Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.

Identification of the Asymmetric Transmission Error and Gear Mesh Dynamic Parameters using Full-Spectrum Responses in a Geared-Rotor System

A dominant source of vibration in geared-rotor systems is the gear mesh fault parameters.They include the asymmetric transmission error(TE),phases of TE,the gear mesh stiffness,the gear mesh damping,and the gear runouts.The present work deals with the experimental identification of the aforementioned parameters.A mathematical model of a geared-rotor system has been developed using Lagrangian dynamics.Equations of motion are transformed into the frequency domain using the full-spectrum response analysis.These transformed equations are used to develop an identification algorithm(IA)based on least-squares fit to estimate the TE and gear mesh dynamic parameters.The system IA is initially verified using numerical simulations.The robustness of the algorithm is checked by introducing white Gaussian noise in the simulated responses.A geared-rotor experimental rig was developed and used to measure responses at gear locations in two orthogonal directions.Measured responses are transformed in the frequency domain using the full-spectrum analysis and used in the present novel IA to identify the gear parameters.The identified parameters are validated by comparing the numerically generated full-spectrum response using experimentally estimated parameters and that from the experimental rig.

Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation

A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.

A Successive Shift Box-Counting Method for Calculating Fractal Dimensions and Its Application in Identification of Faults

Fractal dimensions of a terrain quantitatively describe the self-organizedstructure of the terrain geometry. However, the local topographic variation cannot be illustrated bythe conventional box-counting method. This paper proposes a successive shift box-counting method,in which the studied object is divided into small sub-objects that are composed of a series of gridsaccording to its characteristic scaling. The terrain fractal dimensions in the grids are calculatedwith the successive shift box-counting method and the scattered points with values of fractaldimensions are obtained. The present research shows that the planar variation of fractal dimensionsis well consistent with fault traces and geological boundaries.

AN INTERNATIONAL VIEW OF DIFFERENT APPROACHES FOR FAULTS DETECTION AND IDENTIFICATION IN NUCLEAR POWER PLANTS

An extensive survey of computer based systems that apply different approaches for faults diagnostics and identifications in nuclear power plants (NPPs) was presented. In the light of reviewed material, the classification criteria were developed. The classification of computational techniques (class of computing devices, class of programming languages, and simulation programs) was discussed. The classification of theoretical aspects applied (brief aspects, and detailed aspects) in computer based diagnostic systems were established. The classification of metholology applied (symbolic reasoning methodology, event based methodology, and function based methodology) in the diagnostic systems was also depicted. In the end, the personal comments on the reviewed material, and scope of the study were described.

Intelligent identification method and application of seismic faults based on a balanced classification network

This study combined fault identification with a deep learning algorithm and applied a convolutional neural network(CNN)design based on an improved balanced crossentropy(BCE)loss function to address the low accuracy in the intelligent identification of seismic faults and the slow training speed of convolutional neural networks caused by unbalanced training sample sets.The network structure and optimal hyperparameters were determined by extracting feature maps layer by layer and by analyzing the results of seismic feature extraction.The BCE loss function was used to add the parameter which is the ratio of nonfaults to the total sample sets,thereby changing the loss function to find the reference of the minimum weight parameter and adjusting the ratio of fault to nonfault data.The method overcame the unbalanced number of sample sets and improved the iteration speed.After a brief training,the accuracy could reach more than 95%,and gradient descent was evident.The proposed method was applied to fault identification in an oilfield area.The trained model can predict faults clearly,and the prediction results are basically consistent with an actual case,verifying the effectiveness and adaptability of the method.

Analysis and Identification on Fault of Rub-Impact between Rotor and Stator

According to the background of the rub impact faults of aerial engines and industrial turbines, two kinds of test rigs, on the base of the dynamics model, are established to study the rub impact faults between rotor and stator with free supports. The orbit of the vibration of rotor displacement is respectively examined on the four impact conditions, which are the normal state with no impact, the early sharp impact statement, the semi sharp impact statement and the terminal blunt impact statement. The route to chaos, appearing with the early sharp impact, is observed for the first time. By analyzing the frequency domain characteristics of the experimental data on four impact conditions, it is testified that the appearance of the sub harmonic vibrations of the order 1/3 and 1/4 is the effective evidence to judge whether or not the blade has initial light rub impact. When there are only the harmonic vibrations of the order of 1/1 and 1/2, the blade stator rub impact faults have become very serious.

Ellipsoidal bounding set-membership identification approach for robust fault diagnosis with application to mobile robots

A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).

The Identification and Modeling of the Volcanic Weathering Crust in the Yingcheng Formation of the Xujiaweizi Fault Depression, Songliao Basin

Through the analysis of core descriptions, well-logs, seismic data, geochemical data and structural settings of the volcanic rock of the Yingcheng Formation in the Xujiaweizi fault depression, Songliao Basin, and the geological section of the Yingcheng Formation in the southeast uplift area, this work determined the existence of volcanic weathering crust exists in the study area. The identification marks on the volcanic weathering crust can be recognized on the scale of core, logging, seismic, geochemistry, etc. In the study area, the structure of this crust is divided into clay layer, leached zone, fracture zone and host rocks, which are 5-118 m thick （averaging 27.5 m）. The lithology of the weathering crust includes basalt, andesite, rhyolite and volcanic breccia, and the lithofacies are igneous effusive and extrusive facies. The volcanic weathering crusts are clustered together in the Dashen zone and the middle of the Xuzhong zone, whereas in the Shengshen zone and other parts of the Xuzhong zone, they have a relatively scattered distribution. It is a major volcanic reservoir bed, which covers an area of 2104.16 km2. According to the geotectonic setting of the Songliao Basin, the formation process of the weathering crust is complete. Combining the macroscopic and microscopic features of the weathering crust of the Yingcheng Formation in Xujiaweizi with the logging and three-dimensional seismic sections, we established a developmental model of the paleo uplift and a developmental model of the slope belt that coexists with the sag on the Xujiaweizi volcanic weathering crust. In addition, the relationship between the volcanic weathering crust and the formation and distribution of the oil/gas reservoir is discussed.

Transmission line fault-cause identification method for large-scale new energy grid connection scenarios

The accurate fault-cause identification for overhead transmission lines supports the operation and maintenance personnel in formulating targeted maintenance strategies and shortening the time of inspecting faulty lines.With the goal of achieving“carbon peak and carbon neutrality”,the schemes for clean energy generation have rapidly developed.Moreover,new energy-consuming equipment has been widely connected to the power grid,and the operating characteristics of the power system have significantly changed.Consequently,these have impacted traditional fault identification methods.Based on the time-frequency characteristics of the fault waveform,new energy-related parameters,and deep learning model,this study proposes a fault identification method suitable for scenarios where a high proportion of new energy is connected to the power grid.Ten parameters related to the causes of transmission line fault and new energy connection scenarios are selected as model characteristic parameters.Further,a fault identification model based on adaptive deep belief networks was constructed,and its effect was verified by field data.

Using structure restoration maps to comprehensively identify potential faults and fractures in compressional structures

Faults and fractures of multiple scales are frequently induced and generated in compressional structural system. Comprehensive identification of these potential faults and fractures that cannot be distinguished directly from seismic profile of the complex structures is still an unanswered problem. Based on the compressional structural geometry and kinematics theories as well as the structural interpretation from seismic data, a set of techniques is established for the identification of potential faults and fractures in compressional structures. Firstly, three-dimensional(3D) patterns and characteristics of the faults directly interpreted from seismic profile were illustrated by 3D structural model. Then, the unfolding index maps, the principal structural curvature maps, and tectonic stress field maps were obtained from structural restoration. Moreover, potential faults and fractures in compressional structures were quantitatively identified relying on comprehensive analysis of these three maps. Successful identification of the potential faults and fractures in Mishrif limestone formation and in Asmari dolomite formation of Buzurgan anticline in Iraq demonstrates the applicability and reliability of these techniques.

Fault Location Identification for Localized Intermittent Connection Problems on CAN Networks

The intermittent connection（IC）of the field-bus in networked manufacturing systems is a common but hard troubleshooting network problem,which may result in system level failures or safety issues.However,there is no online IC location identification method available to detect and locate the position of the problem.To tackle this problem,a novel model based online fault location identification method for localized IC problem is proposed.First,the error event patterns are identified and classified according to different node sources in each error frame.Then generalized zero inflated Poisson process（GZIP）model for each node is established by using time stamped error event sequence.Finally,the location of the IC fault is determined by testing whether the parameters of the fitted stochastic model is statistically significant or not using the confident intervals of the estimated parameters.To illustrate the proposed method,case studies are conducted on a 3-node controller area network（CAN）test-bed,in which IC induced faults are imposed on a network drop cable using computer controlled on-off switches.The experimental results show the parameters of the GZIP model for the problematic node are statistically significant（larger than 0）,and the patterns of the confident intervals of the estimated parameters are directly linked to the problematic node,which agrees with the experimental setup.The proposed online IC location identification method can successfully identify the location of the drop cable on which IC faults occurs on the CAN network.

An Efficient Fuzzy Logic Fault Detection and Identification Method of Photovoltaic Inverters

Fuzzy logic control(FLC)systems have found wide utilization in several industrial applications.This paper proposes a fuzzy logic-based fault detection and identification method for open-circuit switch fault in grid-tied photovoltaic(PV)inverters.Large installations and ambitious plans have been recently achieved for PV systems as clean and renewable power generation sources due to their improved environmental impacts and availability everywhere.Power converters represent the main parts for the grid integration of PV systems.However,PV power converters contain several power switches that construct their circuits.The power switches in PV systems are highly subjected to high stresses due to the continuously varying operating conditions.Moreover,the grid-tied systems represent nonlinear systems and the system model parameters are changing continuously.Consequently,the grid-tied PV systems have a nonlinear factor and the fault detection and identification(FDI)methods based on using mathematical models become more complex.The proposed fuzzy logic-based FDI(FL-FDI)method is based on employing the fuzzy logic concept for detecting and identifying the location of various switch faults.The proposed FL-FDI method is designed and extracted from the analysis and comparison of the various measured voltage/current components for the control purposes.Therefore,the proposed FL-FDI method does not require additional components or measurement circuits.Additionally,the proposed method can detect the faulty condition and also identify the location of the faulty switch for replacement and maintenance purposes.The proposed method can detect the faulty condition within only a single fundamental line period without the need for additional sensors and/or performing complex calculations or precise models.The proposed FL-FDI method is tested on the widely used T-type PV inverter system,wherein there are twelve different switches and the FDI process represents a challenging task.The results shows the superior and accurate performance of the proposed FL-FDI method.

Fault Identification and Health Monitoring of Gas Turbine Engines Using Hybrid Machine Learning-based Strategies

Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to component degrada-tions that are caused by fouling and erosion.The proposed hybrid framework involves integrating both supervised recur-rent neural networks and unsupervised self-organizing maps methodologies,where the former is developed to extract ef-fective features that can be associated with the engine health condition and the latter is constructed for fault severity modeling and tracking of each considered degradation mode.Advantages of our proposed methodology are that it ac-complishes fault identification and health monitoring objectives by only discovering inherent health information that are available in the system I/O data at each operating point.The effectiveness of our approach is validated and justified with engine data under various degradation modes in compressors and turbines.

Research of Risk Identification and Prevention of Underground Pressure Pipelines Damage Caused by External Disturbance

External disturbance is an important cause of underground pressure pipeline damage,which leads to accidents,and it is crucial to study the risk of damage caused by external disturbance and come up with proper prevention and control measures.We reviewed literature on risk identification of underground pressure pipelines damage due to external disturbance was conducted,and a list of risk factors was formed.Based on the list of risk factors,fault tree analysis was carried out on underground pressure pipelines damage caused by external disturbances,and risk prevention and control measures were proposed through the calculation of minimum cut sets,minimum path sets,and structural importance,in hopes of providing reference for the normal operation of underground pressure pipelines.

FAULT IDENTIFICATION IN HETEROGENEOUS NETWORKS USING TIME SERIES ANALYSIS

Fault management is crucial to pro vi de quality of service grantees for the future networks, and fault identification is an essential part of it. A novel fault identification algorithm is proposed in this paper, which focuses on the anomaly detection of network traffic. Since the fault identification has been achieved using statistical information in mana gement information base, the algorithm is compatible with the existing simple ne twork management protocol framework. The network traffic time series is verified to be non-stationary. By fitting the adaptive autoregressive model, the series is transformed into a multidimensional vector. The training samples and identif iers are acquired from the network simulation. A k-nearest neighbor classif ier identifies the system faults after being trained. The experiment results are consistent with the given fault scenarios, which prove the accuracy of the algo rithm. The identification errors are discussed to illustrate that the novel faul t identification algorithm is adaptive in the fault scenarios with network traff ic change.

Parameter identification algorithm for fault location using one terminal data based on frequency domain

This paper presents a novel algorithm of fault location for transmission line.Solving the network spectrum equations for different frequencies the fault can be located accurately by this algorithm with one terminal data of voltage and current,and the identified parameters,such as fault distance, fault resistance,and opposite terminal system resistance and inductance.The algorithm eliminates the influence of the opposite system impedance on the fault location accuracy,which causes the main error in traditional fault location methods using one terminal data.A method of calculating spectrum from sampled data is also proposed.EMTP simulations show the validity and higher accuracy of the fault location algorithm compared to the existing ones based on one terminal data.

Fault Identification of Power Grid Based on Wide-Area Differential Current and K-Means Clustering

A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associated domain is defined, and the relationship of positive sequence current fault component for the association domain boundaries is sought, then the conception of positive sequence fault component differential current for node IED association domains is introduced. The information of the positive sequence fault component differential current gathered by node IEDs is selected as the object of K-means clustering. The node IEDs of fault associated domains can be classified into one category, and the node IEDs of non-fault associated domains are classified into another category. With the fault area minimum principle, the group of node IEDs about fault associated domains can be obtained. The overlap of fault associated domains for different nodes is the fault area. A large number of simulations show that the algorithm proposed can identify fault domains with high accuracy and no influence by the operating mode of the system and topological changes.

A Novel Parsimonious Neurofuzzy Model Applied to Railway Carriage System Identification and Fault Diagnosis

In this paper, we suggest a novel parsimonious neurofuzzy model realized by RBFNs for railway carriage system identification and fault diagnosis. To overcome the curse of dimensionality resulting from high dimensional input variables, in our developed model the features extracted from the available observations are regarded as the input variables by adopting the higher-order statistics(HOS) technique. Such a constructed model is also applied to a practical railway carriage system, simulation results indicate that the developed neurofuzzy model possesses strong identification and fault diagnosis ability.

Single Phase-to-Ground Fault Line Identification and Section Location Method for Non-Effectively Grounded Distribution Systems Based on Signal Injection

A diagnostic signal current trace detecting based single phase-to-ground fault line identifica- tion and section location method for non-effectively grounded distribution systems is presented in this paper.A special diagnostic signal current is injected into the fault distribution system,and then it is detected at the outlet terminals to identify the fault line and at the sectionalizing or branching point along the fault line to locate the fault section.The method has been put into application in actual distribution network and field experience shows that it can identify the fault line and locate the fault section correctly and effectively.