Conference Paper

High impedance arcing fault detector for three-wire power distribution networks

Telecomunicacion Basque Country University
DOI: 10.1109/MELCON.2000.879677 Conference: Electrotechnical Conference, 2000. MELECON 2000. 10th Mediterranean, Volume: 3
Source: IEEE Xplore


A new approach to a high impedance fault (HIF) detector is presented, suitable for the European scene where the power distribution systems are usually three-phase and three-wire configured. This paper aims to be a contribution to the proposals that, in the last twenty years, have been trying to solve the HIF problem with no definitive solution known yet. The purpose of the study presented is to design an electric arc detector and characterise the danger of the fault looking at fault context conditions. The detector system input signals are the three individual phase currents which comply with 3I0=I1+I2+I3≠0 at the three-wire configuration. First, a continuous HIF context conditions study is proposed: overcurrent or reclosing in every phase, noticeable load variation monitoring and 3I0 monitoring. Secondly, a short, medium and long-term statistical analysis is performed both with odd harmonics (third, fifth, seventh and ninth) and even harmonics (second and fourth) of the I0 current. Thirdly, the estimated arc probability is calculated and combined with context conditions in order to determine a diagnosis. The proposed detector has been tested on 100 unfaulted condition event records (some have leakage current) and 32 staged fault records on different surfaces. The test results presented in the paper are satisfactory both in the sense of dependability and sensitivity.

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    • "Methods based on frequency domain analysis take advantage of the fact that HIF creates wide-spectrum voltage and current arcs. Low frequency spectral behavior of magnitude and phase [10] and the energy content of even and odd harmonics, and inter-harmonics [11] have been utilized for HIF detection. The methods based on time domain and frequency domain analysis benefit from low computational burden and simplicity. "
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    ABSTRACT: A new high impedance fault (HIF) detection method using time-frequency analysis for feature extraction is proposed. A pattern classifier is trained whose feature set consists of current waveform energy and normalized joint time-frequency moments. The proposed method shows high efficacy in all the detection criteria defined in this paper. The method is verified using the real-world data, acquired from HIF tests on three different materials (concrete, grass, and tree branch) and under two different conditions (wet, and dry). Several non-fault events, which often confuse HIF detection systems, were simulated, such as capacitor switching, transformer inrush current, non-linear loads, and power electronics sources. A new set of criteria for fault detection is proposed. Using these criteria the proposed method is evaluated and its performance is compared with the existing methods. These criteria are accuracy, dependability, security, safety, sensibility, cost, objectivity, completeness, and speed. The proposed method is compared with the existing methods, and it is shown to be more reliable, and efficient than its existing counterparts. The effect of choice of pattern classifier on method efficacy is also investigated.
    Full-text · Dataset · Dec 2014
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    • "Also arc detection method has been proposed [6]. In frequency domain, using Fourier transform, several articles have been published based on harmonic components [7], [8]; inter harmonic component [9], and high frequency spectra [10]. Other methods that try to reduce the limitation of the Fourier analysis are: Kalman filtering [11]; use of the fractal theory [12]; and use of neural networks [13]. "
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    ABSTRACT: A novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented in this paper. Using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and feature extraction, feature selection is done by principal component analysis and Bayes classifier is used for classification. HIF and ILC data was acquired from experimental tests and the data for transients was obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying HIFs from other events.
    Full-text · Article · Nov 2005 · IEEE Transactions on Power Delivery
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    • "In these methods some devices are used to provide low impedance by catching the fallen conductor [2] Electrical methods Time domain Ratio ground relay [3] Proportional relay algorithm [4] A smart relay based on time domain feature extraction [5] Arc detection method [6] Frequency domain Several papers have been published based on harmonic component using Fourier transform [7] [8] Inter harmonic component [9] High frequency spectra [10] A method based on Kalman filtering [11] A method based on fractal theory [12] Wavelet transform Wavelet transform (WT) can be used to analyze the transient behavior of a signal in both time domain and frequency domain. Recently WT has been proposed to achieve a better solution for digital signal processing of HIF signals [13] Table 2 Computation techniques and their roles in the proposed methods Method Number Decomposition and feature extraction Dimension reduction or feature selection Classifier 1 WT GA Bayes 2 WT PCA NN inrush current and no load line switching, was obtained by simulating the same feeder using EMTP. "
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    ABSTRACT: Two methods, one based on genetic algorithm (GA) and one based on neural networks (NN), are proposed for high impedance fault (HIF) detection in distribution systems. These methods are used to discriminate HIFs from isolator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and feature extraction in both methods. In one method, GA is used for feature vector reduction and Bayes for classification. In the other method, principal component analysis (PCA) is applied for feature vector reduction and NN for classification. HIF and ILC data was acquired by experimental tests and the data for other faults was obtained by simulating a real network using EMTP. Results show that either of the proposed procedures can be used to identify HIF from other events efficiently.
    Full-text · Article · Sep 2005 · Electric Power Systems Research
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