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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A physics‐informed learning technique for fault location of DC microgrids using traveling waves

Abstract Fast and accurate fault location in DC power systems is of particular importance to ensure their reliable operation. One of the approaches for implementing a fast‐tripping protection scheme is to use Traveling waves (TW) initiated by a fault scenario. This paper proposes a physics‐informed machine learning approach that utilizes TWs for fault location in DC microgrids. TWs are extracted by the so‐called multiresolution analysis which identifies the TW's wavelet coefficients for multiple frequency ranges. This paper deploys Parseval's theorem to find the energy of wavelet coefficients as a quantitative metric for describing TWs. The hypothesis of this paper is that once the Parseval energy curves for a specific cable are extracted, they can be utilized to locate faults along with that cable regardless of the DC system in which the cable is deployed. The fault location algorithm uses Parseval energy curves to train a Gaussian Process (GP) estimator. With the Parseval energy values of measured current at the protection device location, the GP estimator is able to estimate fault locations with high accuracy. The effectiveness of the proposed algorithm is verified by simulating a DC microgrid system in PSCAD/EMTDC.

Paruthiyil, Sajay Krishnan↗
DOE OSTI2020

Fault-tolerant grid frequency measurement algorithm during transients

Many critical electric grid operations rely on accurate grid frequency measurements. Unfortunately, the measurement accuracy can be easily undermined by power system transient faults. During a power system transient fault, the power grid voltages and currents are usually highly distorted by high-frequency components. What is worse, the power grid signals could have discontinuity during some system transient faults such as phase angle jump, and the discontinuity could result in large measurement errors to state-of-the-art grid measurement algorithms. In this study, a fault-tolerant grid frequency measurement algorithm during transients is proposed. The new algorithm consists of two stages. The first stage is a transient detector, and it can detect the occurrence of system transient faults instantaneously. The second stage is the intelligent frequency estimator, and it will adapt its measurements according to the transient detector. The performance of the algorithm is evaluated under different steady-state and transient conditions. Both dependability and security of the fault-tolerant algorithm are assessed by using PSCAD simulation data and IEEE Standard test data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Open Circuit Switch Fault Management Method of a Multi-Phase Synchronous Buck Converter for EV Charging Application

Multi-phase synchronous buck converter (MSBC) is becoming popular for the electric vehicle (EV) charging application because of its higher efficiency, smaller passive component sizes, bidirectional power flow, and output current ripple reduction through interleaving. This converter also provides higher reliability than the traditional single phase converter since its modular structure enables fault tolerant operation. Fault management in the converter requires the detection of switch fault, shut down of the faulty phase, and reconfiguration of the PWM signals for the healthy phases to avoid circulating current and high output current ripple. In addition, it is desirable that the fault management method can be implemented using the micro-controller unit (MCU) or digital signal processor (DSP), which are widely used to control power converters. This paper presents a fault management method for detecting switch faults and reconfiguring healthy phases using a DSP. The method is validated through simulations and experiments using a commercial off-the-shelf power stack.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Collaborative Fault Tolerant Control of Non-Signalized Intersections for Connected and Autonomous Vehicles

With the potential of increased penetration of connected and autonomous vehicles (CAVs), intersectional signal control faces new challenges in terms of its operation and implementation. One possibility is to fully make use of the communication capabilities of CAVs so that intersectional signal control can be realized by CAVs alone – this leads to non-signalized intersectional operation for traffic networks in urban areas. In this paper, the state-of-the-art on collaborative fault tolerant control schemes for complex systems will be briefly described. This is then followed by the formulation of operational fault tolerant control that realizes the collaborative fault tolerance functionality at CAVs operational level in response to possible individual vehicle faults, where detailed modelling using vehicle movement dynamics will be described together with the construction of fast fault diagnosis and a collaborative fault tolerant control algorithm. A simple example will be given as well to demonstrate the proposed algorithm together with the discussions on other issues such as randomness of the system, communication errors and full energy consideration. These leads to several future directions of the research for the traffic flow control of non-signalized intersections with 100% penetration of CAVs.

Wang, Hong↗
DOE OSTI2023

Fault Clearing Operation in Low-Frequency High-Voltage AC Systems

Abstract—Low-frequency high-voltage ac (LF-HVac) system has been proposed as an alternative to HVdc and HVac systems to reduce transmission losses and improve grid flexibility. LF-HVac system employs back-to-back (BTB) converters to interconnect and control the voltage and power interchange between two power grid operating at different frequencies. While there are multiple studies on LF-HVac system in the literature, the fault clearing method in the LF-HVac system has not been analyzed. Therefore, this paper focuses on fault clearing operation in LFHVac system and proposes a novel method to reduce fault current by changing operating frequency of power source and converter. The performance of the proposed method is validated using detailed models of BTB converters developed in PSCAD/EMTDC. The ability to change operating frequency at the onset of a fault condition allows breakers to rapidly clear the fault and recloses. The results obtained by using the proposed method substantially diminish the fault current.

Kim, Woosung↗
DOE OSTI2021

Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. These HVAC systems in smart buildings rely 'on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learning-based framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.

Xu, Shichao↗
DOE OSTI2023

Dynamic Bayesian Networks for Fault Prognosis

A dynamic Bayesian Network (DBN)-based fault prognosis framework is proposed in this study to predict the future fault probabilities of gradual faults. The proposed framework utilizes the trend in prediction error generated from data driven forecasting models to estimate the future fault beliefs. The accuracy and scalability of the proposed method is evaluated using the data from a Modelica-based virtual testbed. Overall, the developed framework demonstrates good potential in estimating future fault probabilities of gradual faults.

Pradhan, Ojas↗
DOE OSTI2025

ATTNChecker: Highly-Optimized Fault Tolerant Attention for Large Language Model Training

Large Language Models (LLMs) have demonstrated remarkable performance in various natural language processing tasks. However, the training of these models is computationally intensive and susceptible to faults, particularly in the attention mechanism, which is a critical component of transformer-based LLMs. In this paper, we investigate the impact of faults on LLM training, focusing on INF, NaN, and near-INF values in the computation results with systematic fault injection experiments. We observe the propagation patterns of these errors, which can trigger non-trainable states in the model and disrupt training, forcing the procedure to load from checkpoints. To mitigate the impact of these faults, we propose ATTNChecker, the first Algorithm-Based Fault Tolerance (ABFT) technique tailored for the attention mechanism in LLMs. ATTNChecker is designed based on fault propagation patterns of LLM and incorporates performance optimization to adapt to both system reliability and model vulnerability while providing lightweight protection for fast LLM training. Evaluations on four LLMs show that ATTNChecker on average incurs on average 7% overhead on training while detecting and correcting all extreme errors. Compared with the state-of-the-art checkpoint/restore approach, ATTNChecker reduces recovery overhead by up to 49×.

Liang, Yuhang [University of Alabama - Birmingham]↗
DOE OSTI2024

Wasatch Fault Structure from Machine Learning Arrival Times and High-Precision Earthquake Locations

Abstract On 18 March 2020, a magnitude 5.7 earthquake hit the Salt Lake valley in the state of Utah, United States. Using a dense geophone deployment and machine learning (ML), an additional several thousand events were detected and located. Currently, both the mainshock and the majority of the aftershocks are suspected to have occurred on or near a deeper portion of the Salt Lake segment of the Wasatch fault—part of a large range-bounding fault system thought to be capable of generating an Mw 7.2 earthquake. However, a small subset of aftershocks may have occurred on a portion of the more steeply, eastward dipping, and poorly understood West Valley fault. Unfortunately, the catalog locations and lack of focal mechanisms for this subset of aftershocks provide only a crude constraint on the true fault structure. To better illuminate fault structure, we relocate the ML-generated catalog with a range of magnitudes from −2 to 4.6, using: (1) NonLinLoc, a nonlinear location algorithm, (2) source-specific station terms, and (3) waveform coherence. We further compute first-motion focal mechanisms for 68 events. Results of the relocation suggest a simpler, minimally listric Wasatch fault geometry, contrary to what has been previously proposed. We also find that analysis of the focal mechanisms and waveform similarity indicates minimal event similarity throughout the Magna sequence, suggesting a highly complex and heterogeneous rupture zone, as opposed to rupture on a single plane. These findings suggest an increased seismic hazard due to the overall shallowness of the earthquake sequence and highly varied rupture mechanisms.

Geochemistry & Geophysics↗
DOE OSTI2022

Market Barriers and Drivers for the Next Generation Fault Detection and Diagnostic Tools

Commercial buildings in the U.S. consume as much as 30% excess energy compared to buildings that operate fault free and efficiently. Fault detection and diagnostic (FDD) platforms help to continually identify operational inefficiencies and maintain low-carbon performance. However, the recommendations generated by FDD tools need to be implemented by technicians, resulting in delays or lost savings opportunities. Recent research advances showed fault AUTOcorrection integrating with commercial FDD offerings filled this gap. Seven innovative AUTOcorrection algorithms were integrated into two FDD platforms and deployed across four buildings. The enhanced tools successfully correct faults focusing on incorrectly programmed schedules, override not released, control hunting, rogue zone, and suboptimal setpoints. Although its technical efficacy has been proven in the field, fault AUTO-correction is still early in the deployment cycle and opportunities and barriers need to be understood to reach its full potential in market transformation. This paper broadly introduces the new technology that automatically corrects HVAC faults. The authors describe in detail technology potential, market barriers, and enablers for scalability based on field testing results and interviews with the FDD providers and facility managers. The interviewees agreed that AUTO-correction can reduce the extent to which savings are dependent upon human intervention, scale building operators’ ability to act on FDD findings (especially for facilities with small operation teams), and achieve significant savings. To enable scalable deployment, future efforts are needed to overcome the barriers such as cybersecurity and accountability concerns from building operators and standardization of control parameters used in building automation systems.

Pritoni, Marco↗
DOE OSTI2021

Open Source Fault-tolerant Grid Frequency Measurement for Solar Inverters

The Discrete Fourier transform (DFT) based measurement algorithms are one of the most common measurement algorithms for grid parameter estimation such as rms, phase angle, frequency. Over the past few years, many DFT based algorithms have been developed to enhance its measurement accuracy under steady-state and/or dynamic grid conditions. For example, an adaptive band-pass filter utilizing exponential modulation filter has been proposed to reduce measurement errors at the presence of large frequency deviations. Measurement accuracy of different algorithms including FIR filter, extended Kalman filtering (EKF), and enhanced DFT method have been compared in detail under different grid conditions. Two artificial signals that have 90-degree phase difference were constructed by the Clarke transformation to address the frequency spectrum leakage of DFT. A multi-module approach was developed to enhance both steady-state and dynamic measurement accuracies, in which each module was developed to eliminate some specific errors. Besides DFT-based measurement algorithms, some signal model-based algorithms have been developed to further improve the accuracy under dynamic conditions. However, a key drawback of the state-of-the-art algorithms is that they cannot perform measurements accurately during system transient faults. In the Blue Cut Fire event, there was a phase angle jump of about 26 degrees in the voltage waveform during the transient fault. The phase angle jump fault will cause waveform discontinuity, and these algorithms will fail to provide reliable measurements during this period because they typically assume the waveform to be measured is continuous, no matter what method (DFT, PLL, EKF, FIR, or Taylor WLS) is used for estimation. In fact, the measurement errors during the system transient faults like phase-jump is not required in the IEEE Standard. As a result, although a measurement instrument can pass the strict IEEE Standard, it could still be the source of the problem in the future if we have similar system transient faults, which could happen again. Therefore, developing the fault-tolerant measurement technology is the key to solve the problem.

14 SOLAR ENERGY↗
DOE OSTI2022

SRF Cavity Fault Classification and Prediction at Jefferson Lab

Over the last few years several machine learning projects at Jefferson Lab have had a common focus to optimize operation of superconducting RF (SRF) cavities in the Continuous Electron Beam Accelerator Facility (CEBAF). In this talk we highlight work to identify and classify types of faults from C100-type cavities and then to extend those capabilities to provide real-time fault prediction. Early prediction may enable mitigation strategies to prevent some types of faults. In our approach we apply a two-step fault prediction pipeline. In the first step, a model distinguishes between faulty and normal signals. In the second step, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. In additional to model performance, we also highlight challenges in working with real-world data and challenges for deploying models.

Tennant, C.↗
DOE OSTI2023

Identification of Faults Susceptible to Induced Seismicity Final Report

The primary objective of this project (referred to as the State of Stress project or SoS project) was to predict the presence of faults susceptible to movement in the presence of fluid injection as a consequence of vertical pressure migration from the storage unit to the crystalline basement. These faults included those difficult to resolve with traditional interpretations of surface seismic: faults with no or minimal offset, faults in the crystalline basement where no seismic reflectors are present, subseismic faults (due to size), and faults undetected (e.g., due to unfavorable orientation with surface seismic survey).

25 ENERGY STORAGE↗
DOE OSTI2024

Constraining induced seismicity hazard on the Neck of the Desert fault for the proposed Iron Mountain, Utah carbon sequestration project

As significantly more focus is placed on carbon sequestration as a means to help mitigate the progression of climate change, the need to adequately characterize an injection target for its induced seismicity potential becomes more apparent. In the Iron Mountain project in Southern Utah, a single fault is identified in the Neck of the Desert near Iron Mountain. The dip of the fault is unknown, and the geomechanical stability of the fault (i.e., distribution of Coulomb failure functions (CFFs)) is highly dependent upon the dip. For some dips, such as 15 degrees, the hazard from injection is low. For other dips, such as 75 degrees, the hazard is high. Another key consideration is the depth of penetration of the fault. Should the fault not penetrate the stress and pressure perturbed region from injection, it will not be induced to fail. Thus, understanding the dip and the depth of penetration of the fault will greatly improve our understanding of the likelihood of inducing an earthquake.

58 GEOSCIENCES↗
DOE OSTI2024

An Advanced Synchronized Time Digital Grid Twin Testbed for Relay Misoperation Analysis of Electrical Fault Type Detection Algorithms

Distributed energy resources and the number of relays are expected to rise in modern electrical grids; consequently, relay misoperations are also expected to grow. Relays can detect electrical fault types using an internal algorithm and can display the result using light indicators on the front of the relay. However, some relays’ internal algorithms for predicting types of electrical faults could be improved. This study assesses a relay’s external and internal algorithms with an Advanced Synchronized Time Digital Grid Twin (ASTDGT) testbed with paired relays. A misoperation relay analysis focused on measuring the accuracy of using the boundary admittance (the external algorithm) versus the set-default (the internal algorithm) relay method to determine the electrical fault types was performed. In this study, the internal and external relay algorithms were assessed with a synchronized time digital grid twin testbed using a real-time simulator. This testbed evaluated two sets of logic at the same time with the digital grid twin and paired relays in the loop. Different types of electrical faults were simulated, and the relays’ recorded events and electrical fault light indicator states were collected from the human–machine interfaces. This ASTDGT testbed with paired relays successfully evaluated the relay algorithm misoperations. The boundary admittance method had an accuracy of 100% for line-to-line, line-to-ground, and line-to-line ground faults.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1973

Seismicity and recent faulting in eastern California and western and central Nevada: A preliminary report

The author has identified the following significant results. ERTS-1 imagery covering the eastern California-Nevada seismic belt were utilized to study the fault pattern in relation to the distribution of earthquake epicenters and Quaternary volcanic rocks. Many suspected faults not previously mapped were identified. These include several suspected shear zones in Nevada, faults showing evidence of recent breakage, and major lineaments. Highly seismic areas are generally characterized by Holocene faulting and Quaternary volcanic activity. However, several major fault segments showing evidence of recent breakage are associated with little or no seismicity. The tectonic pattern strongly suggests that the eastern California-Nevada seismic belt coincides with a major crustal rift associated with zones of lateral shear. New data on potentially active fault zones have direct practical applications in national and local earthquake hazard reduction programs. Positive contacts have been made with Kern and Ventura Counties to make results of this investigation available for application to their earthquake hazards definition projects.

Abdel-Gawad, M.↗
NASA NTRS1973

Evaluation of feasibility of mapping seismically active faults in Alaska

The author has identified the following significant results. The sharp bend in the Alaska Range near 65 deg N, 150 deg W in now thought to enclose a corner of the northwesterly migrating north Pacific lithospheric plate. Subduction of the plate beneath the continent is believed, on the basis of hypocentral distribution, to occur along Cook Inlet and the eastern flanks of the Aleutian and Alaska Ranges as far northward as Mt. McKinley. The nature of tectonic deformation here, particularly in the area of the bend in the Alaska Range, is understandably complex. The Denali fault is thought to be a transform character in the vicinity of Mt. McKinley (i.e., it is thought to be the surface along which the oceanic plate separates from the continental plate). On the ERTS-1 imagery, however, it appears that there are a number of sub-parallel faults which branch off of the Denali fault in a southwesterly direction. Slippage along these would tend to squeeze material around the inside of the band rather than the plate being directly underthrust. All of these sub-parallel faults are seismically active. The right-lateral fault-plane solution obtained for this event is consistent with the concept of slippage around the bend on a set of sub-parallel faults in the manner postulated. The best images to show these features are 1066-20444 and 1266-20572.

Gedney, L. D.↗
NASA NTRS1975

Modeling transient faults in TMR computer systems

In this paper we report the development of a technique for modeling transient faults in redundant computer systems. Transient faults are characterized by their arrival rate and their duration. Fault detection, transient recovery, and the effect of permanent faults are included. A fault occurrence/recovery status state diagram is drawn to illustrate the operational status of the system while undergoing faults. The state diagram is used to formulate the equations for the mission failure probability. The techniques are then applied to a triple modular redundant computer system.

Merryman, P. M.↗