Fault Location Result Research Articles

Spatio-temporal information analytics based performance-driven industrial process monitoring framework with cloud–edge-device collaboration

Smart manufacturing in the context of Industry 4.0 presents new characteristics, such as large-scale, complex sub-processes cooperation, and integrated information management, which makes comprehensive performance-driven process monitoring difficult to be achieved in practice. Cloud–edge-device collaboration brings new perspectives for modern industries. This paper presents a performance-driven process monitoring framework in cloud–edge-device collaborative architecture, including a spatio-temporal information analytics based method and its application to a hot strip mill process (HSMP). Firstly, an integrated performance-driven process monitoring framework is proposed, where different monitoring tasks are deployed in the device, edge, and cloud layers, respectively. Secondly, a new performance-related spatio-temporal information analytics based fault detection and localization method is developed. In detail, temporal kernel canonical correlation analysis (CCA) is employed to extract the canonical correlation features with nonlinearities in the temporal domains, and spatial CCA is designed to form the spatial features. Based on the spatio-temporal features, the equipment operation condition monitoring model is developed in the device layer for real-time fault localization, and the process monitoring model is deployed in the edge for fault detection. Furthermore, to give a high-level evaluation of the operating status of the process, a qualitative trend analysis based operation risks evaluation model is deployed in the cloud. Different from the traditional architectures, the data processing capability of each layer is fully utilized in the proposed one, and the function is relevant to the enterprise management system. It provides a topological basis with clear priorities for subsequent process regulation and optimization. Finally, an actual HSMP is introduced to testify the performance of the framework. From the results, the fault detection rates are improved in comparison with traditional methods, and the fault localization results are consistent with the actual situations.

High pass filter based traveling wave method for fault location in VSC-Interfaced HVDC system

Due to the difficult topography and bad weather conditions across large distances, dc line breakdown is a serious danger to the safety of high-voltage direct-current systems (HVDC). Thus, there is a need of an accurate and reliable fault location method to safely isolate the dc grid in the occurrence of a dc fault. Traveling wave (TW) fault location techniques have gained in popularity over the decades, with the expectation that power networks will become considerably more credible and accurate throughout. However, in the existing traveling wave based location, the signal processing methodology for extracting the wave front consumes too many computer resources, necessitating the development of a new location method with a fast response and high accuracy. In this paper, a new algorithm of TW based on high pass filter is proposed, it can differentiate the TW peak more easily by removing the oscillation in a signal. To ensure more accuracy double ended method (Type D) is used to obtain the location for the fault. The theory behind this new algorithm is presented in detail. High pass filter is designed for this purpose and its validity tested on point to point (P2P) dc simulation model which is created in PSCAD software to gather the voltage data from both terminals during transient. Following that the proposed fault location algorithm is executed in the MATLAB environment to get the fault location results. A comparison of the proposed algorithm with two popular methods namely the wavelet transforms (WT) and the single ended method highlights the superiority.

Electromagnetic Time Reversal Fault Location Method Based on Characteristic Frequency of Traveling Waves

In its application to locate ground faults, the electromagnetic time reversal (EMTR) method uses voltage from only a single point. However, when a high-impedance fault (HIF) occurs, the reflected signal from the fault point is weak, leading to an error between the simulated and actual fault location results. This paper proposes a new EMTR method based on the characteristic frequency of the traveling wave reflecting from the fault point. First, the transient signal spectrum under different fault resistances was analyzed, and the reasons for the inapplicability of the existing EMTR method under HIF conditions were discussed. Subsequently, a single-end signal expression with fault resistance was deduced, and the applicability of the EMTR method, which involves calculating only the energy of the fault current at the characteristic frequency, was verified. A reduced-scale experiment and a simulation in PSCAD were performed, and the EMTR method and proposed method were compared. The results showed that the proposed method can be applied to different complex lines, and it outperforms the conventional EMTR method in the event of HIFs.

Fault location of untransposed double-circuit transmission lines based on an improved Karrenbauer matrix and the QPSO algorithm

Some double-circuit transmission lines are untransposed, which results in complex coupling relations between the parameters of the transmission lines. If the traditional modal transformation matrix is directly used to decouple the parameters, it can lead to large errors in the decoupled modal parameter, errors which will be amplified in the fault location equation. Consequently, it makes the fault location results of the untransposed double-circuit transmission lines less accurate. Therefore, a new modal transformation method is needed to decouple the parameter matrix of untransposed double-circuit transmission lines and realize the fault location according to the decoupled modal parameter. By improving the basis of the Karrenbauer matrix, a modal transformation matrix suitable for decoupling parameters of untransposed double-circuit transmission lines is obtained. To address the difficulties in solving the fault location equation of untransposed double-circuit transmission lines, a new fault location method based on an improved Karrenbauer matrix and the quantum-behaved particle swarm optimization (QPSO) algorithm is proposed. Firstly, the line parameter matrix is decomposed into identical and inverse sequence components using the identical-inverse sequence component transformation. The Karrenbauer matrix is then transformed to obtain the improved Karrenbauer matrix for untransposed double-circuit transmission lines and applied to identical and inverse sequence components to solve the decoupled modal parameter. Secondly, based on the principle that voltage magnitudes at both ends are equal, the fault location equation is expressed using sequence components at each end, and the QPSO algorithm is introduced to solve the equation. Finally, the feasibility and accuracy of the proposed method are verified by PSCAD simulation. The simulation results fully demonstrate that the innovative improvement on the basis of the traditional modal transformation matrix in this paper can realize the modal transformation of the complex coupling parameters of the untransposed double-circuit transmission lines. It causes almost no errors in the decoupling process. The QPSO algorithm can also solve the fault location equation more accurately. The new fault location method can realize the accurate fault location of untransposed double-circuit transmission lines.

Effect of Stray Capacitance in a Distribution Network on the Accuracy of Fault Location Based on Electromagnetic Time Reversal Method

The existing electromagnetic time reversal (EMTR) method for determining the location of faults in a distribution system does not consider stray capacitors in the system, leading to inaccurate fault location results. In this article, the influence of stray capacitors at the end and middle of the line on the EMTR method without considering the capacitance in the backward-time process was analyzed, the current signal energy of the fault location was derived, and the fault location was determined. A reduced-scale experimental platform of the EMTR method was built, and the influence of stray capacitors at observation points and middle of lines on the fault location results was verified. Finally, a fault location platform for a 33-node distribution network was built in power systems computer aided design (PSCAD) to explore the impact of stray capacitance on the fault location accuracy. The results showed that the error increases with the increase of the stray capacitance. This article found that considering the stray capacitance in the backward-time process can help determine the fault location more accurately. Besides, the signal observation point setting at low time constant area can also improve the accuracy when stray capacitance cannot be determined.

Multi-section frequency dependent line model enabled underground cable fault location in MMC-HVDC grids using dynamic state estimation

Line parameters of underground cables (UGCs) in MMC-HVDC grids experience severe frequency dependent characteristics. For fault location of UGCs in MMC-HVDC grids, existing methods usually neglect the frequency dependent line parameters, which limits the fault location accuracy. To overcome this issue, a dynamic state estimation based fault location (DSEFL) method for UGCs in the MMC-HVDC grid is proposed in this paper. Firstly, the dynamic line model of the UGC based on the multi-section multi-layer model is formulated to consider both frequency dependent and distributed characteristics of the line parameters. Next, the DSE algorithm is used to solve the dynamic line model and the consistency indicator with respect to the determined fault location and resistance. Due to the complexity of the multi-layer model, a multi-step algorithm is designed to efficiently obtain the fault location result. Numerical experiments on MMC-HVDC grids verify that the proposed DSEFL method presents higher fault location accuracy for different fault types, locations, and resistances. compared to the existing methods. In addition, the computation burden, effects of measurement noises, parameter errors, and sampling rates are also discussed.

The online monitoring method of single-phase grounding fault in medium voltage distribution network based on multi-measuring point information

To reduce the error of single-phase ground fault and improve the security and stability of distribution network operation, an online monitoring method for single-phase ground fault in a medium voltage distribution network based on multi-measuring point information is proposed. Based on the analysis of single-phase grounding transient characteristics, the fault location is determined from the information of multiple measuring points according to the characteristics analysis results to realize the fault subsection location, identify the fault line section, and improve the location accuracy. Based on the fault location results, fault monitoring is realized through the “four-in-one” intelligent distribution terminal. The experimental results show that the accuracy of the monitoring results of this method is kept above 95%, and the monitoring error value of grounding fault is kept at about 1%, so the monitoring results have high reliability.

Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix

Aiming at the difficulty of fault location of multi-source transmission lines, this paper proposes a fault location method for multi-terminal transmission lines based on a fault branch judgment matrix. The fault traveling wave signal is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the IMFs sensitive components that can characterize the fault characteristics of the target signals are selected by constructing a correlation-rearrangement entropy function. The arrival time of fault signals at the endpoint has been accurately calibrated by combining them with the Teager Energy Operator (TEO). To eliminate the influence of wave velocity and fault time on the location results, this paper proposes a two-terminal location method based on the line mode component to improve the location accuracy. On this basis, combined with the fault branch judgment matrix, the accurate location of multi-terminal transmission line faults is realized. This method has been shown to have high accuracy in detecting traveling wave heads, accurately judging fault branches, and producing a small error in fault location results. Compared with the existing multi-terminal transmission line fault location algorithm, it has obvious advantages and meets the needs of actual working conditions.

Research on the Application of Uncertainty Quantification (UQ) Method in High-Voltage (HV) Cable Fault Location

In HV cable fault location technology, line parameter uncertainty has an impact on the location criterion and affects the fault location result. Therefore, it is of great significance to study the uncertainty quantification of line parameters. In this paper, an impedance-based fault location criterion was used for an uncertainty study. Three kinds of uncertainty factors, namely the sheath resistivity per unit length, the equivalent grounding resistance on both sides, and the length of the cable section, were taken as random input variables without interaction. They were subject to random uniform distribution within a 50% amplitude variation. The relevant statistical information, such as the mean value, standard deviation and probability distribution, of the normal operation and fault state were calculated using the Monte Carlo simulation (MCS) method, the polynomial chaos expansion (PCE) method, and the univariate dimension reduction method (UDRM), respectively. Thus, the influence of uncertain factors on fault location was analyzed, and the calculation results of the three uncertainty quantification methods compared. The results indicate that: (1) UQ methods are effective for simulation analysis of fault locations, and UDRM has certain application prospects for HV fault location in practice; (2) the quantification results of the MCS, PCE, and UDRM were very close, while the mean convergence rate was significantly higher for the UDRM; (3) compared with the MCS, PCE, and UDRM, the PCE and UDRM had higher accuracy, and MCS and UDRM required less running time.

Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning.

Objective The processing and analysis of medical rehabilitation information data in tertiary hospitals is a hot research topic. Combining medical data analysis with machine learning algorithms to improve data mining efficiency is a problem that needs to be solved at present. This paper proposes an autonomous perception model of sports medicine rehabilitation equipment based on a deep learning algorithm for sports medical rehabilitation data. Methods This paper cites a deep learning multi-dimensional perception model for medical rehabilitation equipment autonomous perception. The model utilizes the automatic overhaul of medical rehabilitation equipment based on deep belief networks. This paper extracts features through a multi-layer neural network and obtains fault location results of medical rehabilitation equipment through softmax. Results In similarity prediction, the accuracy rate of the first three kinds of feedback containing the target answer is 77%. The accuracy rate of the target answers included in the top five kinds of feedback was 92%. Conclusion In this study, it is feasible to apply deep learning to the quality control information system of sports rehabilitation medical equipment. This improves the management efficiency of medical rehabilitation equipment to a certain extent.

New Algorithm for 2-terminal Transmission Line Fault Location Integrating Voltage Phasor Feature and Phase Angle Jump Checking

The low precision and instability of fault location results of power system transmission lines is a major problem that affects the safe operation of power systems, which needs to be solved more effectively. With the increasing coverage of the synchronic Phasor Measurement Unit (PMU) in power systems, it has laid a good foundation for the research and application of high-precision fault location method based on 2-termnal synchronous measurement of phasor. Based on this, a new high-precision fault location algorithm integrating voltage phasor feature and phase-angle jump checking is proposed in this paper. Firstly, the synchronous fundamental positive sequence voltage and current phasors of the fault line are extracted; and then all the roots(including real and virtual roots) are calculated by using the condition that the fundamental positive sequence voltage phasor at the fault point is equal; finally the unique real root represents the actual fault point can be determined by using the feature that the check function of phase-angle will step-jump when crossing the fault point from left to right, and then the purpose of high precision fault location is realized. This algorithm has the characteristics of strong versatility, high precision and immunity to transition resistance. The results of simulation and example show the feasibility and effectiveness of the fault location algorithm the fault location algorithm proposed in this paper.

Fault diagnosis mechanism of gears and bearings for coal mining equipment in a complex geological environment

The traditional research method of fault diagnosis mechanism has poor stability, which leads to the difference of fault diagnosis and location results. Therefore, under the complex geological environment, a new research method of fault diagnosis mechanism of gear and bearing for coal mining equipment is proposed. This method calculates gears and bearings’ yield strength by analyzing coal mining equipment’s bearing capacity elasticity. According to the fitting degree, the equipment sample’s projection space is confirmed, the fault features of gear and bearing are extracted by segmentation algorithm, the optimal fitness is set by positioning algorithm, the location of fault center is obtained, and the fault mechanism diagnosis is studied. Experimental results show that compared with the traditional method, the proposed method is more stable, and the difference in fault diagnosis results is minimal. It can be seen that this method is more suitable for fault diagnosis of coal mining equipment.

ПРОДОЛЬНОЕ СОПРОТИВЛЕНИЕ ОДНОЦЕПНОЙ ЛЭП С ДВУМЯ ГРОЗОЗАЩИТНЫМИ ТРОСАМИ В ЗАДАЧЕ ДИСТАНЦИОННОГО ОМП

One of the most important tasks in remote fault location in a 110 kV+ network is modeling the power lines taking into account the electromagnetic and electrostatic interaction of their conductive elements. A common approach is to use the mathematical models recommended by the guidelines for calculating short-circuit currents for relay protection. However, these models of power transmission lines take into account the geometric mean distance between the wires and the lightning protection cables, which means they average the values of the mutual induction impedances. In cases where wires and cables are located asymmetrically relative to each other, this leads to an overestimation of the zero-sequence impedance of power lines and, as a consequence, to an underestimation of the single-phase short-circuit current. This situation can affect the results of remote fault location. To eliminate this drawback, the article proposes a combined equivalent circuit for a single-circuit power transmission line with two lightning protection cables, based on a combination of methods of phase coordinates and symmetric components. The given equivalent circuit of power transmission lines allows calcula¬ting the mode parameters using the method of symmetric components and, at the same time, simulating those of interest for calculating power lines using phase coordinates.

Incremental Formula-Based Fix Localization

Automatically fixing bugs in software programs can significantly reduce the cost and improve the productivity of the software. Toward this goal, a critical and challenging problem is automatic fix localization, which identifies program locations where a bug fix can be synthesized. In this paper, we present AgxFaults, a technique that automatically identifies minimal subsets of program statements at which a suitable modification can remove the error. AgxFaults works based on dynamically encoding semantic of program parts that are relevant to an observed error into an unsatisfiable logical formula and then manipulating this formula in an increasingly on-demand manner. We perform various experiments on faulty versions of the traffic collision avoidance system (TCAS) program in the Siemens Suite, programs in Bekkouche’s benchmark, and server real bugs in the Defects4J benchmark. The experimental results show that AgxFaults outperforms single-path-formula approaches in terms of effectiveness in finding fix localization and fault localization. AgxFaults is better than program-formula-based approaches in terms of efficiency and scalability, while providing similar effectiveness. Specifically, the solving time of AgxFaults is 28% faster, and the running time is 45% faster, than the program-formula-based approach, while providing similar fault localization results.

Single Pole‐to‐Ground Fault Location Method for MMC‐HVDC System Using Wavelet Decomposition and DBN

Accurate and reliable fault location technology is essential to the stable operation of the modular multilevel converter‐high voltage direct current (MMC‐HVDC) system. Aiming at the difficulty of locating high‐resistance ground faults on MMC‐HVDC transmission lines, this paper proposes a method for fault location of transmission lines based on wavelet transform and deep belief network (DBN). First, the wavelet transform is used to decompose the original single pole ground fault voltage waveform, and then the high‐frequency and low‐frequency components obtained are used as training samples to train different DBN models, the final fault location results are obtained by superimposing the outputs of each model at last. The ±250 kV double‐ended MMC‐HVDC system model is established by using PSCAD/EMTDC, which can simulate the faults of different positions and different transition resistances. In order to verify the fault location performance of the proposed method, it is compared with two machine learning fault location methods. The results show that this method can accurately and reliably locate the single pole‐to‐ground fault of the transmission line with transition resistance of up to 10 000 Ω at low sampling frequency of 20 kHz. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Research on Portable Fault Message Analysis Device Based on Mobile Terminals

As the scale of the power grid expands and the complexity of the power grid structure increases, timely acquisition and correct analysis of grid fault information is important for us handling failure and recovering the power supply. When we are facing of complex grid faults, It can help us get accurate assessment of fault nature, fault location result and failure severity. This paper proposes a portable Fault Message Analysis Device based on mobile terminals, which can help field personnel to obtain fault recording information of different sources in different substations, and complete data remote transmission sharing by wireless transmission module. This device can confirm the fault point and judge the correctness of the protection action behavior through differential calculation and analysis of multi-source recording data. It has powerful functions of analysis and communication, which can help improve the level of fault analysis and processing efficiency, reduce the time of fault recovery and improve the reliability of power supply.

Failure Analysis of Interconnection Fault in a PoP Module Applied for Mobile Phone

Abstract A typical early fault case of some mobile phones was studied in this paper. The failure phenomenon was manifested as that the mobile phones could not be powered on after one year in their service, with the malfunction rate having reached about 2% of the whole yield in the same manufacturing lot as the fault machines returned from client side continuously. The results of fault location showed that there was interconnection failure in the main chip module, which used package on package (PoP) packaging. A series of experimental technique was used for the failure analysis, such as 3D X-ray inspection, shadow moiré test, microsectioning, scanning electron microscope (SEM), electron backscattered diffraction (EBSD) analysis, etc. The results showed that some solder joints' through cracking in the PoP module was the immediate cause of the mobile phones' malfunction, and the main reason for the cracking was thermal-mechanical fatigue with recrystallization microstructure degradation. It is important to further study on the technological method for controlling grain orientations in electronic interconnections.

Robust Wide Area Fault Location Considering Network Parameters Error

Network parameters error may seriously affect the accuracy of fault location results, even making them unacceptable in extreme cases. This paper proposes a new method that incorporates these errors in wide area fault location problem using phasor measurement units to estimate inaccurate network parameters employing robust state estimation. Our proposed method is composed of two stages. The first stage or the primary stage provides an initial point for the second stage, which is the main contribution of this paper. Additionally, the set of measurements contaminated with bad data could be obtained from the first stage, and its intersection with leverage measurements are then eliminated from the measurement set before feeding it into the second stage. Finally, fault point and faulted line parameters are estimated by the main (second) stage of the method. The proposed method has been applied to two test systems, namely Institute of Electrical and Electronics Engineers (IEEE) 14-bus and IEEE 57-bus systems, to evaluate its accuracy. Simulation results indicate high accuracy and consistency of the proposed method.

A New Theory for Locating Line Fault in Power System: Simulation Part

Three time-reversal-based fault location methods are derived from the theoretical part of this paper, which is named currents-in-time-domain method, forward-currents-in-frequency-domain method, and forward-currents-in-frequency-domain-with-compensation method, respectively. The first proposed method utilizes only the decomposed transient currents in the time domain to calculate the RMS values of the fault currents of assumed fault in a lossless mirror line. The location where the peak value is located is the calculated fault location. The second method uses the forward current in the frequency domain to calculate the RMS values of the fault currents of assumed fault in a loss mirror line. The location where the minimum value is located is the calculated fault location. The third method first calculates the RMS values of fault currents of assumed fault in lossless mirror line, and the fault location is selected from the peak value. Then the final fault location result is compensated by the pre-calculated error for each assumed fault location. A two-terminal AC transmission system is established for testing these three methods. The frequency-dependent lines including homogeneous and inhomogeneous lines are taken into the simulated system for a large number of tests.

Intelligent Localization of Transformer Internal Degradations Combining Deep Convolutional Neural Networks and Image Segmentation

Industrial 4.0 placed higher demands on the field of intelligent equipment monitoring. The transformer is one of the critical power devices, its intelligent monitoring and fault positioning require in-depth studies. In this study, an efficient fault localization method for transformer internal thermal faults was proposed by introducing different deep convolutional neural networks (CNNs) and image segmentation. First, the transformer monitoring images of temperature and velocity fields in fault conditions were simulated using the lattice Boltzmann method (LBM), and the images were also used to highlight features information. In practice, transformer degradation does not frequently occur, so that the fault samples for deep learning are insufficient. To solve this problem, a transfer learning method was employed. Subsequently, fault locations were defined as classification labels, and different CNN's were used to classify the labels to achieve the fault localization results. Next, image segmentation was performed to extract the features of fault areas and simplify the data volumes. Likewise, the CNN's were employed to perform the fault localization again. Afterward, since the monitoring sensors were not located everywhere in a transformer in practical applications, information of partial monitoring areas where the monitoring sensors located was trained following a similar procedure. After image segmentation, the average fault localization accuracy using the information obtained by sensors decreased from 97.95% to 94.42%, while the data volume was reduced to nearly 1% of the original one. Besides, the average calculation time per iteration decreased by 8.816%, while the loss value was reduced by 37.68%. Finally, the Friedman hypothesis test and Nemenyi post hoc test were performed to compare the evaluation indicators of different networks, and the performance of GoogLeNet in this case was considered the best.