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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An introduction to neuromorphic computing and its potential impact for unattended ground sensors

Neuromorphic computers are hardware systems that mimic the brain’s computational process phenomenology. This is in contrast to neural network accelerators, such as the Google TPU or the Intel Neural Compute Stick, which seek to accelerate the fundamental computation and data flows of neural network models used in the field of machine learning. Neuromorphic computers emulate the integrate and fire neuron dynamics of the brain to achieve a spiking communication architecture for computation. While neural networks are brain-inspired, they drastically oversimplify the brain’s computation model. Neuromorphic architectures are closer to the true computation model of the brain (albeit, still simplified). Neuromorphic computing models herald a 1000x power improvement over conventional CPU architectures. Sandia National Labs is a major contributor to the research community on neuromorphic systems by performing design analysis, evaluation, and algorithm development for neuromorphic computers. Space-based remote sensing development has been a focused target of funding for exploratory research into neuromorphic systems for their potential advantage in that program area; SNL has led some of these efforts. Recently, neuromorphic application evaluation has reached the NA-22 program area. This same exploratory research and algorithm development should penetrate the unattended ground sensor space for SNL’s mission partners and program areas. Neuromorphic computing paradigms offer a distinct advantage for the SWaP-constrained embedded systems of our diverse sponsor-driven program areas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks

Land cover datasets are essential for modeling and analysis of Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, often limited due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The fusion of hyperspectral, multispectral, and terrain datasets was performed using unsupervised and supervised classification techniques over a ~343 km 2 area, and a high-resolution (5 m) vegetation classification map was generated. An unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters. We employed a quantitative method to add supervision to the unlabeled clusters, producing a fully labeled vegetation map. We then developed convolutional neural networks (CNNs) using the multi-sensor fusion datasets to map vegetation distributions using the original classes and the classes produced by the unsupervised classification method. To validate the resulting CNN maps, vegetation observations were collected at 30 field plots during the summer of 2016, and the resulting vegetation products developed were evaluated against them for accuracy. Our analysis indicates the CNN models based on the labels produced by the unsupervised classification method provided the most accurate mapping of vegetation types, increasing the validation score (i.e., precision) from 0.53 to 0.83 when evaluated against field vegetation observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evaluating Image Classification Deep Convolutional Neural Network Architectures for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition-based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available, and physical modeling is often not feasible. Additionally, using data-based models, which make decisions based on raw sensor data, allow features to be learned instead of manually determined. In this work, deep convolutional neural network (CNN) architectures are investigated for their ability to estimate the RUL of turbofan engines. To improve the accuracy of the models, CNN architectures, which have proven successful in image classification, are implemented and tested. Specifically, the blocks used in the Visual Geometry Group (VGG) architecture, inception modules used in the GoogLeNet architecture, and residual blocks used in the ResNet architecture are incorporated. To account for varying flight lengths, the input to the models is a window of time series data collected from the engine under test. Window locations at the climb, cruise, and descent stages are considered. To further improve the RUL estimations, multiple overlapping windows at each location are used. This increases the amount of training data available and is found to increase the accuracy of the resulting RUL estimations by averaging the estimates from all overlapping segments. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set, and high prognosis accuracy was achieved. Furthermore, this work expands on the model developed and used in the 2021 PHM Society Data Challenge, which received second place.

convolutional neural networks↗
DOE OSTI2024

Achieving Cyber-Resilience for Power Systems using a Learning, Model-Assisted Blockchain Framework

The secure integration and management of distributed energy resources (DER) and power aggregators in the electric grid requires secure communications and a physics-aware Command and Control (C2) strategy. A Blockchain (BC)-based overlay network was developed to provide a security layer for the existing power grid network that mitigates risks in current and legacy network and C2 protocols. By integrating a Model-Assisted Machine Learning (MAML) framework with a Secure Blockchain Overlay Network (SBON) a defense-in-depth strategy was achieved. In our approach, the MAML framework leveraged a smart contract framework to gather network data and learn the dynamics of DER to develop detection strategies for attacks targeting sensors and actuators used by DER. The MAML framework learned dynamical systems models for individual DERs to detect sensor attacks. For DER we utilized a Digital Twin (DT) to accelerate the learning process for a model resistant to stealthy attacks. The project created DT for PV inverters and BESS. The DTs were coupled with a model-assisted, data-driven learning of DER behavior. Specifically, we evaluated architectures for model-based learning with model-free fine-tuning. Additionally, differential privacy techniques were used to obfuscate data, while still allowing the computation of attack detection results based on obfuscated data. The SBON developed leverages a private permissioned blockchain network orchestrated with the Hyperledger Fabric framework. To connect the cyber world, which orchestrates the blockchain fabric, and the physical world where the power network resides, we developed a system implementation to enable the secure interaction of the physical world and the abstracted blockchain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks

Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. Our results demonstrate that networks with ReLU and leaky ReLU activation functions not only achieved superior accuracy in self-motion estimation from novel optic flow patterns but also exhibited greater robustness under challenging conditions. The advantages offered by ReLU and leaky ReLU may stem from their ability to induce sparser representations than GELU and Mish do. Our work characterizes the encoding of optic flow in neural networks and highlights how the sparseness induced by ReLU may enhance robust and accurate self-motion estimation from optic flow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Using stochastic activity networks to study the energy feasibility of automatic weather stations

Automatic Weather Stations (AWSs) are systems equipped with a number of environmental sensors and communication interfaces used to monitor harsh environments, such as glaciers and deserts. Designing such systems is challenging, since designers have to maximize the amount of sampled and transmitted data while considering the energy needs of the system that, in most cases, is powered by rechargeable batteries and exploits energy harvesting, e.g., solar cells and wind turbines. To support designers of AWSs in the definition of the software tasks and of the hardware configuration of the AWS we designed and implemented an energy-aware simulator of such systems. The simulator relies on the Stochastic Activity Networks (SANs) formalism and has been developed using the Möbius tool. In this paper we first show how we used the SAN formalism to model the various components of an AWS, we then report results from an experiment carried out to validate the simulator against a real-world AWS and we finally show some examples of usage of the proposed simulator.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

ML Clustering to Identify Natural Gas Pipeline Infrastructure Vulnerabilities

The network of more than 2.5 million miles of natural gas pipelines the U.S. are exposed to a range of vulnerabilities, ranging from extreme weather, to human behavior, and increasingly, to cyber threats. Advanced, data-driven analytics, including the use of machine learning and artificial intelligence, afford an opportunity to identify potential vulnerabilities, better understand risks, and help mitigate vulnerabilities in the network. At NETL, machine learning is being used to explore pipeline vulnerabilities and identify significant clusters of failure events, including failures due to weather, human behavior, and materials. These findings are being used to support the development of new technologies, including sensors, but can also be used to evaluate and inform mitigation strategies to reduce risks and vulnerabilities throughout the network.

Bauer, Jennifer↗
DOE OSTI2018

Substrate-Wide Confined Shear Alignment of Carbon Nanotubes for Thin Film Transistors

To exploit their charge transport properties in transistors, semiconducting carbon nanotubes must be assembled into aligned arrays comprised of individualized nanotubes at optimal packing densities. However, achieving this control on the wafer-scale is challenging. Here, solution-based shear in substrate-wide, confined channels is investigated to deposit continuous films of well-aligned, individualized, semiconducting nanotubes. Polymer-wrapped nanotubes in organic ink are forced through sub-mm tall channels, generating shear up to 10 000 s -1 uniformly aligning nanotubes across substrates. The ink volume and concentration, channel height, and shear rate dependencies are elucidated. Optimized conditions enable alignment within a ±32° window, at 50 nanotubes µm-1, on 10 × 10 cm 2 substrates. Transistors (channel length of 1–5 µm) are fabricated parallel and perpendicular to the alignment. The parallel transistors perform with 7× faster charge carrier mobility (101 and 49 cm 2 V -1 s -1 assuming array and parallel-plate capacitances, respectively) with high on/off ratio of 105. The spatial uniformity varies ±10% in density, ±2° in alignment, and ±7% in mobility. Deposition occurs within seconds per wafer, and further substrate scaling is viable. Compared to random networks, aligned nanotube films promise to be a superior platform for applications including sensors, flexible/stretchable electronics, and light emitting and harvesting devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Distributed Acoustic Sensing to Estimate the Permeability

Optical fiber in a borehole can be interrogated with distributed acoustic sensors (DAS) to capture fracture displacements with the potential to map surrounding fracture networks. We designed a laboratory experiment to test the capability of DAS to determine borehole flow characteristics, and we show that for the first time DAS can be used to remotely estimate permeability. Optical fiber was wrapped around a bead filled pipe and the pressure drop and flow velocity were measured to directly calculate permeability. A machine learning model using statistical features from continuous DAS estimated the bulk permeability. Fluid interactions with the permeable material demonstrate insufficient resolution using DAS amplitude-based measurements for estimating pressure drop to infer permeability. Variations in the spectral domain relate DAS measurements to the pressure drop and provide consistent permeability estimates. Resolution with DAS is sufficient to estimate permeability and provides a reliable method to monitor at depth in borehole conditions.

58 GEOSCIENCES↗
DOE OSTI2019

Sono-Assisted Surface Energy Driven Assembly of 2D Materials on Flexible Polymer Substrates: A Green Assembly Method Using Water

The challenges in achieving a green and scalable integration of two-dimensional (2D) materials with flexible polymer substrates present a major barrier for the application of 2D materials, such as graphene, MoS2, and h-BN for flexible devices. Here, we create a sono-assisted surface energy driven assembly (SASEDA) method that can achieve foot-scale to micrometer-scale assembly of 2D materials, form a conductive network in as short as 10 s, and build hierarchical and hybrid flexible devices such as sensors, resistors, and capacitors by using water as the dispersion solvent. SASEDA highlights two counterintuitive innovations. First, we use an 'unfavorable' solvent (i.e., water) for both 2D materials (e.g., graphene, MoS2, and h-BN) and polymer substrates (e.g., polydimethylsiloxane) to drive the assembly process. Second, we use a weak sono-field (0.3 W/cm2) generated by a regular sonication bath cleaner to enhance the assembly efficiency and reorganize and unify the assembly network. This method and its principle pave the way toward affordable large-scale 2D material-based flexible devices.

2D material↗
DOE OSTI2019

Self-powered Wireless Dual-mode Langasite Sensor for Pressure/Temperature Monitoring of Nuclear Reactors

Safety issues in nuclear power plants are of extreme importance. Numerous sensors are used within nuclear power plants for sensing and monitoring of its critical operation parameters. As currently implemented, during massive losses of power across the plant in accidents, the backup batteries for the monitoring sensors will ultimately be drained and can cease to operate. The overall objective of this NEUP project is to develop a novel self-powered wireless hybrid sensor which can accurately monitor both pressure and temperature using a single device, even in the extreme harsh environments of severe nuclear accidents, without the requirement of external electricity. At Virginia Tech side, a heat pipe assisted thermoelectric generator (TEG) energy harvester and associated power management and data transmission circuits were developed. The energy harvester can produce electrical power continuously, even in loss of plant and external power situations, providing power for important data transmission for accident diagnosis. The self-powered sensing system could be integrated directly onto key nuclear components including pipes, pump housings, heat exchangers, reactor vessels, and shielding structures inside and outside of the reactor core. Two TEG based energy harvesters which can work in relatively low (50-250 ºC) and high temperature ranges (250-350 ºC) were established and tested. In the prototypes, heat pipes were introduced to reduce the heat resistances of the device at the hot and cold ends. The power output of the heat pipe assisted TEG module is 2.22 W, which is 6 times more than one using an aluminum rod. In-lab tests showed that these devices canmeet the requirement to power the sensors and circuit during normal and off-normal conditions.Amodel was then built to analyze the thermal network in the energy harvester, and was used to optimize its performance and reduce its geometry size. The design and testing of the vibrational energy harvester were completed. The harvester, has shown to effectively increase the vibrational potential of the main coolant pump, though it has been shown that thermal energy has a much higher potential for the powering of sensors. In this project, a self-start charger circuit for the energy harvester was designed to regulate and manage the power. An energy storage element was incorporated into the energy harvesting module for sensors and wireless transceivers in an off-normal situation. The signal conditioning and wireless communication electronic circuits were developed and tested in the lab. An important consideration for an energy harvester within a nuclear power plant is the level of radiation it will encounter during operation. The two main sources of radiation in a nuclear power plant are gamma and neutron radiation. Aradiation experiment was conducted in the Westinghouse Electric Company during which the prototypes were placed in a 106 rad radiation environment to test their ability to operate in the extreme environment. The results showed that this level of radiation had little effect on the performance of the TEGmaterial and heat pipe heat exchanging system. Its influence on the circuit was still under research, however the need for more development on radiation hardened electronics is needed. Pressure measurement based on piezoelectric crystal resonators has advantages over the commercially available methods like capacitive, electromagnetic, optical, potentiometric, and piezo resistive strain gauges. Since the output of piezoelectric based sensors are digital in format, it has a very high resolution, high accuracy, and long-term stability. Quartz is one of the commonly used piezoelectric material in digital pressure sensors design. This material is elastic in nature, providing high stability, high repeatability, high elasticity, repeatable mechanical behavior for many cycles of operation, and is free of hysteresis. The quartz resonator has a high Q, which means that very high vibration can be driven with the very low electric power. Langasite (LGS) is a new promising piezoelectric material that combines the advantageous properties of quartz with a better performance at high temperatures. LGS will not undergo any phase transitions up to its melting temperature of 1473 C, and hence, the application of Langasite in measuring pressure at high temperatures is promising. In this project, the design and fabrication of the piezo oscillator crystal for temperature and pressure measurement has been completed. The entire sensor has been fabricated and tested to see its sensitivity to temperature and pressure changes. The design and optimization of the radiation shielding systems that can effective protect the duel-mode Langasite sensor inside reactor core were demonstrated. To maximize the radiation shielding effectiveness, different types of materials are investigated based upon recent developments of radiation shielding systems including aluminum + Boron carbide and bismuth borosilicate glass B2O3 in addition to tungsten/B4C. New materials based upon metatheses is included as potential shielding to mitigate neutron fluencies. The radiation shielding effectiveness of selected composites are evaluated within a broader neutron spectrum from thermal, epithermal, fast (1 MeV) and 15 MeV neutrons. Performance analysis was also conducted to evaluate the effects of selected composite systems.Aconceptual design was also proposed for the packaging and integration of the radiation shielding system with the sensor, and its applicability within the reactor core was discussed. At end of the project, all components necessary for a complete sensing and monitoring system, including a langasite sensor, electrodes, the TEG harvester, an energy storage device, and power management and transmission electronics were integrated to demonstrate feasibility in powering the sensor. Finally, integration of each component into a full package, and a full demonstration were done.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Embedded Sensing in Additive Manufacturing Metal and Polymer Parts: A Comparative Study of Integration Techniques and Structural Health Monitoring Performance

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications.

additive manufacturing↗
DOE OSTI2022

CONGO²: Scalable Online Anomaly Detection and Localization in Power Electronics Networks

Rapid and accurate detection and localization of electronic disturbances simultaneously are important for preventing its potential damages and determining potential remedies. Existing anomaly detection methods are severely limited by the low accuracy, the expensive computational cost and the need for highly trained personnel. There is an urgent need for a scalable online algorithm for in-field analysis of large-scale power electronics networks. Here in this paper, we propose a fast and accurate algorithm for anomaly detection and localization of power electronics networks: stratified colored-node graph (CONGO2). This algorithm hierarchically models the change of correlated waveforms and then correlated sensors using the colored-node graph. By aggregating the change of each sensor with its neighbors’ inputs, we can spontaneously identify and localize the anomaly that cannot be detected by data collected from a single sensor. As our proposed method only focuses on the changes within a short time frame, it is highly computational efficient and only needs small data storage. Thus, our method is ideal for online and reliable anomaly detection and localization of large-scale power electronic networks. Compared to existing anomaly detection methods, our method is entirely data-driven without training data, highly accurate and reliable for wide-spectrum anomalies detection, and more importantly, capable of both detection and localization. Thus, it is ideal for infield deployment for large-scale power electronic networks. As illustrated by a distributed energy resources (DERs) power grid with 37-node, our method can effectively detect and localize various cyber and physical attacks.

42 ENGINEERING↗
DOE OSTI2022

Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation

Abstract There is a growing interest in developing data‐driven reduced‐order models for atmospheric and oceanic flows that are trained on data obtained either from high‐resolution simulations or satellite observations. The data‐driven models are non‐intrusive in nature and offer significant computational savings compared to large‐scale numerical models. These low‐dimensional models can be utilized to reduce the computational burden of generating forecasts and estimating model uncertainty without losing the key information needed for data assimilation (DA) to produce accurate state estimates. This paper aims at exploring an equation‐free surrogate modeling approach at the intersection of machine learning and DA in Earth system modeling. With this objective, we introduce an end‐to‐end non‐intrusive reduced‐order modeling (NIROM) framework equipped with contributions in modal decomposition, time series prediction, optimal sensor placement, and sequential DA. Specifically, we use proper orthogonal decomposition (POD) to identify the dominant structures of the flow, and a long short‐term memory network to model the dynamics of the POD modes. The NIROM is integrated within the deterministic ensemble Kalman filter (DEnKF) to incorporate sparse and noisy observations at optimal sensor locations obtained through QR pivoting. The feasibility and the benefit of the proposed framework are demonstrated for the NOAA Optimum Interpolation Sea Surface Temperature (SST) V2 data set. Our results indicate that the NIROM is stable for long‐term forecasting and can model dynamics of SST with a reasonable level of accuracy. Furthermore, the prediction accuracy of the NIROM gets improved by almost one order of magnitude by the DEnKF algorithm.

Pawar, Suraj↗
DOE OSTI2021

Solar Transposition Modeling via Deep Neural Networks With Sky Images

This article presents a notable advance toward the development of a new method of increasing the single-axis tracking photovoltaic (PV) system power output by improving the determination and near-term prediction of the optimum module tilt angle. The tilt angle of the plane receiving the greatest total irradiance changes with Sun position and atmospheric conditions including cloud formation and movement, aerosols, and particulate loading, as well as varying albedo within a module's field of view. In this article, we present a multi-input convolutional neural network that can create a profile of plane-of-array irradiance versus surface tilt angle over a full 180° arc from horizon to horizon. As input, the neural network uses the calculated solar position and clear-sky irradiance values, along with sky images. The target irradiance values are provided by the multiplanar irradiance sensor (MPIS). In order to account for varying irradiance conditions, the MPIS signal is normalized by the theoretical clear-sky global horizontal irradiance. Using this information, the neural network outputs an N -dimensional vector, where N is the number of points to approximate the MPIS curve via Fourier resampling. The output vector of the model is smoothed with a Gaussian kernel to account for error in the downsamping and subsequent upsampling steps, as well as to smooth the unconstrained output of the model. Furthermore, these profiles may be used to perform near-term prediction of angular irradiance, which can then inform the movement of a PV tracker.

42 ENGINEERING↗
DOE OSTI2016

In-situ trainable intrusion detection system

A computer implemented method detects intrusions using a computer by analyzing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such that the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: An artificial neural network approach

Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ET i ) to daily evapotranspiration (ET d ) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation ( R S ) to be the most robust reference variable explaining the ratio between ET d and ET i . This study aims to contribute in ET i upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation ( R Sd / R Si ) as a factor for converting ET i to ET d . This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict R Sd from R Si followed by using the R Sd / R Si ratio to convert ET i to ET d across different terrestrial ecosystems. Using R Si and R Sd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing R Sd and further used it to upscale ET i to ET d . The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geographic locations. R S -based upscaled ET d produced a significant linear relation ( R 2 = 0.65 to 0.69), low bias (-0.31 to -0.56 MJ m -2 d -1 ; approx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m -2 d -1 ; approx. 10 %) with the observed ET d , although a systematic overestimation of ET d was also noted under persistent cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN training reduced the systematic overestimation tendency in predominantly overcast days. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANN-driven R S -based ET i upscaling method and was found to produce lowest RMSE under cloudy conditions. Sensitivity analysis revealed variable sensitivity of the method to biome selection and high ET d prediction errors in forest ecosystems are primarily associated with greater rainfall and cloudiness. As a result, the overall methodology appears to be promising and has substantial potential for upscaling ET i to ET d for field and regional-scale evapotranspiration mapping studies using polar orbiting satellites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes

Malicious activities on measurements from sensors like Phasor Measurement Units (PMUs) can mislead the control center operator into taking wrong control actions resulting in disruption of operation, financial losses, and equipment damage. In particular, false data attacks initiated during power systems transients caused due to abrupt changes in load and generation can fool the conventional model-based detection methods relying on thresholds comparison to trigger an anomaly. In this paper, we propose a Koopman mode decomposition (KMD) based algorithm to detect and identify false data attacks in real-time. The Koopman modes (KMs) are capable of capturing the nonlinear modes of oscillation in the transient dynamics of the power networks and reveal the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements. The Koopman-based spatio-temporal nonlinear modal analysis is used to filter out the false data injected by an attacker. The performance of the algorithm is illustrated on the IEEE 68-bus test system using synthetic attack scenarios generated on GridSTAGE, a recently developed multivariate spatio-temporal data generation framework for simulation of adversarial scenarios in cyber-physical power systems.

Nandanoori, Sai Pushpak↗