usability of arc fault circuit interrupters with network

Scientia Iranica A (2020) 27(2), 607{613

Sharif University of TechnologyScientia Iranica

Transactions A: Civil Engineeringhttp://scientiairanica.sharif.edu

Usability of arc fault circuit interrupters with networkfunction

H.-S. Konga and W.-J. Rab;�

a. Department of Fire Safety, Kyungil University, 38428, 50 Gamasilgil, Hayangup Gyeongsan Gyeongbuk, South Korea.b. Hetko Inc., Gayang-Technotown #602-2, 07531, 217 Heojun-ro, Gangseo-Gu Seoul, South Korea.

Received 28 August 2017; accepted 3 March 2018

KEYWORDSNetwork;Arc Fault CircuitInterrupter (AFCI);Electricitymanagement;External monitoring;Interruption causeanalysis.

Abstract. The existing arc fault circuit interrupters have the function of interruptingoverloads, ground fault, and arc fault; however, they have neither the monitoring functionto allow external monitoring nor the noti�cation function to notify the �re safety manager.This study investigates the arc fault circuit interrupters with the network function, whichhave not been studied so far. We intend to install these arc fault circuit interrupters inplaces such as server rooms, pigsties, chicken farms, markets, cultural assets, skyscrapers,and factories, where large loss may occur if the �re safety managers do not recognize theelectricity shutdown, in order to increase the e�ciency of electricity management. If anoverload, a short circuit, or an arc is detected when power is supplied to the load, themicroprocessor generates a trip signal and cuts o� the power by this trip signal. Thissituation is monitored in real time by external monitoring and noti�ed to the �re safetymanager. The �re safety manager can immediately recognize the situation when the arcfault circuit interrupter interrupts the circuit and takes the necessary action to manage theelectricity e�ciently.© 2020 Sharif University of Technology. All rights reserved.

1. Introduction

According to the statistics of Korea Electric SafetyCorporation in 2015, 7,500 � 9,000 electric �res occureach year, accounting for about 20% of the total 40,000� 45,000 �re counts in Korea. In terms of the causesof electric �res, electric sparks (arc faults), accountingfor 78% of the total, are the main cause of electrical�res, followed by overloads, accounting for 10.2%, andground fault, accounting for 3.9%, respectively, whileother causes account for 7.8% of the electric �res [1].Therefore, it is necessary to detect and interrupt theelectric spark e�ectively in order to prevent electric�re. The electric spark can be due to various reasons

*. Corresponding author.E-mail address: woong-jae [email protected] (W.-J. Ra)

doi: 10.24200/sci.2018.5086.1088

such as deterioration of the electric wire, defectiveelectric product, and faulty contact of the electricwire [2]. In case of the electric spark, the temperaturerises instantaneously from several thousands to tensof thousands degrees Celsius. It is very dangerousas it can cause �re in a few seconds. In the arc�re simulation results of the National Institute ofStandards and Technology (NIST), it was observedthat one room would burn in 40 seconds after leavingan arc fault without interrupting it [3]. It is necessaryto introduce arc fault circuit interrupters in Korea toprevent �re caused by electric sparks in advance. Itis possible to secure safe electricity management if wecan externally monitor the identi�cation and analysisof the interruption causes through the network.

Previous studies on arc fault or arc fault circuitinterrupters include the work of Qi et al. (2017).They provided an in-depth analysis of the parametersfor Discrete Wavelet Transform (DWT) applied to

608 H.-S. Kong and W.-J. Ra/Scientia Iranica, Transactions A: Civil Engineering 27 (2020) 607{613

detection of arc faults that could greatly improve arcdefect detection performance. In their study, themethod of optimal parameters estimation for discretewavelet transform was utilized for arc fault detection inlow-voltage residential power networks. The in uenceof three parameters was investigated: the choice ofmother wavelet, level of decomposition, and samplingfrequency. A performance criterion was de�ned andused to compare the in uences of 550 combinationsof these three parameters on the arc fault detectionperformance for di�erent loads in two household appli-ances. The study showed that the choice of these threeparameters greatly in uenced the arc fault detectionperformance [3].

Guo et al. (2016) built a series arc fault generatorbased on UL1699 in their study. Experiments werecarried out under di�erent load conditions. Loop cur-rent waveforms with and without series arc fault wereobtained. Firstly, the current signal was decomposedand reconstructed by wavelet transform. Then, theirregular degrees of signals in each frequency bandwere quanti�ed with approximate entropy algorithmand the feature vectors of current signals were ob-tained. Finally, all the feature vectors were used asinput variables to Support Vector Machine (SVM).The series arc fault could be identi�ed by classifyingthose feature vectors with SVM. It was shown thatthe feature vectors obtained by wavelet approximateentropy algorithm could diagnose series arc fault [4].

Chae et al. (2016) presented a detection algorithmfor series DC arc faults to improve safety issues inDC microgrids in their research. To identify seriesarc faults accurately and quickly in DC microgrids, aseries DC arc fault detection algorithm using relativemagnitude comparison was proposed. The proposedalgorithm detected the time of occurrence of an arcfault based on the magnitudes of the load current inboth the time and frequency domains, simultaneously.The algorithm accurately worked for a DC microgridwith multiple switching converters, as it relied only onrelative current information [5].

Jovanovic et al. (2016) presented a novel methodbased on a single-phase Active Power Filter (APF)for series arc faults detection in an AC electricalinstallation. The APF's reference current was usedas the starting point for their method to test a largevariety of loads. The proposed method was validated atthe simulation level using �rst the MATLAB softwareand then the Hardware-In-the-Loop (HIL) approachexperimentally with an FPGA Altera Stratix III proto-typing board. The results obtained in this work showedthat series arc faults could be successfully detected withan APF only by updating its digital control with thearc fault detection functionality, instead of designingan arc-fault detection-speci�c device from the verybeginning [6].

Liu et al. (2016) used the approach of radialbasis function neural network (RBFNN) to identifythe occurrence of serial arc faults. The DiscreteWavelet Transform (DWT) was employed to obtain thetime-frequency domain characteristics of line currentwaveforms for representing the serial arc fault pat-terns. This study compared the results with two othermethods in Detection of Sub-spectrum Energy (DSE)and High Frequency Detection by Wavelet Transform(HFDWT). It was observed that RBFNN had betterability than DSE and HFDWT in the detection of serialarc faults [7].

Hatton et al. (2016) reported the results of aproject to develop a DC Arc Generator (AG) suitablefor use in the development and testing of DC arc faultdetectors [8].

Zhu et al. (2016) discussed a testbed that hadbeen developed for the purpose of testing arc faultdetectors in a laboratory environment using precisereproduction or replay of pre-recorded arc signals. Thetestbed was capable of replaying both the arc signatureand the noise from the power electronic circuits atproper amplitude to represent real-world conditions.Utilization of such a testbed would facilitate the studyof reliable detection algorithms [9].

Zhu et al. (2016) reported the results of develop-ment of a real-time arc fault detection technique thatwas built as a wavelet decomposition based arc detectorusing a TI C2000 platform DSP. The arc fault detectorwas tested on a composite arc signal constructed fromrecordings of real-world inverter noise and real-worldarc events replayed through a high-�delity testbed tocompare the abilities so that the exclusive invertersignals could be di�erentiated from inverter plus arcingsignals. The results demonstrated that the waveletdecomposition and arc discrimination algorithms couldbe implemented in real time on a low-cost DSP [10].

Wang and Balog (2016) presented an e�ectivemethod based on wavelet transform and Support Vec-tor Machines (SVM) for detection of arc faults inDC PV systems. Because of its advantages in time-frequency signal processing, wavelet transform was ap-plied to extract the characteristic features from systemvoltage/current signals. SVM was then used to identifyarc faults. The performance of the proposed techniquewas compared with traditional Fourier transform basedapproaches [11].

Zhao et al. (2016) �rst introduced the dischargecharacteristics and classi�cation of arc faults. Then,the research status of AC AFD methods was sum-marized. Also, this paper analyzed the advantagesand disadvantages of the methods and proposed anew method for arc fault detection based on arc faultdi�erence signal analysis and wavelet transform. Thefundamental waves of the collected current signalswere removed and then, the obtained di�erence signal

H.-S. Kong and W.-J. Ra/Scientia Iranica, Transactions A: Civil Engineering 27 (2020) 607{613 609

was analyzed by the stationary wavelet transformin MATLAB. Afterwards, the modulus maxima of alayer of detail waveform were selected as the featurefor detection. The experimental results showed thatthis method could improve the accuracy of fault arcdetection [12].

Liu et al. (2016) proposed a method based onHilbert-Huang Transform (HHT) and Arti�cial NeuralNetworks (ANN) for AC SSPC arc fault detection.Numerical simulation results together with discussionswere provided, which indicated the e�ectiveness of theproposed fault detection method. Speci�cally, Hilbert-Huang transform based multi-resolution analysis wasadopted to obtain the features of the AC SSPC arccurrent in the measured signal and arti�cial neuralnetworks were adopted to identify the faults based onthe extracted features [13].

Johnson et al. (2011) stated that manufacturerswere developing new Arc Fault Circuit Interrupters(AFCIs). The Distributed Energy Technologies Labo-ratory (DETL) at Sandia National Laboratories (SNL)used multiple recon�gurable arrays with a variety ofmodule technologies, inverters, and Balance Of System(BOS) components to characterize new Photovoltaic(PV) DC AFCIs and Arc Fault Detectors (AFDs).Detection capabilities, characteristics, and nuisancetripping avoidance of the device were the primarypurpose of the testing. The results showed thatsigni�cant noise was injected into the array from theinverter, but AFCI functionality of the device wasgenerally stable [14].

Strobl and Meckler (2010), through the analysisof the measured signals in time and frequency domains,showed that parallel arcing involved signi�cant changesin the current at the primary side of the converterand was therefore easily detectable. Serial arc faults,however, could not usually be detected by a low-frequency analysis of current and voltage signals dueto the speci�c characteristic curve of the photovoltaicmodules, the control concept of the converter formaximum power tracking, and the possible change insolar irradiance [15].

Yao et al. (2012) designed an experimental systemto study the characteristics of series DC arc. Di�erenttests were conducted in order to determine the in u-ence of di�erent factors, such as gap length, current,etc., on the arc [16].

Johnson and Armijo (2014) discussed the di�er-ences between establishing and sustaining arc-faults fora number of di�erent test con�gurations, compared thevariability of arc-fault spectral content for each test,and analyzed the evolution of the RF signature overthe duration of the fault. Their ultimate goal was todetermine the most repeatable `worst case' tests foradoption by UL [17].

Sidhu et al. (2002) described the development of

a multi-sensor device based on four di�erent physicalphenomena for reliable detection of low-level arcingfaults in metal-clad switchgear. The device could alsobe applied to the detection of arcing in power electronicdrives, dry-type transformers, gas insulated switchgear,generator bus-ducts, and other metal-clad electricalapparatuses [18].

Chen and Li (2016) aimed at providing a jointdetection method for Arc Fault Circuit Interrupters(AFCI) serving smart micro grid. In this paper, twomethods to bring PV series arc fault into the PV systemwere recorded by Intensi�ed Charge-Coupled Device(ICCD). A relatively satisfying joint algorithm basedon the two proposed detection variables was put for-ward to prevent unwanted nuisance trips in the systemtransient process. To constantly �t varying electricsignals in the PV system, this detection algorithm alsoadopted dynamic threshold value [19].

Analysis of the existing research studies showsthat most of them suggest methods for reducing arcfault and testing performance of arc fault circuit inter-rupters; however, there is no study on arc fault circuitinterrupters with the network function. Therefore, thisstudy aims to improve the performance of arc faultcircuit interrupter management by introducing networkfunction into the existing arc fault circuit interrupters,considering the case of places where large loss mayoccur if the �re safety manager does not recognize theelectric shutdown quickly, e.g., server rooms.

2. Structure of an existing AFCI without thenetwork function

2.1. Appearance and block diagramFigure 1(a) shows the appearance of an arc fault circuitinterrupter. The test button is located on the frontside of the arc fault circuit interrupter. Figure 1(b)represents the block diagram of the arc fault circuitinterrupter. The arc detection unit, the overloaddetection unit, and the short circuit detection unitdetect arc fault, overloads, and ground fault. Theysend the detection signals to the microprocessor. Themicroprocessor receives the signals and sends them tothe trip function unit to turn o� the power [20].

2.2. Operation mechanismAs shown in Figure 2, when a load is connected tothe arc fault circuit interrupter and power is supplied,real-time monitoring of dangerous signals for electricalhazard factors such as arc fault, overloads, and groundfault is performed. If a dangerous signal such as anoverload, a short circuit, or an arc is detected whilesupplying power to the load, a trip signal is generatedto shut o� the power.

As mentioned above, the existing arc fault cir-cuit interrupters have the functionality of interrupting


Figure 1. Structure of the existing AFCI without thenetwork function.

Figure 2. Operation mechanism of the existing AFCIwithout the network function.

overloads, ground fault, and arc fault; however, none ofthem has the monitoring function to allow for externalmonitoring or the noti�cation function to notify the �resafety manager.

3. Structure of an AFCI with the networkfunction

3.1. Block diagram and circuit diagramFigure 3(a) shows the appearance of an arc fault circuitinterrupter with the network function. The networkconnection unit is located at the bottom of the arcfault circuit interrupter.

Figure 3(b) is the block diagram of the arc faultcircuit interrupter. The arc detection unit, the overloaddetection unit, and the short circuit detection unitdetect arc fault, overloads, and ground fault. Theysend the detection signals to the microprocessor. Themicroprocessor receives the signals and sends them tothe trip function unit to turn o� the power. At thesame time, the microprocessor sends the signals to thenetwork unit.

Figure 3. AFCI with the network function.


Figure 3(c) is a detailed circuit diagram of thenetwork unit. When the network unit receives sig-nals through its Tx and Rx terminals, its outputunit generates the RS232 signals at the TTL level.Generally, RS232 is con�gured with its GND set on\Common" and its Tx and Rx set to select \High"to send and receive signals. The signals from themicroprocessor are converted by the insulation ele-ments, i.e., photo couplers (U10 and U11), enablingthe communication of signals without damaging theexternal network unit connected to the arc fault circuitinterrupter.

3.2. Operation mechanismAs shown in Figure 4, when the load is connectedto the arc fault circuit interrupter and the arc faultcircuit interrupter is turned on by supplying thepower, self-diagnosis is carried out to see whetherthe arc fault circuit interrupter operates normally.If the arc fault circuit interrupter does not operatenormally, an abnormal signal is generated. If the arcfault circuit interrupter operates normally, overloads,ground fault, and arc fault are monitored in real time.If an overload, a short circuit, or an arc is detectedwhile power is supplied to the load, the arc faultcircuit interrupter is tripped to cut o� the power andgenerate a trip signal [21].

This situation is monitored in real time by exter-

nal monitoring and noti�ed to the �re safety manager.The �re safety manager can immediately recognizethe situation when the arc fault circuit interrupterinterrupts the circuit and take the necessary actionto manage the electricity e�ciently. The arc faultcircuit interrupters with the network function can bee�ectively used in server rooms, pigsties, chicken farms,markets, cultural assets, skyscrapers, factories, etc.that require real-time monitoring of the arc fault circuitinterrupters.

Table 1 shows the comparison between the exist-ing arc fault circuit interrupter and the arc fault circuitinterrupter with the network function.

4. Conclusions

An arc fault circuit interrupter with the network func-tion was equipped with the communication functionby a microprocessor to determine whether the causeof the interruption was an overload, an arc, or ashort circuit and transmit and notify the result tothe management system of a remote place throughthe network. The arc fault circuit interrupter allowsthe circuit to be interrupted by remote control ifnecessary. This arc fault circuit interrupter preventsboth electrical accidents and �res more accurately andsupports the communication function for the network.Through the network, the external monitoring system

Figure 4. Operation mechanism of the AFCI with the network function.


Table 1. Comparison between AFCIs with and without network function.

Existing AFCI AFCI with networkfunction

Function of interrupting overloads Yes YesFunction of interrupting ground fault Yes YesFunction of interrupting arc fault Yes YesFunction of external monitoring No YesFunction of remote interruption No Yes

can identify and analyze the interruption causes andimprove the e�ciency of electricity management.

Now, the installation of an arc fault circuit in-terrupter to prevent electric �re must be compulsory,not an option. Especially, it is necessary to activelyintroduce the arc fault circuit interrupters based on thenetwork because they can be used in places where real-time monitoring of the electricity is required; moreover,electricity management can be e�ciently performed bythem.

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Biographies

Ha-Sung Kong majored in Disaster Science in theGraduate School of University of Seoul. His maininterests are �re�ghting facilities, safety policies, disas-ter management, and �re�ghting quali�cation systemsamong others. He currently works in the Departmentof Fire Safety of Kyungil University as an AssociateProfessor.

Woong-Jae Ra majored in Electrical & ElectronicEngineering and Business Administration at YonseiUniversity. He worked in KT Corporation (KoreaTelecom) and Accenture PLC, and performed variousIT and energy related projects. Currently, he isthe CEO of Arcontek Co., Ltd., which is a leadingprofessional manufacturer of AFCI (Arc Fault CircuitInterrupter) in Korea.

usability of arc fault circuit interrupters with network

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