High-impedance faults (HIFs) pose the greatest challenge for distribution system protection, especially for microgrids and distribution networks with distributed generators (DGs) that have flexible operation modes. This paper analyzes the faulty features of HIFs and proposes a HIF detection method that uses empirical wavelet transform (EWT) and differential faulty energy. The proposed method is as follows. First, the various time-frequency components are obtained by utilizing the EWT to decompose the differential faulty energy and adaptively select the feature component with the largest permutation entropy. Second, the permutation variance index is constructed based on the sample point number and feature component energy, and then it is employed to detect HIFs. Finally, low voltage microgrid simulation tests, medium voltage distribution system integrated by DG simulation tests, and field tests show that the proposed method can correctly distinguish HIFs from normal disturbances, including operation mode switches, load switches, capacitor switches, and DG switches. The advantages of the proposed method are also elaborated in detail, from signal preprocessing and feature extraction. 

A High-Impedance Fault Detection Method for Distribution Systems Based on Empirical Wavelet Transform and Differential Faulty Energy

Detection of the high impedance fault (HIF) in distribution systems is significant for power utilization safety. In addition to the low fault currents, traditional approaches are invalid to detect HIFs also due to the diverse characteristics, including the slight HIF nonlinearity during the weak arcing process, the distortion offset caused by the lag of heat dissipation, and the interference of background noise. This paper proposes a distortion-based algorithm to improve the reliability of HIF detection under various conditions. Firstly, the challenges brought by the diversity of HIF distortions are explained according to the field experiments in a 10 kV real-world distribution system. HIFs are classified into five types according to the distortions of their current waveforms. Secondly, a definition of interval slope is introduced to describe waveform distortions. The interval slope is extracted by combining methods of linear least square filtering (LLSF) and Grubbs-criterion-based robust local regression smoothing (Grubbs-RLRS), so that the distortions under different fault conditions can be uniformly described. Thirdly, an algorithm is proposed to judge the features presented by the interval slope, and distinguish from non-fault conditions. Finally, the reliability and security of the proposed algorithm are thoroughly analyzed with real-world HIFs and the simulated HIFs obtained in IEEE 34-bus and IEEE 123-bus systems. Results show the improvements of the proposed algorithm by the comparisons with other advanced algorithms.

Distortion-Based Detection of High Impedance Fault in Distribution Systems

This article presents a new method for effective detection of ac series arc fault (AF) (SAF) and extraction of SAF characteristics in residential buildings, which addresses the challenges with conventional current detection methods in discriminating arcing and nonarcing current due to their similarity. Different from the traditional method, in the proposed method, the differential magnetic flux is coupled to obtain high-frequency signals by putting the live line and the neutral line through the current transformer, which can effectively solve the problem of SAF features disappearing in the trunk-line current. However, similar to the traditional method, the effectiveness of the proposed coupling method could also be compromised when being used in cases with dimmer load and load starting process. This is found to be caused by the presence of high-amplitude pulse phenomenon in the nonarcing signals in these scenarios, which are incorrectly detected as arcing signals in other loads. To address this issue, a short-observation-window singular value decomposition and reconstruction algorithm (SOW-SVDR) is used to enhance the capability to identify SAFs by the coupling method. The proposed method has been implemented and validated according to the UL1699 standard with different types of loads connected to the system and also tested under their starting processes. The experimental results show that the proposed approach is more effective in detecting AFs compared with existing methods.

/pdf/a-coupling-method-for-identifying-arc-faults-based-on-short-12sq6o0d1a.pdf

A Coupling Method for Identifying Arc Faults Based on Short-Observation-Window SVDR

Arc faults in dc microgrids or photovoltaic (PV) systems are difficult to detect and isolate. Especially in location research, there are few related results, and arc fault detection is immature. This article proposes a planar location method that requires only two detection points. Because the height of many dc systems is much smaller than their horizontal dimension, an antenna array and a fault source form a horizontal triangle to locate the dc arc faults. Then, the signal pulses are distinguished and extracted using a cross correlation technique. A neural network (NN) and received signal strength indicators (RSSIs) are used to estimate the arc distance. However, the data from the dual detection points are insufficient for meeting the demands of NN training and may lead to a large location error. Therefore, a data-augmented NN (DANN) technique was introduced into the algorithm to enhance the accuracy and robustness of distance estimation. The experimental verification results show that the method can effectively locate faults under a variety of arcing conditions. Moreover, the proposed method has intrinsic immunity to normal current fluctuations (NCFs). It can be used in PV plants or smart buildings with solar cells to avoid nuisance trips and improve the effectiveness of fault protection.

A Planar Location Method for DC Arc Faults Using Dual Radiation Detection Points and DANN

Accurate modeling of arc is significant for the researches on the high impedance arcing fault (HIAF), which performs variations of nonlinear distortions under different fault conditions. The diversity of the waveform distortions during HIAFs is rarely investigated previously. This paper proposes a black-box HIAF model to simulate the nonlinear distortions of currents with improved controllability and higher accuracy. Firstly, with field HIAFs experimented in a 10 kV distribution network, three major characteristics varying in the distortions of currents are summarized and illustrated, including the offset, extent, and duration. Secondly, a distortion-controllable (DIST-C) HIAF model is proposed based on the heat balance equation after mathematical assumptions and transformations. The three characteristics of distortions can be independently controlled by the proposed model, specifically, by the three parameters with practical meanings. After illustrating the implementation of the model in PSCAD, an automatic parameter determination method is presented by taking advantage of the particle swarm optimization in a Python-PSCAD-MATLAB co-simulation platform. Finally, the controllability and accuracy of the proposed model are verified by comparing it with the existing typical black-box models.

Distortion-Controllable Arc Modeling for High Impedance Arc Fault in the Distribution Network

High impedance arc faults (HIAFs) happening in the medium-voltage distribution system may result in damages to devices and human security. However, great difficulties exist in identifying these faults due to the much weaker features and the varieties when grounded with different surfaces. This paper presents an integrated algorithm to detect the HIAFs with high-resolution waveform data provided by distribution-level PMUs deployed in the system. An improved arc model is proposed, which can continuously imitate the randomness and intermittence during the “unstable arcing period” of arc faults. The integrated algorithm consists of two branches. First, the variations of HIAFs during unstable arcing period are identified with the unified harmonic energy and global randomness index, which can unify the scales of harmonic content in different fault situations and enlarge the disparities from non-fault conditions. Then, the waveform distortions of HIAFs during the stable arcing period are identified with discrete wavelet transform to extract the detailed distribution characteristics. The reliability and security of the proposed algorithm are verified with numerical simulations and field tests in a 10-kV distribution system.

High Impedance Arc Fault Detection Based on the Harmonic Randomness and Waveform Distortion in the Distribution System

Feeder identification is indispensable for distribution networks to locate faults at a specific feeder, especially when measuring devices are insufficient to locate faults more precisely. For the high impedance fault (HIF), the feeder identification is much more challengeable and related approaches are still in the early stage. This paper thoroughly reveals the nonlinearity characteristics of different feeders when a HIF happens at a three-wire system that is with the resonant grounded neutral (RGN). Firstly, the diversity of nonlinearity existing in HIFs is explained from the perspective of energy. Then, the nonlinearity of zero-sequence current that differs between healthy and faulty feeders are deduced theoretically. Effects of the detuning index of Petersen coil, the damping ratio of system, and the length of feeder are all considered. Afterward, these theoretical conclusions are verified by the HIF cases experimented in a real 10kV system. Finally, after indicating the problems of a classic phase-relationship-based algorithm, we suggest an improved method based on the phase differences between the harmonic currents of different feeders. The effectiveness of the method has been verified.

Nonlinearity Characteristic of High Impedance Fault at Resonant Distribution Networks: Theoretical Basis to Identify the Faulty Feeder

Power system analysis (PSA) is the first comprehensive curriculum for the major of electrical engineering at the undergraduate stage. It helps students obtain an overview of the power system, and s...

Mingjie Wei

Papers

High Impedance Arc Fault Detection Based on the Harmonic Randomness and Waveform Distortion in the Distribution System

Distortion-Based Detection of High Impedance Fault in Distribution Systems

Distortion-Controllable Arc Modeling for High Impedance Arc Fault in the Distribution Network

Nonlinearity Characteristic of High Impedance Fault at Resonant Distribution Networks: Theoretical Basis to Identify the Faulty Feeder

Enhancing the course teaching of power system analysis with virtual simulation platform