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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Implementation Plan for Combined Heat and Power Systems VOLTTRON Controller: Performance Monitoring and Real-Time Commissioning Algorithm Verification

Building-integrated cooling, heating, and power (CHP) systems are more efficient than conventional systems at providing local power and thermal energy, and favorable fuel prices are bound to spur their increased adoption. However, to realize the full benefit of the CHP systems, we must ensure persistence of energy efficient operations. Much of the inefficiency in the current building operations can be eliminated by use of automated performance monitoring (PM), real-time commissioning verification (CxV) and automated fault detection and diagnostic (AFDD) tools. Automation can help system operators make intelligent decisions. Remote and continuous monitoring of system conditions and performance will enable better management and integration of CHP with existing building systems. Continuous PM, real-time CxV, and AFDD could alleviate burdens for operations staff, enhance operations and maintenance (O&M), and improve reliability of building and CHP systems. To address the O&M challenges and to provide a means to maximize the rate-of-return of building-integrated CHP systems, the Building Technologies Office (BTO) within the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) initiated a project to design, develop, and field test a VOLTTRON™-based supervisory controller and associated open-source algorithms. These algorithms will ensure real-time optimal operation of a building-integrated CHP system, support electric grid reliability, and lead to achieving the goal of clean, efficient, reliable, and affordable next-generation integrated energy system. Previous report listed the components for which PM, real-time CxV, and AFDD algorithms will be developed, how the algorithms will be tested, and the metrics that will be used to validate the algorithms and their ease of deployment. Deployment of these algorithms in the field will result in a reduction in energy consumption of between 10% and 20% (for both CHP and conventional building systems). This report builds upon the previous report by detailing the process by which PNNL will implement performance monitoring and real-time commissioning algorithms for CHP systems in conjunction with the use of the VOLTTRON CHP economic dispatch agent in host facilities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems

Modern industrial systems are now fitted with several sensors for condition monitoring. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault detection, diagnosis, and prognostics. However, the information valuable for performing these tasks is often clouded in noise and must be mined from high-dimensional data structures. Therefore, this dissertation presents a data analytics framework for performing these condition monitoring tasks using high-dimensional data. Demonstrations of this framework are detailed for challenges related to power generation systems in automobiles, power plants, and aircraft engines. These implementations leverage data collected from state-of-the-art, industry class test-rigs. Results indicate the ability of this framework to develop effective methodologies for condition monitoring of complex systems.

Peters, Benjamin↗
DOE OSTI2021

State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data

Nowadays, transmission system operators require higher degree of observability in real-time to gain situational awareness and improve the decision-making process to guarantee a safe and reliable operation. Digitalization of energy systems allows utilities to monitor the system dynamic performance in real-time at fast time scales. The use of such technologies has unlocked new opportunities to introduce new data driven algorithms for improving the stability assessment and control of the system. Motivated by these challenges, a group of experts have worked together to highlight and establish a baseline set of these common concerns, which can be used as motivation to propose innovative analytics and data-driven solutions. In this document, the results of a survey on 10 transmission system operators around the world are presented and it aims to understand the current practices of the participating companies, in terms of data acquisition, handling, storage, modelling and analytics. The overall objective of this document is to capture the actual needs from the interviewed utilities, thereby laying the groundwork for setting valid assumptions for the development of advanced algorithms in this field.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

All-digital Sensor System for Distributed Downhole Pressure Monitoring in Unconventional Fields

This project developed and validated (through field tests) a new low-cost all-digital pressure sensing technology for in situ distributed downhole pressure monitoring in unconventional oil and gas (UOG) fields. The all-digital sensing technology uses a built-in non-electric analog-to-digital converter (ADC) to transform the pressure information into a combination of binary (ON/OFF) states. As such, the system does not need downhole electronics for signal conditioning and telemetry. The all-digital sensors can be remotely logged over a long distance, and many sensors can be multiplexed for distributed sensing. Based on a review of unconventional wells in the Lower 48 states, the specification of the sensor is to measure pressure up to 69 MPa (10,000 psi) and temperature up to 250°C. A sensor with a helical bourdon sensing element and a digital signal decoder of 50 mm diameter and 109 mm length was constructed. The helical bourdon sensing element was made of 304L stainless steel and filled with motor oil. The digital converter was made up of 8 digital reading pads constructed of high-temperature epoxy with conductive inserts made of stainless steel. The sensor had a linear response to pressure with an accuracy of 0.14 MPa (20 psi). To withstand the high pressure, the sensor was enclosed in a stainless-steel pressure housing with a wall thickness of 5.5 mm, a diameter of 73 mm, and a length of 724 mm. In the laboratory tests, the sensor exhibited no temperature-related effects on the results. The sensor did not show drift over a 14-day test period at elevated pressure. A field test was conducted where the sensor was deployed in a test wellbore at the Quest drilling test facility to a depth of 0 feet over three weeks. The sensor was attached to the production rods, along with a downhole reference sensor of PPS27 type, which is a permanent downhole monitoring system. During the testing phase, the test well annular blow-out preventer was closed, and the well was pressurized at the surface to 11 MPa (1600 psi). The sensor read the elevated bottom hole pressure of 1500 psi. A multiplexing unit was created for the sensor to deploy multiple sensors on one data transmission line in a distributed approach. The multiplexing unit was tested in a simulated environment of 3048 m (10,000 ft) with five sensors distributed. The sensors were pressurized at different intervals. The multiplexed sensors recorded the correct pressure, and the multiplexing did not interfere with the readings. The proposed concept of an all-digital pressure sensor for harsh downhole environments was designed, manufactured, and tested in the laboratory and tested at the field to a up to 69 MPa and 250°C. This technology has high-temperature tolerance and has potential in downhole areas outside oil and gas, such as carbon capture and storage (CCS) and geothermal wells. The sensor concept has been proven in this project, but to create a commercially viable product, manufacturing a sensor with a smaller diameter needs to be performed.

02 PETROLEUM↗
DOE OSTI2026

Novel and Rugged Electromagnetic Wireless System for Long-Term Subsurface CO₂ Plume Monitoring and Saturation Mapping

Novel and rugged electromagnetic wireless system for long-term subsurface CO₂ plume monitoring and saturation mapping is a Phase I SBIR project led by Cyentech Consulting LLC to develop a permanently installed, outside-casing electromagnetic monitoring system for long-term carbon storage monitoring. The project addresses a major challenge in carbon capture and storage: the need for reliable, cable-free downhole monitoring that can detect CO₂ plume movement and saturation changes without compromising well integrity through casing perforations or external umbilical cables.

58 GEOSCIENCES↗
DOE OSTI2021

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

Here we present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.

42 ENGINEERING↗
DOE OSTI2023

Smart Refractory Sensors Development for Corrosion and Erosion Monitoring in High Temperature Systems

To optimize the operation and functioning of high temperature systems such as slagging gasifiers, coal boilers and glass/steel melters, it is important to monitor corrosion and erosion of refractory used in such systems. Corrosion test strategies are generally based on continuous gravimetric and chemical reactivity monitoring at operational temperatures (750°-1500°C). Both thermocouples and failure sensors and arrays would be useful to monitor the health of any refractory or coatings in these systems. Many of such type of sensors are installed into the systems through open access ports within the refractory; however, there are some disadvantages of this approach where corrosive/erosive gas and molten materials can penetrate and compromise the system. The current work presents the development and performance demonstration of smart refractory with embedded high temperature sensors such as thermocouples, thermistors, and various spallation/crack monitoring sensors, which may be used within a variety of refractory brick in different high temperature processes and applications. The main feature of this technology is that electroceramic based sensors are embedded into smart refractory without significantly impact to the intrinsic properties of the refractory. This technology circumvents the need to insert an isolated monolithic, stand-alone sensor into the refractory via an access port. This technological approach guarantees the integrity and the chemical stability of the materials used in the sensor fabrication within the harsh environment and does not introduce molten material (such as slag) penetration pathways within the refractory. One interesting and important aspect of this innovation is that these embedded sensors can be used to in situ monitoring processes such as chemical reactions and at the same time give information and a deeper understanding of the corrosion and erosion process of the refractory within the system. As stated above, the objective of our work is to develop high-temperature sensors composed of electroceramic materials that are chemically stable at high temperatures (750°-1500°C) and high pressures (up to 1000 psi) that can be used in monitoring corrosion and erosion process in refractory used in high energy systems. The high-temperature sensors investigated in this work were composed of various oxide composites directly embedded into the refractory oxides. The composites used for this work were synthesized by a mixed-oxide route. Metal oxides were inserted within a matrix material composed of refractory oxides (Al2O3, ZrO2, etc.). The physical and electrical properties were specifically manipulated by altering the level of percolation of the conductive species (metal oxides) within the refractory constituent (refractory oxide). Prior to the development of the high-temperature sensors, the oxides composites developed in this study were sintered up to 1600°C under oxidizing atmosphere in order to investigate densification, microstructural evolution, phase development, and their thermoelectrical performance as a function of the composition. The 4-point DC conductivity measurements were performed between 100°-1500°C. The sensors were fabricated from the composite materials by 3D-printing or screen-printing methods into the refractory brick during the consolidation process. An example of one of these embedded sensors consisted of an electroceramic-based thermocouple fabricated with two separate oxide composite compositions which were patterned to produce a couple within the interior of a refractory matrix. The thermocouple successfully displayed thermoelectric voltage trend (as a function of temperature), and the voltage was 220.0 mV around 1400 °C. Corrosion tests on the refractory embedded sensors were performed. To evaluate corrosion in the refractory brick an in-house glass composition was prepared and pressed into pellets and delivered into a pre-cut cavity in the brick. Corrosion experiments results showed the glass penetrated the brick over a 90 h period, and the penetration of the glass through the brick could be monitored by both an amperometric and voltametric based sensor. With this experiment, it was demonstrated that the embedded sensor could dynamically monitor the corrosion process.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Preprocessing for Unintended Conducted Emissions Classification with ResNet

Characterization of Unintended Conducted Emissions (UCE) from electronic devices is important when diagnosing electromagnetic interference, performing nonintrusive load monitoring (NILM) of power systems, and monitoring electronic device health, among other applications. Prior work has demonstrated that UCE analysis can serve as a diagnostic tool for energy efficiency investigations and detailed load analysis. While explaining the feature selection of deep networks with certainty is often not fully comprehensive, or in other applications, quite lacking, additional tools/methods for further corroboration and confirmation can help further the understanding of the researcher. This is true especially in the subject application of the study in this paper. Often the focus of such efforts is the selected features themselves, and there is not as much understanding gained about the noise in the collected data. If selected feature and noise characteristics are known, it can be used to further shape the design of the deep network or associated preprocessing. This is additionally difficult when the available data are limited, as in the case which the authors investigated in this study. Here, the authors present a novel work (which is a proposed complementary portion of the overall solution to the deep network classification explainability problem for this application) by applying a systematic progression of preprocessing and a deep neural network (ResNet architecture) to classify UCE data obtained via current transformers. By using a methodical application of preprocessing techniques prior to a deep classifier, hypotheses can be produced concerning what features the deep network deems important relative to what it perceives as noise. For instance, it is hypothesized in this particular study as a result of execution of the proposed method and periodic inspection of the classifier output that the UCE spectral features are relatively close to each other or to the interferers, as systematically reducing the beta parameter of the Kaiser window produced progressively better classification performance, but only to a point, as going below the Beta of eight produced decreased classifier performance, as well as the hypothesis that further spectral feature resolution was not as important to the classifier as rejection of the leakage from a spectrally distant interference. This can be very important in unpredictable low-FNR applications, where knowing the difference between features and noise is difficult. As a side-benefit, much was learned regarding the best preprocessing to use with the selected deep network for the UCE collected from these low power consumer devices obtained via current transformers. Baseline rectangular windowed FFT preprocessing provided a 62% classification increase versus using raw samples. After performing a more optimal preprocessing, more than 90% classification accuracy was achieved across 18 low-power consumer devices for scenarios in which the in-band features-to-noise ratio (FNR) was very poor.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

High-resolution in situ electrochemical NMR with interdigitated electrodes

A system for carrying out electrochemical nuclear magnetic resonance spectroscopy (EC-NMR) is disclosed, along with methods of manufacturing the EC-NMR system, and methods of using the EC-NMR system to monitor electrochemical reactions. The system comprises interdigitated electrodes arranged in a cylindrically symmetric manner. The system allows for nuclear magnetic resonance spectroscopy to be carried out on a sample during electrolysis with minimal effect to its sensitivity.

Tong, YuYe J.↗
DOE OSTI2021

Evaluation of Mode Meters Robust to Forced Oscillations using Field-Measured Data

Mode meters are tools used by power system operators to continuously monitor a system's small-signal stability. They do so by estimating the system's electromechanical modes of oscillation. When a system undergoes a forced oscillation, mode meters may become biased because the two types of oscillation cannot be distinguished. Modified mode meter algorithms robust to this bias have been proposed in prior research, but these studies were based primarily on simulated data. In this paper, modified least squares and Yule-Walker mode meter algorithms are evaluated using field-measured data from phasor measurement units (PMUs). Results show that the sensitivities of the least squares algorithm make it impractical for use given the complexities of real-world forced oscillations. However, the modified Yule-Walker algorithm is shown to perform well and has significant potential for practical deployment in mode meter tools.

Follum, James D.↗
DOE OSTI2023

Analysis of an optical imaging system prototype for autonomously monitoring zooplankton in an aquaculture facility

Traditional approaches to biomonitoring in aquatic systems, such as sample collection, sorting, and identification, require significant time and effort, thereby limiting the spatiotemporal resolution of sample collection. Additionally, collection and preservation of samples for subsequent taxonomic identification and enumeration leads to mortality of organisms. Recent advances in technologies that utilize optical imaging and machine learning have provided new opportunities to expedite biomonitoring and lead to significant cost savings. These technologies can be advantageous to scientists or managers that conduct routine biomonitoring to inform operations, as in the case of aquaculture facilities. The Small Aquatic Organism optical imaging system (SAO) is a high-throughput optical imaging and classification prototype system that relies on computer vision and machine learning (Support Vector Machines, or SVMs) to autonomously identify and enumerate aquatic organisms. The SAO provides a more sustainable method of collecting large volumes of data and has the benefit of being used in situ. In this study, we tested the performance of the SAO in providing comparable results to manual zooplankton community monitoring in ten ponds at an aquaculture facility. We performed a side-by-side study comparing the sampling methods of plankton tow nets, where major zooplankton taxonomic classes were manually identified and enumerated, to sampling with the SAO. Vouchered samples were used to develop a training library for the SAO, where classes consisted of water boatman and zooplankton groups: cladocerans, copepod adults, copepod nauplii, and rotifers. SAO imagery was manually classified and compared with predicted results for validation. Accuracy for the SVM classifier of the SAO was 37.4 %. Convolutional Neural Networks (CNN) and Random Forest classifiers were also applied to SAO imagery and image features for comparison. The best CNN model and our Random Forest model had accuracies of 80.4 % and 46.6 % respectively. Challenges faced included the small size of copepod nauplii and rotifers and the limited resolution of the imaging camera, although there are tradeoffs between imaging resolution and the sample processing rate. Furthermore, our comparison shows that advancement in both optical imaging and ML are needed in order for the SAO prototype to yield comparable results to manual community monitoring in an aquaculture facility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Universal monitored dynamics in multimode bosonic systems

We propose a route to study monitored many-body dynamics in multimode bosonic systems using circuit quantum electrodynamics. In this experimental setting, we construct several bosonic models comprising brickwork circuits built from beam-splitter gates, local parity measurements, and optional on-site Hubbard interactions, and diagnose their monitored dynamics via ancilla purification and a learnability-based probe. Under parity measurements, generic gate sets exhibit behavior that is largely consistent with a conventional measurement-induced phase transition, while a special class of beam-splitter circuits shows an apparent critical-like high-measurement regime in which purification times scale linearly with system size. We show that for realistic noise, gate, and measurement rates, these signatures are observable with near-term circuit QED hardware.

Patel, Shivam [Rutgers U., Piscataway]↗
DOE OSTI2021

A Data-Driven Method for Estimating Behind-the-Meter Photovoltaic Generation in Hawaii

Due to the increasing penetration of distributed behind-the-meter photovoltaic (PV) systems and the installed utility revenue metering limited to monitoring only the net power import/export of the household, it is increasingly challenging for utilities to effectively plan and operate the grid. This paper proposes a methodology that estimates behind-the-meter PV generation using a selected subset of monitored PV systems. It is a data-driven approach, and the PV output is estimated utilizing a statistic regression model. A Minimum Redundancy Maximum Relevance (MRMR) algorithm is applied to preselect the optimal subset of the monitored PV systems. The performance of this approach is compared with a spatial interpolation method and a model-based approach. The proposed method is validated using high-resolution meter data recorded from 18 residential rooftop PV systems located on the island of Maui, Hawaii.

Data-driven modeling↗
DOE OSTI2022

Rock Physics-Based Data Assimilation of Integrated Continuous Active-Source Seismic and Pressure Monitoring Data during Geological Carbon Storage

Summary There has been substantial controversy concerning the role of geological carbon storage (GCS) in sequestering anthropogenic carbon emissions to mitigate climate change and global warming. Arguments center on the inability to monitor a geological storage site precisely and continuously, especially highlighting the associated costs and spatiotemporal trade-offs when using conventional subsurface monitoring techniques (well logs, core samples, chemical tracers, and 4D seismics). Active surveillance of GCS sites is essential for managing and mitigating potential leaks but is also required by regulation. With the goal of enhancing the monitoring capability at GCS sites, we present a rock physics-based joint data assimilation model to study a popular GCS site at Cranfield, Mississippi, USA. Synthetic continuous active-source seismic monitoring (CASSM) data (in the form of Vp and Qp measurements) and wellbore pressure monitoring data are assimilated with an ensemble of reservoir realizations to monitor gas saturation and reservoir pressure changes over a period of 100 years. Synthetic seismic attributes are generated using rock physics models (RPMs) and wellbore pressure monitoring data are extracted from the ground truth. Two assimilation methods, ensemble Kalman filter (EnKF) and ensemble Kalman smoother (EnKS), are tested in an observation system simulation experiment (OSSE) environment to assess the prediction accuracy of the individual and composite observation systems. The joint monitoring system achieves more accurate estimates of gas saturation and pressure, across the time span from start of injection to end of forecast, as compared to a single type of monitoring tool and irrespective of data assimilation algorithm choice. These results indicate that jointly assimilated data from two types of sensors (in this case, crosswell seismic and downhole pressure) may lead to a more risk-reducing monitoring design. One would expect that more data, vis-à-vis inclusion of a new sensor type, will improve the accuracy of any GCS monitoring system. However, from a practical standpoint, one important question is whether such a gain in accuracy is worth the additional cost associated with the new sensor. This paper focuses on quantifying the gain in accuracy, such that a practitioner can answer this question.

Engineering↗
DOE OSTI2024

Zero-power wireless system for crop water content monitoring

Zero power wireless sensors, devices, and systems are used for crop water content monitoring. The sensors consume no power while monitoring for the presence of dry crop conditions. Infrared reflectance from plants is measured and when selected spectral conditions are met, a circuit is closed, activating an alarm, an RFID tag, or a radio transmitter. The deployed sensors consume no power while monitoring, reducing or eliminating the need to change batteries.

Rinaldi, Matteo↗
DOE OSTI2026

Measurement Adequacy for Monitoring Data Center Oscillations

Artificial intelligence (AI) training data centers with periodic load profiles can induce sustained grid oscillations across a wide frequency range, making accurate monitoring essential for reliable power system operation. This report evaluates the adequacy of existing measurement systems for monitoring such oscillations, focusing on phasor measurement units (PMUs) and point-on-wave (POW) measurement systems. The analysis shows that while PMUs are highly effective for monitoring low-frequency electromechanical oscillations, they have inherent limitations in accurately representing higher-frequency oscillations due to constraints imposed by reporting rates and the bandwidth of phasor estimation filters. Even when configured with higher reporting rates, the filtering inherent in the phasor estimation process can significantly attenuate oscillation magnitudes, potentially leading to underestimation of oscillatory behavior. This has important implications for compliance and performance monitoring of large loads. To address the limitations associated with PMU-based monitoring, the report examines the use of high-resolution POW measurements, which can capture oscillations across a broader frequency range. However, continuous POW monitoring introduces practical challenges related to large data volumes, communication bandwidth, and real-time data processing. For this reason, the report also discusses emerging approaches that use POW measurements as a complementary capability alongside PMUs to improve observability of oscillations from large data center loads.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Systems, devices, and methods for performing augmented reality responsive to monitoring user behavior

Systems, devices, and methods are described for performing augmented reality (AR) to assist user performing a task in an environment. An AR device may be configured to capture real-time data. An AR engine may be configured to monitor user behavior from the real-time data responsive to feature extraction from the real-time data, compare the user behavior to pre-defined work procedures, and generate augmented reality objects to be output by the AR device.

Yoon, SuJong↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗