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At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Secured fault detection in a power substation

Systems and methods for fault detection and protection in electric power systems that evaluates electromagnetic transients caused by faults. A fault can be detected using sampled data from a first monitored point in the power system. Detection of fault transients and associated characteristics, including transient direction, can also be extracted through evaluation of sample data from other monitored points in the power system. A monitoring device can evaluate whether to trip a switching device in response to the detection of the fault and based on confirmation of an indication of detection of fault transients at the other monitored points of the power system. The determination of whether to trip or activate the switching device can also be based on other factors, including the timing of receipt of an indication of the detection of the fault transients and/or an evaluation of the characteristics of the detected transients.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Secured fault detection in a power substation

Cui, Tao↗

DOE OSTI2023

On the Investigation of Phase Fault Classification in Power Grid Signals: A Case Study for Support Vector Machines, Decision Tree and Random Forest

In monitoring the power grid, an ability to differentiate between fault types is essential to ensuring electrical safety. Accordingly, this study introduces a fault detection and classification method by considering different machine learning (ML) and feature extraction (FE) methods combinations. Specifically, the proposed method is established in two classification layers; the first layer determines the fault, and the second layer distinguishes the type of fault. Based on the proposed system model, this study seeks to determine the influential data attributes in a power grid signal using FE methods, including fast Fourier transform, power spectral density (PSD), auto-correlation, and wavelet transform (WT). A cross-comparison of the effectiveness of the Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) is also performed to accomplish the classification layers of the proposed method. The designed algorithm is analyzed under the various combinations of FE and ML methods, and outcomes are presented by considering the trade-off between computational complexity and prediction accuracy. The results reveal that the RF-based ML algorithm shows the most accurate classification performance with PSD, and the most time-saving of the models is the DT WT. Also, SVM emerges superior on a subsequent test of the simulated models on real-world signals.

Galbraith, Kelli↗

NASA NTRS1990

Functional requirements for an intelligent RPC

An intelligent remote power controller (RPC) based on microcomputer technology can implement advanced functions for the accurate and secure detection of all types of faults on a spaceborne electrical distribution system. The intelligent RPC will implement conventional protection functions such as overcurrent, under-voltage, and ground fault protection. Advanced functions for the detection of soft faults, which cannot presently be detected, can also be implemented. Adaptive overcurrent protection changes overcurrent settings based on connected load. Incipient and high-impedance fault detection provides early detection of arcing conditions to prevent fires, and to clear and reconfigure circuits before soft faults progress to a hard-fault condition. Power electronics techniques can be used to implement fault current limiting to prevent voltage dips during hard faults. It is concluded that these techniques will enhance the overall safety and reliability of the distribution system.

Aucoin, B. M.↗

DOE OSTI2024

Rotor Magnet Fault Analysis in Permanent Magnet AC Machines Under Load Conditions - All Electric Transportation Systems

This paper studies the performance of a Permanent Magnet (PM) alternating-current (AC) machine when subjected to faults under load conditions. The effect of varying load torques, and unbalances are found to be potential causes of rotor faults, inducing physical magnet defects or broken rotor-PMs. The consequent and immediate impact on the machine quantities is observed through anomalous change in torque-speed characteristics and other vital signatures such as machine back-EMF/flux and motor current signatures (MCS). The present research study develops and illustrates a method to diagnose physical magnet defects (fault) in PMAC machines by estimating the machine torque/back-EMF constant, Ke. The constant Ke is a measure of magnet strength exhibiting the health of rotor PMs, indicative of faults. Advanced research studies and investigations are carried out to establish motor approximations and signature-based analysis as significant viable tools for diagnosing machine faults even under load conditions.

alternating current↗

DOE OSTI2021

Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units

An end-to-end supervised learning method is proposed for fault detection in the electric grid using Big Data from multiple Phasor Measurement Units (PMUs). The approach consists of preprocessing steps aimed at reducing data noise and dimensionality, followed by utilization of six classification models considered for detecting faults. Three of the models were variants of Convolutional Neural Network (CNN) architectures that consider a single type of measurement (voltage, current or frequency) at all PMUs or all types together also at all PMUs. CNN based models were compared to traditional methods of Logistic Regression (LR), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Evaluation was conducted on two-year data measured by PMUs at 37 locations in a large electric grid. Here, the response variable for classification were extracted from the grid-wide outage event log. Experiments show that CNN-based models outperformed traditional methods on one year out-of-sample outage detection over the entire grid.

42 ENGINEERING↗

DOE OSTI2021

Report on High Energy Arcing Fault Experiments

This report documents an experimental program designed to investigate High Energy Arcing Fault (HEAF) phenomena for medium voltage electrical switchgear containing aluminum conductors. This report covers full-scale laboratory experiments using representative nuclear power plant (NPP) three-phase electrical equipment. Electrical, thermal, and pressure data were recorded for each experiment and documented in this report. This report covers four of the fourteen planned medium voltage electrical enclosure experiments. Subsequent reports will document the additional experiments performed in the future. The experiments were performed at KEMA Labs located in Chalfont, Pennsylvania. The experimental design, setup, and execution were completed by staff from the United States Nuclear Regulatory Commission (NRC), the National Institute of Standards and Technology (NIST), Sandia National Laboratories (SNL) and KEMA. In addition, representatives from the Electric Power Research Institute (EPRI) observed some of the experimental setups and execution. The HEAF experiments were performed on four near-identical units of General Electric metal-clad medium voltage switchgear. The three-phase arcing fault was initiated on the primary cable connection bus. All four experiments used the same system voltage (6.9 kV) but varied the current and duration. Real-time electrical operating conditions, including voltage, current and frequency, were measured during the experiments. Heat fluxes and incident energies were measured with plate thermometers and slug calorimeters at various locations around the electrical enclosures. Internal enclosure pressures were measured during the experiments. The experiments were documented with normal and high-speed videography, infrared imaging, and photography. Insights from the experimental series included timing information related to enclosure breach, event progression, mass loss measurements for electrodes and steel enclosures, peak pressure rise, particle analysis, along with visual and thermal imaging data to better understand and characterize the hazard. These results will be used in subsequent efforts to advance the state of knowledge related to HEAF.

42 ENGINEERING↗

DOE OSTI2023

Overview of Potential Hazards in Electric Aircraft Charging Infrastructure

With increasing efforts in electrification of Advanced Air Mobility and electric aircraft, there is a growing need to install new infrastructure to support them. This report highlights potential hazards (non exhaustive) that the installation must be aware of and be prepared to mitigate with increased electrical equipment on site. The hazards can be due to natural causes as well as non natural causes. The report aims to provide starting guidelines for awareness of these hazards and to help plan for mitigation of the same.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1991

An autonomous fault detection, isolation, and recovery system for a 20-kHz electric power distribution test bed

Future space explorations will require long term human presence in space. Space environments that provide working and living quarters for manned missions are becoming increasingly larger and more sophisticated. Monitor and control of the space environment subsystems by expert system software, which emulate human reasoning processes, could maintain the health of the subsystems and help reduce the human workload. The autonomous power expert (APEX) system was developed to emulate a human expert's reasoning processes used to diagnose fault conditions in the domain of space power distribution. APEX is a fault detection, isolation, and recovery (FDIR) system, capable of autonomous monitoring and control of the power distribution system. APEX consists of a knowledge base, a data base, an inference engine, and various support and interface software. APEX provides the user with an easy-to-use interactive interface. When a fault is detected, APEX will inform the user of the detection. The user can direct APEX to isolate the probable cause of the fault. Once a fault has been isolated, the user can ask APEX to justify its fault isolation and to recommend actions to correct the fault. APEX implementation and capabilities are discussed.

Quinn, Todd M.↗

NASA NTRS2018

Electrical Engineering Medium Voltage Protection and Coordination

This presentation covers the basics of the medium voltage electrical system on the Kennedy Space Center. The material will include the process of selective coordination between protective devices and the analysis of a fault that occurred in the electrical system. I will discuss the most common causes of faults found and how the system reacts to the faults at different locations in the subsystem. Explain the importance of the sensitivity and coordination between the devices to protect the equipment it is supplying.

Wessner, Jason R.↗

NASA NTRS1978

The impact of defects on the photovoltaic potential of RTR silicon ribbon

Silicon ribbon grown at a high rate by the ribbon-to-ribbon (RTR) method contains a variety of defects and can exhibit an unusual dendritic morphology. This paper is a study of the effect of the defect structure and dendritic morphology on the photovoltaic potential of RTR ribbon. Defects found in RTR ribbon include grain boundaries, twin planes, stacking faults and dislocations. The electrical activity of these defects is investigated by measuring the diffusion length in the ribbon; by fabricating small area diodes on the ribbon and evaluating their photoresponse; and by using a scanning electron microscope in the electron beam induced current mode. It is concluded that although grain boundaries serve very effectively as recombination centers, they do not occur frequently enough in RTR ribbons to significantly affect the generation current. Dislocation densities up to 1,000,000/sq cm can be tolerated without strongly reducing the photoresponse. Similarly, linear boundaries such as twin planes or stacking faults do not affect the cell performance up to densities of 1000/sq cm.

Baghdadi, A.↗

DOE OSTI2024

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

Abstract A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Blockchain for Fault-Tolerant Grid Operations Version 2.0

This report explores the potential of distributed ledger technology (DLT) as a transformative tool to enhance fault-tolerant operations in electrical distribution systems. Leveraging DLT's core attributes, including an immutable decentralized ledger, distributed consensus mechanisms, and state replication capabilities, this study focuses on three critical use cases. A central aspect of this research centers on the utilization of a consensus-driven ledger, providing actors within the system, such as distributed resources, with access to a reliable data repository. This empowers these actors to collaborate effectively and make informed decisions, all securely recorded on the blockchain. The first use case concentrates on data configuration, utilizing mathematical criteria---particularly, the chi-squared test for gross error detection---to identify trustworthy sensors for advanced decision-making. Building upon this foundation of trust, the second use case, topology identification, accurately determines circuit breaker states, unveiling the distribution network's topology. Ultimately, the third use case leverages this trust to execute switching actions, reconfiguring feeders and restoring power to disconnected customers after fault events. The concept of trust serves as a cornerstone in this approach, marking a departure from traditional fault location, isolation, and service restoration (FLISR) methods. Additionally, the blockchain-based architecture introduces decentralization, empowering disconnected areas to make autonomous decisions, even when communication with a central control center is disrupted. The primary contributions of this report are twofold: (1) a novel approach for evaluating distribution system voltage areas while preserving data ownership and (2) the implementation of interactions between distribution network areas using the actor model. Unlike the previous sequential approach for evaluating the area connection voltages, which required a radial network topology, this study's area model reduction enables a more versatile approach. The area model reduction addresses issues of prolonged data waiting times and multiple points of failure within the previous approach. Notably, the presented evaluation for the reduced network model area connection reveals a significant increase in the differences in voltage magnitudes. Simulation and evaluation of area agents across four distinct cases elucidate the area-level interaction behavior during a fault event. Simulations demonstrate that the proposed distributed FLISR (DFLISR) approach can successfully restore service to an affected area. Varying message delays and message loss probabilities in each simulation case underscore their impacts on restoration times, ranging from 3 min and 32 s to 6 min and 19 s. In contrast, power is not restored in an area in one of our simulation cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2013

Detectors Ensure Function, Safety of Aircraft Wiring

Pedro Medelius waited patiently in his lab at Kennedy Space Center. He had just received word that a colleague was bringing over a cable from a Space Shuttle solid rocket booster to test Medelius new invention. Medelius was calm until his colleague arrived, with about 30 other people. "Talk about testing under pressure," says Medelius. "There were people there from the Navy, the Air Force, and the Federal Aviation Administration." After the group s arrival, Medelius took a deep breath and connected his Standing Wave Reflectometer (SWR) to the cable. He wiggled the cable around, and the display showed a fault (a short or open circuit in wire) about an inch and a half inside the connector on the cable. His colleague questioned the results, because he had already checked that area on the cable. Medelius used the SWR to check again but got the same result. "That is when we took the cable apart and looked inside," Medelius says. "Lo and behold, that was exactly where the fault was." The impetus for Medelius new wire inspection technology came about in 1999 when one of the space shuttles lost power due to a fault somewhere in its more than 200 miles of electrical wiring. "The backup circuit was activated and prevented a major dysfunction, but nevertheless, there was a problem with the wiring," Medelius describes. Even though technicians used a device called a multimeter to measure the electrical current to find which wire had a fault, it could not pinpoint exactly where on the wire the fault was located. For that, technicians had to visually inspect the wire. "Sometimes they would have to remove the whole wire assembly and visually inspect every single wire. It was a very tedious operation because the wires are behind cabinets. They go all over the place in the shuttle," says Medelius. "NASA needed an instrument capable of telling them exactly where the faults were occurring." To meet NASA s needs for a highly precise device to inspect electrical power bundles, wires, and connectors, Medelius devised the SWR. "It came down to what was affected when a wire is short circuited or opened," he says. "We worked out a few equations based on physical principles." The SWR proved very sensitive, and the technology was patented.

Source record↗

DOE OSTI2020

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Here, we present a novel online system identification methodology for monitoring the performance of power systems. This methodology was demonstrated in a gas turbine recuperated power plant designed for a hybrid configuration. A 120-kW Garrett microturbine modified to test dynamic control strategies for hybrid power systems designed at the National Energy Technology Laboratory (NETL) was used to implement and validate this online system identification methodology. The main component of this methodology consists of an empirical transfer function model implemented in parallel to the turbine speed operation and the fuel control valve, which can monitor the process response of the gas turbine system while it is operating. During fully closed-loop operations or automated control, the output of the controller, fuel valve position, and the turbine speed measurements were fed for a given period of time to a recursive algorithm that determined the transfer function parameters during the nominal condition. After the new parameters were calculated, they were fed into the transfer function model for online prediction. The turbine speed measurement was compared against the transfer function prediction, and a control logic was implemented to capture when the system operated at nominal or abnormal conditions. To validate the ability to detect abnormal conditions during dynamic operations, drifting in the performance of the gas turbine system was evaluated. A leak in the turbomachinery working fluid was emulated by bleeding 10% of the airflow from the compressor discharge to the atmosphere, and electrical load steps were performed before and after the leak. This tool could detect the leak 7 s after it had occurred, which accounted for a fuel flow increase of approximately 15.8% to maintain the same load and constant turbine speed operations.

algorithms↗

DOE OSTI2021

A SSTDR Methodology, Implementations, and Challenges

Sequence time-domain reflectometry (STDR) and spread spectrum time-domain reflectometry (SSTDR) detect, locate, and diagnose faults in live (energized) electrical systems. In this paper, we survey the present SSTDR literature for discussions on theory, algorithms used in its analysis, and its more prominent implementations and applications. Our review includes both scientific litera-ture and selected patents. We also discuss future applications of SSTDR.

14 SOLAR ENERGY↗

NASA NTRS1972

Atmosphere revitalization in the space station prototype.

The atmosphere revitalization group (ARG) for the space station prototype (SSP) environmental/thermal control and life support system (ETC/LSS) has been designed to meet a comprehensive set of requirements and constraints for a flight type mission. Design definition is for a modular vehicle temperature and humidity control subsystem, contaminant control equipment, a hydrogen depolarized cell CO2 concentrator, a water electrolysis oxygen generation subsystem, CO2 reduction equipment, and a pressure and composition control subsystem. Performance requirements are for support of a crew of six. Realistic leakage rates and metabolic activity cycles, cabin temperature selectability, and controlled cabin relative humidities are specified for the design. Operational aspects of the subsystem are discussed with emphasis given to the influence of a 180-day, maintainable requirement on system design. Consideration of flight vehicle type packaging, in flight maintenance, data management systems interface and fault detection capability, and electrical supply constraints is covered.

Trusch, R. B.↗