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Detection algorithm of series arc for electrical fire prediction
Abstract
In this paper, we studied on the detection of series arcing which is a main cause of electrical fires in low-voltage indoor wiring system. To distinguish series arcing state from normal one, we proposed a detection method based on that the magnitude of arc voltage included in AC voltage varies at random during series arcing. We designed and fabricated a high-pass filter with the low cut-off frequency of 3 kHz to attenuate AC voltage by -80 dB or below, and to pass arc voltage without distortion. Also, a phase shift algorithm which eliminates the periodic component of arc voltage was used for nonlinear loads like incandescent lamps controlled by a dimmer.
... Many studies have been conducted to measure and test the arc due to electrical failure. Park et al. [30] developed an algorithm to measure the series arc for preventing electrical fires. They proposed a method of measuring the AC voltage that includes the arc voltage in order to distinguish a series arc in a normal state and used a phase shift algorithm for applications with a non-linear load. ...
... Fire prevention analysis for electrical equipment was conducted for arc faults and electrical equipment for heating purposes. In order to prevent fires caused by arc faults occurring in electrical equipment, arc faults were measured via simulation or a method of measuring arc faults was studied [30][31][32]. For electric devices for the purpose of generating heat, the cause of the temperature rising from the normal operating state was analyzed, with the risk verified through experiments [43][44][45][46]. ...
Every year, diverse types of safety accidents cause major damage to human life and property. In particular, failure to suppress safety accidents caused by fires during the early stages can lead to large-scale accidents, which in turn can cause more serious damage than other types of accident. Therefore, this paper presents an analysis of the prevailing research trends and future directions for research on preventing safety accidents due to fire. Since fire outbreaks can occur in many types of places, the study was conducted by selecting the places and causes involved in frequent fires, using fire data from Korea. As half of these fires were found to occur in buildings, this paper presents an analysis of the causes of building fires, and then focuses on three themes: fire prevention based on fire and gas detection; fire prevention in electrical appliances; and fire prevention for next-generation electricity. In the gas detection of the first theme, the gas referred to does not denote a specific gas, but rather to the gas used in each place. After an analysis of research trends for each issue related to fire prevention, future research directions are suggested on the basis of the findings. It is necessary to evaluate the risk, select a detection system, and improve its reliability in order to thoroughly prevent fires in the future. In addition, an active emergency response system should be developed by operating a fire prevention control system, and safety training should be developed after classifying the targets of the training targets appropriately.
... Hall [85] propose une méthode de détection basée sur l'analyse des fréquences supérieures à 1 kHz. Dae-won [86] et Kim [87] n'utilisent que la bande de fréquence supérieure à 3 kHz. Kim [21] a analysé le courant de défaut d'arc à l'aide d'un filtre passe-haut de 10 kHz. ...
... Ayant remarqué une variation aléatoire de la tension pendant l'occurrence d'un défaut d'arc série, Dae Won Park [86] propose la détection d'un défaut d'arc électrique série basée sur l'analyse des composantes hautes fréquences de la tension AC. L'auteur propose l'utilisation d'un filtre passe-haut avec une fréquence de coupure de 3 kHz en utilisant aussi un algorithme de déphasage pour éliminer les composantes périodiques de la tension AC. ...
Accès restreint aux membres de l'Université de Lorraine jusqu'au 2016-05-18
... Among them, the fire caused by insulation damages the creep age arc. Due to arc impedance, the arc fault current is small, cannot be protected by short circuit protection, and the residual current device is needed for electrical fire protection [5]. ...
To solve the problem of the single-phase ground fault and occurrence of electrical fires due to the residual current in substation AC power systems, a residual current intelligent sensing technology is proposed based on ensemble empirical modal decomposition (EEMD), sample entropy (SE) reconstruction, and fire warning technology using a beetle antennae search algorithm. First, through the residual current monitoring device to collect residual current information, EEMD and SE reconstruction for arc-earth fault diagnosis and an analysis of the differences in the current characteristics of each line after reconstruction are used to determine the fault line. Second, residual current, temperature, and operating voltage as input parameters and fire probability are the output parameters. The input–output relationship is established by a back-propagation neural network (BPNN) and optimized by the beetle antennae search (BAS) algorithm to speed up the convergence and improve the prediction accuracy to establish a substation fire warning scheme. Through simulation experiments, this paper proposes the residual current as a monitoring object method can effectively diagnose ground faults and accurately predict the probability of fire occurrence to ensure the safe and stable operation of substations.
... Several detection methods are based on current spectral composition. The analysis is performed in a variety of bands ranging from low frequencies up to 20 kHz [4][5][6][7] and at even higher frequencies [8][9]. Other methods focus specifically on analyzing the odd harmonics [10], more specifically third and fifth harmonics [4][5]11]. ...
This paper presents a method for detecting series arcs for a 230 V AC-50 Hz residential installation, its implementation in an embedded circuit and its performance under real conditions. First, we evaluated the inter-period autocorrelation coefficient r of the line current. The algebraic estimation of the derivative of the signal r was then used to obtain an indicator of arc detection. r was calculated on the current at a sampling rate frequency of 5 kHz and the 50 Hz component was attenuated by an high pass (HP) filter. Tests carried out with domestic appliances with different power loads (resistive load, lamp, vacuum cleaner, dimmer etc) show a value for r which is very stable and close to 1 under normal conditions. The coefficient r weakens in the presence of an arc (initiated by a carbon path) and is no longer constant. The complete algorithm was implemented in real time on a 32-bit ARM Cortex-M3 microcontroller with a 12-bit ADC. The measuring current, which ranges from 0 to more than 20 A, was provided by a passive toroidal inductive probe. Four typical household loads were tested. The results obtained show that detection is operative and very fast (10 ms) with currents which exceed 400 mA.
This paper presents a method for detecting series arcing faults in AC home electrical networks. The proposed algorithm is based on both a Kalman filter, used for identifying fault symptoms and a decision block, which confirms the presence of a series arc fault to activate a tripping signal. The current measured at one end of the power line is estimated using a model of two steady-state variables (X1 and X2). Firstly, residuals and the third order difference of state X2 are used as input parameters of a Fuzzy logic processor for detecting fault symptoms. Secondly, the fault symptoms are processed by a detection logic block, which confirms the presence of an electrical arcing fault.
The algorithm is tested on a variety of loads in single or masking load configurations chosen accordingly to the requirements of the UL 1699 and IEC 62606 standards. The algorithm is also tested in the steady state or at load start (transient state). This method’s performance is studied and discussed in the final part. Experimental results show that the method we propose can detect arcing faults efficiently, avoiding false tripping, whilst taking into account a high degree of diagnosis accuracy and average detection time.
This paper is studying the low voltage arc fault circuit interrupters (AFCI) based on FPGA Signal detection. The arc fault generator is designed to generate artificial low voltage series arc faults. The AFCI signal detection circuits are tested by using the measured voltage and current waveforms from the testing circuits. It is expect to develop high performance signal detection circuits and the key component using in AFCI. Finally, the experimental data with serial arc faults are used to test the detecting methods and compare with the commercial devices. The purposed detecting methods can effectively detect the occurring of series arc faults, and the probability of malfunction is low.
Several authors propose different methods to arc fault detection based on time or frequency characteristics. Among those, some papers present the arcing fault detection using a specific frequency band on the current or voltage. This paper presents an overview of the different frequencies bands proposed by the authors that allow the detection of an arcing fault. To compare these proposed methods, we make a frequency analysis to obtain the frequencies characteristics of arcing fault for different loads signatures (resistive, inductive and nonlinear load). The method we have developed for arcing detection are based on five criterions: Analysis of the current low frequency, the voltage high frequency, the 5th harmonic current and current and voltage magnitude variations. Hardware in the loop approach allows us to test the methods of detection. Finally, our architecture of arcing detection is implemented on Field Program Gate Array (FPGA) prototyping board.
This paper presents a summary of a Honeywell collaborative IRD to provide a review and characterization of existing on-line methods of arc fault detection and protection; to summarize the critical challenges and opportunities for implementing on-line arc fault detection and protection for aerospace next generation electrical power systems including high voltage AC and variable frequency systems.
Since the arc-fault circuit interrupter (AFCI) was commercially introduced in 1998, questions have arisen about how it detect arcs, whether it detects series and parallel arcs, and what types of AFCIs are available. Types other than the original branch/feeder AFCI are emerging. This paper is intended to provide an update regarding answers to those questions, following an earlier paper that introduced the basic functioning of the AFCI (see G.D. Gregory et al., IEEE Trans. Ind. Apps., p. 928-33, 1998).
The arc-fault circuit interrupter is beginning to emerge as a new
product in residential applications in the US to protect against
conditions that may cause fire. This paper provides some of the
background regarding its emergence as a product and the function it is
intended to provide. The evolving technology may also lend itself to
industrial applications, as the potential applications become
sufficiently well understood
Arc Fault Detector with Circuit Interrupter and Early Arc Fault Detection
- Benjamin B Neiger
- Roger M Bradley
- James N Pearse
- William J Rose