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This paper describes a framework of incorporating smart sampling techniques in a probabilistic look-ahead contingency analysis application. The predictive probabilistic contingency analysis helps to reflect the impact of uncertainties caused by variable generation and load on potential violations of transmission limits.
This paper describes a probabilistic look-ahead contingency analysis application that incorporates smart sampling and high-performance computing (HPC) techniques. Smart sampling techniques are implemented to effectively represent the structure and statistical characteristics of uncertainty introduced by different sources in the power system. They can significantly reduce the data set size required for multiple look-ahead contingency analyses, and therefore reduce the time required to compute them. High-performance-computing (HPC) techniques are used to further reduce computational time. These two techniques enable a predictive capability that forecasts the impact of various uncertainties on potential transmission limit violations. The developed package has been tested with real world data from the Bonneville Power Administration. Case study results are presented to demonstrate the performance of the applications developed.
This work is a survey of current trends in applications of PMUs. PMUs have the potential to solve major problems in the areas of power system estimation, protection, and stability. A variety of methods are being used for these purposes, including statistical techniques, mathematical transformations, probability, and AI. The results produced by the techniques reviewed in this work are promising, but there is work to be performed in the context of implementation and standardization. As the smart grid initiative continues to advance, the number of intelligent devices monitoring the power grid continues to increase. PMUs are at the center of this initiative, and as a result, each year more PMUs are deployed across the grid. Since their introduction, myriad solutions based on PMU-technology have been suggested. The high sampling rates and synchronized measurements provided by PMUs are expected to drive significant advancements across multiple fields, such as the protection, estimation, and control of the power grid. This work offers a review of contemporary research trends and applications of PMU technology. Most solutions presented in this work were published in the last five years, and techniques showing potential for significant impact are highlighted in greater detail. Being a relatively new technology, there are several issues that must be addressed before PMU-based solutions can be successfully implemented. This survey found that key areas where improvements are needed include the establishment of PMU-observability, data processing algorithms, the handling of heterogeneous sampling rates, and the minimization of the investment in infrastructure for PMU communication. Solutions based on Bayesian estimation, as well as those having a distributed architectures, show great promise. The material presented in this document is tailored to both new researchers entering this field and experienced researchers wishing to become acquainted with emerging trends.
With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.
The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.
Currently, research on structural health monitoring systems is focused on direct integration of the system into a component or structure. The latter results in a so-called smart structure. One example of a smart structure is a component with integrated strain sensing for continuous load monitoring. Additive manufacturing, or 3D printing, now also enables such integration of functions inside components. As a proof-of-concept, the Fused Deposition Modeling (FDM) technique was used to integrate a strain sensing element inside polymer (ABS) tensile test samples. The strain sensing element consisted of a closed capillary filled with a fluid and connected to an externally mounted pressure sensor. The volumetric deformation of the integrated capillary resulted in pressure changes in the fluid. The obtained pressure measurements during tensile testing are reported in this paper and compared to state-of-the-art extensometer measurements. The sensitivity of the 3D printed pressure-based strain sensor is primarily a function of the compressibility of the capillary fluid. Air- and watertightness are of critical importance for the proper functioning of the 3D printed pressure-based strain sensor. Therefore, the best after-treatment procedure was selected on basis of a comparative analysis. The obtained pressure measurements are linear with respect to the extensometer readings, and the uncertainty on the strain measurement of a capillary filled with water (incompressible fluid) is ±3.1 µstrain, which is approximately three times less sensitive than conventional strain gauges (±1 µstrain), but 32 times more sensitive than the same sensor based on air (compressible fluid) (±101 µstrain).
Although Reinforcement Learning (RL) algorithms have made impressive progress in learning complex tasks over the past years, there are still prevailing short-comings and challenges. Specifically, the sample-inefficiency and limited adaptation across tasks often make classic RL techniques impractical for real-world applications despite the gained representational power when combining deep neural networks with RL, known as Deep Reinforcement Learning (DRL). Recently, a number of approaches to address those issues have emerged. Many of those solutions are based on smart DRL architectures that enhance single task algorithms with the capability to share knowledge between agents and across tasks by introducing Transfer Learning (TL) capabilities. Here this survey addresses strategies of knowledge transfer from simple parameter sharing to privacy preserving federated learning and aims at providing a general overview of the field of TL in the DRL domain, establishes a classification framework, and briefly describes representative works in the area.
Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.
In this research, a smart manufacturing approach was used to enhance the mechanical properties of aluminum (Al) for lightweight applications. The smart manufacturing involved cryomilling of Al powders with and without 5 wt.% magnesium (Mg) powders for varying durations followed by a high-pressure cold spray (HPCS) additive manufacturing process to prepare bulk components. The morphological changes, crystallite size, and composition of the cryomilled powders and cold sprayed (CS’ed) components were examined using scanning electron microscopy (SEM), x-ray diffraction (XRD), and transmission electron microscopy (TEM) techniques. The results showed that the crystallite size reduces with an increase in cryomilling time and the addition of Mg dopant. To test the mechanical properties of the bulk CS’ed components, microhardness tests were performed using a Vickers microhardness tester. Uniaxial tensile tests were also carried out to ascertain the material’s tensile properties. The mechanical testing results showed great improvement in the hardness and tensile strength of CS’ed Al–Mg samples as compared to pure Al samples. Subsequently, fractography analysis of the tensile failed samples was carried out to determine the nature of the failure. Here, the research article also discusses the inherent mechanisms for the improvement in mechanical properties of smart manufactured components as a result of Mg doping and cryomilling.
An experiment to release radioactive particles representative of small-scale venting from an underground nuclear test was conducted to gather data in support of treaty capability development activities. For this experiment, a CO2-driven “air cannon” was used to release lanthanum-140 at ambient temperatures. The radioisotope 140La was chosen as a representative fission fragment with a short half-life and prominent gamma-ray emissions; the choice was also influenced by the successful production and use of 140La with low levels of radioactive contaminants in the Defence Research and Development Canada Field Trials. The source was created through activation of high-purity lanthanum oxide at the TRIGA research reactor of Washington State University, Pullman, Washington. Witness plates and air samplers were laid out in an irregular grid covering the area for which the plume was anticipated to deposit based on climatological wind records. A vehicle-mounted spectrometer, and handheld and backpack instruments ranging from polyvinyl toluene to high purity germanium, were used to survey the plume. Additionally, three soil sampling techniques were investigated. The relative sensitivity and utility of sampling and survey methods are discussed in the context of On-Site Inspection.
The precipitation technique of Mn-IV/itaconic acid redox pair was chosen for grafting meth acrylic acid onto chitosan. This was done to maximize the graft yield and minimize the homopolymer formation to lower extent in addition to prepare water soluble chitosan instead of non-eco friendly acid soluble one. Evidence of grafting was confirmed instrumentally by (FTIR, SEM, and TGA), titrimetrically using carboxyl content and gravimetrically using dry weight method. The mechanism of grafting and factors affected grafting reaction were studied in details, and the optimum reaction conditions were obtained at [chitosan] 2 g, [MnO{sub 2}] 25.9 mmol/100 g sample, [itaconic acid],100 mmol/L, [MAA] 50% bows,[liquor ratio], 25, reaction time, 2 h, and [reaction temperature], 65 °C. It is also seen from the obtained results that, the resultant copolymer showed the following findings: (a) only nitric acid solutions are proved to be a superior solvent for the grafted product; (b) superabsorbent towards water (197 g H{sub 2}O/g polymer) and saline absorbency (60 g saline solution/g polymer) after cross-linked with epichlorohydrin, (c) higher water solubility (21%) at 48% graft yield and (d) 73.5% copper ion removal in comparison with 41% for chitosan.
We briefly review previous applications of machine learning (ML) in power grid analyses and introduce our ongoing effort toward developing a generative-adversarial (GA) model for fast and reliable grid contingency analyses. According to our review, the persisting limitation of traditional ML techniques in grid analyses is the need for an exhaustive amount of training data for model generalization and accurate predictions. GA models overcome this limitation by first learning true data distribution from a small training set, from which new samples assimilating true data are generated with some variations. Subsequently, GA models can transfer learn or super-generalize with increased accuracy, that is, accurately predict n - (k + 2) contingencies from a small n - k training set and generated n - (k + 1) data. The joint effort between Idaho National Lab and Florida State University strives to develop a zero-shot and deep learning-based contingency analysis tool, named Smart Contingency Analysis Neural Network (SCANN), by leveraging the aforementioned advantages of GA models. The basic architecture of SCANN stems from the Latent Encoding of Atypical Perturbations network combined with an adversarial network, and it is designed to generate imbalanced power flow data from learned true data distributions for prediction purposes. Here we also introduce the abstract concept of resilience-chaos plots, a new resilience characterization tool proposed to complement SCANN by aiding in the assessment of large amounts of high-order contingency predictions.
Biogeochemical interfaces such as bacteria-mineral, fungi-mineral, and water-mineral are hot spots for important processes and reactions in all environments. Studying these interfaces are useful over a range of spatial and temporal scales in order to understand elemental cycles, soil formation, restoration, and bioremediation. Advancement of micro- and nano-scale techniques lead to new discoveries about reactions and interactions at the heterogeneous interface of bacteria, fungi, and minerals. The current state-of-art of analytical techniques allows to study solid, liquid, and gas phases and their interactions with biological components and often permits simultaneous study of chemical and physical properties in a site-specific manner. Improved techniques not only increased spatial and temporal resolution with site specificity but also decreased sample preparation, artifact generation, sample size requirements and analytical time. There are limitations and challenges that still need to be overcome while studying the very heterogeneous microbe-mineral interface, but improved accessibility to beam time at the synchrotron facilities and instrument time at the user laboratories offer some solutions. This review chapter focuses on a subset of the available micro- and nano-scale techniques: electron and atomic force microscopy, and X-ray based spectroscopy of traditional and synchrotron-based applications.
Ferromagnetic shape memory alloys are smart materials because they exhibit temperature driven shape memory effect and magnetic field induced strain. Thus two types of energy, i.e. thermal and magnetic, are used to control their shape memory behaviour. Study of critical phenomenon in such materials has received increased experimental and theoretical attention for better understanding of the magnetic phase transition behavior as well as further development of ferromagnetic shape memory materials. In the present study we report the preparation and characterization of bulk Co{sub 45}Ni{sub 25}Ga{sub 30} alloy, prepared by a sequence of arc melting technique followed by homogenization at 1150 °C for 24 hours and ice-water quenching. Structural and magnetic properties of the alloys were studied by means of X-ray diffraction and vibrating sample magnetometer in an applied field range of ±18 kOe equipped with a high temperature oven. We have determined the critical temperature T{sub C} (∼375.5 K) and the critical exponents viz; β=0.40, γ=1.68 and δ=5.2. Asymptotic critical exponents β, γ, and δ obey Widom scaling relation, γ+β=βδ, and the magnetization data satisfy the scaling equation of state for second-order phase transition in the asymptotic critical region.
This article presents a comprehensive investigation into the drying of strawberries using multiple dehydration methods with an emphasis on energy efficiency and sustainability. Initially, an airborne ultrasonic transducer with a frequency of 21 kHz was employed in batch operations to examine the effects of controlling parameters, including applied power, distance from the transducer plate, sample thickness, and sample holder type. The Energy Ratio, defined as the ratio of thermal energy required to evaporate moisture to ultrasonic energy, was observed to reach up to 2.2, particularly during the initial stages of drying. Subsequently, the Smart Dryer integrated airborne ultrasound (US) dehydration, slot jet reattachment (SJR) nozzle convective drying, infrared (IR) drying, and their combinations, enabling a systematic exploration of various drying conditions on the drying time and quality of strawberries. The integration of airborne US with SJR nozzles and IR drying demonstrates a promising approach for optimizing drying processes. This method not only preserves the quality of dried strawberries but also improves the energy factor to 0.88, reducing drying time by 89% compared with other conditions. Key quality attributes of the dried strawberries, such as color and water activity, were evaluated to understand the influence of each drying method. The findings highlight the potential of these integrated drying techniques as sustainable solutions for efficient and high-quality strawberry dehydration.
In this paper, we present an efficient computational framework with the purpose of generating weighted pseudo-measurements to improve the quality of distribution system state estimation (DSSE) and provide observability with advanced metering infrastructure (AMI) against unobservable customers and missing data. The proposed technique is based on a game-theoretic expansion of relevance vector machines (RVMs). This platform is able to estimate the nodal power consumption and quantify its uncertainty while reducing the prohibitive computational burden of model training for large AMI datasets. To achieve this objective, the large training set is decomposed and distributed among multiple parallel learning entities. The resulting estimations from the parallel RVMs are then combined using a game-theoretic model based on the idea of repeated games with vector payoff. It is observed that through this approach and by exploiting the seasonal changes in customers’ behavior the accuracy of pseudo-measurements can be considerably improved, while introducing robustness against bad training data samples. The proposed pseudo-measurement generation model is integrated into a DSSE using a closed-loop information system, which takes advantage of a branch current state estimator (BCSE) to further improve the performance of the designed machine learning framework. This method has been tested on a practical distribution feeder model with smart meter data for verification.
Photoresponse of bimetallic Au-Ag nanoparticle embedded soda glass (Au-Ag@SG) substrate is reported for surface plasmon assisted optical switching using 808 nm excitation. Au-Ag@SG system is made by an ion beam technique where Ag{sup +} is introduced first in the soda glass matrix by ion exchange technique. Subsequently, 400 keV Au{sup +} is implanted in the sample for different fluences, which is followed by an ion beam annealing process using 1 MeV Si{sup +} at a fixed fluence of 2 × 10{sup 16} ions·cm{sup −2}. Characteristic surface plasmon resonance (SPR) peaks around 400 and 550 nm provided evidence for the presence of Au and Ag nanoparticles. An optical switching in the Au-Ag@SG system with 808 nm, which is away from the characteristic SPR peaks of Ag and Au nanoparticles, suggests the possible role of two photon absorption (TPA) owing to the presence of interacting electric dipole in these systems. The role of surface plasmon polariton is emphasized for the propagation of electronic carrier belonging to the conduction electron of Au-Ag system in understanding the observed photoresponse. Unique excitation dependent photoresponse measurements confirm the possible role of TPA process. A competitive interband and intraband transitions in the bimetallic system of Au and Ag, which may be primarily responsible for the observation, are validated qualitatively using finite difference time domain calculations where inter-particle separation of Au and Ag plays an important role. Thus, a smart way of optical switching can be envisaged in noble bimetallic nanocluster system where long wavelength with higher skin depth can be used for communication purpose.
The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.