Types Of Faults Research Articles

Electrical surface breakdown characteristics of micro- and nano-Al2O3 particle co-doped epoxy composites

Abstract Epoxy microcomposites are basic materials for gas-insulated switchgear (GIS) spacers that are subjected to huge electrical stress. Previous works have indicated that nanoparticles are beneficial to dielectric performance. However, surface electrical breakdown, a typical fault in GIS of co-doped micro- and nanoparticles in epoxy composites, is seldom studied. In this work, numerous concentrations of micro- and nano-Al2O3 are co-doped into an epoxy matrix; the surface traps, surface charging, and surface breakdown voltages (V sb) of the co-doped composites are studied, and the influence of micro- and nano-Al2O3 on the electrical surface breakdown is clarified. The results show that V sb first decreases and then increases with the microparticles, and V sb decreases from 25.34 kV to 19.52 kV. As the number of nanoparticles increases, V sb increases and then decreases when the microparticle loading is low, but decreases and then increases when the microparticle loading exceeds 40 wt%. Micro-Al2O3 particles introduce surface shallow traps into epoxy composites, while small amounts of nano-Al2O3 introduce deep traps. Two different mechanisms dominate the surface charging and V sb of epoxy micro-nanocomposites. When the surface conductivity is lower than 7 × 10−14 S, the surface charges are reduced by the suppression of electrode injection as the trap depth increases, and V sb increases. When the surface conductivity exceeds 7 × 10−14 S, the surface charge dissipation rate increases with the surface conductivity and V sb increases as the surface conductivity increases. Our work indicates that co-doped micro- and nano-particles should keep the surface conductivity away from the specic value (7 × 10−14 S) to safeguard insulation properties for GIS spacers.

Fault location and type identification method for current and voltage sensors in traction rectifiers

Fault location and type identification method for current and voltage sensors in traction rectifiers

A novel phaselet-based approach for fault detection and location in HVDC lines

Recently, the popularity of direct current (DC) transmission has surged due to a 30 % reduction in power electronics equipment costs over the last decade. High-Voltage Direct Current (HVDC) transmission lines experience fluctuations in both nominal and faulty states, posing challenges for fault location methods, particularly those based on traveling waves, resulting in imprecise outcomes with an average error margin of ±2 %. This paper investigates a novel fault detection and location approach utilizing wavelet and phaselet transforms. The proposed method focuses on traveling waves in HVDC transmission lines, where the phaselet transform effectively eliminates undesired fluctuations in line currents, improving fault detection accuracy by up to 15 %. By leveraging the traveling time in the faulty line, the exact fault location is calculated with an accuracy of 98.5 %, independent of transmission line parameters, making it applicable to any DC line. Additionally, the method's precision is minimally affected by fault resistance, with <0.5 % deviation even in nonlinear scenarios. Sensitivity analysis conducted on various parameters, including mother wavelet patterns, suggests the optimal solution for each fault type, enhancing detection speed by 20 %. The proposed method is validated using the Cigre HVDC benchmark, confirming its accuracy, speed, and robustness in fault detection and location in HVDC lines. The main contribution of this paper is an accurate, fast, and robust phaselet-based algorithm that reduces fault location errors to <1 % and is applicable to all types of bipolar and monopolar HVDC systems, delivering reliable results for both ground and line faults.

Multi-sensor temporal-spatial graph network fusion empirical mode decomposition convolution for machine fault diagnosis

Multi-sensor time-series data at different locations contains not only temporal correlation information but also spatial correlation information which is treasure for machine fault diagnosis. Existing graph construction methods mainly apply different data analysis methods to connect nodes and edges. Few works, however, consider the location of the sensor itself and temporal correlation information of multi-sensor time-series data. To mine the relationship between spatial information and temporal information, the multi-sensor temporal-spatial graph is constructed in this paper. Hereinto, the different data points of multi-sensor are severed as different nodes which represents the spatial feature information. The temporal information is contained between different nodes of the same sensor. Moreover, an empirical mode decomposition graph convolution network (EGCN) is proposed to extract the feature. Specifically, the traditional graph convolution operator is changed to empirical mode decomposition which can decompose the input features into multiple intrinsic modal features to achieve adaptive feature extraction and improve the representation capability of the network. Finally, the different fault types can be classified by fully connected layers. Experiments from different test rigs demonstrate that the proposed method achieves a diagnostic accuracy exceeding 99 % under limited fault samples.

INTELLIGENT FAULT DETECTION AND LOCATION IN ELECTRICAL HIGH-VOLTAGE TRANSMISSION LINES

Fault location in transmission lines is critical to ensure power systems' reliable and efficient operation. Accurate fault detection and localization are essential to minimize downtime, prevent cascading failures, and maintain the overall stability of the electrical grid. Over the years, various fault location methods have been proposed, ranging from traditional model-based approaches to more sophisticated artificial intelligence techniques. This research presents two fault location methodologies: the Atom search optimization metaheuristic approach (ASO) and machine learning (ML) with cubic spline models. We evaluate the performance of both approaches by considering different fault types, fault distances, and fault resistance. We analyze accuracy and computational efficiency. The findings reveal that the Metaheuristic Approach demonstrates robustness in fault detection and localization under diverse conditions but may suffer from higher computational overhead. In contrast, the hybridization of machine learning and metaheuristic exhibits promising potential in achieving real-time fault localization with improved accuracy.

Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.

Fault imaging using earthquake sequences: a revised seismotectonic model for the Albstadt Shear Zone, Southwest Germany

In Germany, the highest seismic hazard is associated with the Albstadt Shear Zone (ASZ) in the western Swabian Jura, a low mountain range in southwest Germany. The region is affected by continuous micro-seismic activity with the potential for damaging earthquakes (nine events with ML≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge $$\\end{document} 5 in the 20th\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{th}$$\\end{document} century). Within the AlpArray and StressTransfer projects nine temporary seismic stations have been installed in the region of the ASZ to densify the permanent seismic monitoring. In October 2018 and September 2019, the state seismological survey (LED) detected two low-magnitude earthquake sequences with hundreds of events in the area. The temporarily densified local network allows us to systematically analyze these sequences and to search for other sequences by applying a template-matching routine on data from 2018 to 2020. In total, six earthquake sequences could be identified with at least 10 events. The four largest sequences (> 50 events) consist of two fore- and aftershock sequence and two earthquake swarms. Earthquake swarms were so far not observed around the ASZ. Precise relative hypocenter relocations and the determination of fault-plane solutions allow us to propose a seismotectonic model based on the three imaged fault types: (a) The well-known NNE-SSW striking sinistral strike-slip ASZ at depths of 5-10 km, (b) a NW-SE striking dextral strike-slip fault zone at depths of 11-15 km beneath the Hohenzollerngraben (HZG), a shallow, apparently aseismic NW-SE striking graben structure; this NW-SE fault zone possibly is an inherited zone of weakness in the basement and facilitated the development of the HZG and (c) at the intersection of the ASZ with the NW-SE fault zone, complex faulting in form of NNW-SSE striking sinistral strike-slip and normal faulting possibly to accommodate local stresses.

Survey of fault management techniques for edge-enabled distributed metaverse applications

The metaverse, envisioned as a vast, distributed virtual world, relies on edge computing for low-latency data processing. However, ensuring fault tolerance – the system’s ability to handle failures – is critical for a seamless user experience. This paper analyzes existing research on fault tolerance in edge computing over the past six years, specifically focusing on its applicability to the metaverse. We identify common fault types like node failures, communication disruptions, and security issues. The analysis then explores various fault management techniques including proactive monitoring, resource optimization, task scheduling, workload migration, redundancy for service continuity, machine learning for predictive maintenance, and consensus algorithms to guarantee data integrity. While these techniques hold promise, adaptations are necessary to address the metaverse’s real-time interaction requirements and low-latency constraints. This paper analyzes existing research and identifies key areas for improvement, providing valuable research guidelines and insights to pave the way for the development of fault management techniques specifically tailored to the metaverse, ultimately contributing to a robust and secure virtual world.

Three-phase balance relay using numerical techniques experimentally verified on synchronous machines

In this paper, a multifunction three-phase balance relay based on normalized correlation coefficients is proposed to detect and estimate imbalances and perturbations in synchronous machine output signals. Furthermore, fresh definitions of imbalance and disturbance indicators derived using the correlation estimators are introduced, taking into account the changes in the waveform phase displacement, frequency, amplitude, and shape of the machine three-phase waves. Experimental tests are performed on a motor–generator set connected to a three-phase load, which is used to identify and evaluate the imbalance and disturbance conditions of the voltage and current measurements. Extensive tests for different fault types have been presented. The practical results show that the proposed protection can respond quickly to faults, and assess online the level of the imbalance/disturbance with high accuracy. Its running time is within one cycle. In addition, the proposed algorithm’s reliability and accuracy are its most significant attributes, whose percentages exceed 96.6%. The present algorithm considers the impact of both negative and zero sequence components when measuring di-symmetry factors, while some conventional approaches merely rely on the negative sequence component computed for the machine voltages and currents.

Expert label for explainable fault diagnosis and for unknown fault generalization

This study proposes the expert label that incorporates specific process knowledge into sample labels to enhance the explainability of diagnostic results. Traditional sample label is a one-hot encoding vector without inherent physical meaning, limiting the diagnostic scope to predefined fault types without explainability. In contrast, expert label as multi-label provides multi-dimensional information including disturbance type, unit location, and associated variable type. Since multi-label can accommodate an exponential number of fault types, the diagnosis models based on expert label have the potential to generalize unknown faults. Meanwhile, we develop the process multi-scale feature mixture network (PMFMN), which excels in deep feature extraction through modules including multi-scale decomposition, multiple pattern representation, and Fourier attention mechanism. The effectiveness of PMFMN with expert label is validated through case studies in two chemical processes, which further improves the fault diagnosis rate, provides reliable diagnostic results with explainability, and can diagnose unknown faults.

A Literature Review of Fault Detection and Diagnostic Methods in Three-Phase Voltage-Source Inverters

This review paper offers a comprehensive examination of the various types of faults that occur in inverters and the methods used for their identification. The introductory segment investigates the internal component failures of voltage-source inverters (VSIs), examining their failure rates and the consequent effects on the overall system performance. Subsequently, this paper classifies and clarifies the potential malfunctions in components and sensors, placing particular emphasis on their frequency of occurrence and the severity of their impact. The examination encompasses issues associated with transistors, including open circuits, short circuits, gate firing anomalies, as well as failures in capacitors, diodes, and sensors. Following this, the paper delivers a comparative assessment of fault diagnosis techniques pertinent to each type of component, appraised against specific criteria. The concluding section encapsulates the findings for each fault category, delineates the fault detection and diagnosis (FDD) methodologies, analyzes the outcomes, and provides recommendations for future scholarly investigation.

Review on Short-Circuit Protection Methods for SiC MOSFETs

SiC MOSFETs have been a game-changer in the domain of power electronics, thanks to their exceptional electrical traits. They are endowed with a high breakdown voltage, reduced on-resistance, and superior thermal conductivity, which make them supremely suitable for high-power and resilient applications across aviation, automotive, and renewable energy sectors. Despite their intrinsic advantages, SiC MOSFETs also necessitate advanced safeguarding mechanisms to counteract the vulnerability to short-circuit conditions due to their lower short-circuit robustness. This review paper offers an in-depth analysis of the array of short-circuit protection (SCP) methods applied to SiC MOSFETs. This paper scrutinizes techniques such as desaturation detection, di/dt detection, gate charge characteristics monitoring, two-dimensional monitoring, Rogowski coil-based detection, and two-stage turn-off strategies. The paper meticulously explores the operational principles, merits, and limitations of each method, with an emphasis on their adaptability to various fault types, including hard switching faults and load-induced faults. This review acts as a thorough compendium, guiding the choice of pertinent SCP strategies, ensuring the secure and efficient functioning of SiC MOSFETs in demanding applications.

Research on Fault Diagnosis of Drilling Pump Fluid End Based on Time-Frequency Analysis and Convolutional Neural Network

Operating in harsh environments, drilling pumps are highly susceptible to failure and challenging to diagnose. To enhance the fault diagnosis accuracy of the drilling pump fluid end and ensure the safety and stability of drilling operations, this paper proposes a fault diagnosis method based on time-frequency analysis and convolutional neural networks. Firstly, continuous wavelet transform (CWT) is used to convert the collected vibration signals into time-frequency diagrams, providing a comprehensive database for fault diagnosis. Next, a SqueezeNet-based fault diagnosis model is developed to identify faults. To validate the effectiveness of the proposed method, fault signals from the fluid end were collected, and fault diagnosis experiments were conducted. The experimental results demonstrated that the proposed method achieved an accuracy of 97.77% in diagnosing nine types of faults at the fluid end, effectively enabling precise fault diagnosis, which is higher than the accuracy of a 1D convolutional neural network by 14.55%. This study offers valuable insights into the fault diagnosis of drilling pumps and other complex equipment.

Crack fault diagnosis in rotor bearing system by transient and study state time domain analysis

Online condition monitoring methodologies for rotor dynamic systems are an emerging trend and an essential for safe system operation. During operation fatigue cracks in rotor shaft can lead to catastrophic failures if not identified at early stage. Therefore, these systems require a reliable crack fault detection methodology. The vibrations may induce in the system due to different type of faults, like shaft transverse cracks, oil whirl and whip. To make the detection methodologies robust and reliable, redundancy in crack detection methodologies is vital. The present research is a foolproof procedure to detect the travers fatigue crack in the rotor shaft. Rotor response, orbital pattern, and response FFT during transient (runup/cost down) and study state operations at 2X & 3X sub critical speeds are analyzed to identify the presence of crack in rotor bearing system. The rotor system is analyzed by numerical Finite Element Modeling (FEM) simulations and experimental means. Open Wedge Crack (OWC) and Breathing Wedge Crack (BWC) are modeled and their behavior is studied. The effectiveness of Newton Rapson method, Houbolt numerical method, and Harmonic Balance Method (HBM) in crack diagnosis are studied. Numerical FEM results are validated with experimental and published literature results. FEM simulated and experimental response FFT shows similar response behavior with presence of crack with 98 % correlation and less than 2 % error. FEM simulated and literature response orbital pattern at 2X sub critical backward speed are similar with less than 3 % error (97.76 % close) and at 2X sub critical forward speed are similar with less than 1 % error (99.33 %).

Research on Wind Turbine Fault Detection Based on CNN-LSTM

With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

Prior knowledge embedding convolutional autoencoder: A single-source domain generalized fault diagnosis framework under small samples

The proposed transfer learning-based fault diagnosis models have achieved good results in multi-source domain generalization (MDG) tasks. However, research on single-source domain generalization (SDG) is relatively scarce, and the limited availability of small training samples is seldom considered. Therefore, to address the insufficient feature extraction capability and poor generalization performance of existing models on single-source domain small sample data, a novel single-source domain generalization fault diagnosis (SDGFD) framework, the prior knowledge embedded convolutional autoencoder (PKECA), is proposed. During the training phase, first, single-source domain data are used to construct prior features based on the time domain, frequency domain, and time-frequency domain. Second, a prior knowledge embedding structure based on the convolutional autoencoder is built, which compresses the prior knowledge and original vibration data into a high-dimensional space of consistent dimensions, embedding the prior knowledge into the features corresponding to the vibration data using a mean squared error loss function. Subsequently, the proposed centroid-based self-supervised learning (CBSSL) strategy further constrains high-dimensional features, improving the generalization ability. The designed sparse regularized activation (SRA) function significantly enhances the regularization effect on features. During the testing phase, it is only necessary to input the data from the unknown domain to identify the fault types. The experimental results show that the proposed method achieves superior performance in fault diagnosis tasks involving cross-speed, time-varying speed, and small sample data in SDGFD, demonstrating that PKECA has strong generalizability. The code can be found here: https://github.com/John-520/PKECA. © 2024 Elsevier Science. All rights reserved

A Novel Strategy for Multitype Fault Diagnosis in Photovoltaic Systems Using Multiple Regression Analysis and Support Vector Machines

This study focuses on analyzing common fault types in photovoltaic (PV) modules, employing fault diagnosis methods based on machine learning technology to enhance the accuracy and efficiency of diagnosing faults in solar power systems. Initially, we collected relevant data from the solar power system and used data analysis techniques to identify system faults, designing a human-machine monitoring interface for practical application. Furthermore, the experimental results proved that the system could accurately identify eight major types of faults, including solar panel output circuits, energy storage batteries, maximum power point tracking (MPPT) controllers, inverters, dust accumulation, loosening of mounting rack screws, damage to the mounting rack foundation, and deformation of the mounting rack structure. Particularly in the detection of dust accumulation, we developed a new method of estimating power generation from multiple regression analysis (MRA), which closely aligns the estimated power output with the actual power output, highlighting the significant impact of dust accumulation on the efficiency of solar power systems. Next, by integrating voltmeters and support vector machines (SVM) into the solar PV array modules, we are able to quickly and accurately measure and locate short-circuit and open-circuit faults in bypass diodes. Ultimately, the proposed PV fault diagnosis strategy includes diagnostics for dust accumulation and mounting frame faults, making it particularly suitable for areas with severe air pollution and frequent earthquakes, providing a comprehensive fault diagnosis solution.

Wind energy system fault classification using deep CNN and improved PSO‐tuned extreme gradient boosting

AbstractIntelligent fault diagnosis for wind energy systems requires identifying unique characteristics to differentiate various fault types effectively, even when data discrepancy occurs due to the unpredictable and dynamic nature of its environment. This article addresses some of the challenges of fault classification in wind energy systems by proposing an integrated approach that combines deep learning features with a resampled supervisory control and data acquisition (SCADA) dataset. The methodology involves resampling the imbalanced SCADA dataset using synthetic minority oversampling technique (SMOTE) and near‐miss undersampling techniques, extracting deep learning features using deep convolutional neural network, and feeding them into an XGBoost (extreme gradient boosting) classifier with tuned parameters using adaptive elite‐particle swarm optimization (AEPSO). The effectiveness of the proposed method is demonstrated through validation conducted on a different imbalanced dataset showing superior performance metrics in terms of accuracy. Additionally, the study contributes to methodological advancements in wind turbine fault diagnosis by providing a rigorous framework for fault classification. It is confirmed that utilizing the extracted deep learning features into the resampled data can significantly affect the classification performance metrics. Furthermore, the proposed integrated approach shows significance for fault diagnosis enhancement in wind energy systems and advancing the field towards more efficient and reliable operation.

Fault diagnosis method for oil-immersed transformers integrated digital twin model

To address the problems of low accuracy in fault diagnosis of oil-immersed transformers, poor state perception ability and real-time collaboration during diagnosis feedback, a fault diagnosis method for transformers based on the integration of digital twins is proposed. Firstly, fault sample balance is achieved through Iterative Nearest Neighbor Oversampling (INNOS), Secondly, nine-dimensional ratio features are extracted, and the correlation between dissolved gases in oil and fault types is established. Then, sparse principal component analysis (SPCA) is used for feature fusion and dimensionality reduction. Finally, the Aquila Optimizer (AO) is introduced to optimize the parameters of the Kernel Extreme Learning Machine (KELM), establishing the optimal AO-KELM diagnosis model. The final fault diagnosis accuracy reaches 98.1013%. Combining transformer digital twin models, real-time interaction mapping between physical entities and virtual space is achieved, enabling online diagnosis of transformer faults. Experimental results show that the method proposed in this paper has high diagnostic accuracy and strong stability, providing reference for the intelligent operation and maintenance of transformers.

Tectonic ledges at the Ladoga Lake bottom

For the past few years, the Institute of Limnology of the Russian Academy of Sciences has been conducting geological and geomorphological studies of the bottom of Lake Ladoga. Starting from 2018, underwater photo and video cameras developed at the Institute began to be used in these studies. The use of this new research tool led to the discovery of a number of new facts about the structure of the bottom of Lake Ladoga. In particular, at the bottom of the northern part of Lake Ladoga, three subvertical ledges were found, composed of bedrock, up to 70–100 meters high. According to a number of features, such as the bedrock composition of rocks, morphology and spatial correspondence to known fault zones, these ledges can be recognized as morphological expressions of tectonic objects – faults of presumably fault type. The absence of visible traces of glacial processing on the bedrock blocks suggests the Holocene age of the formation of these ledges and, accordingly, the existence of modern active tectonic movements in the area of Lake Ladoga.