High impedance fault detection: A review

Wiley Series in Microwave and Optical Engineering

This paper presents a method for the detection of series arc faults in electrical circuits, which has been developed starting from an experimental characterization of the arc fault phenomenon and an arcing current study in several test conditions. Starting from this, the authors have found that is it possible to suitably detect arc faults by means of a high-resolution low-frequency harmonic analysis of current signal, based on chirp zeta transform, and a proper set of indicators. The proposed method effectiveness is shown by means of experimental tests, which were carried in both arcing and nonarcing conditions and in the presence of different loads, chosen according to the UL 1699 standard requirements.

Arc Fault Detection Method Based on CZT Low-Frequency Harmonic Current Analysis

Arc faults have always been a concern for electrical systems, as they can cause fires, personnel shock hazard, and system failure. Existing commercialized techniques that rely on pattern recognition in the time domain or frequency domain analysis using a Fourier transform do not work well, because the signal-to-noise ratio is low and the arc signal is not periodic. Instead, wavelet transform (WT) provides a time and frequency approach to analyze target signals with multiple resolutions. In this paper, a new approach using WT for arc fault analysis in dc systems is proposed. The process of detecting an arc fault involves signal analysis and then feature identification. The focus of this paper is on the former. Simulation models are synthesized to study the theoretical results of the proposed methodology and traditional fast Fourier transform analysis on arcing faults. Experimental data from the dc system of a photovoltaic array is also shown to validate the approach.

Arc Fault and Flash Signal Analysis in DC Distribution Systems Using Wavelet Transformation

This paper presents a dc arc model to simplify the study of a critical issue in dc microgrids: series faults. The model is derived from a hyperbolic approximation of observed arc voltage and current patterns, which permit analyzing the arc in terms of its resistance, power, energy, and quenching condition. Recent faults staged by the authors on a dc microgrid yielded enough data to develop an arc model for three fault types: constant-gap speed, fixed-gap distance, and accelerated gap. The results in this paper compare experimental and simulation results for the three fault types. It is concluded that because the instantaneous voltage, current, power, and energy waveforms produced by the model agree well with experimental results, the model is suitable for transient simulations.

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A DC Arc Model for Series Faults in Low Voltage Microgrids

Discrete wavelet transform optimal parameters estimation for arc fault detection in low-voltage residential power networks

An embedded system for AC series arc detection by inter-period correlations of current

https://hal.univ-lorraine.fr/hal-01397359/document

Series arcing detection by algebraic derivative of the current

https://hal.univ-lorraine.fr/hal-02065585/document

Kalman filter and a fuzzy logic processor for series arcing fault detection in a home electrical network

This article is focused on low-frequency, spectral and correlation analysis to serial arc fault electrical system in order to detect arc fault. This analysis can be used to improve the security of power supplies in the automotive, aerospace and photovoltaic systems. More precisely, we study the influence of arc ignition on the signal characteristics. The experimental test bench is composed by an arc generator, a robotic cylinder and different loads. Three types of arcs ignitions are considered: carbonized path, contact opening and over-voltage. 0.5 kHz - 500 kHz spectrum is measured before and during the start phase of the arc and stabilization. The results show the possibility to determine the cause of the arc fault ignition. The shape of low frequency arc voltage is estimated from the current measurement with a very high fidelity where the load is known. Finally we study the possibility to locate series arcs by analyzing the correlation function of the Radio Frequency (RF) signals from two Rogowski coils inserted at two distinct points of the circuit. The calibration procedure performed reveals a mean velocity in the system of about 24 cm/ns.

Patrick Schweitzer

Papers

Discrete wavelet transform optimal parameters estimation for arc fault detection in low-voltage residential power networks

An embedded system for AC series arc detection by inter-period correlations of current

Series arcing detection by algebraic derivative of the current

Kalman filter and a fuzzy logic processor for series arcing fault detection in a home electrical network

Arc Fault Analysis and Localisation by Cross-Correlation in 270 V DC