Analysis and Research on 10kV Distribution Network Faults

The 10kV distribution network is an essential component of the power system,and its stable operation is crucial for ensuring reliable power supply.However,various factors can lead to faults in the distribution network.In order to enhance the safety and reliability of power distribution,this paper focuses on the analysis of faults in the 10kV distribution network caused by natural factors,operational factors,human factors,and equipment factors.It elucidates the various hazards resulting from distribution network faults and proposes corresponding preventive measures for different types of faults in the 10kV distribution network.The aim is to mitigate or reduce the impact of distribution network faults,ensuring the safe and stable operation of the distribution system.

Fault Line Selection Method Considering Grounding Fault Angle for Distribution Network

In the distribution network system with its neutral point grounding via arc suppression coil, when single-phase grounding fault occurred near zero-crossing point of the phase voltage, the inaccuracy of the line selection always existed in existing methods. According to the characteristics that transient current was different between the fault feeder and other faultless feeders, wavelet transformation was performed on data of the transient current within a power frequency cycle after the fault occurred. Based on different fault angles, wavelet energy in corresponding frequency band was chosen to compare. The result was that wavelet energy in fault feeder was the largest of all, and it was larger than sum of those in other faultless feeders, when the bus broke down, the disparity between each wavelet energy was not significant. Fault line could be selected out by the criterion above. The results of MATLAB/simulink simulation experiment indicated that this method had anti-interference capacity and was feasible.

Application of Vertical Electrical Sounding in Mapping Lateral and Vertical Changes in the Subsurface Lithologies: A Case Study of Olbanita, Menengai Area, Nakuru, Kenya

Much study has been done in the study area linking Vertical Electrical Sounding (VES) interpreted results to lithologies in the subsurface though only tend to indicate the vertical changes with the aim of mapping the occurrence of groundwater aquifers. Several boreholes have been drilled in the study area, though not much has been done to compare the vertical and lateral lithologic changes in the study area. This research is based on VES modelled geoelectric layers compared from point to point and using borehole logs as control data to establish inferences of certain lithology in the subsurface. The inversion of each VES curve was obtained using an AGI Earth Imager ID inversion automated computer program and resistivities and thicknesses of a geoelectric model were estimated. The analyzed VES data interpretation achieved using the curve matching technique resulted in mapping the subsurface of the area as portraying H-type;ρ1 > ρ2 ρ3, K-type;ρ1 ρ2 > ρ3, A-type;ρ1 ρ2 ρ3, Q-type;ρ1 > ρ2 > ρ3, representing 3-Layer subsurface and subsequently a combination of HK, HA and KHK types of curves representing 4-Layer and 5-Layer in the subsurface. The analysis further deployed the use of the surfer software capabilities which combined the VES data to generate profiles running in the west-east and the north-south direction. A closer analysis of the curve types indicates that there exists a sequence showing a shifting of the order of arrangement between the west and the east fragments which incidentally coincides with VES points 8, 9 and 10 in the West-East profiles. The lateral change is noted from the types of curves established and each curve indicates a vertical change in the subsurface. Control log data of lithologies from four boreholes BH1, BH2, BH3 and BH5 to show a qualification that different resistivity values portent different lithologies. Indeed, an analysis at borehole BH3 lithologies is dominated by either compacted rocks or soils, insinuating a scenario of compression experienced in this part of the subsurface which confirmed compression of subsurface formations. A correlation of the VES curve types and their change from one point to another in the study area are evident. This change supported by the surfer generated profiles from the modeled VES data show that there exists and inferred fault line running in the north-south in the area. The inferred fault line by VES mapping, is magnificently outlined by the geological map. There is exuded evidence from this study that the application of VES is able to help map the lateral and the vertical changes in the subsurface of any area but the evidence of the specific lithologies has to be supported by availability of borehole log control data. The VES data was able to enumerate vertical layering of lithologies, lateral changes and even mapping vertical fault line in the study area.

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.

Robust fault analysis in transmission lines using Synchrophasor measurements

As more electric utilities and transmission system operators move toward the smart grid concept,robust fault analysis has become increasingly complex.This paper proposes a methodology for the detection,classification,and localization of transmission line faults using Synchrophasor measurements.The technique involves the extraction of phasors from the instantaneous three-phase voltages and currents at each bus in the system which are then decomposed into their symmetrical components.These components are sent to the phasor data concentrator(PDC)for real-time fault analysis,which is completed within 2–3 cycles after fault inception.The advantages of this technique are its accuracy and speed,so that fault information may be appropriately communicated to facilitate system restoration.The proposed algorithm is independent of the transmission system topology and displays high accuracy in its results,even with varying parameters such as fault distance,fault inception angle and fault impedance.The proposed algorithm is validated using a three-bus system as well as the Western System Coordinating Council(WSCC)nine bus system.The proposed algorithm is shown to accurately detect the faulted line and classify the fault in all the test cases presented.

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.

Fault Line Detection Using Waveform Fusion and One-dimensional Convolutional Neural Network in Resonant Grounding Distribution Systems

Effective features are essential for fault diagnosis.Due to the faint characteristics of a single line-to-ground(SLG)fault,fault line detection has become a challenge in resonant grounding distribution systems.This paper proposes a novel fault line detection method using waveform fusion and one-dimensional convolutional neural networks(1-D CNN).After an SLG fault occurs,the first-half waves of zero-sequence currents are collected and superimposed with each other to achieve waveform fusion.The compelling feature of fused waveforms is extracted by 1-D CNN to determine whether the fused waveform source contains the fault line.Then,the 1-D CNN output is used to update the value of the counter in order to identify the fault line.Given the lack of fault data in existing distribution systems,the proposed method only needs a small quantity of data for model training and fault line detection.In addition,the proposed method owns fault-tolerant performance.Even if a few samples are misjudged,the fault line can still be detected correctly based on the full output results of 1-D CNN.Experimental results verified that the proposed method can work effectively under various fault conditions.