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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.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Cyber-Attack Detection for Active Neutral Point Clamped (ANPC) Photovoltaic (PV) Converter using Kalman Filter

With the upgrading of communication technology, cyber threats to power converters are increasing. In this paper, a model-based cyber-attack detection methodology is proposed for the interleaved Active neutral point clamped (ANPC) Photovoltaic (PV) converter. The proposed methodology identifies cyber-attacks using two estimations based on the Kalman filter and the state-space model of the ANPC PV converter. The residual between the two estimations is analyzed from a statistical perspective. Based on the Cumulative Sum (CUSUM) Control Chart, the standard errors shift of the residual is used to identify cyber-attacks. Besides, to validate the feasibility of the proposed methodology, several conditions are considered in the simulation, including white noise in the sensor, irradiance variation, and cyber-attacks in the PV converter. Furthermore, the simulation result demonstrates the proposed method can identify cyber-attacks in the ANPC PV converter.

14 SOLAR ENERGY↗
DOE OSTI2020

Cybersecurity Attacks in Vehicular Sensors

Today's modern vehicles contain anywhere from sixty to one-hundred sensors and exhibit the characteristics of Cyber-Physical-Systems (CPS). There is a high degree of coupling, cohesiveness, and interactions among vehicle's CPS components (e.g., sensors, devices, systems, systems-of-systems) across sensing, communication, and control layers. Cyber-attacks in the sensing or communication layers can compromise the security of the control layer. This paper provides a detailed review of potential cyber threats related to the sensing layer. Notably, the focus is mainly towards two categories of sensors: vehicle dynamics sensors (e.g., Tire Pressure Monitoring Systems (TPMS), magnetic encoders, and inertial sensors) and environment sensors (e.g., Light Detection and Ranging (LiDAR), ultrasonic, camera, Radio Detection and Ranging (Radar) systems, and Global Positioning System (GPS) units). Furthermore, the paper also offers perspectives through existing countermeasures from literature and stresses the need for data-driven cybersecurity solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Cyber-Secure and Safe Operation of Solar Photovoltaic Power Distribution Systems

Solar photovoltaic (PV)-rich power distribution systems are networked Cyber-Physical Systems (CPS). These are control systems where multiple computing nodes and diverse intelligent agents interact with the physical world in real-time. However, the presence of networked components renders them vulnerable to potential cyber-attacks, cyber-intrusions, and other malicious events. This is because these systems depend on the measurements reported from their heterogeneous sensors. This makes them vulnerable to potential cyber-attacks where malicious agents can compromise the sensors or the communication networks carrying the sensor measurements. This paper proposes a novel methodology for enhancing the cyber-security and cyber-resilient post-attack safe operation of solar PV-rich power distribution systems against potential cyber-attacks through the Dynamic Watermarking (DW), using online system identification. The resiliency of the proposed technique is tested and validated with several attack scenarios on both a lab-scale 3kW grid-connected PV inverter and a Hardware-in-the-Loop (HiL) system. The proposed approach can be applied to other types of power distribution systems to enhance their cyber-secure and cyber-resilient safe operation. This paper thereby contributes to the field of cyber-security of Cyber-Physical Energy Systems (CPES).

Kim, Jaewon↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2023

Designing an Intrusion Detection for an Adjustable Speed Drive System Controlling a Critical Process

In this article, we address the cyber-security problem of industrial control systems (ICSs) when their sensor measurements may be compromised due to an attacker who has intercepted those measurements via a network. We introduce a general-purpose method “Dynamic Watermarking (DW)” to detect potential cyber-intrusions on speed sensor measurements within industrial control systems, which deploy an adjustable speed drive (ASD) to control a critical process. The DW method is injecting a random private low-amplitude signal with a zero mean Gaussian distribution, “watermark”, into one of the input phase voltages powering the ASD system. The watermark signal propagates through the system including pulse width modulation (PWM) power conversion stage and motor, then ultimately appears in the speed sensor measurements. By deploying two statistical DW tests with two proper thresholds, the system can detect potential cyber-intrusions or unobservable cyber-attacks such as replay attacks and false data injection attacks (FDIA). The DW method tested on a laboratory-scale ASD system experimentally to protect the system against cyber-intrusions. This system, powered by a commercial PWM drive operating at 208 V, 3-phase, and 3.7 kW, served as our experimental platform.

42 ENGINEERING↗
NASA NTRS1981

On-line structural parameter identification

Algorithms are presented for on-line parameter identification of structural dynamic systems. As an example, they are used to calculate the parameters of a modal model of a flexible beam. The algorithms are tested using hardware consisting of a 12 ft. beam with four voice coil actuators and nine noncontacting displacement sensors. They are programmed in a CDC Cyber 175 digital computer which provides input command signals for the actuators, reads the sensor data, and processes the algorithm to calculate consistent estimates of the modal parameters of the beam. Experimental results are compared with those of simulation analysis.

Thau, F. E.↗
DOE OSTI2022

Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system

To support the reliable and resilient operation of modular reactors and microreactors, anticipatory control strategies have been proposed for achieving faster-than-real-time predictions and decision-making capabilities in anticipation of potential anomalies, including setpoint changes and cyber incidents. Here this work presents how anticipatory control strategies can be implemented via model predictive control (MPC) of a single heat pipe’s temperature. Considering the uncertainty in developing and applying MPC, this work evaluates MPC performance given three different model forms: a linear response surface model, an artificial neural network (ANN), and an autoregressive model with exogenous input (ARX). This work also evaluates the impacts of different input biases and variance on MPC performance in order to account for potential sensor reading variations due to cyber incidents. We observe that nonparametric models such as the ANN and ARX result in more fluctuated control actions compared to the MPC applied to the linear response surface model. However, when the cyber incidents are of a large magnitude, the linear response surface model produces smaller feasible regions than the nonparametric models under identical constraints.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Quantized Constant-Q Gabor Atoms for Sparse Binary Representations of Cyber-Physical Signatures

Increased data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present new challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different continuous wavelet transform (CWT) reconstruction formulas are presented and tested under different signal to noise ratio (SNR) conditions. A sparse superposition of Nth order Gabor atoms worked well against a synthetic blast transient using the wavelet entropy and an entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for sparse feature extraction and dictionary-based machine learning across multiple sensor modalities.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2019

A Survey on Social Media to Enhance the Cyber-PhysicalSocial Resilience of Smart Grid

Smart sensor that enables power grid resiliency at distribution network is a very expensive solution and its implementation has been very sluggish. Social media has great potential to become an alternate abstract information channel that can replace/reinforce smart sensor capabilities towards a more resilient power grid. In this paper, we present a survey on the progress of utilizing social media as a platform to extract and analyse abstract data to produce intelligent information. This information can be utilized to plan and implement resources towards power grid resiliency from micro level (consumers) to macro level (cities/states). This paper discusses the reliability of social sensors, various concepts relating to social media and their implementation to solve problems, and how we can develop a framework based on the social media towards a resilient power grid. The realization of social media framework for information will benefit as robust communication and control channels that can be used together with prediction models to improve the accuracy and effectiveness of system operators' decision-making.

DISASTER RESPONSE, HASHTAGS, POWER GRID RESILIENCE↗
DOE OSTI2023

Proactive Intrusion Detection and Mitigation System

SAND2023-05661O The proactive intrusion detection and mitigation system (PIDMS) provides grid-edge situational awareness for cybersecurity defense by capturing real-time distributed energy resource (DER) network traffic and performance data with a novel approach that improves the detection and prevention of cyber-physical attacks. The PIDMS addresses the grid-edge security gap with real-time analysis of both network traffic and photovoltaic performance data to deliver a novel, cyber-physical intrusion detection system (IDS) approach that increases the accuracy and effectiveness of detection and mitigation. This hybrid IDS analysis enables dual monitoring that increases the workload of the adversary; both cyber and physical data would have to be simultaneously spoofed to evade detection. Furthermore, monitoring and analyzing cyber data are insufficient in some cases. For example, in an insider threat aimed at disrupting inverter grid-support functions where proper credentials and authentication are achieved, only the altered PV performance would indicate abnormal behavior. All in all, the PIDMS provides novel capabilities for: • Distributed, real-time cyber-physical detection and mitigation analysis • Cybersecurity defense for grid-edge systems • Analysis framework that can provide situational awareness across the transmission, distribution, and DER systems The PIDMS sensor is designed to collect cyber-physical data, process the data using machine-learning algorithms, detect abnormal events, and deploy mitigations. With these goals, the main functional PIDMS objectives are: • Capability to collect cyber-physical data • Onboard storage of cyber-physical data • Peer-to-peer communication • Computationally efficient machine-learning algorithms • Online cyber-physical data analysis • Alerting/visualization capabilities • Mitigation deployment capability with bump-in-the-wire (BITW) implementation Each of these functional objectives enable PIDMS to perform effective cyber-physical intrusion detection and mitigation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Jones, Christian↗
DOE OSTI2022

Cyber Infrastructure for the Smart Electric Grid

As electric power systems undergo a transformative upgrade with the integration of advanced technologies to enable the smarter electric grid, professionals who work in the area require a new understanding of the evolving complexity of the grid. Cyber Infrastructure for the Smart Electric Grid delivers a comprehensive overview of the fundamental principles of smart grid operation and control, smart grid technologies, including sensors, communication networks, computation, data management, and cyber security, and the interdependencies between the component technologies on which a smart grid's security depends. The book offers readers the opportunity to critically analyze the smart grid infrastructure needed to sense, communicate, compute, and control in a secure way.

communication networks↗
DOE OSTI2021

An Active Detection Scheme for Sensor Spoofing in Grid-tied PV Systems

In this paper an active detection scheme for sensor spoofing (manipulated externally via a cyber attack) in grid-tied PV systems is discussed. The core of the proposed active detection scheme is to introduce a private (secret) watermarking signal into the control inputs of the DC-DC converter and DC-AC inverter stages to detect any malicious spoofing (manipulation) of voltage/current sensor measurements controlling both the DC-DC converter maximum power point tracking (MPPT) stage and the DC-AC inverter of the grid-tied PV system. Several types of possible spoofing mechanisms (attack models) are discussed. The proposed sensor spoofing attack detector system consists of injecting a small magnitude of digital watermarking signal (DWS) and conduct three statistical watermark tests on the reported sensor measurements to determine if a) the proposed system is healthy and operating as expected b) if sensor signals were spoofed (manipulated) externally or c) if a particular sensor is malfunctioning due to a faulty hardware. It is shown via extensive simulations that the proposed DWS approach is robust in detecting malicious external manipulation of sensors controlling the grid tied PV system. A testing platform is currently under development and the experimental results will be discussed in the conference presentation.

Ibrahim, Hasan↗
DOE OSTI2014

Development and evaluation of a prototype tracking system using the treatment couch

Purpose: Tumor motion increases safety margins around the clinical target volume and leads to an increased dose to the surrounding healthy tissue. The authors have developed and evaluated a one-dimensional treatment couch tracking system to counter steer respiratory tumor motion. Three different motion detection sensors with different lag times were evaluated. Methods: The couch tracking system consists of a motion detection sensor, which can be the topometrical system Topos (Cyber Technologies, Germany), the respiratory gating system RPM (Varian Medical Systems) or a laser triangulation system (Micro Epsilon), and the Protura treatment couch (Civco Medical Systems). The control of the treatment couch was implemented in the block diagram environment Simulink (MathWorks). To achieve real time performance, the Simulink models were executed on a real time engine, provided by Real-Time Windows Target (MathWorks). A proportional-integral control system was implemented. The lag time of the couch tracking system using the three different motion detection sensors was measured. The geometrical accuracy of the system was evaluated by measuring the mean absolute deviation from the reference (static position) during motion tracking. This deviation was compared to the mean absolute deviation without tracking and a reduction factor was defined. A hexapod system was moving according to seven respiration patterns previously acquired with the RPM system as well as according to a sin{sup 6} function with two different frequencies (0.33 and 0.17 Hz) and the treatment table compensated the motion. Results: A prototype system for treatment couch tracking of respiratory motion was developed. The laser based tracking system with a small lag time of 57 ms reduced the residual motion by a factor of 11.9 ± 5.5 (mean value ± standard deviation). An increase in delay time from 57 to 130 ms (RPM based system) resulted in a reduction by a factor of 4.7 ± 2.6. The Topos based tracking system with the largest lag time of 300 ms achieved a mean reduction by a factor of 3.4 ± 2.3. The increase in the penumbra of a profile (1 × 1 cm{sup 2}) for a motion of 6 mm was 1.4 mm. With tracking applied there was no increase in the penumbra. Conclusions: Couch tracking with the Protura treatment couch is achievable. To reliably track all possible respiration patterns without prediction filters a short lag time below 100 ms is needed. More scientific work is necessary to extend our prototype to tracking of internal motion.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

CONTROL AND DATA ACQUISITION IN A CYBER-PHYSICAL MIDSTREAM TESTBED

This thesis presents the development of a laboratory-scale cyber–physical midstream pipeline testbed designed to address this gap and support research in industrial control systems security. The platform integrates pumps, valves, sensors, programmable logic controllers (PLCs), and a human–machine interface (HMI) to emulate the monitoring and control architecture of real pipeline operations. The physical process is implemented as a closed-loop liquid circulation system designed to replicate flow behavior characteristic of midstream pipeline infrastructure. The testbed enables real-time data acquisition of key process variables, including flow rate and pressure facilitating the generation of datasets representative of normal pipeline operation. A threat model encompassing common ICS attack vectors was developed, including sensor spoofing, command injection, false data injection, denial-of-service attacks, and relay manipulation. Multiple attack scenarios were implemented and evaluated to demonstrate how cyber intrusions targeting sensors, actuators, networks, and software propagate into measurable physical consequences in pipeline flow and pressure. The developed platform serves as a practical, cost-effective environment for experimentation, education, and future cybersecurity research in midstream pipeline systems.

42 ENGINEERING↗
NASA NTRS1985

Atmospheric Modeling And Sensor Simulation (AMASS) study

A 4800 band synchronous communications link was established between the Perkin-Elmer (P-E) 3250 Atmospheric Modeling and Sensor Simulation (AMASS) system and the Cyber 205 located at the Goddard Space Flight Center. An extension study of off-the-shelf array processors offering standard interface to the Perkin-Elmer was conducted to determine which would meet computational requirements of the division. A Floating Point Systems AP-120B was borrowed from another Marshall Space Flight Center laboratory for evaluation. It was determined that available array processors did not offer significantly more capabilities than the borrowed unit, although at least three other vendors indicated that standard Perkin-Elmer interfaces would be marketed in the future. Therefore, the recommendation was made to continue to utilize the 120B ad to keep monitoring the AP market. Hardware necessary to support requirements of the ASD as well as to enhance system performance was specified and procured. Filters were implemented on the Harris/McIDAS system including two-dimensional lowpass, gradient, Laplacian, and bicubic interpolation routines.

Parker, K. G.↗
NASA NTRS2004

Southeast Ecological Observatory Network (SEEON) Workshop on Ecological Sensors and Information Technology. Report on Second SEEON Workshop

A fundamental goal of the new National Science Foundation (NSF) initiative National Ecological Observatory Network (NEON) is to provide timely and broad access to the ecological data collected at NEON sites. Information management and data collection will be critical components to achieving this goal and a successful NEON implementation. The Southeast Ecological Observatory Network (SEEON) working group recognized the importance of information management and sensor technology in its first planning workshop and recommended that interested parties in the region come together to discuss these subjects in the context of the needs and capabilities of a southeast regional ecological observatory network. In February 2004, 28 participants from 14 organizations including academic institutions, state and federal agencies, private and non-profit entities convened at the Space Life Sciences Laboratory (SLSL) at the Kennedy Space Center, Florida for two days of presentations and discussions on ecological sensors and information management. Some of the participants were previously involved in the first SEEON workshop or other meetings concerned with NEON, but many were somewhat new to the NEON community. Each day focused on a different technical component, i.e. ecological sensors the first day and cyber-infrastructure the second day, and were structured in a similar manner. The mornings were devoted to presentations by experts to help stimulate discussions on aspects of the focal topic held in the afternoon. The formal and informal discussions held during the workshop succeeded in validating some concerns and needs identified in the first SEEON workshop, but also served to bring to light other questions or issues that will need to be addressed as the NEON planning and design stages move forward. While the expansion of the SEEON community meant that some of the presentation and discussion time was needed to help bring the newcomers up to speed on the goals, objectives and current status of the various NEON efforts, the additional perspectives and technical expertise included in this workshop helped fuel some valuable interdisciplinary discussions that will need to continue to bring SEEON and NEON to fruition. Participants agreed that continued discussions of SEEON are needed , to keep up the momentum and that the southeast region must continue to be represented at the national level. It is vital that the all'the regions continue to push things forward for NEON to succeed.

Jones, Lori N.↗
DOE OSTI2014

FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced State-Awareness of Resilient Hybrid Energy System

Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a Fuzzy-Neural Data Fusion Engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms, 2) anomalous behavior detector using self-organizing fuzzy logic system, and 3) artificial neural network based system modeling and prediction. The improved control system state-awareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory’s (INL) hybrid energy system facility know as HYTEST. Experimental results demonstrate that the proposed FN-DFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold based alarm systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗