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DOE OSTI2023

Assessment of the Phase-to-Ground Fault Apparent Admittance Method with Phase/Ground Boundaries to Detect Types of Electrical Faults for Protective Relays Using Signature Library and Simulated Events

Protective relays in electric power grids recognize the types of electrical faults in a few seconds. The most common detection method to detect the types of electrical faults is based on measuring the angle between the zero and negative sequence currents. However, it is not completely accurate because the phase to phase ground and phase to ground electrical faults could have the same detection conditions. Therefore, engineers need to plot the events after an electrical fault to observe the nature of the incidents in detail. In this study, the phase to ground fault apparent (PGFA) admittance method with phase/ground boundaries identified the types of electrical faults located in distribution power lines and feeders. This method was based on measuring the PGFA admittance magnitudes for the faulted and non-faulted phases, resulting in greater than zero and near zero, respectively. The PGFA admittance algorithm was built with MATLAB/ Simulink software and tested with signature library and grid simulation events. The PGFA method with phase/ground boundaries was evaluated with the confusion matrix. The measured and predicted values matched in more than 90% of the tests, and the PGFA admittance method with phase/ground boundaries presented an accuracy of 94.3% and a precision of 100%.

Piesciorovsky, Emilio↗

DOE OSTI2022

Assessment of the Phase-to-Ground Fault Apparent Admittance Method with Phase/Ground Boundaries to Detect Types of Electrical Faults for Protective Relays Using Signature Library and Simulated Events

Protective relays in electric power grids recognize the types of electrical faults in a few seconds. The most common detection method to detect the types of electrical faults is based on measuring the angle between the zero and negative sequence currents. However, it is not completely accurate because the phase-to-phase-ground and phase-to-ground electrical faults could have the same detection conditions. Therefore, engineers need to plot the events after an electrical fault to observe the nature of the incidents in detail. In this study, the phase-to-ground fault apparent (PGFA) admittance method with phase/ground boundaries identified the types of electrical faults located in distribution power lines and feeders. This method was based on measuring the PGFA admittance magnitudes for the faulted and nonfaulted phases, resulting in greater than zero and near zero, respectively. The PGFA admittance algorithm was built with MATLAB/Simulink software and tested with signature library and grid simulation events. The PGFA method with phase/ground boundaries was evaluated with the confusion matrix. The measured and predicted values matched in more than 90% of the tests, and the PGFA admittance method with phase/ground boundaries presented an accuracy of 94.3% and a precision of 100%.

42 ENGINEERING↗

DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗

DOE OSTI2022

A Novel Fault Diagnosis Approach for Rolling Bearing Based on CWT and Adaptive Sparse Representation

Extraction and enhancement of weak impulse signature is the key of rolling bearing fault prognostics in which case the features are often weak and covered by noise. Tunable Q-factor wavelet transform (TQWT), as an emerging wavelet construction theory developed in a frequency domain explicitly, has the advantages of matching with the specific oscillation behavior of signal components. In this article, an adaptive sparse representation (ASR) method is proposed, which integrates the sparse code shrinkage (SCS) and parameter optimization into TQWT. However, direct application of ASR is difficult to extract fault signatures at the early stage or low-speed operation due to weak fault symptoms and background noise. A novel fault diagnosis strategy based on continuous wavelet transform (CWT) and ASR is investigated. CWT owns significant advantages on multiscale subdivision and weak signal detection. The results of simulated and experimental vibration signal analyses verify the effectiveness of the proposed method in accurately extracting weak impulse features from the noise environment.

Yuan, Xing↗

DOE OSTI2024

Seismic Features Predict Ground Motions During Repeating Caldera Collapse Sequence

Abstract Applying machine learning to continuous acoustic emissions, signals previously deemed noise, from laboratory faults and slowly slipping subduction‐zone faults, demonstrates hidden signatures are emitted that describe physical details, including fault displacement and friction. However, no evidence currently exists to demonstrate that similar hidden signals occur during seismogenic stick‐slip on earthquake faults—the damaging earthquakes of most societal interest. We show that continuous seismic emissions emitted during the 2018 multi‐month caldera collapse sequence at the Kı̄lauea volcano in Hawai'i contain hidden signatures characterizing the earthquake cycle. Multi‐spectral data features extracted from 30 s intervals of the continuous seismic emission are used to train a gradient boosted tree regression model to predict the GNSS‐derived contemporaneous surface displacement and time‐to‐failure of the upcoming collapse event. This striking result suggests that at least some faults emit such signals and provide a potential path to characterizing the instantaneous and future behavior of earthquake faults.

58 GEOSCIENCES↗

DOE OSTI2025

Automatic speech recognition predicts contemporaneous earthquake fault displacement

Abstract Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec-2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kīlauea volcano on the island of Hawai’i. We pre-train the Wav2Vec-2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligence models developed for encoding audio of speech may have important applications in studying active fault zones.

58 GEOSCIENCES↗

DOE OSTI2023

Induced microseismicity and tremor signatures illuminate different slip behaviours in a natural shale fault reactivated by a fluid pressure stimulation (Mont Terri)

SUMMARY Fault slip induced by fluid perturbation in shale formations may only lead to a sparse seismicity. However, fault slip may strongly impact the integrity of shale formations that serve as caprocks for geological reservoirs holding buoyant fluids such as CO2, natural gas or hydrogen. A better understanding of the fluid reactivation processes of fault and the seismic triggering process is therefore critical for reservoir monitoring and fault stability. Here we analyse the seismic responses of a shale fault exposed to fluid pressurization during an in situ field-scale injection experiment at ∼340 m depth in the Mont Terri underground research laboratory (Switzerland). Two main types of seismic signals are observed as the fault was activated and started to slowly slip. After an aseismic phase, we observed tremor signatures and an increase in noise amplitude, which were directly associated with the slowly propagating fault slip in response to fluid injection. These signatures were later followed by micro-earthquakes that seem to occur further away from the fluid-pressurized area. We interpret these micro-earthquakes to be triggered by stress perturbations from the main slip growth. These two classes of seismic responses therefore highlight two different processes. Tremors seem to be a more direct observation for the fluid-induced slip propagation than micro-earthquakes. Even hidden in the noise, they precede earthquake failures, thus providing a useful tool for monitoring fluid leakage activated by slow deformation on low permeable shale faults, with applications for sealing integrity of caprocks.

De Barros, Louis (ORCID:0000000255419162)↗

DOE OSTI2021

Fault location in High Voltage Multi-terminal dc Networks Using Ensemble Learning

Precise location of faults for large distance power transmission networks is essential for faster repair and restoration process. High Voltage direct current (HVdc) networks using modular multi-level converter (MMC) technology has found its prominence for interconnected multi-terminal networks. This allows for large distance bulk power transmission at lower costs. However, they cope with the challenge of dc faults. Fast and efficient methods to isolate the network under dc faults have been widely studied and investigated. After successful isolation, it is essential to precisely locate the fault. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. In this paper, we discuss a novel data-driven ensemble learning based approach for accurate fault location. Here we utilize the eXtreme Gradient Boosting (XGB) method for accurate fault location. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Detecting faults in wind turbines

A wind turbine generator fault detection method is described. The method includes obtaining a first signal from a generator of a wind turbine and a second signal from a vibration sensor coupled to the wind turbine, the first signal representing an output current of the generator, and the second signal being a time-sampled signal representing vibrations of a bearing in the wind turbine. Determining a shaft rotation frequency signal from the first signal, the shaft rotation frequency signal representing a time-varying rotational speed of a shaft of the wind turbine. Resampling an envelope of the second signal based on the shaft rotation frequency signal to provide a third signal, the third signal being an angular sampled signal. Detecting, by the at least one processor, a fault in the bearing of the wind turbine by identifying a characteristic signature of a bearing fault in the third signal.

Qiao, Wei↗

DOE OSTI2022

Detecting faults in wind turbines

Qiao, Wei↗

DOE OSTI2021

Use of Traveling Wave Signatures in Medium-Voltage Distribution Systems for Fault Detection and Location

Protection systems are a critical feature for the proper operation of a power grid. Protection systems should be able to quickly detect, identify, locate, and isolate the faults within a time frame that prevents damage to grid components and reduces customer outages. As more inverter-based photovoltaics (PV) are integrated into the grid, phasor domain based protection schemes become more inaccurate due to low levels of fault current from inverters or respond too slowly to accurately identify fault conditions. This points to the need to identify and develop novel protection approaches for distribution systems with high penetration of inverter based PV. The main objective of the project is to develop new time domain-based protection schemes to enable more accurate and faster detection, identification and location of faults in distribution systems and/or microgrids with high penetration of inverter-based PV. Our approach aims to exploit the traveling waves that are generated during a fault or other disturbances in the grid.

14 SOLAR ENERGY↗

DOE OSTI2021

Signature of transition to supershear rupture speed in the coseismic off-fault damage zone

Most earthquake ruptures propagate at speeds below the shear wave velocity within the crust, but in some rare cases, ruptures reach supershear speeds. The physics underlying the transition of natural subshear earthquakes to supershear ones is currently not fully understood. Most observational studies of supershear earthquakes have focused on determining which fault segments sustain fully grown supershear ruptures. Experimentally cross-validated numerical models have identified some of the key ingredients required to trigger a transition to supershear speed. However, the conditions for such a transition in nature are still unclear, including the precise location of this transition. In this work, we provide theoretical and numerical insights to identify the precise location of such a transition in nature. We use fracture mechanics arguments with multiple numerical models to identify the signature of supershear transition in coseismic off-fault damage. Furthermore, we then cross-validate this signature with high-resolution observations of fault zone width and early aftershock distributions. We confirm that the location of the transition from subshear to supershear speed is characterized by a decrease in the width of the coseismic off-fault damage zone. We thus help refine the precise location of such a transition for natural supershear earthquakes.

42 ENGINEERING↗

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 OSTI2024

Five Year Wildfire Risk Reduction Action Plan for the Electric Power Industry

The U.S. DOE and the Electric Power Research Institute convened a wildfire advisory group to bring together knowledge of ignition risks, electric utility needs, the applicability of available technology, and existing technology gaps. The outcome is a five-year action plan, containing recommendations on RD&D projects, that if completed by the year 2030, may accelerate the power industry’s ability to substantially reduce wildfire ignition risks. When reviewing this document please consider it is a snapshot in time for the years 2023 and 2024, could be obsolete by the end of the decade. The following list comprises the titles and topics for the proposed follow-on demonstrations: 1. Hybrid Undergrounding RD&D, 2. Live Downed Conductor Detection RD&D, 3. Fault Energy Reduction RD&D, 4. Advanced Inspection and Response Drone, 5. Fault and PQ Event Signature Repository, 6. Advanced and Intelligent Sensor Nodes, 7. Fire Friendly Asset Coatings and Coverings, and 8. Environmental Monitoring Action Plan.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Imaging the crust and uppermost mantle structure of Portugal (West Iberia) with seismic ambient noise

SUMMARY We present a new high-resolution 3-D shear wave velocity (Vs) model of the crust and uppermost mantle beneath Portugal, inferred from ambient seismic noise tomography. We use broad-band seismic data from a dense temporary deployment covering the entire Portuguese mainland between 2010 and 2012 in the scope of the WILAS project. Vertical component data are processed using phase correlation and phase weighted stack to obtain empirical Green functions (EGFs) for 2016 station pairs. Further, we use a random sampling and subset stacking strategy to measure robust Rayleigh-wave group velocities in the period range 7–30 s and associated uncertainties. The tomographic inversion is performed in two steps: First, we determine group-velocity lateral variations for each period. Next, we invert them at each grid point using a new trans-dimensional inversion scheme to obtain the 3-D shear wave velocity model. The final 3-D model extends from the upper crust (5 km) down to the uppermost mantle (60 km) and has a lateral resolution of ∼50 km. In the upper and middle crusts, the Vs anomaly pattern matches the tectonic units of the Variscan Massif and Alpine basins. The transition between the Lusitanian Basin and the Ossa Morena Zone is marked by a contrast between moderate- and high-velocity anomalies, in addition to two arched earthquake lineations. Some faults, namely, the Manteigas–Vilariça–Bragança fault and the Porto–Tomar–Ferreira do Alentejo fault, have a clear signature from the upper crust down to the uppermost mantle (60 km). Our 3-D shear wave velocity model offers new insights into the continuation of the main tectonic units at depth and contributes to better understanding the seismicity of Portugal.

Silveira, Graça (ORCID:0000000221102554)↗

DOE OSTI2023

Condition monitoring of permanent magnet AC machines for all-electric transportation systems: State of the art

The current state of the art on emerging and efficient techniques for condition monitoring of permanent magnet (PM) alternating-current (AC) machines deployed in electric vehicle (EV) applications is presented. The discussion includes the most common and specific types of faults in PM motors, such as rotor demagnetisation and stator inter-turn faults, respectively. Fault indicators, such as voltage (v s ) and current (i s ) signals and machine signatures based on motor back electromotive force (EMF) (E B ) and magnetic flux (φ), are taken into account as a measuring quantity in diagnosing motor faults. Other signatures, including thermal analysis, acoustic noise, and vibrations, are also illustrated as some of the emerging techniques in estimating the performance of EV motors while under operations. In addition, various fault modelling methods, condition monitoring techniques, and comprehensive approaches applied in diagnosing the effect of machine faults during its incipient stages are illustrated. Since most of the fault diagnostic techniques discussed here include only machine-based quantities as fault indices/indicators, the provided solutions are therefore found to be more reliable and accurate for diagnosing the motor faults. This comprehensive review study is inclusive of the existing fault diagnostic techniques, which are currently employed in industrial and commercial practices, in addition to the new methodologies proposed by the authors. All the given condition monitoring schemes therefore seem significantly vital in estimating the state of health of PM AC machines while under operation in all-electric transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

The High-Frequency Signature of Slow and Fast Laboratory Earthquakes

Tectonic faults fail through a spectrum of slip modes, ranging from slow aseismic creep to rapid slip during earthquakes. Understanding the seismic radiation emitted during these slip modes is key for advancing earthquake science and earthquake hazard assessment. In this work, we use laboratory friction experiments instrumented with ultrasonic sensors to document the seismic radiation properties of slow and fast laboratory earthquakes. Stick-slip experiments were conducted at a constant loading rate of 8 μm/s and the normal stress was systematically increased from 7 to 15 MPa. We produced a full spectrum of slip modes by modulating the loading stiffness in tandem with the fault zone normal stress. Acoustic emission data were recorded continuously at 5 MHz. We demonstrate that the full continuum of slip modes radiate measurable high-frequency energy between 100 and 500 kHz, including the slowest events that have peak fault slip rates <100 μm/s. The peak amplitude of the high-frequency time-domain signals scales systematically with fault slip velocity. Stable sliding experiments further support the connection between fault slip rate and high-frequency radiation. Experiments demonstrate that the origin of the high-frequency energy is fundamentally linked to changes in fault slip rate, shear strain, and breaking of contact junctions within the fault gouge. Our results suggest that having measurements close to the fault zone may be key for documenting seismic radiation properties and fully understanding the connection between different slip modes.

58 GEOSCIENCES↗

DOE OSTI2023

Model Agnostic Bayesian Framework for Online Anomaly/Event Detection in PMU Data

Phasor measurement units (PMU) are integral to the modernization and automation plan of the electric power industry. A PMU data signature contains system-level events (e.g., faults, generation/load change, etc.) and any measurement/device-related errors. Therefore, the reliable and resilient operation of power systems is equivalent to the quality of the PMU data and the situation awareness provided by its data signature. Despite recent progress, current state-of-the-art methods are not fool-proof and have certain limitations tracing an error/abnormality to sensor sub-components and grid systems. This is because of technical challenges imposed by the scarcity of the labeled information, loss of data quality, and non-stationarity of data. In this paper, we consider the online PMU data stream as an output of a stochastic process and pose the anomaly/event detection as a changepoint detection problem dealing with detecting parameter changes in the underlying stochastic processes. The proposed model-agnostic framework relies on: (a) feature extraction utilizing the minimum volume enclosing ellipsoids (MVEE) method from raw PMU observations and (b) a Bayesian framework of changepoint detection. The validity of the proposed methodology is discussed through numerical experiments on real-world utility-scale PMU data.

Hossain, Ramij Raja↗