. This paper presents a SGridSim micro-grid simulator tool that simplifies the modeling of both the voltage and power distribution features in a desired micro-grid. The SGridSim tool accomplishes this simplified modeling by using Effective Node-to-Node Complex Impedance (EN2NCI) models of components that typically make-up a micro-grid. The term EN2NCI models means that the impedance based components of a micro-grid are modeled as single impedances tied between their respective voltage nodes on the micro-grid. Hence the benefit of the presented SGridSim tool are 1) simulation of a micro-grid is performed strictly in the complex-domain; 2) faster simulation of a micro-grid by avoiding the simulation of detailed transients. An example micro-grid model was built using the SGridSim tool and tested to simulate both the voltage and power distribution features with a total absolute relative error of less than 6%.

43 PARTICLE ACCELERATORS↗
DOE OSTI2013

Assessing the Importance of Nonlinearities in the Development of a Substructure Model for the Wind Turbine CAE Tool FAST: Preprint

Design and analysis of wind turbines are performed using aero-servo-elastic tools that account for the nonlinear coupling between aerodynamics, controls, and structural response. The NREL-developed computer-aided engineering (CAE) tool FAST also resolves the hydrodynamics of fixed-bottom structures and floating platforms for offshore wind applications. This paper outlines the implementation of a structural-dynamics module (SubDyn) for offshore wind turbines with space-frame substructures into the current FAST framework, and focuses on the initial assessment of the importance of structural nonlinearities. Nonlinear effects include: large displacements, axial shortening due to bending, cross-sectional transverse shear effects, etc.

17 WIND ENERGY↗
DOE OSTI2019

Distributed Barrier Certificates for Safe Operation of Inverter-Based Microgrids

Inverter-interfaced microgrids differ from the traditional power systems due to their lack of inertia. Vanishing timescale separation between voltage and frequency dynamics makes it critical that faster-timescale stabilizing control laws also guarantee by-construction the satisfaction of voltage limits during transients. In this article, we apply a barrier functions method to compute distributed active and reactive power setpoint control laws that certify satisfaction of voltage limits during transients. Using sum-of-squares optimization tools, we propose an algorithmic construction of these control laws. Numerical simulations are provided to illustrate the proposed method.

Kundu, Soumya↗
DOE OSTI2018

CNC Module for Turning: A New CAM Application

The integrated CAM application for the KOMPAS-3D environment known as CNC Module for Turning is considered. This application permits automated development of control programs for numerically controlled lathes. The user may readily adapt the CAM application for specific production conditions and machine tools, thanks to features such as the following: representation of the cutting tools by means of parametric 3D models, the possibility of including fixed CNC cycles in the control programs, and the use of the Python language for programming of the postprocessors.

42 ENGINEERING↗