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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Systems and methods for monitoring traffic on industrial control and building automation system networks

Technologies relating to monitoring communications traffic to detect potential attacks on industrial control system networks and building automation system networks are described herein. In an embodiment, a monitoring device receives a plurality of communications from a control network. The monitoring device transmits the communications to a computing device. Based on the communications, the computing device generates a listing of devices that communicated by way of the control network over a period of time, and computes a volume of traffic between each pair of devices in the listing of devices. The computing device then outputs a graphical user interface (GUI) by way of display, the GUI comprising data indicative of the computed volumes of traffic, which may be indicative of a potential attack on the control network.

Jenkins, Chris↗
DOE OSTI2024

Losing Control of Your Linear Network? Try Resilience Theory

Resilience of cyber-physical networks to unexpected failures is a critical need widely recognized across domains. For instance, power grids, telecommunication networks, transportation infrastructures, and water treatment systems have all been subject to disruptive malfunctions and catastrophic cyberattacks. Following such adverse events, we investigate scenarios where a node of a linear network suffers a loss of control authority over some of its actuators. These actuators are not following the controller's commands and are instead producing undesirable outputs. The repercussions of such a loss of control can propagate and destabilize the whole network despite the malfunction occurring at a single node. To assess system vulnerability, we establish resilience conditions for networks with a subsystem enduring a loss of control authority over some of its actuators. Furthermore, we quantify the destabilizing impact on the overall network when such a malfunction perturbs a nonresilient subsystem. We illustrate our resilience conditions on two academic examples, on an islanded microgrid and on the linearized IEEE 39-bus system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evaluating Named Data Networking for Industrial Control System [Slides]

Current proposed work is: See if the inherent security that comes with Named Networking (NDN) can be applied to Industrial Control Systems; and, Every packet is required to be cryptographically signed which makes every single piece of data communicated in the system secure and authenticated.

42 ENGINEERING↗
DOE OSTI2021

Ripple-Type Control for Enhancing Resilience of Networked Physical Systems

Distributed control agents have been advocated as an effective means for improving the resiliency of our physical infrastructures under unexpected events. Purely local control has been shown to be insufficient, centralized optimal resource allocation approaches can be slow. In this context, we put forth a hybrid low-communication saturation-driven protocol for the coordination of control agents that are distributed over a physical system and are allowed to communicate with peers over a “hotline” communication network. According to this protocol, agents act on local readings unless their control resources have been depleted, in which case they send a beacon for assistance to peer agents. Our ripple-type scheme triggers communication locally only for the agents with saturated resources and it is proved to converge. Moreover, under a monotonicity assumption on the underlying physical law coupling control outputs to inputs, the devised control is proved to converge to a configuration satisfying safe operational constraints. The assumption is shown to hold for voltage control in electric power systems and pressure control in water distribution networks. Numerical tests corroborate the efficacy of the novel scheme.

distributed control↗
DOE OSTI2023

Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation

Here this paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve DG operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The first stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DG operating points, in the second stage, we develop local control schemes that steer the DG to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DG operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Optimizing Signal Timing Control for Large Urban Traffic Networks Using an Adaptive Linear Quadratic Regulator Control Strategy

We discuss how traffic signal control is important for intersection safety and efficiency. However, most traffic signal control methods are designed for individual intersections or corridors. Although some adaptive control systems have been developed, the methods used are often proprietary and not published, making it difficult to evaluate their effectiveness. This study proposes an adaptive multi-input and multi-output traffic signal control method that not only can improve network-wide traffic operations in terms of reduced traffic delay and energy consumption, but also is more computationally feasible than existing centralized signal control methods. Considering intersection interactions, a linear dynamic traffic system model was built and adaptively updated to reflect how the signal control input of each intersection affects network-wide vehicle travel delay. Based on the system model, an adaptive linear-quadratic regulator (LQR) was designed to minimize both traffic delay and incremental changes in the control input. The proposed control method was evaluated in a microscopic traffic simulation environment with a 35-intersection network of Bellevue City, Washington. Simulation results show that the proposed method had shorter average traffic delays in the network when compared with the traffic delays controlled by the state-of-the-art max-pressure, self-organizing traffic lights, and independent deep Q network methods.

42 ENGINEERING↗
DOE OSTI2022

Learning Local Volt/VAR Controllers Toward Efficient Network Operation with Stability Guarantees: Preprint

This paper considers the problem of voltage regulation in distribution network. The primary motivation is to keep voltages within pre-assigned operating limits by commanding the reactive power output of distributed energy resources (DERs) deployed in the grid. We develop a framework for developing local Volt/Var control that comprises of two main steps. In the first, exploiting historical data and for each DER, we learn a function representing desirable equilibrium points for the power network. These points approximate solutions of an Optimal Power Flow problem. In the second, we propose a control scheme for steering the network towards these favorable configurations. Theoretical conditions are derived to formally guarantee the stability of the developed control scheme and numerical simulations illustrate the effectiveness of the proposed approach.

data-driven control↗
DOE OSTI2025

Optimizing Traffic Signal Control to Enhance Transportation Efficiency and Maximize Pedestrian Benefits in the Road Network

Increasing urban mobility requirements demand efficient transportation system strategies for both vehicular and pedestrian movement. This study enhances the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) approach, originally tailored for vehicular traffic signal timing, to incorporate pedestrian traffic dynamics. The improved algorithm considers crucial metrics such as Eco_PI, assesses vehicle fuel consumption by factoring in stops and delays, and addresses pedestrian waiting time, crucial for system efficiency while acknowledging driver waiting time impact. Utilizing Digital Twin simulation along the MLK Smart Corridor in Chattanooga, Tennessee, the algorithm's performance is compared for various pedestrian control scenarios. To evaluate the effectiveness of DGMARL, this study compared DGMARL-enabled signal management with automated pedestrian traffic detection and an actuated signal management system (real-word baseline) with pedestrian recall, which predetermingly enforces a pedestrian phase every cycle. Findings indicate substantial improvements with DGMARL, showing a 28.29% enhancement in vehicle Eco_PI, a 60.55 % reduction in pedestrian waiting time, and a 55.74% decrease in driver stop delay, on average, compared to the baseline actuated signal timing plan.

Kumarasamy, Vijayalakshmi K [The University of Ten↗
DOE OSTI2020

Network-cognizant voltage droop control

The present disclosure provides techniques for network-cognizant droop control in power systems, such as a power distribution system. An example device includes a processor configured to determine, based on (i) a model representing a structure of a power system that includes a plurality of energy resources and (ii) an indication of predicted uncontrollable power injections in the power system, for each controllable energy resource in the plurality of energy resources, a respective value of a first droop coefficient and a respective value of a second droop coefficient. The processor may be further configured to cause at least one controllable energy resource in the plurality of energy resources to modify an output power of the at least one energy resource based on the respective value of the first droop coefficient and the respective value of the second droop coefficient.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Ripple-Type Control for Enhancing Resilience of Networked Physical Systems: Preprint

Distributed control agents have been advocated as an effective means for improving the resiliency of our physical in-frastructures under unexpected events. While purely local control has been shown to be insufficient, centralized optimal resource allocation approaches can be slow. In this context, we put forth a hybrid low-communication saturation-driven protocol for the coordination of control agents that are distributed over a physicalsystem and are allowed to communicate with peers over a 'hot-line' communication network. According to this protocol, agents act upon on local readings unless their control resources have been depleted, in which case they send a beacon for assistance to peer agents. Our ripple-type scheme triggers communication locally only for the agents with saturated resources, and is proved to converge. Moreover, under a monotonicity assumption on the underlying physical law coupling control outputs to inputs, the devised control is proved to converge to a configuration satisfying safe operational constraints. The assumption is shown to hold for voltage control in electric power systems and pressure control in water distribution networks. Numerical tests on both networks corroborate the efficacy of the novel scheme.

distributed control↗
DOE OSTI2024

Information-incorporated gene network construction with FDR control

Abstract Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Automated Cyber Security Testing Platform for Industrial Control Systems

Nuclear Power Plants (NPPs) are a complex system of coupled physics controlled by a network of Programmable Logic Controllers (PLCs). These PLCs communicate process data across the network to coordinate control actions with each other and inform the operators of process variables and control decisions. Networking the PLCs allows more effective process control and provides the operator more information which results in more efficient plant operation. This interconnectivity creates new security issues, as operators have more access to the plant controls, so will bad actors. As plant networks become more digitized and encompass more sophisticated controllers, the network surface exposed to cyber interference grows. Understanding the dynamics of these coupled systems of physics, control logic, and network communications is critical to their protection. The research into the cybersecurity of the Operational Technologies of NPPs is developing and requires a platform that can allow high fidelity physics simulations to interact with digital networks of controllers. This will require three main components: a network simulation environment, a physics simulator, and virtual PLCs (vPLC) that represent typical industry hardware. A platform that incorporates these three components to provide the most accurate representation of actual NPP networks and controllers is developed in this paper.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Firmware Tampering Detection in Heavy-Duty Vehicles through J1939 CAN Analysis

Modern heavy-duty vehicles rely on complex networks of Electronic Control Units (ECUs) that communicate using the J1939 protocol. While this system makes it easier to update and configure vehicle components, it also opens the door to serious cybersecurity risks if not properly secured. This work investigates the potential for firmware tampering through the J1939 communication protocol, which enables ECU configuration and reprogramming over the Controller Area Network (CAN) bus. By monitoring CAN traffic during legitimate maintenance operations and reverse-engineering OEM diagnostic software, we identified common and proprietary J1939 message identifiers, authentication patterns, and vulnerabilities within Unified Diagnostic Services (UDS). These findings demonstrate that inadequate authentication mechanisms can allow malicious actors to alter ECU firmware or disable safety functions, posing severe operational and safety risks. Our analysis contributes to the development of vehicle intrusion detection systems capable of recognizing abnormal reprogramming activity and future firmware fingerprinting methods to verify software integrity across ECUs. This work highlights the importance of standardizing secure firmware authentication across manufacturers to strengthen cyber resilience in heavy-duty vehicle systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Potential Cooling Energy Savings of Economizer Control and Artificial-Neural-Network-Based Air-Handling Unit Discharge Air Temperature Control for Commercial Building

Heating, ventilation, and air-conditioning (HVAC) systems play a significant role in building energy consumption, accounting for around 50% of total energy usage. As a result, it is essential to explore ways to conserve energy and improve HVAC system efficiency. One such solution is the use of economizer controls, which can reduce cooling energy consumption by using the free-cooling effect. However, there are various types of economizer controls available, and their effectiveness may vary depending on the specific climate conditions. To investigate the cooling energy-saving potential of economizer controls, this study employs a dry-bulb temperature-based economizer control approach. The dry-bulb temperature-based control strategy uses the outdoor air temperature as an indicator of whether free cooling can be used instead of mechanical cooling. This study also introduces an artificial neural network (ANN) prediction model to optimize the control of the HVAC system, which can lead to additional cooling energy savings. To develop the ANN prediction model, the EnergyPlus program is used for simulation modeling, and the Python programming language is employed for model development. The results show that implementing a temperature-based economizer control strategy can lead to a reduction of 7.6% in annual cooling energy consumption. Moreover, by employing an ANN-based optimal control of discharge air temperature in air-handling units, an additional 22.1% of cooling energy savings can be achieved. In conclusion, the findings of this study demonstrate that the implementation of economizer controls, especially the dry-bulb temperature-based approach, can be an effective strategy for reducing cooling energy consumption in HVAC systems. Additionally, using ANN prediction models to optimize HVAC system controls can further increase energy savings, resulting in improved energy efficiency and reduced operating costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Wireless Sensing and Communication Capability from In-Core to a Monitoring Center

Significant cost savings can be made if electrical cables can be replaced by wireless technology in current Nuclear Power Plants (NPP) and in advance reactor designs. Wireless technology can also provide in-core opportunities by significantly reducing the number of penetrations in the pressure vessel, cost and complexity of sensor installation and by increasing the efficiency of current and advanced reactors. Unlike other deployment scenarios for an industrial environment, operators need to have centralized control over all the networks. Centralized control will reduce implementation costs, provide single point control and enable monitoring of network devices, improve security, and enhance connectivity. Micro-sensors that can simultaneously monitor temperature and pressure within a fuel rod inside nuclear reactors will enable preventative actions during abnormal operating conditions. This ability could avert accidents and enable the expedient development of accident tolerant fuels. A novel micro-sensor suite (~ mm) to simultaneously measure multiple parameters such as temperature, strain, pressure, and neutron/gamma flux inside a fuel rod is being developed for use in reactors. The necessary communication architecture is also being developed to transmit measurement signals from the core to the plant's data cloud or control room. A three three-tier strategy has been developed to support wireless transmission of in-core measurements to the control room or to a secure cloud platform for control, analytics, and decision-making purposes. 1. In-core: data signal from in-core to outside of the pressure vessel within the containment building 2. Containment building: data signal from inside to the outside of the containment building and into the balance of the plant network 3. Balance of the plant network: information transmitted to the data cloud and control room This plan presents a wireless sensing and communication system for use within a reactor core and elsewhere. The communication technology is advantageous to compensate for network equipment failures and adverse data transmission conditions. Wireless technology will significantly increase the resiliency of the plants network system. The wireless system naturally provides multiple transmission path capability and data redundancy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗