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

Abstract

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

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

“…From this comparison, it can be seen that the architecture of the proposed method based on a Kalman filter has the important advantage to detect arcing faults in the transient regime considering different maskingtype configurations (including EMI tests) on a larger number of loads (simple and combined). Also, the greatest advantage of the proposed method compared to proposed approaches [4,6,7,9,14,25] is the using of an adaptive thresholding mechanism which can avoid efficiently unwanted trips in the process of fault detection without requiring complex training tasks as are used in approaches [27][28][29]. Moreover, concerning the time response, the proposed method gives satisfactory results and exceeds the requirements defined by the standard IEC 62606 (tripping time=0.12 s for line current = 32 A).…”

“…Equations (4) and (5) represent the state and measurement equations respectively. The recursive estimation of X1 and X2, in the presence of measurement noise, begins with their initialization using initial values, and a previous assignment of the error covariance matrix P(n − 1) = Q.…”

“…The error is defined as: e(n) = x(n) −X(n) and its discrete-time derivative as: e(n + 1) = x(n + 1) −X(n + 1). Substituting Equations (13) and (4) in this last expression allow us to obtain Equation (14).…”

“…Different arcing fault detection approaches have been proposed in the literature, with spectrum analysis being the detection method most often used. Spectral analyze of the line current focusing on third and fifth harmonics [1][2][3][4][5] is performed in a variety of bands ranging from low frequencies up to 20 kHz. In [6] a fault detection algorithm based on the energy density spectrum computation $ Email address: edwin-milton.calderon-mendoza@univ-lorraine.fr (Edwin Calderon-Mendoza) uses an existing structure of a shunt active power filter.…”

Pseudorandom Dynamic Test Power Signal Modeling and Electrical Energy Compressive Measurement Algorithm

“…During recent years, the electrical networks have been growing more and more complex, with the applications of distributed renewable electricity sources (such as photovoltaic power supply) and the use of dynamic loads (such as electric arc furnaces, high-speed railway train and electric vehicle charging station), the current amplitude fluctuations of the dynamic loads have shown complex random characteristics, such as the quick and random dynamic changes [1]- [4]. Under these dynamic conditions, the electrical energy measured by electricity meters may produce significant measurement errors, in some cases, the error can be as large as -37.7 % [5].…”