Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “power system monitoring”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Assessment of Condition Monitoring Methods and Technologies for Inservice Inspection and Testing of Nuclear Power Plant Components

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to explore the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components. The advanced technologies considered in this work are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), physics-based models, and digital twins (DT). The interest in the application of advanced technologies for condition monitoring in nuclear power plants continues to grow, and current and future licensees are expected to implement advanced technologies as part of their inservice inspection (ISI) and inservice testing (IST) programs. This report delineates the outcomes of an exploratory investigation into the implementation of advanced condition monitoring technologies to address ISI and IST requirements. A thorough review was conducted of the existing regulatory requirements for ISI and IST, along with an analysis of associated industry practices. Additionally, a state-of-the-art assessment was performed on advanced condition monitoring technologies frequently employed in non-nuclear sectors. This research incorporated two nuclear-specific case studies to illustrate the application of these technologies within the current nuclear fleet. The report provides an exhaustive discussion on the technical challenges, considerations, and opportunities associated with the deployment of advanced condition monitoring technologies. The following are key considerations in the application of advanced technologies for the ISI and IST of nuclear power plant components: • Developing adequate verification and validation procedures to confirm the functional and non-functional requirements, • Developing technical capabilities to conduct real-time asset condition monitoring, • Establishing guidance and protocol for modeling and simulation tools to continuously meet regulatory requirements, • Addressing trustworthiness, explainability, and interpretability of ML/AI methods, • Evaluating maintenance activities to maintain an adequate safety margin and avoid undesirable conditions, • Establishing cybersecure condition monitoring programs associated with a computer-based software system, and • Establishing standardized evaluation metrics for advanced condition monitoring programs. Interest in the use of advanced technologies for condition monitoring in ISI and IST programs continues to grow, and the technology is expected to experience rapid and wide industry adoption in the near future. Adoption of advanced technologies for condition monitoring could have novel and unique impacts on regulatory activities associated with ISI and IST programs. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of ISI and IST programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Flexible Transformers for Resilient and Adaptable Power Systems

This paper presents experience with grid ready flexible transformer unit, which is in service for two years without any difficulty. Transformer unit is capable of changing short circuit impedance on load and it is equipped with state-of-the-art monitoring system.

field validation↗
DOE OSTI2026

Validating Protection System Behavior with Machine Learning in a Master State Overseer

As power system protection devices continue the widespread transition from analog to digital, they become increasingly intricate. The internal functions and communication between critical grid components must now be significantly more complex to keep up with the demands of the modern smart grid. This brings increased difficulty in maintenance and monitoring, making it harder to identify potential misoperation, power anomalies, and cyber threats. Such issues are often only pinpointed after an exhaustive and costly post-mortem analysis, when a major outage or damage has already occurred. A solution is needed for validating protection systems as they operate, independently evaluating grid state and confirming whether the protection system is behaving accordingly. As opposed to incident response, this acts as a constant verification mechanism that raises a flag when subtler issues are noticed, catching them earlier and preventing larger incidents. This work presents the implementation of such a system, expanding on the prototype developed by the authors in a previous paper. This is accomplished with a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. Additionally, this system is contextualized within a larger, modular Master State awareness Overseer (MSO) framework, responsible for monitoring, analyzing, and managing an electric grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Microseismicity Modulation Due To Changes in Geothermal Production at San Emidio, Nevada, USA

Brief cessations of geothermal production can induce seismicity, a phenomenon that has drawn increasing attention in recent years. Such observations are rare, and the underlying mechanism requires careful analysis. In April 2022, a dense seismic and hydrologic monitoring system was deployed at the San Emidio geothermal field, Nevada, to accompany a planned power plant shutdown. Using the dense seismic array data, we detected and located ∼1,800 microseismic events (MSEs) and developed a high-resolution tomographic P-wave velocity model. We observed substantially increased microseismicity during shutdown. Most MSEs occurred on pre-existing normal faults, which are contained within extremely low-velocity zones that are likely damaged, fluid-filled, and hydraulically connected to nearby production wells. Hydrologic data show rapid fluid pressure increases of <60 kPa following the shutdown. We suggest that the cessation of production rapidly increased fluid pressures along pre-existing fault zones, activating critically stressed fault patches and fractures and producing microseismicity.

Guo, Hao [University of Wisconsin-Madison, WI (Uni↗
DOE OSTI2022

Time Sequence Machine Learning-Based Data Intrusion Detection for Smart Voltage Source Converter-Enabled Power Grid

Smart inverters of distributed energy resources can enable cloud computing, condition monitoring, result visualization, remote control, and peer-to-peer energy trading in advanced power systems. However, the advent of data injection attacks in the communication architecture can alter measurement characteristics of power grids and have devastating consequences. In this article, we propose a time sequence machine learning-based anomaly detection methodology for detecting cyber intrusion into control signal setpoints and dc voltage signal measurement bias of the voltage source converter (VSC) in wind generators. We first investigated the effects of four types of denial of service, tampering signal, and stealthy-type data intrusion attacks on smart VSCs and overall wind farms. We then proposed a novel time sequence machine learning-based intrusion detection framework that can be implemented to detect different cyberattacks in the VSCs. The performance of the proposed framework has been compared with that of autoencoder and clustering-based intrusion detection framework. The proposed framework was validated by using the IEEE 39 bus power system in the presence of four wind farms in different locations. Using several metrics for intrusion detection performance, we validated the effectiveness of the proposed framework.

42 ENGINEERING↗
DOE OSTI2022

Line Faults Classification Using Machine Learning on Three Phase Voltages Extracted from Large Dataset of PMU Measurements

An end-to-end supervised learning method is developed to classify transmission line faults in a twoyear field-recorded dataset that includes synchronized measurements of three-phase voltages recorded by 38 Phasor Measurement Units (PMU) sparsely located in in the US Western Grid interconnection. Statistical analysis is performed to extract features from this large dataset to train Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) classifiers initially. The training further leverages a simulated dataset from a synthetic grid with 12 PMUs to increase the number of faults of types infrequently seen in the field-recorded dataset. Training the classification models with the combined dataset resulted in a classification accuracy of 97.7%. This is a significant improvement over 89.7% to 92.5% accuracy obtained by relying on the field-recorded dataset alone.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Partitioning of Radionuclides in Various Streams of the Mo-99 Purification Process

The Universal Neutron Irradiator (UNI) was designed to irradiate a small volume of uranium solution and to achieve high fission power without creating a large radionuclide (RN) inventory. The UNI system is equipped with a fully enclosed off-gas system with online hydrogen gas monitoring to prevent the production of an explosive atmosphere, as well as storage cylinders for capturing fission gases. It also includes a catalytic recombiner in the target capsule (to immediately recombine radiolytic hydrogen and oxygen gas in the system) and access ports for sampling the target solution and capsule headspace. In UNI Phase I experiments, 50 mL of uranyl sulfate solution was irradiated at 0.3–0.5 W/mL fission power using a tantalum target assembly to produce photoneutrons via irradiation with an electron beam. The purposes of the Phase I experiments were to test the efficacy of adding 200 ppm Fe 2+ to the target solution to prevent uranyl peroxide precipitation under representative irradiation conditions, and to monitor iodine speciation in the irradiated uranyl sulfate solution. The focus of the UNI Phase II experiments was to use an irradiated uranyl sulfate solution containing a mixture of fission products and monitor their distribution in various streams during the primary recovery column (titania-based column) that separates Mo-99 from uranium. Irradiations were designed to create sufficient activity for tracking their distribution in various processing streams using a combination of gamma counting and inductively coupled plasma mass spectrometry (ICP-MS) analyses. Obtained data will be used to better understand the composition of waste streams, accumulation of RN on the titania column, and identify what RNs accumulate in the uranyl sulfate solution. Furthermore, this knowledge will allow us to determine waste classifications for various streams produced during the Mo-99 purification process and develop an appropriate waste clean-up strategy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Laser ablation spectrometry for studies of uranium plasmas, reactor monitoring, and spent fuel safety

Nuclear security is one of the defining challenges of our time. Nuclear threats range from deliberate dispersal of radioactive material to contaminate the vital infrastructure to diversion and smuggling of special nuclear material for clandestine nuclear programs and nuclear terrorism, respectively. There is an associated need to develop and sustain the nuclear forensics capabilities, which requires the understanding of complex processes that occur in plasmas of nuclear materials. The area of nuclear safety has seen a resurgence of public interest, and there is a concomitant need to safely store used nuclear fuel and detect structural material failure in nuclear power systems, especially in innovative reactor designs envisioned for future adoption. Laser-produced plasmas are complicated extreme environments that can generate intense and rich, highly specific signatures of nuclear and radiological materials, which can then be explored in a wide range of applications. They include interdiction and rapid detection of nuclear materials, including their isotopic composition, detection over long distances, laboratory simulation of weapons effects, monitoring the condition of structural materials in dry cask storage containers, and novel instrumentation for nuclear power systems. We present a compilation of recent representative examples of the application of laser spectroscopy, and laser-induced breakdown spectroscopy in particular, to nuclear safety and security problems. A case is made that spectroscopic techniques based on laser-produced plasmas offer complementary, and sometimes unique, capabilities that motivate the continued exploration of their efficient production and understanding of the signatures they produce

(020.3260) Isotope shifts, (140.3440) Laser-induce↗
DOE OSTI2021

Sensors with Intelligent Measurement Platform and Low-cost Equipment (SIMPLE) for Monitoring and Control of Medium Voltage Distribution Systems with High Penetration of Intermittent Distributed Energy Resources

The primary focus of the SIMPLE project was to develop, commission, test and introduce intelligent voltage and current sensor platform with enhanced characteristics (accuracy, bandwidth and harmonic range) and high measurement granularity for medium voltage distribution system monitoring, protection and control. The enhancements incorporated existing (commercially available) sensors and made them suitable for applications such as voltage regulation, frequency support, fault detection and location, distribution system state estimation, power quality measurements and electrical distribution network topology processing. The development of the intelligent sensor platform was driven by the need for high-fidelity monitoring and control of medium voltage distribution feeder to enable higher penetration of renewable resources and microgrid operation.

14 SOLAR ENERGY↗
DOE OSTI2022

Validation of Power Distribution Models using Load Flow Analysis in an ADMS Environment

Electric utilities are facing the need for better monitoring, analysis, and control of their distribution systems. An accurate mathematical model is a key to both the development of cutting-edge, scalable model-based algorithms and the assessment of emerging technologies such as distributed energy resources (DER) for grid planning and operation. However, the constantly evolving nature of power distribution systems poses challenges to maintaining accurate models. In this paper, we propose a novel load flow based approach to validate power distribution models. Networked equipment models described according to the Common Information Model (CIM) standard and a measurement model are used to formulate the distribution load flow problem. First, a system admittance matrix (Ybus) is derived from device-level CIM parameters. Next, the operational parameters (dynamic Ybus and nodal injections) are extracted from the measurement model using sensor configuration and equipment state. An iterative power flow method is then used to compute nodal voltages and branch flows that are compared against the measurement data to find any inconsistencies in the networked equipment model. This approach is implemented within GridAPPS-D, an open-source standards-based platform for advanced distribution management system (ADMS) application development, and demonstrated on the IEEE 13-bus, 123-bus, and 8500-node test feeders.

Common information model, model validation, power ↗
DOE OSTI2020

An Intelligent Adaptable Monitoring Package. Final Report

The “Intelligent Adaptable Monitoring Package” project was a four-year effort that demonstrated the feasibility of integrated sensing packages at tidal and wave energy sites. Such integration is generally required by the breadth of sensors required to understand environmental effects at marine energy sites and the operational difficulty of deploying, maintaining, and recovering such sensors. Over the course of the project, the Adaptable Monitoring Package (AMP) was deployed in multiple settings, each corresponding to a project budget period: - Budget Period 1: Demonstration of cabled deployment at Pacific Northwest National Laboratory’s Marine Science Laboratory. The deployment highlighted AMP hardware endurance over a 4-month deployment in a tidally-dominated environment and laid the groundwork for machine learning algorithms to detect and classify targets present in active sonar data. - Budget Period 2: Demonstration of an autonomous deployment at PacWave South off the coast of Newport, Oregon. The deployment highlighted the stability of AMP hardware and software, with the autonomous package collecting data on a duty cycle over a 1.5-month deployment. - Budget Period 3: Demonstration of an autonomous deployment powered by a wave energy converter at the U.S. Navy’s Wave Energy Test Site. The deployment highlighted the potential of wave energy to power ocean observatories and led to the development of machine learning algorithms to detect and classify targets in optical camera data. In aggregate, this project’s greatest success was demonstrating the AMP’s flexibility in a range of deployment scenarios. Each budget period represented a “first of a kind” demonstration of integrated instrumentation – cabled AMP, autonomous AMP, wave-energy powered AMP – and each deployment helped to identify and set goals for the next. Further, despite the exploratory nature of these deployments, each one achieved high system up-time and proved that flexible integration of multiple sensors in a single package represents a viable strategy for marine energy environmental monitoring. The key lessons learned from the project are: - Without continuous power, either from a shore cable or in situ source, many of the benefits of integration are lost (If continuous power is not available, the ability to detect rare events is lost, as is the ability to minimize the risk of behavioral changes through adaptive sensing. However, even on a duty cycle, there is still value in being able to acquire synchronous data from multiple sensors.); and - Observations from a moving platform present substantially greater data processing challenges than those from stationary platforms. Finally, these deployments also demonstrate an important truth: successful integration alone does not guarantee that relevant data are collected. To grow the knowledge base about environmental interactions with marine energy converters, integrated systems, like the AMP, need to include the right sensor mix and connect the data pipelines to effective processing algorithms. These deployments establish a strong foundation for future collaborations with the environmental research community: not only to understand the environmental effects of marine energy, but also to improve our general ability to study life in the sea.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

2023 Reftek Wrangler Digitizer Type Approval Evaluation

Sandia National Laboratories has tested and evaluated the Reftek Wrangler digitizer. The Wrangler digitizers are intended to record sensor output for seismic and infrasound monitoring applications. The purpose of this digitizer evaluation is to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self-noise, dynamic range, system noise, response, passband, and timing. The Wrangler digitizers are being evaluated for potential use in the International Monitoring System (IMS) and On-site Inspection (OSI) components of Comprehensive Nuclear-Test-Ban Treaty (CTBT).

58 GEOSCIENCES↗
DOE OSTI2023

Digital holography for real-time non-invasive monitoring of larval fish at power plant intakes

Effective evaluation of technological and operational approaches to reduce entrainment of marine organisms at cooling water intake structures (CWIS) requires accurate organism-sensing systems. Current detection methods lead to large temporal data gaps, require tedious manual analysis, and are fatal to organisms. In this report we describe integrating deep learning with a non-lethal, non-intrusive imaging method—digital holography—to rapidly detect fish larvae. Laboratory experiments demonstrated that the instrument could successfully image fish larvae at flow rates exceeding ranges seen in CWIS. Holograms of two fish larvae species, in the presence of bubbles and detritus, were collected to build a large database for training a lightweight convolutional neural network. The model achieves 97% extraction accuracy in quantifying larvae, and distinguishing them from other particles, including detritus and bubbles, when applied to a dataset of manually classified images, exceeding previous metrics for non-lethal, accurate, and real-time detection. These results demonstrate the potential of in situ holographic imaging for monitoring endangered larval fish species at power plant intake structures, and for high-fidelity, real-time applications in monitoring aquatic ichthyoplankton.

endangered species↗
DOE OSTI2024

Online Dynamic Cyber-Attack Diagnosis in Power Electronics Systems Based on Few-Shot Learning

With increasing exposure to software-based sensing and control, power electronics systems are facing higher risks of cyber-physical attacks. To ensure system stability and minimize potential economic losses, it is critical to monitor the operating states and detect those attacks at the early stage. However, anomaly detection and diagnosis of attacks are still challenging, especially when labeled anomaly data is difficult or even infeasible to obtain. To overcome this problem, we propose a Few-Shot Learning (FSL) based approach for cyber-attack diagnosis leveraging the waveform data. To the best of our knowledge, this work is the first attempt at leveraging FSL for cyber-attack diagnosis in power electronics systems. Extensive experimental results demonstrate that our proposed approach can achieve comparable diagnosis accuracy with the state-of-the-art data-driven methods using less than 0.04% of the training samples.

Li, Qi↗
DOE OSTI2022

Data-driven cyber-attack detection for photovoltaic systems: A transfer learning approach

With increasing exposure to software-based sensing and control, power systems are facing higher risks of cyber/physical attacks. Here, to ensure system stability and minimize the potential economic losses, it is imperative to monitor the operating states and detect those attacks at the early stage. In this paper, a transfer learning method is proposed to detect cyber-attacks in photovoltaic (PV) systems with much less training data. First of all, two PV systems with a different number of PV inverters and power ratings are analyzed and their attack models are studied. Next, an attack detection Convolutional Neural Network (CNN) model was trained with rich amount of data from PV #1. Then, transfer learning was proposed to transfer the well-trained features from PV #1 to PV #2. Lastly, the attack detection model on PV #2 was trained based on the transferred CNN model. The experiment results show that the proposed transfer learning method achieves better accuracy and a faster convergence rate with a much less training dataset than conventional deep learning.

14 SOLAR ENERGY↗
DOE OSTI2022

An underground, wireless, open-source, low-cost system for monitoring oxygen, temperature, and soil moisture

Abstract. The use of wireless sensor networks to measure soil parameters eliminates the need to remove sensors for field operations, such as tillage, thus allowing long-term measurements without multiple disturbances to soil structure. Wireless sensors also reduce above-ground cables and the risk of undesired equipment damage and potential data loss. However, implementing wireless sensor networks in field studies usually requires advanced and costly engineering knowledge. This study presents a new underground, wireless, open-source, low-cost system for monitoring soil oxygen, temperature, and soil moisture. The process of system design, assembly, programming, deployment, and power management is presented. The system can be left underground for several years without the need to change the battery. Emphasis was given on modularity so that it can be easily duplicated or changed if needed and deployed without previous engineering knowledge. Data from this type of system have a wide range of applications, including precision agriculture and high-resolution modelling.

54 ENVIRONMENTAL SCIENCES↗