Parallel Arc Faults Research Articles

Testing the operation of protection devices against parallel arc breakdown and spark gaps in the event of a ground fault

Testing the operation of protection devices against parallel arc breakdown and spark gaps in the event of a ground faultTHE PURPOSE. Develop a laboratory rig for testing parallel arc fault and spark gap protection devices for tripping in the event of a ground fault. Conduct laboratory rig tests with subsequent development of requirements for the use of parallel arc fault and spark gap protection devices for tripping in the event of a ground fault.METHODS. Mathematical and statistical methods of analysis were used to achieve the stated objective. Laboratory tests of parallel arc fault and spark gap protection devices for different network parameters for correct tripping in the event of a ground fault.RESULTS. Laboratory tests of parallel arc fault and spark gap protection devices for correct tripping in the event of a ground fault were conducted with different network parameters. The oscilloscope recorded the shape of the voltage and current curve in the event of a ground fault and the tripping time of the devices. The tested devices demonstrated the ability to recognize and disconnect a ground fault. The tripping time of the devices varies, however, does not exceed the established requirements.CONCLUSION. A laboratory stand has been developed for testing parallel arc breakdown and spark gap protection devices for operation during a ground fault. This stand makes it possible to test devices for adequate operation during a ground fault with subsequent certification. The tests conducted demonstrate the need to develop mandatory requirements for the device to standardize their operating algorithm. Therefore, the use of this protection device can contribute to safer operation of low-voltage electrical networks by preventing emergency situations and reducing the risk of fires. This is an important technical aspect for improving the reliability and safety of power supply.

Comprehensive Experimental Database and Model Fitting for Electric Arc Behavior in Aircraft Environments

The More Electric Aircraft (MEA) paradigm advocates weight reduction by transitioning to electrical components, resulting in increased system voltage and higher arc fault risk. This paper presents a methodology for creating a database of nearly one thousand tests simulating parallel arc faults, using Andrea's arc model for accurate representation. Objectives include the creation of a robust experimental database and the use of a simplified model to analyze arcing behavior. The setup follows the UL 746A standard and considers various pressure and electrode separation conditions. The database includes 960 experiments with PVC and PTFE materials, and model fitting ensures accurate representation of arc currents and voltages. Error analysis shows consistent model performance across materials and highlights sensitivity to electrode spacing, pressure, and source voltage. The paper concludes with a clear 30% error threshold for model validity, enhancing the understanding of the applicability of Andrea's model under various experimental scenarios in aircraft environments.

Photovoltaic system DC series arc fault: a case study

<p>Photovoltaic (PV) systems are becoming increasingly popular; however, arc faults on the direct current (DC) side are becoming more widespread as a result of the effects of aging as well as the trend toward higher DC voltage levels, posing severe risk to human safety and system stability. The parallel arc faults present higher level of current as compared with the series arc faults, making it more difficult to spot the series arc. In this paper and for the aim of condition monitoring, the features of a DC series arc fault are analyzed by analysing the arc features, performing model’s simulation in PSCAD, and carrying out experimental studies. Various arc models are simulated and investigated; for low current arcs, the heuristic model is used where a set of parameters established. Moreover, the heuristic model’s simulated arc has been shown to be compatible with the experimental data. The features of arc noise in the electrode separation region and steady-arcing states with varied gap widths are investigated. It has been discovered that after an arc fault occurs, arc noise increases, notably in the frequency range below 50 kHz; where this property is useful for detecting DC series arc faults. Besides that, variations in air gap width are more sensitive to frequencies under 5 kHz.</p>

Intelligent DC Arc-Fault Detection of Solar PV Power Generation System via Optimized VMD-Based Signal Processing and PSO–SVM Classifier

In a solar photovoltaic (PV) power generation system, arc faults including series arc fault (SAF) and parallel arc fault (PAF) may occur due to aging of joints or other reasons. It may lead to a major safety accident, such as fire, if the high temperature caused by the continuous arc fault is not identified and solved in time. Because the SAF without drastic current change is difficult to detect, an intelligent detection algorithm based on the optimized variational mode decomposition and the support vector machine (SVM) is investigated in this article. The proposed algorithm uses the variational mode decomposition to extract the fault information from current signals, and then screens the statistical information of the signals in each frequency band by the proposed adaptive feature screening. The features to be strongly correlated with classification are taken as inputs into the SVM optimized by the particle swarm optimization for classification finally. The proposed intelligent framework not only can accurately identify the SAF occurring at different locations, but also identify the PAF. Moreover, it can also maintain good diagnosing results under the occurrence of dynamic shading, inverter startup, and SAF under wind blowing. In addition, single-series PV string and solar PV power generation systems in different countries are also used to examine the universal ability of the proposed algorithm. As for experimental results, the detection accuracy is more than 98.21% under all examined conditions.

Detection Algorithms of Parallel Arc Fault on AC Power Lines Based on Deep Learning Techniques

Detection Algorithms of Parallel Arc Fault on AC Power Lines Based on Deep Learning Techniques

Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques

The unwanted electric discharge usually relates to arc phenomena between two connectors. The energy from an arc might fuse the electric wiring and be responsible for a fire. Various researches have been investigated for safety operations to improve detected techniques for arc diagnosis. There are two types of arc faults: parallel and series arcs. A parallel arc happens among two electrical lines, or line and ground, due to degrading insulation or contamination. On the other hand, a series arc might result from releasing connections in the wiring. The system’s current can be significantly increased by parallel arc fault compared with the series arc. In this work, the electrical behavior of the system is investigated during parallel arc faults to understand the arcing characteristics from different cases, identify electrical characteristics that are useful and reliable for the diagnosis process, and determine the location of the fault based on current or voltage of the faulted system. Eight learning techniques are adopted to detect arc fault in this study. Parallel arc signals were analyzed in the time and frequency domains, and unique characteristics of the current are extracted using Fourier analysis as an indicator for diagnosing an arc fault. This research can be used to improve arc-fault detector reliability and robustness.

Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms

Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors.

Series And Parallel Arc Fault Detection Based on Discrete Wavelet VS. FFT Techniques

Arc problems are most commonly caused by electrical difficulties such as worn cables and improper connections. Electrical fires are caused by arc faults, which generate tremendous temperatures and discharge molten metal. Every year, flames of this nature inflict a great lot of devastation and loss. A novel approach for identifying residential series and parallel arc faults is presented in this study. To begin, arc faults in series and parallel are simulated using a suitable simulation arc model. The fault characteristics are then recovered using a signal processing technique based on the fault detection technique called Discrete Wavelet Transform (DWT), which is built in MATLAB/Simulink. Then came db2, and one level was discovered for obtaining arc-fault features. The suitable mother and level of wavelet transform should be used, and try to compare results with conventional methods (FFT-Fast Fourier Transform). MATLAB was used to build and simulate arc-fault models with these techniques.

Series and Parallel Arc Fault Detection in Electrical Buildings Based on Discrete Wavelet Theory

Electrical issues such as old wires and faulty connections are the most common causes of arc faults. Arc faults cause electrical fires by generating high temperatures and discharging molten metal. Every year, such fires cause a considerable deal of destruction and loss. This paper proposes a new method for detecting residential series and parallel arc faults. A simulation model for the arc is employed to simulate the arc faults in series and parallel circuits. The fault features are then retrieved using a signal processing approach called Discrete Wavelet Transform (DWT) designed in MATLAB/Simulink based on the fault detection algorithm. Then db2 and one level were found appropriate mother and level of wavelet transform for extracting arc-fault features. MATLAB Simulink was used to build and simulate the arc-fault model.

Wavelet-Analysis-Based Singular-Value-Decomposition Algorithm for Weak Arc Fault Detection via Current Amplitude Normalization

A wavelet-analysis-based singular-value-decomposition (WASVD) algorithm augmented by the current amplitude normalization (CAN) is proposed in this study to tackle the challenge of weak arc-fault detection. First, an experimental platform is prepared for producing bus-current data from normal operations and weak arc faults. Then, the CAN and the wavelet analysis (WA) are performed on collected signals. Moreover, the coefficients in each layer of the WA are exploited to construct a correspondent Hankel matrix for acquiring the WASVD coefficients by the singular value decomposition (SVD). In order to filter the irrelevant components automatically, the coordinates of AC/DC current components are located by the entropy index calculation on WASVD coefficients. The performances of conventional arc-fault detection algorithms with dependence on manual set-up will be severely degraded if loads, current amplitudes or working environments undergo drastic changes. The proposed algorithm avoids this dilemma by subtracting one arbitrary processed normal signal from all signals of the same system to reduce the noise floor instead of manual adjustment. In addition, signals are reconstructed to have features extracted for the support vector machine (SVM) to make the final diagnostic judgment. The effectiveness of the proposed WASVD-CAN algorithm is verified by various experiments including single load and parallel loads with different operating current amplitudes and an arc generator mounted in series with each load respectively generating series or parallel arc faults. The results show that the proposed algorithm, without manually retuning parameters, has achieved outstanding detection accuracies in comparison with traditional weak arc-fault detection methods.

A New Method for Detecting Series Arc Fault in Photovoltaic Systems Based on the Blind-Source Separation

Photovoltaic networks are outdoor infrastructures, faced to different harsh conditions, which may experience various failures such as parallel arc fault (PAF) and series arc fault (SAF). Although PAF is more severe than SAF, the detection of SAF is more problematic, and its hazards including fire and the risk of staff electrocution are more serious. This paper proposes a new method for timely and reliable detection of SAF in photovoltaic systems. In this method, one of the blind-source separation algorithms called the principal component analysis (PCA) is employed. This method separates the nondependent components of some measurable quantities such as voltage and current using eigenvectors of their covariance matrix. In a normal condition, these signals include the dc component, switching components, and the network disturbances. When a SAF occurs, some new components by the electrical arc also add to the system. Using PCA, a suitable index is derived to discriminate signatures of SAF from the other components. Performance of the method is evaluated using plenty of experiments in different conditions.

Design of a DC Series Arc Fault Detector for Photovoltaic System Protection

With the skyrocketing growth in global photovoltaic (PV) power capacity, fault detection of PV systems has gained prodigious importance in recent years. It has been known that in PV system the dc series arc fault is more difficult to be detected than the parallel arc fault. With the proposed technologies of characteristic extraction, filtering, and fault identification, a novel efficient detection algorithm protecting a PV system against the dc series arc fault is proposed. The detection algorithm is realized on an arc fault detector (AFD) based on a cost-effective TMS320F28335 digital signal processor. To verify the feasibility and effectiveness of the proposed AFD, a relevant experiment PV system is conducted to compare with two commercial AFDs by various testing states. A great deal of empirical evidence shows that the detection performance of the proposed AFD outperforms that of the commercially available AFDs. The research may be of some reference value for similar works in the future.

An arc fault diagnosis algorithm using multiinformation fusion and support vector machines.

Arc faults in low-voltage electrical circuits are the main hidden cause of electric fires. Accurate identification of arc faults is essential for safe power consumption. In this paper, a detection algorithm for arc faults is tested in a low-voltage circuit. With capacitance coupling and a logarithmic detector, the high-frequency radiation characteristics of arc faults can be extracted. A rapid method for computing the current waveform slope characteristics of an arc fault provides another characteristic. Current waveform periodic integral characteristics can be extracted according to asymmetries of the arc faults. These three characteristics are used to develop a detection algorithm of arc faults based on multiinformation fusion and support vector machine learning models. The tests indicated that for series arc faults with single and combination loads and for parallel arc faults between metallic contacts and along carbonization paths, the recognition algorithm could effectively avoid the problems of crosstalk and signal loss during arc fault detection.

Arc Fault Detection and Localization in Photovoltaic Systems Using Feature Distribution Maps of Parallel Capacitor Currents

Arc faults threaten the safe operation of photovoltaic (PV) systems. An arc fault detection and localization approach using parallel capacitors is proposed. A PV system has been analyzed and tested with five capacitors paralleled with the branches in the system. Series and parallel arc faults at nine locations have been tested in the system. When an arc occurred, current pulses were generated in the capacitors and their amplitudes and polarities were obtained through Hall current sensors. Discrete wavelet transformation was performed on the capacitor currents and the distributions of their amplitudes, frequency spectrums, and polarities are here reported. The results indicate that the distributions are unique under different fault types and locations, which could be used to detect and localize arc faults in PV systems. Moreover, the amplitudes of the capacitor currents can also help to localize a series arc fault within a PV string. Finally, the proposed approach is validated by a double-fault test.

The Detection of Parallel Arc Fault in Photovoltaic Systems Based on a Mixed Criterion

This paper presents the problem of parallel arc fault in the DC side of a photovoltaic (PV) system. First, an experimental platform for arc fault of a PV system was built to simulate the DC side parallel arc fault in the PV system. By extracting the current waveform at the exit of PV panels, a study was conducted on the characteristics of the current during parallel arc fault. The result of Fourier transform on the current suggests that the high-frequency component of the current mainly concentrates in the frequency range of 126-250 kHz. According to the analysis of arc fault characteristic in time domain and frequency domain, reversal current maximum, modulus maximum, and energy are chosen as the criteria to detect parallel arc fault. And finally, a mixed criterion is proposed to overcome the shortcomings of the above-mentioned criteria and its reliability is verified by experiments. It provides a theoretical basis for the detection of the parallel arc fault in the PV system.

Low cost arc fault detection in aerospace applications

The protection of electrical plant, equipment, and components in aerospace applications is currently a hot research topic, with particular attention being paid to parallel arc fault detection. It is well known that many conventional thermal magnetic breakers do not offer adequate protection against arc faults, and the results can be similar to those produced by a short-circuit; electric faults are one of the main causes of failures in aircrafts, and their effects can be even catastrophic. For these reasons, it is mandatory to employ effective protection devices in order to quickly detect and isolate the failures. Overcurrent and overload protection is well carried out by conventional thermal and magnetic breakers; unfortunately, reliability is not assured when the fault is due to an arc.

Research on the Detection of Parallel Arc Faults Based on the Characteristic Analysis in Time Domain

Arc fault is one of main cause of fires which bring great loss and damage every year. Researchers show that the conventional circuit breakers probably fail to interrupt the circuit when an arc fault occurs. So, it is very important and necessary to find a new way to detect the arc fault. This thesis focuses on discussing the special characters of parallel arc faults, which are the main cause of fires. In this paper, not only the data of parallel faults, but also the normal working data of different apparatus are obtained. Two significant characters will be described and they will be the key criterions to detect the appearance of parallel arc faults. At last, we test the correctness of this method and get good results.