Series Arc Fault Detection Research Articles

Low voltage AC series arc fault detection method based on parallel deep convolutional neural network

The complexity and concealment of series arc fault threaten the safety of household power supply system. Detection of series arc faults using threshold and characteristics extracted from current or voltage may affected by load type and connection mode. Based on AlexNet, a new parallel deep convolutional neural network detection method is proposed in this paper. The series arc fault and normal operation current signal data of three types of load totally 7200 sets were collected respectively. Using the collected data, a training data set and four test data sets were constructed to train and test the proposed convolutional neural network. The experimental results show that the detection accuracy of parallel AlexNet is higher than that of AlexNet, with a maximum of 16.75 percentage points. The stability of parallel AlexNet is about twice as high as that of AlexNet, and the maximum is close to four times.

Design of fault arc detection device based on STM32

The detection of the load current signal is one of the effective methods for judging whether an arc fault occurs in the low-voltage distribution line; according to the national standard GB/T 31143-2014 General Requirements for Arc Fault Protection Devices (AFDD), the analog series fault arc is built. The experimental platform studies the characteristics of the current waveform when the fault arc occurs. The db4 wavelet function is used as the wavelet basis function to decompose and reconstruct the current waveform after noise reduction, and extract the wavelet high-frequency component, calculate the periodic variance value of the wavelet high-frequency component, and detect the arc fault according to this value; The feasibility and effectiveness of the detection algorithm are verified. The arc fault detection algorithm is transplanted to the STM32 platform, and the fault arc detection device based on STM32 is designed. The device can realize the functions of current signal acquisition, data processing and series arc fault detection and recognition. Experiments with resistive loads, LED light, vacuum cleaners and microwave ovens have shown that the device can detect series arc faults with high reliability and will not cause false positives without generating fault arcs.

Series Arc Fault Detection and Localization in DC Distribution System

Series arc faults can cause fire hazards in dc distribution systems. During a series arc fault, the line impedance usually increases rapidly and the arc current includes high-frequency components. To capture the arc-induced high-frequency signals, parallel capacitors are added to the circuit. The characteristics of the currents through these capacitors permit fault detection and localization. This paper determined that a Rogowski coil could be constructed with adequate bandwidth to cover the relevant frequency band of 1 kHz-1 MHz, found that the amplitude of the pulse and the difference in the integrated fast Fourier transform of the current signal are well correlated with the series fault, described experiments to demonstrate that, over the range tested, the two parameters are robust with respect to the pressure of the atmosphere in which the arc forms, the electrode material, the speed at which the electrodes separate to initiate the arc, the switch operation, the load-type and the load change, and demonstrated that within the well-known limits of traveling wave localization, such localization can be used based on the capacitor current pulse information after it has been validated by the spectrum analysis as a fault and not a spurious signal. The proposed approach was demonstrated in two low-voltage dc cable circuits.

Kalman filter–based approach for detection of series arc fault in photovoltaic systems

Kalman filter–based approach for detection of series arc fault in photovoltaic systems

Analysis of the Effects of Arc Volt–Ampere Characteristics on Different Loads and Detection Methods of Series Arc Faults

Owing to the shortcomings of existing series arc fault detection methods, based on a summary of arc volt–ampere characteristics, the change rule of the line current and the relationship between the voltage and current are deeply analyzed and theoretically explained under different loads when series arc faults occur. A series arc fault detection method is proposed, and the software flowchart and principles of the applied hardware implementation are given. Finally, a prototype of an arc fault detection device (AFDD) with a rated voltage of 220 V and a rated current of 40 A is developed. The prototype was tested according to experimental methods provided by the Chinese national standard, GB/T 31143-2014. The experimental results show that the proposed detection method is simple and practical, and can be implemented using a low-cost microprocessor. The proposed method will provide good theoretical guidance in promoting the research and development of an AFDD.

Series Arc Fault Detection of Indoor Power Distribution System Based on LVQ-NN and PSO-SVM

When a series arc fault occurs in indoor power distribution system, current value of circuit is often less than the threshold of the circuit breaker, but the temperature of arc combustion can be as high as thousands of degrees, which can lead to electrical fire. The arc fault experimental platform is used to collect circuit current data of normal work and arc fault. Five types of loads which are commonly used in indoor distribution system, such as resistive and inductive in series load, resistive load, series motor load, switching power load and eddy current load, are chosen. This paper uses four features of current in time domain, i.e. current average, current pole difference, difference current average and current variance. Ten features of current in frequency domain are extracted by Fast Fourier Transform (FFT). The learning vector quantization neural network (LVQ-NN) is designed to judge the load type. The support vector machine optimized by particle swarm optimization (PSO-SVM) is designed to detect the arc fault. The simulation results show the effectiveness of the proposed method.

Research on Series Arc Fault Detection Based on Higher-Order Cumulants

At present, the detection methods on series arc faults are mainly based on the current of the main circuit, which probably results in misjudgment because of the singularity of the normal working current in a nonlinear load. What’s worse, is the arc fault characteristic in the small power branch is easily ignored in the current of the main circuit, which leads to missed judgment. To solve these problems, a detection method based on coupling signal acquisition and higher-order cumulant identification is presented, through the electromagnetic coupling mechanism of high frequency current. By analyzing the coupling signal of the high-frequency current and using the higher-order cumulant algorithm during arcing process, the kurtosis of the coupling signal is calculated. In this paper, the unified threshold value on the kurtosis in various conditions is obtained. The results show that the novel method can be effectively used to detect and identify the series arc faults.

Detection of Series Arcs Using Load Side Voltage Drop for Protection of Low Voltage DC Systems

Low voltage dc distribution grids face issues associated with arc faults, aggravated by the absence of current zero crossing. The focus of this paper is to comprehensively develop a method of series arc fault detection at the load side power electronics, based on the electrode dependent initial voltage drop occurring at the arc initiation. The proposed arc detection algorithm is described along with the structure and time constants of the designed bandpass filter. The operational boundaries of the arc detection algorithm are defined for copper electrodes depending on the set threshold voltage and the system parameters, like grid inductance, resistance, and the load capacitance. Further, the detection time and the zone of guaranteed positive detection are depicted. These are validated through test simulations on the state space model of the system. Finally, experimental validation of the proposed scheme is carried out, wherein, a series arc is generated in the dc circuit and the programmed micro-controller provides a real time signal upon detecting the arcing event. The results on variation in detection time with set threshold voltage are also presented experimentally.

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

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.

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

In this work, an in-depth analysis providing the optimal parameters estimation for discrete wavelet transform (DWT) applied to detection of series arc faults in the household AC power network is presented. The influence of three parameters was investigated: the choice of mother wavelet, level of decomposition and sampling frequency. The line current was used as input for all analyses. A performance criterion based on the energy computation of line currents with and without arc faults was defined and used to compare the influence of 550 combinations of these three parameters on the arc fault detection performances for different loads, including two household appliances. The study showed that the right choice of these three parameters greatly influences the arc fault detection performances. Moreover, for each tested load a frequency range providing maximal arc fault detection performances is identified. The study showed also that the choice of the mother wavelet is less critical than the two other parameters.

The Detection of Series Arc Fault in Photovoltaic Systems Based on the Arc Current Entropy

This paper presents a series arc-fault detection algorithm for photovoltaic (PV) systems, which relies on the quantum probability model theory. The algorithm determines the presence of the arc by calculating the modified Tsallis entropy of the PV panel current. Based on the calculated entropy, the algorithm is able to differentiate an arc state (when the current variations are chaotic) from a no-arc state (when the current variations are ordered). The proposed algorithm enables arc-fault detection on a plug-and-play principle, requiring no prior information about the PV system in which it operates. The operation of the algorithm was first simulated on the prerecorded data from the system with the PV panel simulator and the commercial PV inverter. A laboratory prototype of the detector was then built and tested in a real 1.6-kW grid-connected PV system with different PV inverter, without any additional parameter adjustments. Both the sensitivity and the robustness of the detector were confirmed. The tests have shown that both the sustained series arcs and the small sparking series arcs were successfully detected, and there was no false detection due to the MPP tracker operation, PV current step change, or inverter turn on transient.

A Series Arc Fault Detection Strategy for Single-Phase Boost PFC Rectifiers

This paper proposes a series arc fault detection algorithm which incorporates peak voltage and harmonic current detectors for single-phase boost power factor correction (PFC) rectifiers. The series arc fault model is also proposed to analyze the phenomenon of the arc fault and detection algorithm. For arc detection, the virtual dq transformation is utilized to detect the peak input voltage. In addition, multiple combinations of low- and high-pass filters are applied to extract the specific harmonic components which show the characteristics of the series arc fault conditions. The proposed model and the arc detection method are experimentally verified through a boost PFC rectifier prototype operating under the grid-tied condition with an artificial arc generator manufactured under the guidelines for the Underwriters Laboratories (UL) 1699 standard.

Research on Low-voltage Series Arc Fault Detection Method Based on Least Squares Support Vector Machine

Arc fault is one of the important reasons of electrical fires. In virtue of cross talk, randomness and weakness of series arc faults in low-voltage circuits, very few of techniques have been well used to protect loads from series arc faults. Thus, a novel detection method based on support vector machine is developed in this paper. If series arc fault occurs, high frequency signal energy in circuit will increase a lot, and current cycle integrals are variable and erratic. However, high frequency signal energy will be influenced by cross talk in a nearby branch circuit. Besides, current cycle integrals will also vary while the working states of circuit changed. To better describe series arc faults, two characteristics include high frequency signal energy and current integral difference are extracted as support vectors. Based on these support vectors, least squares support vector machine is used to distinguish series arc faults from normal working states. The validity of the developed method is verified via an arc fault experimental platform set up. The results show that series arc faults are well detected based on the developed method.

Research on Low-Voltage Series Arc Fault Detection Based on Higher-Order Cumulants

Arc faults are one of the main reasons of electrical fires. For the difficulty of series arc fault detection, very few of techniques have successfully protected loads from arc faults in low-voltage circuits, especially in China. When series arc faults occur, shoulders will appear in load currents. The shoulder widths of non-arc faults such as normal load arcs are stable, while those of arc faults are variable because the appearance of arc faults is erratic. Therefore, a novel detection method based on shoulder characteristics was proposed. To better capture shoulder widths, original currents were firstly converted into pulses. Then, the pulse widths were used to detect arcs and the fourth-order cumulants of their differential were used to distinguish arc faults from normal operations. Finally, an arc fault detection device (AFDD) prototype was developed for test. The results show this prototype can discriminate arc faults effectively from normal operations.

A Low-Cost Power-Quality Meter With Series Arc-Fault Detection Capability for Smart Grid

This paper presents a low-cost digital single-phase power-quality measurement device for consumer use with a wide range of features, including series arc-fault detection, load trip on failure, and phase/neutral line wiring mix up indication. A wavelet multiresolution analysis technique was utilized for the voltage transient event detection and the current drop pattern recognition, specifically to arc fault. The last feature also involved the use of adaptive thresholding, peak detection, and repetition frequency calculation. A computationally efficient and accurate Goertzel filter was used for total harmonic distortion calculation. In addition, this meter can measure phase fundamental frequency (using the zero-crossing technique), rms values, and power. MATLAB and MathCAD packages were used to build and simulate arc-fault model and phase voltage distortion, to design and test part of the developed algorithms, which were further implemented in Embedded C and Assembler programming languages. A prototype circuit board with the required sensors and relay, analog isolation, indication, user controls, communication link, and a low-cost microchip microcontroller (MCU) dsPIC33 was designed and built to validate implemented algorithms and conduct experiments.