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Anomaly Identification of Synchronized Voltage Waveform for Situational Awareness of Low Inertia Systems
Inverter-based resources (IBRs) such as photovoltaics (PVs), wind turbines, and battery energy storage systems (BESSs) are widely deployed in low-carbon power systems. However, these resources typically do not provide the inertia needed for grid stability, resulting in a low-inertia power system. IBRs and lack of inertia have been known to cause anomalies such as waveform distortions and wideband oscillations in power systems due to the limited inertia level, leading to increased generation trips and load shedding. Here, to achieve effective anomaly identification, this paper proposes a synchro-waveform-based algorithm utilizing real-time synchronized voltage waveform measurements from waveform measurement units (WMUs). In the proposed method, different physical characteristics, as well as statistical features, are extracted from synchronized voltage waveform measurements to filter anomalies. Then, the anomaly identification approach based on the random forest is developed and deployed into the FNET/GridEye system considering trade-offs among accuracy, computational burden, and deployment cost. Moreover, four WMUs are specially designed and deployed on Kauai Island to receive instantaneous synchronized voltage waveform measurements. To verify the performance of the proposed algorithm, different experiments are carried out with collected field test data. The result demonstrates that the performance of the proposed synchro-waveform-based anomaly categorization algorithm can accurately identify anomalies 95.35% of the time, which has comparable performance among benchmarking algorithms.
Operable adaptive sparse identification of systems: Application to chemical processes
Not provided.
Identification and Quantification of Technetium Species in Hanford Waste Tank AN-102
Technetium-99 (Tc) generated from the fission of 235U and 239Pu in high yields is one of the most difficult contaminants to be addressed at the U.S. Department of Energy Hanford Site. In strongly alkaline solutions typifying Hanford tank waste, Tc exists as pertechnetate (TcO4-) (oxidation state VII) as well as in reduced forms (oxidation state < VII) collectively known as non-pertechnetate species. Designing strategies for effective Tc management, including separation and immobilization, necessitates understanding the molecular structure of the non- pertechnetate species and their identification in the actual tank waste samples, which would facilitate development of new treatment technologies effective for dissimilar Tc species. Toward this objective, a spectroscopic library of the Tc(I) [fac-Tc(CO)3]+ and Tc(IV, VII) compounds was generated using a range of techniques and applied to the characterization of the actual tank waste supernatant collected from the tank 241-AN-102 at Hanford, WA. A sample of the 241-AN-102 tank waste supernatant was processed to adjust Na concentration to about 5.6 M and remove 137Cs by spherical resorcinol-formaldehyde (sRF) ion exchange resin. Cesium-loaded sRF column was eluted with 0.5 M HNO3. As received AN-102, Cs-depleted AN-102 effluent, and sRF eluate fractions were comprehensively characterized for chemical composition and speciation of Tc using 99Tc nuclear magnetic resonance spectroscopy and X-ray absorption spectroscopy. It was demonstrated for the first time that non-pertechnetate Tc present in the 241-AN-102 tank waste is composed of several low-valent Tc species, including the Tc(I) [fac-Tc(CO)3]+ and Tc(IV) compounds. This is the second experimental observation of the [fac-Tc(CO)3]+ species in the Hanford tank waste and the first demonstration of multiple forms of non-pertechnetate species existing simultaneously in the waste, cumulatively highlighting their importance for the waste processing.
Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks
Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.
Moving toward automated µFTIR spectra matching for microplastic identification: addressing false identifications and improving accuracy
Abstract Infrared spectroscopy is a widely used tool for studying microplastics and identifying microparticles. Researchers rely on spectral libraries to differentiate between synthetic and natural materials. Unfortunately, spectral library matching is not perfect, and best practices require researchers to use time consuming, manual peak matching to assess spectral matches. Moving toward automated matching requires increased confidence in the matching process. Using spectra matching software may increase the efficiency of particle identification, however some matching strategies may confuse natural materials such as cotton, silk, and plant matter with common classes of synthetics such as polyesters and polyamides. In this experiment, we prepared 22 pristine sample materials from natural and synthetic sources and measured micro-Fourier transform infrared (µFTIR) spectra in transmission mode for each sample using a Thermo Nicolet iN10 MX instrument. The collected spectra were then input into two spectral library matching systems (Omnic Picta and Open Specy), using a total of five identification routines. Next, we placed a subset of four pristine microplastic materials in a biologically active river system for two weeks to simulate environmental samples. These simulated environmental samples were processed using 10% hydrogen peroxide for 24 h to remove organic contamination and then identified using the strongest performing library. We found that libraries with fewer sample spectra produced lower correlation matches and that using derivative correction greatly reduced the number of inaccuracies in identifying materials as either natural or synthetic. We also found that environmental fouling reduced the correlation value of library matches when compared to pristine particles, however the effect was not consistent across the four materials tested. Overall, we found that the accuracy of automated library matching in the tested systems and processing routines varied from 64.1 to 98.0% for distinguishing between natural and synthetic materials, and that a high Hit Quality Index (HQI) did not always correlate with accuracy. These results are important for the microplastic field, demonstrating a need to rigorously test spectral libraries and processing routines with known materials to ensure identification accuracy.
Radioisotope Identification with List-Mode Gamma Ray Data: A rigorous assessment on the value of temporal information applied to radioisotope identification.
This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with “confuser” sources, or spectra with similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research rigorously examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and information theory. We further propose a basic classification model that can utilize spectral or temporal data (or both) to determine if the incorporation of temporal information can improve radioisotope identification. The findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated.
Advanced Sensor Deployment for Distribution System State Estimation and Fault Identification
Distribution systems are currently facing steep operational challenges as a result of the rapidly increasing integration of renewables and other distributed energy resources (DERs) at both the primary and secondary circuit levels. Distribution utilities and system operators have traditionally had some visibility of their primary circuits using low-frequency supervisory control and data acquisition systems, and they have had very poor if not zero visibility of the secondary circuits where the presence of DERs is constantly increasing. Therefore, this paper presents simulation studies to demonstrate the benefits of an advanced, high-fidelity sensor technology, called as the Meta-Alert System (MAS), developed by Electrical Grid Monitoring, Ltd. (EGM), on the distribution grid. First, a reliable model of the EGM sensors is developed, and then two use cases, distribution system state estimation (DSSE) and fault identification are simulated to evaluate the performance of the MAS technology. Simulation results on the Electric Power Research Institute J1 feeder demonstrate that the MAS can effectively participate in system-level DSSE programs and can detect and locate faults faster than traditional distribution protection schemes.
Deep-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials
Not provided.
Active Suspension Parameters Identification: An Algebraic Approach and its Application to Suspension Travel Control
The parameters of an active suspension need to be identified online, such that the suspension control system can be adapted to mechanical wear and load change. Recursive least squares and observer-based methods are frequently utilized to fulfill this purpose. However, they can yield slow parameter identification due to their asymptotic nature. We propose an algebraic identifier to estimate the parameters of an active suspension online, which does not maintain an asymptotic convergence phase. Simulation results demonstrate the effectiveness of the proposed algebraic approach.
Development of phenomena identification and ranking table for Westinghouse lead fast reactor’s safety
The Westinghouse Lead-cooled Fast Reactor (LFR) is a medium-size, passively safe, economic, Gen-IV nuclear reactor. An important effort within the Westinghouse LFR program is the development of the safety analysis methodology, which comprises computer code development, model development, and experimental testing. A key initial task associated with the development of the safety analysis methodology is the identification of processes and phenomena that affect the plant's capability to meet selected safety performance indicators. This is accomplished through the development of a Phenomena Identification and Ranking Table (PIRT) for selected accident scenarios which, for this specific PIRT effort, included selected postulated design basis accidents and hypothetical beyond design basis accidents in LFRs. This paper describes the role of PIRT in the development of the Westinghouse LFR safety analysis methodology and the process used in the PIRT development. Specifically, the Westinghouse LFR PIRT assessed importance of pertinent phenomena and identified gaps in their knowledge-base by evaluating current modeling capabilities and data available for validation. The ultimate goal was to provide guidance on computer code development and validation efforts and to prioritize testing to support LFR design and licensing. The key phenomena and processes that are deemed highly important for the safety performance indicators, but for which the state of knowledge is low, are presented. The testing program and analyses development are planned to address significant phenomena in the PIRT.
Representative identification of spectra and environments (RISE) using k-means
Not provided.
Milling stability identification using Bayesian machine learning
This paper describes automated identification of the milling stability boundary using Bayesian machine learning and experiments. The Bayesian machine learning process begins with the user’s initial beliefs about milling stability. This “prior” is a distribution that uses all available information, which may be based only on experience or may be informed by physics-based model predictions. Experiments are then completed to update this prior by calculating the “posterior,” a modified probabilistic description of the milling stability limit based on the new information. The approach is demonstrated and results are presented for both numerical and experimental cases.
Identification of Serine-Containing Microcystins by UHPLC-MS/MS Using Thiol and Sulfoxide Derivatizations and Detection of Novel Neutral Losses
Microcystins (MCs) are hepatotoxic cyclic heptapeptides produced by cyanobacteria, and their structural diversity has led to the discovery of more than 300 congeners to date. However, with known amino acid combinations, many more MC congeners are theoretically possible, suggesting many remain unidentified. Herein, two novel serine (Ser)-containing MCs were putatively identified in a Lake Erie cyanobacterial harmful algal bloom (cyanoHAB), using high-resolution UHPLC-MS as well as thiol and sulfoxide derivatization procedures. These MCs contain an α,β-unsaturated carbonyl on methyl dehydroalanine (Mdha) residue that undergoes Michael addition to produce a thiol-derivatized MC. Derivatization reactions using various thiolation reagents were followed by MS/MS, and two Python codes were used for data analysis and structural elucidation of MCs. Two novel MCs containing Ser at position 1 (i.e., next to Mdha) were putatively identified as [Ser 1 ]MC-RR and [Ser 1 ]MC-YR. Using thiol- and sulfoxide-modified [Ser 1 ]MCs, identifications were confirmed by the observation of specific neutral losses of the oxidized thiols or sulfoxides in CID-MS/MS spectra in both positive and negative electrospray ionization (ESI) modes. These novel neutral losses are unique for MCs with Mdha and an adjacent Ser residue. In conclusion, data suggest that a gas-phase reaction occurs between oxygen from adjacent Ser residue and sulfur of the Mdha-bonded thiol or sulfoxide, which leads to the formation and detection of stable cyclic MC ions in MS/MS spectra at m/z values corresponding to the loss of oxidized thiols or oxidized sulfoxides from Ser 1 -containing MCs.
Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification
Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.
Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements
Light detection and ranging (LiDAR) measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables identifying the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data and the associated supervisory control and data acquisition and meteorological data enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction.
Identification of a novel deFADding activity in human, yeast and bacterial 5; to 3; exoribonucleases
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