High impedance fault detection in distribution network based on S-transform and average singular entropy

When a high impedance fault(HIF)occurs in a distribution network,the detection efficiency of traditional protection devices is strongly limited by the weak fault information.In this study,a method based on S-transform(ST)and average singular entropy(ASE)is proposed to identify HIFs.First,a wavelet packet transform(WPT)was applied to extract the feature frequency band.Thereafter,the ST was investigated in each half cycle.Afterwards,the obtained time-frequency matrix was denoised by singular value decomposition(SVD),followed by the calculation of the ASE index.Finally,an appropriate threshold was selected to detect the HIFs.The advantages of this method are the ability of fine band division,adaptive time-frequency transformation,and quantitative expression of signal complexity.The performance of the proposed method was verified by simulated and field data,and further analysis revealed that it could still achieve good results under different conditions.

High Impedance Fault Detection Based on Attention Mechanism and Object Identification

Detection of high impedance faults(HIFs)has been traditionally a main challenge in the protection of distribution systems,since they do not generate enough current to be reliably detected by conventional over-current relays.Data-based methods are alternative HIF detection methods which avoid threshold settings by training a classification or regression model.However,most of them lack interpretability and are not compatible with various distribution networks.This paper proposes an object detection-based HIF detection method,which has higher visualization and can be easily applied to different scenarios.First,based on the analysis of HIFs,a Butterworth band-pass filter is designed for HIF harmonic feature extraction.Subsequently,based on the synchronized data provided by distribution-level phasor measurement units,global HIF feature gray-scale images are formed through combining the topology information of the distribution network.To further enhance the feature information,a locally excitatory globally inhibitory oscillator region attention mechanism(LEGIO-RAM)is proposed to highlight the critical feature regions and inhibit useless and fake information.Finally,an object detection network based You Only Look Once(YOLO)v2 is established to achieve fast HIF detection and section location.The obtained results from the simulation of the proposed approach on three different distribution networks and one realistic distribution network verify that the proposed method is highly effective in terms of reliability and generalization.

Arc flash fault detection in wind farm collection feeders based on current waveform analysis

High impedance faults(HIFs) are easy to occur in collective feeders in wind farms and may cause the cascading of wind generators tripping. This kind of faults is difficult to be detected by traditional relay or fuse due to the limited fault current values and the situation is worse in wind farms. The mostly adopted HIF detection algorithms are based on the 3rd harmonic characteristic of the fault zero-sequence currents, whereas these 3rd harmonics are very easy to be polluted by wind power back-to-back converters. In response to this problem, the typical harmonic characteristic of HIF arc flash based on Mayr’s arc model is first analyzed, and the typical fault waveforms of HIF in wind farm are presented. Then the performance of the harmonic based HIF detection algorithm is discussed,and a novel detection algorithm is proposed from the viewpoint of time domain, focusing on the convex and concave characteristic of zero-sequence current at zerocrossing points. A HIFs detection(HIFD) prototype implementing the proposed algorithm has been developed.The sensitivity and security of the algorithm are proved byfield data and RTDS experiments.

Dynamic State Estimation Based Protection of Mutually Coupled Transmission Lines

Mutually coupled lines create challenges for legacy protection schemes.In this paper,a dynamic state estimation based protection(EBP)method is proposed to address these challenges.The method requires GPS synchronized measurements at both ends of the line and a high fidelity model of the protected line.The paper presents the dynamic model of the protected line and its impact on the performance of the protection scheme.Numerical simulations prove that the method can correctly identify faults,independent of position and type.The work also demonstrates the advantages of the proposed method versus legacy protection functions such as distance protection and line differential.These advantages include reliable and faster detection of internal low impedance faults,inter-circuit faults,and high impedance faults,even in cases of 1)partially coupled lines and 2)lack of measurements in adjacent lines.