Fault Position Research Articles

Seismogeological model of the terrigenous vendian deposits on the north–eastern slope of the Nepa-Botuoba anteclise

A seismogeological model of the junction zone of the Nepa-Botuoba anteclise and the Ygyattinskaya depression was built based on the results of a comprehensive interpretation of deep drilling and seismic data. The position and propagation of regional high-rank faults have been specified, and a model of tectonic faults of the study area has been constructed. All faults of the study area were divided into three classes: those that cut the entire sedimentary cover; developed in the lower part of the sedimentary section, fading in the Cambrian salt pack; complicating the upper post-salt complex of rocks. Criteria for identifying thinning zones were developed and were made use of to identify oil and gas perspective objects within the Talakh, Ulakhan and Kharystan productive horizons.

Deep learning-based application for fault location identification and type classification in active distribution grids

The high penetration of distributed energy resources, especially weather-dependent sources, even at the edge of the distribution grids, has increased the power system uncertainties and drastically shifted the operational status quo for the system operators. For the operators to ensure the uninterrupted electricity supply of the end-consumers, the fast and accurate response to fault events is of critical importance. This paper proposes a data-driven fault location identification and types classification application based on the continuous wavelet transformation and convolutional neural networks optimally configured through Bayesian optimization. This application leverages the proliferation of high-resolution measurement devices in distribution networks. It can locate the exact position of the short-circuit faults and classify them into eleven different types. Its intrinsic models grasp the spatial characteristics and the converted in frequency domain temporal ones of the three-phase voltage and current timeseries measurements stemming from the field devices, thus increasing the operators’ visibility of their networks in real-time. We conduct simulations through synthetic data, which we provide in an open-source repository, that replicate a wide range of fault occurrence scenarios with eleven different types, with the resistance ranging from 50Ω to 2kΩ and with duration from 20ms to approximately 2s, under noise conditions injected by devices and load variability. The results showcase the efficacy of the proposed method reaching an accuracy of 91.4% for fault detection, 93.77% for correct branch identification, 94.93% for fault type classification, and RMSE value of 2.45% for location calculation.

A phase diagram approach to the detection and location of faulty capacitor units in A 115-kV capacitor bank based on unbalanced current arguments

The protection of traditional high voltage capacitor banks relies on an unbalance relay which operates when an internal fuse is blown. However, the unbalance relay cannot indicate cause or position of the fault. Thus, an operator wastes time and human resources investigating the fault issues. To address this issue, a method to locate the fault position in a capacitor bank is developed in this study. The study was simulated by using PSCAD software and modeled on the 115-kV system of the Electricity Generating Authority of Thailand (EGAT). Case studies involving faults with varied phases, side and branch connections, row connections, and inception angles were considered. Moreover, the magnitude and argument of the current phase and unbalanced current were analyzed to identify the fault location in a capacitor bank. The performance of the proposed method was verified via comparison with that of traditional methods and the results of laboratory experiments. In addition, various voltage systems were observed to verify the flexibility and accuracy of the proposed method. The results demonstrate that the proposed method was more efficient than traditional methods at locating the fault position in a capacitor bank.

OPGW positioning and early warning method based on a Brillouin distributed optical fiber sensor and machine learning.

A method of optical fiber composite overhead ground wire (OPGW) positioning based on a Brillouin distributed optical fiber sensor and machine learning is proposed. A distributed Brillouin optical time-domain reflectometry (BOTDR) and Brillouin optical time-domain analyzer (BOTDA) are designed, where the ranges of BOTDR and the BOTDA are 110km and 125km, respectively. An unsupervised machine learning method density-based spatial clustering of applications with noise (DBSCAN) is proposed to automatically identify the splicing point based on the Brillouin frequency shift (BFS) difference of adjacent sections. An adaptive parameter selection method based on k-distance is adapted to overcome the parameter sensitivity. The validity of the proposed DBSCAN algorithm is greater than 96%, which is evaluated by three commonly external validation indices with five typical BFS curves. According to the clustering results of different fiber cores and the tower schedule of the OPGW, the connecting towers are distinguished, which is proved as a 100% recognition rate. According to the identification results of different fiber cores of both the OPGW cables and tower schedule, the connecting towers can be distinguished, and the distributed strain information is extracted directly from the BFS to strain. The abnormal region is positioned and warned according to the distributed strain measurements. The method proposed herein significantly improves the efficiency of fault positioning and early warning, which means a higher operational reliability of the OPGW cables.

Research of energy characteristics and fault position detection for cracked rotor system

Early crack faults will introduce subtle local changes in the whole rotor system, which may bring difficulties to the fault detection process due to external disturbing factors. Meanwhile, judging the damaged position of rotor systems might be tough only through displacement or velocity. To overcome these issues, the harmonic balance-energy power flow analysis (HB-EPFA) is presented to investigate the rotor system with a transverse crack. Besides, a novel indicator (Time-averaged power flow) is proposed to evaluate the crack faults. For the energy characteristic analysis, the cracked rotor appears a special amplitude-jumping phenomenon, even in the case of shallow crack depth. The influence of crack depth on power flow values under different rotating speeds is obtained. Furthermore, the existence of cracks will lead to the rotation shaft presenting an abrupt energy-changing zone, which can be used to detect crack position. Relevant experiments are applied to validate the proposed detection method, and the experimental results agree with the theoretical analysis. The conclusions achieved in this paper can provide a reference for fault diagnosis of rotor systems in engineering practice.

3C-3D tunnel seismic reverse time migration imaging: A case study of Pearl River Delta Water Resources Allocation Project

In recent years, tunnel boring machines (TBMs) have been widely used because of their fast construction speed and economical benefits, and the need to obtain geological information ahead of the tunnel to ensure the TBM tunnel construction safety has also arisen. Faced with the complex geological environment, seismic forward-prospecting is the primary method for predicting the unfavorable geological structure in front of the tunnel face. As a crucial step in seismic forward-prospecting method, migration can image geological characteristics and help learn the distribution of unfavorable geology. To achieve all-round imaging of unfavorable geology, we suggested a three-component (3C) three-dimensional (3D) tunnel seismic reverse time migration imaging method. The acoustic wave equation is employed for the forward and backward wavefield extrapolation after the P/S-wave separation to eliminate the imaging artifacts from severe converted waves in tunnels. GPU parallelization is used to accelerate the calculation to further adapt to the rapid excavation of TBMs. The proposed method is validated in two typical numerical models, especially for 3C seismic data that provides all-round geological information to achieve enhanced imaging energy and artifact reduction, which demonstrates the significance of 3C seismic data to migration imaging. This case study was conducted in the Pearl River Delta Water Resources Allocation Project in Guangdong Province, China. The reliability of 3C3D tunnel seismic reverse time migration imaging in field tunnel engineering was investigated. Two consecutive detections were performed, and then the spatial positions of faults and fractures in front of the tunnel face were accurately judged by the combination of the two imaging results. Based on the detection results, the excavation rate was slowed down and the tunnel lining support was increased. The TBM then successfully passed through the risk areas. This case study can serve as a reference for other tunnels with similar concerns regarding the necessity of 3C seismic data and consecutive detections.

Power supply arm protection scheme of high-speed railway based on wide-area current differential

When fault occurs on cross-coupling autotransformer (AT) power supply traction network, the up-line and down-line feeder circuit breakers in the traction substation trip at the same time without selectivity, which leads to an extended power failure. Based on equivalent circuit and Kirchhoff’s current law, the feeder current characteristic in the substation, AT station and sectioning post when T–R fault, F–R fault, and T–F fault occur are analyzed and their expressions are obtained. When the traction power supply system is equipped with wide-area protection measurement and control system, the feeder protection device in each station collects the feeder currents in other two stations through the wide-area protection channel and a wide-area current differential protection scheme based on the feeder current characteristic is proposed. When a short-circuit fault occurs in the power supply arm, all the feeder protection devices in each station receive the feeder currents with time stamp in other two stations. After data synchronous processing and logic judgment, the fault line of the power supply arm can be identified and isolated quickly. The simulation result based on MATLAB/Simulink shows that the power supply arm protection scheme based on wide-area current differential has good fault discrimination ability under different fault positions, transition resistances, and fault types. The verification of measured data shows that the novel protection scheme will not be affected by the special working conditions of the electrical multiple unit (EMU), and reliability, selectivity, and rapidity of relay protection are all improved.

On the Accuracy of Correlation Estimator Based Fault Location Methods in Transmission Lines via Polynomial Chaos and Regression Analysis

The electromagnetic time reversal (EMTR) method has recently shown a novel advantage in locating faults along transmission lines. Under the ideal condition for EMTR, the key parameters in the transmission line between the direct and reversed phases are assumed to be identical. Thus, the fault position can be pinpointed via the cross-correlation matching theory. However, due to natural and geographical causes, the parameters vary along the line; therefore, accurately obtaining the parameters is difficult. In this article, we first investigate the impact of uncertain parameters on the performance of EMTR-based fault location methods. It has been observed that the performance is insensitive to the variation of the parameters in the lossless case of transmission lines. But in the case of the overhead line above the lossy ground, a significant error in fault location is witnessed. So, under parameter uncertainty, a novel approach to finding the confidence fault interval is proposed, based on utilizing the polynomial chaos and regression analysis. A simulation case with uncertain parameters for different scenarios is presented to validate the performance of the proposed approach.

Fault identifiability and pseudo-data-driven fault localization in a DC microgrid

Post-fault maintenance and power restoration in low voltage direct current (LVDC) microgrids are highly dependent on the fault localization criteria. This paper investigates the localization of an LVDC fault without communiations. In this paper, state-space modelling is firstly employed to investigate the identifiability of a DC fault in a linerized DC (LVDC) network. We show that a DC fault in is not identifiable in and unknown multi-bus DC network with local measurements, i.e. when they are outnumbered by the total states. In line with such theory, the localization of DC fault is proposed to be embedded in reclosing process to reduce the number of states during identification. And then, a pseudo-data-driven method is proposed to localize an LVDC fault. Combining an enhanced analytical approach and model-based artificial neural network, the proposed method can broadly localize the position of both underdamped and over-damped DC faults without communications. The robustness against higher fault level, low sampling rate, full-range fault position, sampling noises and source variations have been validated using time-domain simulations with Matlab/Simulink.

An Innovative PEMFC Magnetic Field Emulator to Validate the Ability of a Magnetic Field Analyzer to Detect 3D Faults

An original non-invasive methodology of the fuel cell diagnosis is proposed to identify different positions of the faults in Proton Exchange Membrane Fuel Cell (PEMFC) stacks from external magnetic field measurements. The approach is based on computing the external magnetic field difference between normal and faulty PEMFC operating conditions. To evaluate the external magnetic field distribution, in this paper, we propose an improved design of the magnetic field analyzer. This analyzer amplifies the magnetic field around the cell to perform an accurate detection of the fault position. Moreover, the main contribution of this work is represented by conceiving and implementing a 3D multi-physical current distribution emulator of a proton exchange membrane fuel cell. The new concept of a proton exchange membrane fuel cell emulator has been specially designed to emulate the magnetic field of a real fuel cell stack. This emulator concept is also beneficial for a new model of the fuel cell, which implies a multi-physical coupling between electrochemical electric conduction and the generated magnetic field. Finally, finally, the numerical model and the emulator have been involved in the realization of numerical simulations and experimental analysis to prove the ability of the system to detect and localize 3D faults.

Advanced detection for faults in front of coal mine roadways using seismic scattered waves

An unsuspected fault in front of a roadway is a key hidden disaster-causing factor that can result in major accidents in coal mines; therefore, the accurate advanced detection of faults is important for the safe coal mining. However, existing seismic advanced detection methods are difficult to execute because of the narrow space limits of roadways. Therefore, on the basis of the generalized theory of scattered waves and the scenes of advanced detections in roadways, we construct a new and superior advanced detection method using scattered waves. In this method, a virtual equivalent survey line perpendicular to the roadway strike is established to reconstruct scattered wave records and a polarization filter function is built to dynamically and accurately extract different types of scattered waves in the target area. An analysis of numerical simulations indicates the following. 1) The true reflection position of a fault in front of a rock roadway is closely related to its dip, and the proposed method can accurately extract and image the scattered multiwaves generated at the true reflection position to identify the inclination of the fault, which can avoid the arc-shaped illusion problem caused by the false reflection signal. 2) Under the condition that the scattered waves at the breakpoint of a broken coal seam are significantly disturbed by the coal seam waveguide effect, the fault inclination of the coal roadway can be determined by the joint imaging and interpretation on the reflected channel wave of the hanging wall breakpoint and the scattered wave of the footwall breakpoint. A field study was performed to verify the effectiveness of the proposed method. The results indicate that this method can effectively identify the position and inclination of faults and provide an effective new means for the accurate detection of faults in front of coal mine roadways.

Prediction of Photovoltaic (PV) Thermal Fault Location Using the Catadioptric Device

Photovoltaic (PV) farms are subject to thermal faults that can degrade and reduce module efficiency. The currently used method of detecting thermal fault is time-consuming and hard to locate the fault position, especially in large PV areas. It can result in additional flaws, such as igniting the PV farm. We provide a new approach for monitoring and detecting PV thermal faults through a catadioptric device (CD) that offers fast and continual detection. As an early development stage, in this study, we concentrate on building a mathematical model that can determine the thermal fault coordinate based on two object images from different CD positions. The experiment then verifies the model, and the parameter variation is performed to find the coordinate prediction characteristic. Also, a case study simulation on large PV system monitoring was performed to figure out the thermal fault localization process. The result shows that the mathematical model can be used to determine the coordinate position of the thermal fault with acceptable measurement error. The parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x_k</i> tends to affect the average measurement error of the coordinate prediction and the error gradient of each axes couple. Greater the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x_k</i> , smaller measurement and gradient error can be achieved. In addition, the case study simulation result shows that the thermal fault position can be predicted with the worst PE of less than 10%, with a MAPE, MAE, and RMSE in a reasonable value. Also, the sensitivity pattern can be used for CD condition monitoring.

COMPREHENSIVE ANALYSIS OF MAGNETIC AND GRAVITY DATA BASED ON VOLUMETRIC GRAVITY-MAGNETIC MODELING ALONG THE GEOTRAVERSE IN THE SHU-SARYSU SEDIMENTARY BASIN

The purpose of the studies presented in the article is to obtain the information of the deep structures along the Shu-Sarysu geotraverse, to determine the regularities in the distribution of mineral deposits and predictive assessment of the research area prospects for searching for hydrocarbons and other minerals. To achieve this purpose, a complex of high-precision gravitational and magnetometric measurements was used. There was shown a high efficiency of the integrated interpretation of 3D gravimagnetic modeling when studying the geological structure of the crystalline basement (assessment of the morphology of its surface, material-petrophysical mapping of the rocks that make up the basement) and the sedimentary cover (assessment of the morphology of the reference horizons, studying their material composition); when identifying and clarifying the position of discontinuous faults, conducting lineament analysis in order to decipher the block structure of the studied areas; when building 3D volumetric models of geophysical parameters; when substantiating predictive and search geophysical criteria for detecting oil and gas geological structures, both in the thickness of sedimentary formations and in the rocks of the crystalline basement. The progress of these methods is conditioned by the widespread introduction into practice of modern computer technologies for processing and interpreting data.

Seismic Response of Mountain Tunnels by Comprehensive Analysis Methods and Feasible Aseismic Measures

In mountainous area, earthquake is an inevitable factor during the construction of tunnel. Earthquake can cause great damage to faults and mountain slopes, and at the same time, the deformation and failure of mountain tunnels are closely related to faults and landslides. This study analyzes the impacts of faults and landslides on the mountain tunnel by numerical modelling, model test and field investigation, and discusses the corresponding engineering countermeasures. Mountain tunnel through fault tends to be damaged severely because it may be shorn by fault dislocation. However, the fractured tunnel may undergo even severer damage due to earthquake wave. Various factors, including earthquake wave, condition of surrounding rock, width of fault, relative position of fault and tunnel, fault friction velocity, fault activity and lining section type, affect the seismic performance of tunnel through fault. In addition, buffer layer, grouting, shock absorption gap, sectional tunnel lining with flexible joints, fiber reinforced concrete lining, and ultra-excavating are all available aseismic or anti-dislocation measures. The damage mode and degree of landslide to tunnel are related to the type of tunnel, and the different relative positions of the tunnel and the sliding surface controls damages. Landslide prevention and control engineering measures mainly include weight loss, drainage, construction of retaining works, improvement and reinforcement of soil and rock properties of sliding zone, etc.

Partial Discharge Detection and Defect Location Method in GIS Cable Terminal

The complex structure of gas-insulated switchgear (GIS) cable terminals leads to serious electric field concentration, which is a frequent fault position of a high-voltage cable system. At present, due to the differences in the frequency bands of sensors, various partial discharge detection technologies have certain differences in their scope of application and anti-interference performance, resulting in a low defect detection rate in GIS cable terminals. In this paper, a comprehensive diagnosis scheme is proposed, which integrates transient earth voltage (TEV), ultra-high frequency (UHF), high frequency (HF), and ultrasonic methods. Two abnormal discharge defects of GIS terminals in two 220 kV substations in Tianjin were tracked and monitored, and the joint diagnosis was carried out using the proposed scheme; the type of discharge defect and the phase sequence of the defect were determined, and the UHV was employed to precisely locate and analyze the defect source. Finally, through the disassembly analysis and electric field simulation of the GIS cable terminal, the accuracy and effectiveness of the discharge detection and location method were verified, providing a typical detection demonstration for the defect diagnosis of a GIS cable terminal.

Advanced fault location scheme for superconducting cables based on deep learning algorithms

This paper addresses the challenge of fault localization in power grids which incorporate Superconducting Cables (SCs) and presence of inverter-connected generation, by proposing a novel data-driven fault location scheme. The developed fault location algorithm utilizes the transformation of time domain fault current and voltage signatures, to time–frequency domain and exploits the advantages of a Convolutional Neural Network (CNN) to estimate the fault position along SCs. The proposed algorithm has been tested using a verified model of SC, and the results revealed that it can provide precise fault localization for a wide range of fault scenarios, including different fault types, fault resistance values and fault inception angles. Furthermore, the proposed scheme robustness has been verified against different influencing factors accounting for very small increments of fault location, additive noise and different value of sampling frequency. For validation purposes, the effectiveness of the CNN-based algorithm has been compared with other data-driven algorithms and the relevant advantages have been highlighted.

Ultrasonic guided wave testing on pipeline corrosion detection using torsional T(0,1) guided waves

Ultrasonic guided wave testing (UGWT) is used in rapid screening to detect, locate and classify corrosion defects. This non-destructive testing technique can perform wide-range inspection from a single point, thus reducing the time and effort required for NDT. However, the mode conversion phenomena and the dispersive nature of the guided waves make corrosion detection difficult. Hence, the parametric studies on the response signals of a T (0, 1) wave from pipe defects were presented in this paper. Firstly, a mathematical model of 6-inch schedule 40 pipes was developed. The corrosion profile of various geometries was then constructed on the outer surface of the pipeline by varying the circumferential length and depth. The numerical study was performed to analyse the characteristics of the response signals when a torsional guided wave impinges on the corroded pipelines. A five-cycle Hanning tone-burst signal with a central frequency of 30k Hz was used throughout the study. The results demonstrated that mode conversion to a flexural mode F (1, m) occurs when the stimulated T (0, 1) strikes non-symmetric defects. Nonetheless, as the circumferential extent of the corrosion increased, the response signals tended to behave symmetrically, and there was less mode conversion detected. Thus, the presence of flexural mode F (1, m) can be used as the criteria to distinguish symmetric and asymmetric faults. In addition, the results demonstrated that the reflection coefficients increase monotonically with the defect's depth due to the increases in the estimated cross-sectional area loss (ECL). As a result, a more significant proportion of the transmission wave was reflected. These findings serve as guidelines for on-site inspections. With the known speed of guided wave propagation, it is possible to precisely forecast the position of faults.

A Precise Stator Ground Fault Location Method for Large Generators Based on Potential Analysis of Slot Conductors

The stator ground fault location of large generators can guide rapid maintenance. In this paper, a location criterion without fault transition resistance is constructed by using the fundamental potential and the third harmonic potential of the faulty winding simultaneously. The above potentials of the faulty winding are corresponding to the fault position. To precisely locate the grounding fault, it is necessary to clarify the analytical relationship between the faulty winding potential and the fault position. However, the existing methods often take the coil potential as the unit to analyze the winding potential, which has theoretical errors for the generators with short-pitch winding. Aiming at this problem, a more precise analysis method for winding potential is established by taking the slot conductor potential as the unit. To solve the location criterion, the optimization model for each slot conductor of the faulty phase is constructed. The slot conductor with the smallest optimal objective function value is judged as the faulty slot conductor, and the corresponding decision variable is judged as the fault position. Simulation and experiments verify the effectiveness of the proposed method, which can still meet the needs of engineering applications under multiple adverse conditions.

Fault diagnosis in district heating networks

District Heating Networks (DHNs), which dispatch thermal energy from a heat source to end-users by means of a heat carrier, are composed of pipes that can be affected by faults that endanger system reliability. Thus, reliable diagnostic approaches have to be employed to evaluate the health state of the DHN.In the framework of the ENERGYNIUS research project, the authors of this paper developed a diagnostic approach aimed at detecting and identifying the most frequent faults that affect DHN pipes, i.e., water leakages, heat losses and pressure losses. The diagnostic approach detects and identifies pipe faults by coupling a DHN model with an optimization algorithm. As a result, the health indices of each pipe of the DHN, the fault position, its type and magnitude are provided. This study investigates the capability of the diagnostic approach by using two datasets, in which challenging faults were hypothetically implanted in the DHN of the campus of the University of Parma.The diagnostic approach successfully detected and identified both faults, by also accurately assessing fault magnitude. In addition, the relative error with which each DHN variable is predicted is lower than 0.06 %.

Identification of localized defects and fault size estimation of taper roller bearing (NBC_30205) with signal processing using the Shannon entropy method in MATLAB for automobile industries applications

Rotating machine is a common class of machinery in most of the industry and the main root cause of machinery failure is a faulty bearing. Bearings are most widely used in various types of machine elements ranging from small to heavy machinery and the common cause of machinery failure is a fault in bearings. Bearing faults can be external or internal which mainly depends on different operating conditions and these faults may cause severe damage to rotating components in machinery. Signal processing methods have traditionally been used to diagnose faults in tapered roller element bearings. A wavelet transform is the most common and effective tool for understanding and analyzing the vibration signal of bearings as it is responded quickly and observed sudden changes along with the transient impulses in the signal caused by faults in the different parts of bearing elements. In this article, localized fault's position and size on the outer ring of tapered roller bearing were investigated. Three different real values wavelets (DB2, Meyer, and Morlet) are analyzed as per Simple Sensitivity index criteria. Finally, experiments are carried out with four sets of bearing having fault on outer racing of bearing, and for the estimation of fault size, the setup was misaligned at ranging (0.00mm–1.50 mm) with a uniform deviation of 0.50 mm for each experiment. Shannon entropy was calculated for the identification of localized size of the faults with wavelets nomenclature, the result of DB2, Morlet, and Meyer wavelets at high-frequency zone are presented.The scanning electron microscope (SEM) has been taken for the estimation of size of the fault. The proposed method has been successfully implemented for measuring defect width and size. Also, it has been observed that with increased magnification level from 0.00 mm to 0.50 mm, the crack width of the faulty bearing was increased by 0.813 mm, and whenever on further increase in magnification level of 0.50, 1.00 mm and 1.50 mm the crack width of the faulty bearing was increased by 2.568 mm and 3.856 respectively.