Research on Asymmetric Fault Location of Wind Farm Collection System Based on Compressed Sensing

Aiming at the problem that most of the cables in the power collection systemof offshore wind farms are buried deep in the seabed,whichmakes it difficult to detect faults,this paper proposes a two-step fault location method based on compressed sensing and ranging equation.The first step is to determine the fault zone through compressed sensing,and improve the datameasurement,dictionary design and algorithmreconstruction:Firstly,the phase-locked loop trigonometric functionmethod is used to suppress the spike phenomenon when extracting the fault voltage,so that the extracted voltage valuewillnot have a large error due to the voltage fluctuation.Secondly,theλ-NIM dictionary is designed by using the node impedancematrix and the fault location coefficient to further reduce the influence of pseudo-fault points.Finally,the CoSaMP algorithmis improved with the generalized Jaccard coefficient to improve the reconstruction accuracy.The second step is to use the ranging equation to accurately locate the asymmetric fault of the wind farm collection system on the basis of determining the fault interval.The simulation results show that the proposedmethod ismore accurate than the compressedsensingmethod andimpedancemethod in fault section location and fault location accuracy,the relative error is reduced from 0.75%to 0.4%,and has a certain anti-noise ability.

Adaptive restarting method for LCC-HVDC based on principle of fault location by current injection

The existing LCC-HVDC transmission project adopts the fixed-time delay restarting method.This method has disadvantages such as non-selectivity,long restart process,and high probability of restart failure.These issues cause a secondary impact on equipment and system power fluctuation.To solve this problem,an adaptive restarting method based on the principle of fault location by current injection is proposed.First,an additional control strategy is proposed to inject a current detection signal.Second,the propagation law of the current signal in the line is analyzed based on the distributed parameter model of transmission line.Finally,a method for identifying fault properties based on the principle of fault location is proposed.The method fully considers the influence of the long-distance transmission line with earth capacitance and overcomes the influence of the increasing effect of the opposite terminal.Simulation results show that the proposed method can accurately identify the fault properties under various complex fault conditions and subsequently realize the adaptive restarting process.

Accurate Fault Location Modeling for Parallel Transmission Lines Considering Mutual Effect

:A new accurate algorithms based on mathematical modeling of two parallel transmissions lines system(TPTLS)as influenced by the mutual effect to determine the fault location is discussed in this work.The distance relay measures the impedance to the fault location which is the positive-sequence.The principle of summation the positive-,negative-,and zero-sequence voltages which equal zero is used to determine the fault location on the TPTLS.Also,the impedance of the transmission line to the fault location is determined.These algorithms are applied to single-line-to-ground(SLG)and double-line-to-ground(DLG)faults.To detect the fault location along the transmission line,its impedance as seen by the distance relay is determined to indicate if the fault is within the relay’s reach area.TPTLS under study are fed from one-and both-ends.A schematic diagrams are obtained for the impedance relays to determine the fault location with high accuracy.

Small-current grounding fault location method based on transient main resonance frequency analysis

The small-current grounding fault in distribution network is hard to be located because of its weak fault features.To accurately locate the faults,the transient process is analyzed in this paper.Through the study we take that the main resonant frequency and its corresponding component is related to the fault distance.Based on this,a fault location method based on double-end wavelet energy ratio at the scale corresponding to the main resonant frequency is proposed.And back propagation neural network(BPNN)is selected to fit the non-linear relationship between the wavelet energy ratio and fault distance.The performance of this proposed method has been verified in different scenarios of a simulation model in PSCAD/EMTDC.

Fault Location Method for STATCOM Connected Transmission Lines Using CCM

Determining the fault location using conventional impedance based distance relay in the presence of FACTS controllers is a challenging task in a transmission line. A new distance protection method is developed to locate the fault in a transmission line compensated with STATCOM with simple calculations. The proposed protection method considers the STATCOM injected/absorbed current to correct the fault loop apparent impedance and accordingly calculates the actual distance to the fault location. The comprehensive equations needed for apparent impedance calculation are also outlined and the performance is evaluated and tested with a typical 400 KV transmission system for different fault types and locations using MATLAB/SIMULINK software. The evaluation results indicate that the new protection method effectively estimates the exact fault location by mitigating the impact of STATCOM on distance relay performance with error less than 0.3%.

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.

Improved fault location method for AT traction power network based on EMU load test

The autotransformer(AT)neutral current ratio method is widely used for fault location in the AT traction power network.With the development of high-speed electrified railways,a large number of data show that the relation between the AT neutral current ratio and the distance from the beginning of the fault AT section to the fault point(Q-L relation)is mostly nonlinear.Therefore,the linear Q-L relation in the traditional fault location method always leads to large errors.To solve this problem,a large number of load-related current data that can be used to describe the Q-L relation are obtained through the load test of the electric multiple unit(EMU).Thus,an improved fault location method based on the back propagation(BP)neural network is proposed in this paper.On this basis,a comparison between the improved method and the traditional method shows that the maximum absolute error and the average absolute error of the improved method are 0.651 km and 0.334 km lower than those of the traditional method,respectively,which demonstrates that the improved method can effectively eliminate the influence of nonlinear factors and greatly improve the accuracy of fault location for the AT traction power network.Finally,combined with a shortcircuit test,the accuracy of the improved method is verified.

Fault location by one-terminal measurement in distribution network

Presents the theory behind, the system design of the acquisition of parameters for and the experiment on the fault location by one terminal measurement in actual distribution network, and some of laws governing the on site acquisition of parameters and fault location established through experimental research on actual power distribution lines.

T-Shaped Transmission Line Fault Location Based on Phase-Angle Jump Checking

In order to effectively solve the dead-zone and low-precision of T-shaped transmission line fault location,a new T-shaped transmission line fault location algorithm based on phase-angle jump checking is proposed in this paper.Firstly,the 3-terminal synchronous fundamental positive sequence voltage and current phasors are extracted and substituted into the fault branch distance function to realize the selection of fault branch when the fault occurs;Secondly,use the condition of the fundamental positive sequence voltage phasor at the fault point is equal to calculate all roots(including real root and virtual roots);Finally,the phase-angle jump check function is used for checking calculation,and then the only real root can be determined as the actual fault distance,thereby achieving the purpose of high-precision fault location.MATLAB simulation results show that the proposed new algorithm is feasible and effective with high fault location accuracy and good versatility.

Auxiliary Fault Location on Commercial Equipment Based on Supervised Machine Learning

As the fundamental infrastructure of the Internet,the optical network carries a great amount of Internet traffic.There would be great financial losses if some faults happen.Therefore,fault location is very important for the operation and maintenance in optical networks.Due to complex relationships among each network element in topology level,each board in network element level,and each component in board level,the con-crete fault location is hard for traditional method.In recent years,machine learning,es-pecially deep learning,has been applied to many complex problems,because machine learning can find potential non-linear mapping from some inputs to the output.In this paper,we introduce supervised machine learning to propose a complete process for fault location.Firstly,we use data preprocessing,data annotation,and data augmenta-tion in order to process original collected data to build a high-quality dataset.Then,two machine learning algorithms(convolutional neural networks and deep neural networks)are applied on the dataset.The evaluation on commercial optical networks shows that this process helps improve the quality of dataset,and two algorithms perform well on fault location.

Multi-Branch Fault Line Location Method Based on Time Difference Matrix Fitting

The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system.In this paper,the multi-branch mainline is decomposed into single branch lines,transforming the complex multi-branch fault location problem into a double-ended fault location problem.Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines,the endpoint reference time difference matrix S and the fault time difference matrix G were established.The time variation rule of the fault-traveling wave arriving at each endpoint before and after a fault was comprehensively utilized.To realize the fault segment location,the least square method was introduced.It was used to find the first-order fitting relation that satisfies the matching relationship between the corresponding row vector and the first-order function in the two matrices,to realize the fault segment location.Then,the time difference matrix is used to determine the traveling wave velocity,which,combined with the double-ended traveling wave location,enables accurate fault location.

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods

Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems.However,traditional intelligent methods limit the use of the physical structures and data information of power networks.To this end,this study proposes a fault diagnostic model for distribution systems based on deep graph learning.This model considers the physical structure of the power network as a significant constraint during model training,which endows the model with stronger information perception to resist abnormal data input and unknown application conditions.In addition,a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability.This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults.In addition,a multi-task learning framework is constructed for fault location and fault type analysis,which improves the performance and generalization ability of the model.The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model.Finally,different fault conditions,topological changes,and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model.Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods.

Fast Single-phase Fault Location Method Based on Community Graph Depth-first Traversal for Distribution Network

With the increasing complexity of distribution network structures originating from the high penetration of renewable energy and responsive loads,fast and accurate fault location technology for distribution networks is a prerequisite for rapid isolation of faults and restoration of the power supply.In this paper,a fault location method based on community graph depth-first traversal is proposed for fast location of single-phase ground faults in distribution networks.First,this paper defines the fault graph weight of the vertices in the distribution network graph model,which can be used to reflect the topology of the vertices and fault points as well as the fluctuation of the vertices’currents.Then,the vertices on the graph model are clustered by using an improved parallel louvain method(IPLM).Finally,the community formed by IPLM is used as the smallest unit for depth-first traversal to achieve fast and accurate location of the fault section.The paper develops a distribution network graph model of IEEE 33-bus system on the graph database for testing.And three other methods are selected for comparison with IPLMDF.The test results show that IPLMDF can achieve fast and accurate fault location when half of the nodes in the distribution network are equipped with D-PMUs.When some of the D-PMUs lose time synchronization,it is still possible to locate the fault section,and at the same time,the locating results can be avoided by falling into local optimal solutions.

Fault Location Approach to Distribution Networks Based on Custom State Estimator

This paper presents a properly designed branchcurrent based state estimator(BCBSE)used as the main core ofan accurate fault location approach(FLA)devoted to distribution networks.Contrary to the approaches available in the literature,it uses only a limited set of conventional measurementsobtained from smart meters to accurately locate faults at busesor branches without requiring measurements provided by phasor measurement units(PMUs).This is possible due to themethods used to model the angular reference and the faultedbus,in addition to the proper choice of the weights in the stateestimator(SE).The proposed approach is based on a searchingprocedure composed of up to three stages:①the identificationof the faulted zones;②the identification of the bus closest tothe fault;and③the location of the fault itself,searching onbranches connected to the bus closest to the fault.Furthermore,this paper presents a comprehensive assessment of the proposedapproach,even considering the presence of distributed generation,and a sensitivity study on the proper weights required bythe SE for fault location purposes,which can not be found inthe literature.Results show that the proposed BCBSE-basedFLA is robust,accurate,and aligned with the requirements ofthe traditional and active distribution networks.

Experimental studies on impedance based fault location for long transmission lines

In long transmission lines,the charging current caused by the shunt capacitance decreases the accuracy in impedance based fault location.To improve the accuracy of fault location,this paper presents a novel scheme,where two Digital Fault Recorders(DFRs)are installed in a line.They can send the transient data of the faults to the both ends of a line.To estimate the distance of a fault,impedance based fault location methods are applied with transient fault data of both ends protection relays and both DFRs installed in a line.To evaluate the proposed scheme,a laboratory setup has been developed.In the lab,several faults have been simulated and associated voltages and currents are injected to a relay IED to compare experimental results.

Distributed processing based fault location,isolation,and service restoration method for active distribution network

Active distribution network(ADN)is a solution for power system with interconnection of distributed energy resources(DER),which may change the network operation and power flow of traditional power distribution network.However,in some circumstances the malfunction of protection and feeder automation in distribution network occurs due to the uncertain bidirectional power flow.Therefore,a novel method of fault location,isolation,and service restoration(FLISR)for ADN based on distributed processing is proposed in this paper.The differential-activated algorithm based on synchronous sampling for feeder fault location and isolation is studied,and a framework of fault restoration is established for ADN.Finally,the effectiveness of the proposed algorithm is verified via computer simulation of a case study for active distributed power system.

Fault Location in Distribution System Using Convolutional Neural Network Based on Domain Transformation

Distribution lines are integral parts of the modern power system,which can affect the security and stability of power supply directly.An effective power system protection scheme should be able to detect all occurring faults as soon as possible.There are two tasks in fault diagnosis.One is the fault classification,where high accuracy rates have already achieved.Thus,this paper focuses on the other task,i.e.fault location.Enlightened by Fourier transform,this paper proposes an online data-driven method,which transforms signals from time domain to image domain through signal-to-image(SIG)algorithm and then process the transformed images with framework based on convolutional neural network(CNN).On the one hand,we can extract more crucial characteristic and information from image domain.On the other hand,the CNN-based structure is much smaller than others.It needs less memory space and would be easier to be transplanted to hardware platform.Moreover,the proposed algorithm does not require synchronous devices.The numerical comparison shows that the proposed SIG-CNN fault location model achieves robust and accurate results compared with other data-driven algorithms.

Hybrid classifier for fault location in active distribution networks

This paper presents a fast hybrid fault location method for active distribution networks with distributed generation(DG)and microgrids.The method uses the voltage and current data from the measurement points at the main substation,and the connection points of DG and microgrids.The data is used in a single feedforward artificial neural network(ANN)to estimate the distances to fault from all the measuring points.A k-nearest neighbors(KNN)classifier then interprets the ANN outputs and estimates a single fault location.Simulation results validate the accuracy of the fault location method under different fault conditions including fault types,fault points,and fault resistances.The performance is also validated for non-synchronized measurements and measurement errors.

A novel fault location method for distribution networks with distributed generations based on the time matrix of traveling-waves

To improve location speed,accuracy and reliability,this paper proposes a fault location method for distribution networks based on the time matrix of fault traveling waves.First,an inherent time matrix is established according to the normalized topology of the target distribution network,and a post-fault time matrix is obtained by extracting the head data of initial waves from traveling wave detection devices.A time determination matrix is then obtained using the difference operation between the two matrices.The features of the time determination matrix are used for fault section identification and fault distance calculation,to accurately locate faults.The method is modified by considering economic benefits,through the optimal configuration of detection devices of traveling waves when calculating fault distances.Simulation results show that the proposed method has good adaptation with higher fault location accu-racy than two other typical ones.It can deal with faults on invalid branches,and the error rate is under 0.5%even with connected DGs.