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At least 667 records · Page 37

Physics-Informed Neural Networks for PDE-Constrained Optimization and Control

The goal of optimal control is to determine a sequence of inputs for maximizing or minimizing a given performance criterion subject to the dynamics and constraints of the system under observation. This work introduces Control Physics-Informed Neural Networks (PINNs), which simultaneously learn both the system states and the optimal control signal in a single-stage framework that leverages the system’s underlying physical laws. While prior approaches often follow a two-stage process-modeling, the system first and then devising its control—the presented novel framework embeds the necessary optimality conditions directly into the network architecture and loss function. We demonstrate the effectiveness of the novel methodology by solving various open-loop optimal control problems governed by analytical, one-dimensional, and two-dimensional partial differential equations (PDEs).

97 MATHEMATICS AND COMPUTING↗

Water Network Shape-Dependence of Local Interactions with the Microhydrated –NO 2 – and –CO 2 – Anionic Head Groups by Cold Ion Vibrational Spectroscopy

We report the structural evolutions of water networks and solvatochromic response of the CH 3 NO 2 – radical anion in the OH and CH stretching regions by analysis of the vibrational spectra displayed by cryogenically cooled CH 3 NO 2 – ·(H 2 O) n=1–6 clusters. The OH stretching bands evolve with a surprisingly large discontinuity at n = 6, which features the emergence of an intense, strongly red-shifted band along with a weaker feature that appears in the region assigned to a free OH fundamental. Very similar behavior is displayed by the perdeuterated carboxylate clusters, RCO 2 – ·(H 2 O) n=5–7 (R = CD 3 CD 2 ), indicating that this behavior is a general feature in the microhydration of the triatomic anionic domain and not associated with CH oscillators. Electronic structure calculations trace this behavior to the formation of a “book” isomer of the water hexamer that adopts a configuration in which one of the water molecules resides in an acceptor–acceptor–donor (AAD) (A = acceptor, D = donor) H-bonding site. Excitation of the bound OH in the AAD site explores the local network topology best suited to stabilize an incipient –XO 2 H–OH–(H 2 O) 2 intracluster proton-transfer reaction. These systems thus provide particularly clear examples where the network shape controls the potential energy landscape that governs water network-mediated, intracluster proton transfer. The CH stretching bands of the CH 3 NO 2 – ·(H 2 O) n=1–6 clusters also exhibit strong solvatochromic shifts, but in this case, they smoothly blue-shift with increasing hydration with no discontinuity at n = 6. Furthermore, this behavior is analyzed in the context of the solute-ion polarizability response and partial charge transfer to the water networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Air Traffic Control Improvement Using Prioritized CSMA

Version 7 simulations of the industry-standard network simulation software "OPNET" are presented of two applications of the Aeronautical Telecommunications Network (ATN), Controller Pilot Data Link Communications (CPDLC) and Automatic Dependent Surveillance-Broadcast mode (ADS-B), over VHF Data Link mode 2 (VDL-2). Communication is modeled for air traffic between just three cities. All aircraft are assumed to have the same equipage. The simulation involves Air Traffic Control (ATC) ground stations and 105 aircraft taking off, flying realistic free-flight trajectories, and landing in a 24-hr period. All communication is modeled as unreliable. Collision-less, prioritized carrier sense multiple access (CSMA) is successfully tested. The statistics presented include latency, queue length, and packet loss. This research may show that a communications system simpler than the currently accepted standard envisioned may not only suffice, but also surpass performance of the standard at a lower cost of deployment.

Robinson, Daryl C.↗

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Hageman, Jacob↗

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Hageman, Jacob J.↗

Control of Complex Dynamic Systems by Neural Networks

This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.

Spall, James C.↗

Inverter Controls for Smooth Transition Operation of Networked Microgrids

Islanding sections of a distribution grid as microgrids and networking these microgrids offer promising opportunities for improving system reliability; however, this approach may need distributed energy resources (DERs) to switch between grid-connected and islanded operation without affecting the system stability. The Thevenin impedance of the power system at the DER point of common coupling changes due to networking actions. It is critical to have smooth transitions between modes to ensure stable system operation and to avoid nuisance tripping. This paper presents the design of a grid-forming (GFM)-capable inverter control scheme to reliably operate in distribution systems with networked microgrids. Control mechanisms for smooth transitions between grid-following (GFL) and GFM inverter modes are presented. First, we test the control mechanism on a simple two-source power system through electromagnetic transient (EMT) simulations using a digital real-time simulator. Then, we add multiple such DERs to the EMT model of a real-world distribution feeder and demonstrate stable operation between mode transitions during the networking of the microgrids.

grid forming controls↗

Advanced control techniques for modern inertia based inverters

In this research three artificial intelligent (AI)-based techniques are proposed to regulate the voltage and frequency of a grid-connected inverter. The increase in the penetration of renewable energy sources (RESs) into the power grid has led to the increase in the penetration of fast-responding inertia-less power converters. The increase in the penetration of these power electronics converters changes the nature of the conventional grid, in which the existing kinetic inertia in the rotating parts of the enormous generators plays a vital role. The concept of virtual inertia control scheme is proposed to make the behavior of grid connected inverters more similar to the synchronous generators, by mimicking the mechanical behavior of a synchronous generator. Conventional control techniques lack to perform optimally in nonlinear, uncertain, inaccurate power grids. Besides, the decoupled control assumption in conventional VSGs makes them nonoptimal in resistive grids. The neural network predictive controller, the heuristic dynamic programming, and the dual heuristic dynamic programming techniques are presented in this research to overcome the draw backs of conventional VSGs. The nonlinear characteristics of neural networks, and the online training enable the proposed methods to perform as robust and optimal controllers. The simulation and the experimental laboratory prototype results are provided to demonstrate the effectiveness of the proposed techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Pilots Rate Augmented Generalized Predictive Control for Reconfiguration

The objective of this paper is to report the results from the research being conducted in reconfigurable fight controls at NASA Ames. A study was conducted with three NASA Dryden test pilots to evaluate two approaches of reconfiguring an aircraft's control system when failures occur in the control surfaces and engine. NASA Ames is investigating both a Neural Generalized Predictive Control scheme and a Neural Network based Dynamic Inverse controller. This paper highlights the Predictive Control scheme where a simple augmentation to reduce zero steady-state error led to the neural network predictor model becoming redundant for the task. Instead of using a neural network predictor model, a nominal single point linear model was used and then augmented with an error corrector. This paper shows that the Generalized Predictive Controller and the Dynamic Inverse Neural Network controller perform equally well at reconfiguration, but with less rate requirements from the actuators. Also presented are the pilot ratings for each controller for various failure scenarios and two samples of the required control actuation during reconfiguration. Finally, the paper concludes by stepping through the Generalized Predictive Control's reconfiguration process for an elevator failure.

Soloway, Don↗

Detection of Control Injection Attacks using Energy Data Anomalies in CNC Machining

The widespread adoption of networked devices, sophisticated automation, and data-driven processes in the industry - also known as Industry 4.0 - has boosted the quantity and quality of manufacturing products. With these benefits, however, comes a substantial increase in the attack surface of these systems. In addition to affecting the readiness and the quality of critical products, the attacks against manufacturing processes and systems carry the potential to have severe physical consequences, including human injury and death. In this paper we present the results of a remote network-based control injection attack on a CNC mill. Specifically, we focus on the impact of this type of the attack on the movement of CNC mill during operation. Evaluating the physical effect of these attacks on a workpiece, we provide machine agnostic, affordable, and scalable solution for their monitoring. We then demonstrate a simple threshold-based method for the detection of these attacks and evaluate the effectiveness of detection.

Taylor, Curtis↗

Data-Driven Optimal Control of Traffic Signals for Urban Road Networks

This paper studies the issue of data-driven optimal control design for traffic signals of oversaturated urban road networks. The signal control system based on the store and forward model is generally uncontrollable for which the controllable decomposition is needed. Instead of identifying the unknown parameters like saturation rates and turning ratios, a finite number of measured trajectories can be used to parametrize the system and help directly construct a transformation matrix for Kalman controllable decomposition through the fundamental lemma of J. C. Willems. On top of that, an infinite-horizon linear quadratic regulator (LQR) problem is formulated considering the constraints of green times for traffic signals. The problem can be solved through a two-phase data-driven learning process, where one solves an infinite-horizon unconstrained LQR problem and the other solves a finite-horizon constrained LQR problem. The simulation result shows the theoretical analysis is effective and the proposed data-driven controller can yield desired performance for reducing traffic congestion.

Liu, Tong↗

Architecture and Methods for Substation SCADA Cybersecurity: Best Practices

There are over 3000 electricity providers in the United States, encompassing investor and publicly owned utilities as well as electric cooperatives. There has been ongoing trends to increasingly automate and provide remote control and monitoring of electric energy delivery systems. The deployment of computer network technologies has increased the efficiency and reliability of electric power infrastructure. However, the increased use of digital communications has also increased the vulnerability to malicious cyber attacks [1]. In 2004 the National Research Councils (National Academies) formed a committee of specialists to address these vulnerabilities and propose possible solutions with an objective to prioritize the R&D needs for developing countermeasures. The committee addressed many potential concerns in the electric power delivery system and classified them based upon different criteria and presented recommendations to minimize the gap between the academic research directions and the needs of the electric utility industry. The complexity and diversity of the electric power delivery system in the U.S. has opened many ports for attackers and intruders [1]. This complexity and diversity is attributed to the fact that power delivery system is a network of substations, transmission and distribution lines, sub-networks of controlling, sensing and monitoring units, and human operator involvement for running the system [1]. Accordingly, any incident such as the occurrence of a fault or disturbance in this complex network cannot be deferred and should be resolved within an order of milliseconds, otherwise there is risk of large-scale outages similar to the occurrences in India and the U.S. in 2003 [2]. There are three main vulnerabilities in supervisory control and data acquisition (SCADA) systems commonly identified—physical vulnerability, cyber vulnerability and personal vulnerability [1]. In terms of cyber threats, SCADA systems are the most critical elements in the electric power grid in the U.S. Unauthorized access to a SCADA system could enable/disable unexpected equipment (such as disable the protection system or a circuit breaker) which could cause large scale disruptions of electric power delivery. This paper provides an overview of power system SCADA technologies in transmission substations (Section 2) and summarizes the best practices for implementing a cyber security program. After introducing SCADA system operations in Section 2, a description of the security challenges for SCADA systems is presented in Section 3. In Section 4, NECRC Critical Infrastructure Protection standards CIP-002 through CIP-009 are summarized. An overview of industry best practices is presented in Section 5.

97 MATHEMATICS AND COMPUTING↗

A Networked Microgrid Framework and Testbed for Communication, Controls, and Optimization Testing

This paper presents the development and experimental results of a networked AC microgrid testbed located at Oak Ridge National Laboratory. The testbed comprises two, 480V three-phase, four wire microgrids designed to operate standalone, grid-tied, or as a network of microgrids. This testbed represents both the state of the industry, by incorporating grid-assets commonly found in real microgrids, and the state of the art, as it is a platform to evaluate advanced controllers. The main elements of this networked microgrid testbed are presented in this paper including a Scenario Manager, local microgrid controls, and a networked microgrid control. The Scenario Manager has the objective of emulating real-world conditions. The local microgrid controller oversees standalone, grid-tied, or islanded operation. The microgrid control is a higher-level control that coordinates interaction between islanded microgrids. This paper delves into these controllers and validates their operation in the networked microgrid testbed showcasing the operational flexibility and advance control capabilities.

Ferrari Maglia, Max↗

A data-driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems

The increasing integration of distributed energy resources, including demand-side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data-driven network optimisation approach is proposed to coordinate the control of distributed PVs and smart buildings in distribution networks considering the uncertainties of solar power, outdoor temperature and heat gain associated with building thermal dynamics. These uncertain parameters have a significant impact on the operation and control of distributed PVs and smart buildings, bringing challenges to the distribution system operation. In the proposed data-driven distributionally robust optimisation (DRO) approach, the Wasserstein ball is used to construct an ambiguity set for the uncertain parameters, which does not require the probability distributions to be known. Furthermore, a conditional value-at-risk is incorporated into the Wasserstein-based DRO model and converted into a computationally tractable mixed-integer convex optimisation problem. Benchmarked with robust optimisation and chance-constrained programming, the proposed data-driven model can give a less conservative robust solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗

MPC-Based Local Voltage Control Strategy of DGs in Active Distribution Networks

The increasing penetration of distributed generators (DGs) exacerbates the voltage violations in active distribution networks (ADNs). The commonly used centralized voltage control may aggravate the burden of computation and communication. Local voltage control can realize fast response to frequent voltage fluctuations. However, effective local control for DGs still needs to be further investigated. In this paper, a model predictive control (MPC)-based local voltage control strategy of DGs is proposed to cope with voltage violation problems and enhance the adaptation to the DG volatility. DGs are regulated with local Q-V control curves to respond to voltage fluctuations rapidly. Based on the dynamic network partition, a unified control curve for DGs is set in the same area to realize efficient operation. To further improve the performance of voltage control, the parameter tuning of control curves is regularly conducted based on decentralized inter-area coordination with the rolling of the control horizon. Finally, case study is verified on the modified IEEE 33-node distribution system. The proposed local control strategy of DGs can effectively improve the voltage profile and reduce power losses of the whole network without high computation and communication burden.

active distribution network (ADN)↗

The influence of preexisting host rock damage on fault network localization

The transition from stable to unstable fracture propagation occurs when fractures begin to interact and link. Thus, fracture network coalescence controls how rocks and engineered structures fail. Here, to constrain the factors that influence localization in shear zones under brittle conditions, we build discrete element method models with a rough fault embedded in a shear zone. We add varying numbers of diffuse, randomly-placed weaknesses to examine the influence of diffuse damage on fracture network localization. The number of weaknesses controls the localization behavior of the fault network and the final fault geometry. We quantify localization using the Gini coefficient of the fracture volume, which measures the nonuniformity in a population. Each model generally increases in localization toward failure. However, models with more diffuse damage experience delocalization phases that are superimposed on the overall trend of increasing localization. The observed link between delocalization and host rock damage may help explain the varying localization of low magnitude seismicity in southern California. Models with more diffuse damage produce more complex fault geometries comprised of several parallel strands of wing cracks. The propagation of these wing cracks reduces the shear stress acting on the model boundaries, indicating that this fracture development increases the mechanical efficiency of the system.

58 GEOSCIENCES↗

A Network Scheduling Model for Distributed Control Simulation

Distributed engine control is a hardware technology that radically alters the architecture for aircraft engine control systems. Of its own accord, it does not change the function of control, rather it seeks to address the implementation issues for weight-constrained vehicles that can limit overall system performance and increase life-cycle cost. However, an inherent feature of this technology, digital communication networks, alters the flow of information between critical elements of the closed-loop control. Whereas control information has been available continuously in conventional centralized control architectures through virtue of analog signaling, moving forward, it will be transmitted digitally in serial fashion over the network(s) in distributed control architectures. An underlying effect is that all of the control information arrives asynchronously and may not be available every loop interval of the controller, therefore it must be scheduled. This paper proposes a methodology for modeling the nominal data flow over these networks and examines the resulting impact for an aero turbine engine system simulation.

distributed control↗

Systems Level Regulation of Rhythmic Growth Rate and Biomass Accumulation in Grasses

Objectives: Several breakthroughs have been recently made in our understanding of plant growth and biomass accumulation. It was found that plant growth is rhythmically controlled throughout the day by the circadian clock through a complex interplay of light and phytohormone signaling pathways. While plants such as the C4 energy crop sorghum (Sorghum bicolor (L.) Moench) and possibly the C3 grass Brachypodium distachyon also exhibit daily rhythms in growth rate, the molecular details of its regulation remain to be explored. A better understanding of diurnally regulated growth behavior in grasses may lead to species-specific mechanisms highly relevant to future strategies to optimize energy crop biomass yield. Here we propose to devise a systems approach to identify, in parallel, regulatory hubs associated with rhythmic growth in C3 and C4 plants. We propose to use rhythmicity in daily growth patterns to drive the discovery of regulatory network modules controlling biomass accumulation. Description: The project is divided in three main parts: 1) Performing time-lapse imaging and growth measurement in B. distachyon and S. bicolor to determine growth rate dynamic during the day/night cycle. Identifying growth-associated genes whose expression patterns follow the observed growth dynamics using deep sequencing technology, 2) identifying regulators of these genes by screening for DNA-binding proteins interacting with the growth-associated gene promoters identified in Aim 1. Screens will be performed using a validated yeast-one hybrid strategy paired with a specifically designed B. distachyon and S. bicolor transcription factor libraries (1000 clones each), and 3) Selecting 50 potential growth regulators from the screen for downstream characterization. The selection will be made by using a sytems biology approach by calculating the connectivity between growth rate, rhythmic gene expression profiles and TF expression profile and determine which TF is likely part of a hub and a potential regulator of plant growth. We will confirme the 50 DNA-protein interactions using transient transcriptional assays in B. distachyon and S. bicolor, and perform further testing in vivo by measuring growth parameters in transgenic loss- or gain-of-function lines for the selected transcription factors (25 B. distachyon and 3 S. bicolor lines). In 2016 the Principal Investigator relocated the laboratory to The Scripps Research Institute (TSRI) where the project has continued with an end date of 08/31/17. Accomplishments: We successfully collected large datasets of gene expression form the model grass Brachypodium. We used and developed bioinformatics analysis tools to investigate the structure, dynamics and robustness of circadian regulated gene expression in Brachypodium. Relevant Discoveries: We were able to determine that the endogenous circadian clock appears to play a much more subdued role in growth regulation in Brachypodium, that has been demonstrated in either Arabidopsis, or crop plants like Rice, Corn and Soybean. This led to our conclusion that Brachypodium unfortunately is unlikely to serve as an informative model for understanding how growth regulation in plants is under the control of circadian network circuitry. However, we were able to leverage our datasets in Brachypodium to inform us and reinforce a large collaborative study on gene networks governing cell wall deposition in Arabidopsis. This led to a major and highly cited publication relevant to improving biomass production.

09 BIOMASS FUELS↗