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 method for petal-shaped distribution network with inverter-interfaced distributed generators

In this paper,a fault location method for the petal-shaped distribution network(PSDN)with inverter-interfaced distributed generators(IIDGs)is proposed to shorten the time of manual inspection.In order to calculate the fault position,the closed-loop structure of the PSDN is skillfully exploited,and the common control strategies of IIDGs are considered.For asymmetrical faults,a fault line identification formula based on the negative-sequence current phase differences is presented,and a fault location formula only utilizing the negative-sequence current amplitudes is derived to calculated the fault position.For symmetrical faults,the positive-sequence current at both ends of lines and the current output from IIDGs are used to identify the fault line,and the positive-sequence current on multiple lines are used to pinpoint the fault position.In this method,corresponding current phasors are separated into amplitudes and phases to satisfy the limitation of communication level.The simulation results show that the error is generally less than 1%,and the accuracy of the proposed method is not affected by the fault type,fault position,fault resistance,load current,and the IIDG penetration.

Overview of Distribution Network Fault Location

According to the existing research, the fault section location and fault location of passive distribution network and active distribution network are reviewed. Among them, fault location of passive distribution network mainly introduces fault segment location based on transient state and steady state quantity and fault location based on transient quantity. The active distribution network mainly introduces the fault segment location based on the current amount and the switching capacity based on the distribution network topology. On this basis, the difficulties of fault location in the distribution network at present are analyzed, and the future development is prospected.

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.

Distribution Network Equipment Location and Capacity Planning Method Considering Energy Internet Attribute from the Perspective of Life Cycle Cost

For facing the challenges brought by large-scale renewable energy having access to the system and considering the key technologies of energy Internet,it is very necessary to put forward the location method of distribution network equipment and capacity from the perspective of life cycle cost.Compared with the traditional energy network,the equipment capacity problem of energy interconnected distribution network which involves in electricity network,thermal energy network and natural gas network is comprehensively considered in this paper.On this basis,firstly,the operation architecture of energy interconnected distribution network is designed.Secondly,taking the grid connection location and configuration capacity of key equipment in the system as the control variables and the operation cost of system comprehensive planning in the whole life cycle as the goal,the equipment location and capacity optimization model of energy interconnected distribution network is established.Finally,an IEEE 33 bus energy mutual distribution grid system is taken for example analysis,and the improved chaotic particle swarmoptimization algorithm is used to solve it.The simulation results show that the method proposed in this paper is suitable for the equipment location and capacity planning of energy interconnected distribution network,and it can effectively improve the social and economic benefits of system operation.

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.

Transient Fault Locating Method Based on Line Voltage and Zero-mode Current in Non-solidly Earthed Network

为解决配电网小电流接地故障时故障点定位困难的问题，提出一种基于线电压和零模电流暂态分量的故障定位新方法。该方法借助馈线自动化（feeder automation，FA）系统实现，不需要额外增加设备。馈线终端检测线电压和零模电流信号，利用暂态线电压的希尔伯特变换与暂态零模电流的乘积计算故障方向参数，FA主站根据故障点前后方向参数极性相反的特征确定故障点所在的线路区段。介绍基于线电压的小电流接地故障检测和接地故障相确定的算法。数字仿真与试验结果表明该方法是正确可行的。

Single-phase earth fault current distribution between optical fiber composite overhead ground wire and ordinary ground wire in transmission system

It is important for the safety of transmission system to accurately calculate single-phase earth fault current distribution.Features of double sided elimination method were illustrated.Quantitative calculation of single-phase earth fault current distribution and case verification were accomplished by using the loop method.Influences of some factors,such as single-phase earth fault location and ground resistance of poles,on short-circuit current distribution were discussed.Results show that:1) results of the loop method conform to those of double sided elimination method;2) the fault location hardly influences macro-distribution of short-circuit current.However,current near fault location is evidently influenced;and 3) the short-circuit current distribution is not so sensitive to the ground resistance of poles.

3-Phase Fault Finding in Oil Field MV Distribution Network Using Fuzzy Clustering Techniques

This paper studies an existing 13.8 kilovolt distribution network which, serves an oil production field spread over an area of approximately 60 kilometers square, in order to locate any fault that may occur anywhere in the network using fuzzy c-mean classification techniques. In addition, Sections 5 and 6 introduce two different methods for normalizing data and selecting the optimum number of clusters in order to classify data. Results and conclusions are given to show the feasibility for the suggested fault location method. Suggestion for future related research has been provided in Section 8.

Location of Asymmetric Ground Fault Using Virtual Injected Current Ratio and Two-stage Recovery Strategy in Distribution Networks

Sparse measurements challenge fault location in distribution networks.This paper proposes a method for asymmetric ground fault location in distribution networks with limited measurements.A virtual injected current vector is formulated to estimate the fault line,which can be reconstructed from voltage sags measured at a few buses using compressive sensing(CS).The relationship between the virtual injected current ratio(VICR)and fault position is deduced from circuit analysis to pinpoint the fault.Furthermore,a two-stage recovery strategy is proposed for improving reconstruction accuracy of the current vector,where two different sensing matrixes are utilized to improve the incoherence.The proposed method is validated in IEEE 34 node test feeder.Simulation results show asymmetric ground fault type,resistance,fault position and access of distributed generators(DGs)do not significantly influence performance of our method.In addition,it works effectively under various scenarios of noisy measurement and line parameter error.Validations on 134 node test feeders prove the proposed method is also suitable for systems with more complex structure.

Optimal Partitioning of Distribution Networks for Micro-Grid Operation

A great concern for the modern distribution grid is how well it can withstand and respond to adverse conditions. One way that utilities are addressing this issue is by adding redundancy to their systems. Likewise, distributed generation (DG) is becoming an increasingly popular asset at the distribution level and the idea of microgrids operating as standalone systems apart from the bulk electric grid is quickly becoming a reality. This allows for greater flexibility as systems can now take on exponentially more configurations than the radial, one-way distribution systems of the past. These added capabilities, however, make the system reconfiguration with a much more complex problem causing utilities to question if they are operating their distribution systems optimally. In addition, tools like Supervisory Control and Data Acquisition (SCADA) and Distribution Automation (DA) allow for systems to be reconfigured faster than humans can make decisions on how to reconfigure them. As a result, this paper seeks to develop an automated partitioning scheme for distribution systems that can respond to varying system conditions while ensuring a variety of operational constraints on the final configuration. It uses linear programming and graph theory. Power flow is calculated externally to the LP and a feedback loop is used to recalculate the solution if a violation is found. Application to test systems shows that it can reconfigure systems containing any number of loops resulting in a radial configuration. It can connect multiple sources to a single microgrid if more capacity is needed to supply the microgrid’s load.

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.

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.

A fault segment location method for distribution networks based on spiking neural P systems and Bayesian estimation

With the increasing scale of distribution networks and the mass access of distributed generation,traditional central-ized fault location methods can no longer meet the performance requirements of speed and high accuracy.There-fore,this paper proposes a fault segment location method based on spiking neural P systems and Bayesian estimation for distribution networks with distributed generation.First,the distribution network system topology is decoupled into single-branch networks.A spiking neural P system with excitatory and inhibitory synapses is then proposed to model the suspected faulty segment,and its matrix reasoning algorithm is executed to obtain a preliminary set of location results.Finally,the Bayesian estimation and contradiction principle are applied to verify and correct the ini-tial results to obtain the final location results.Simulation results based on the IEEE 33-node system validate the feasi-bility and effectiveness of the proposed method.

A voltage resonance-based single-ended online fault location algorithm for DC distribution networks

A novel single-ended online fault location algorithm is investigated for DC distribution networks. The proposed algorithm calculates the fault distance based on the characteristics of the voltage resonance. The Prony's method is introduced to extract the characteristics. A novel method is proposed to solve the pseudo dual-root problem in the calculation process. The multiple data windows are adopted to enhance the robustness of the proposed algorithm. An index is proposed to evaluate the accuracy and validity of the results derived from the various data windows. The performances of the proposed algorithm in different fault scenarios were evaluated using the PSCAD/EMTDC simulations. The results show that the algorithm can locate the faults with transient resistance using the 1.6 ms data of the DC-side voltage after a fault inception and offers a good precision.

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.

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.

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.

Identification Method for Single-line-to-ground Faults with Line Break Based on Phasor Measurement in Distribution Networks

The single-line-to-ground faults with line breaks(SLGFs-LBs)occur more and more frequently in distribution networks and can cause major safety accidents.It is difficult to distinguish the single-line-to-ground faults(SLGFs)in resonant grounding systems and ungrounding systems due to the same electrical characteristics on the source side and uncertain operation conditions of distribution networks.This paper proposes a method for distinguishing SLGFs-LBs and SLGFs.First,the source-side and load-side voltage characteristics of SLGFs and SLGFs-LBs are analyzed,and the phase difference between the voltages of the fault phase and non-fault phase on the load side is selected as the identification criterion.Phasor measurement units(PMUs)are selected as measuring devices.Then,the effects of operation conditions and external devices in distribution networks on the proposed method are discussed,and the phase errors caused by them are calculated to correct the identification method.Finally,the field testing and simulation experiments are conducted to verify the effectiveness and robustness of the proposed method.

Faulty Feeder Identification in Resonant Grounding Distribution Networks Based on Deep Learning and Transfer Learning

Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-based multi-labelclassification framework to reliably distinguish the faulty feeder.Three different neural networks (NNs) including the multilayerperceptron, one-dimensional convolutional neural network (1DCNN), and 2D CNN are built. However, the labeled data maybe difficult to obtain in the actual environment. We use thesimplified simulation model based on a full-scale test field (FSTF)to obtain sufficient labeled source data. Being different frommost learning-based methods, assuming that the distribution ofsource domain and target domain is identical, we propose asamples-based transfer learning method to improve the domainadaptation by using samples in the source domain with properweights. The TrAdaBoost algorithm is adopted to update theweights of each sample. The recorded data obtained in the FSTFare utilized to test the domain adaptability. According to ourvalidation and testing, the validation accuracies are high whenthere is sufficient labeled data for training the proposed NNs.The proposed 2D CNN has the best domain adaptability. TheTrAdaBoost algorithm can help the NNs to train an efficientclassifier that has better domain adaptation. It has been thereforeconcluded that the proposed method, especially the 2D CNN, issuitable for actual distribution networks.