Goette, Josef

Grichting, Benjamin

Jacomet, Marcel

English

Berner Fachhochschule: ARBOR

Cascaded Fuzzy Logic based Arc FaultDetection in Photovoltaic ApplicationsSummaryBenjamin Grichting, Josef Goette and Marcel JacometBern University of Applied Sciences, HuCE-microLabCH-2501 Biel-Bienne, SwitzerlandAbstractArc faults in photo-voltaic (PV) systems are dangerous events and can create huge damage. If the inverter has theinformation of an existing arc fault in the system, it can capture counter measures. We present an algorithm which detectssuch arc faults with only low demands in computation power and resolution.I. INTRODUCTIONIn the United States, each year over 20’000 fires are attributed to electrical malfunction. These fires result in 280deaths each year [1]. Arc faults in a privately installed PV system can cause such a fire. The heating power of anarc can easily emblaze surrounding material. Arc faults occur at critical points, as for example in rusty contacts, indefect modules, or in connector blocks. If mechanical load, like wind, provokes a short interrupt, a constant arc atthese critical points can occur. In Switzerland, such fires have already occured at PV Plant Mt Soleil near St-Imier, in2006 a considerable number of arcs ocurred in defective modules in the PV Plant on the roof of the Bern Universityof Applied Science. In the last years, also in Germany several fires were reported [2].So the National Electrical Code (NEC) 2011 [4] dictates an Arc Fault Circuit Interrupter (AFCI) for DC PV systemsoperating at 80 VDC or higher. We expect that such a law is soon passed also in Europe too, thus all inverters musthave a unit to detect arc faults in PV systems. Until now however, only a few systems exhibit such a detector. Forinverter manufacturers it is interesting to know, how to combine the existing parts with a new detector. So the algorithmhas to be as simple as possible such that it can operate in already existing controllers.We here present an algorithm that achieves the mentioned goals. It has low computational demands and works withmost standard ADC’s contained in controllers. It bases on our experiences we made with the measurements describedin Section II. With the help of these measurements we made an analysis (Section III) to find some characteristics todevelop the final algorithm (Section IV).II. PROPOSED APPROACHThe arc detector must be a part of the inverter. It detects an arc fault in the PV plant. The PV plant is always anothersystem and cannot be easily modeled. Therefore we do not consider methods like impedance spectroscopy because ofthe high complexity of the complete system. Instead our algorithm bases on measurements of the input signals to theinverter. Figure 1(a) shows the block diagram of the automated measuring setup. The measurement unit is composed ofAutomaticMeasurementUnitup to > 100mGridVIVI~=iinIin VinvinIarc Varc (a) Automatic Measuring System−80−60−40−200Magnitude (dB)102 103 104 105 106 107 108−180−90090Phase (deg)AC Current FilterFrequency  (rad/s)(b) Bode Plot of AC Current FilterFig. 1. The whole measurement system with different arc locations and frequency characteristics.a Data Acquisition (DAQ) Box NI DAQ USB-6566 controlled by a laptop. We measure 6 channels: Varc =ˆ DC voltageover arc; Iarc =ˆ DC current in arc; Iin =ˆ DC inverter input current; Vin =ˆ DC inverter input voltage; iin =ˆ AC invertersource: https://doi.org/10.24451/arbor.7527 | downloaded: 5.3.2020input current; vin =ˆ AC inverter input voltage. The channel Varc serves as trigger source. If a trigger event occurs, 2.7 sof all channels with 0.5 s pre-trigger are recorded with a sampling rate of 2 MSamples per second and a resolution of16 bits. The recording time is limited by the buffer size of the DAQ Box, which is in our case 32 MSamples.A. Front EndThe front end is divided into two parts: The first part is mobile and is always coupled with the arc fault generator(see details in II-B) and measures Varc and Iarc. We condition the signals with a first order low-pass filter with cutoff-frequency of approximately 1.5 kHz. These signals are not relevant for our algorithm and serve only as trigger sources.The more important second part is near of the inverter. It consists an AC and a DC section. We need DC values todetermine the operating point of the inverter. More interesting for us are the AC measurements. The current is measuredover a toroidal transformer with subsequently filtering. Figure 1(b) shows the Bode-plot of the iin measurements. Wesee a bandpass characteristic with corner frequencies at approximately 1 kHz and 100 kHz. The high-frequency slopeis −40 dB/DEC, so at the Nyquist frequency of 1 MHz we have already a damping of 40 dB.The Bode-plot of vin measurements looks the same, since we use for decoupling the high voltage an RC high-passfilter followed by a corresponding low-pass filter of second order.B. Arc Fault GeneratorThe norm UL1699B [3] from Underwriter Laboratories (UL)1 describes how the different arc faults have to becreated. The arc fault generator consists of the arc-producing electrodes and a parallel break contact. If the breakcontact is pressed, the current flows over the electrodes, the steel wool between the predefined electrode distanceburns, and a constant electric arc appears between the electrodes. The UL standard dictates a polycarbonate tubearound the electrodes containing the steel wool. Because in our measurements in most cases no arc could be produced,we have performed all measurements without this tube.In addition to the dictated distances of the electrodes, measurements with varying distance have also been done: Thecurrent flows over the electrodes and simultaneously the distance becomes larger. The motivation for this method is toprevent a burning of the steel wool which makes it much easier to detected the inception of an arc fault at the inputof the inverter.C. DatabaseDuring the first half of 2012, we have built a database with several hundred measurements of arc faults. Allmeasurements have been done in collaboration with the PV Lab of the Bern University of Applied Sciences in Burgdorf.2With the help of this database, the measurements can easily be selected by their properties, like input voltage, arcpower, distance of the electrodes and the like. Various constellations of arc location, supply, and distances are possible.In the project description on the website of our HuCE microLab, we make available a link to this database.3III. ANALYSISA. Frequency SpectrumHere we analyze the change of the frequency spectrum when an arc fault occurs. We compute the Fast FourierTransform (FFT) before (Ino−arc[f ]) and one during (Iarc[f ]) the arc is burning. To obtain a number describing howlarge the signal energy is in a given bandwidth, we build the absolute sum in this band.Sno−arc =fmax∑fmin|Ino−arc[f ]| , Sarcfmax∑fmin|Iarc[f ]| (1)To scale the relation between Sarc and Sno−arc we calculate N according toN =Sarc − Sno−arcSarc + Sno−arc(2)to abstain a number between −1 and 1 which represents the change in frequency spectrum when a narc turns on.We first look at a system consisting a high voltage battery stack as supply and an ohmic load (8.25 Ω). Since the largeinput capacity of inverters short-circuits nearly all frequencies, below we refer only to the current spectrum. Figure2(a) shows this situation. An interesting observation is that the most energy of the arc fault noise is visible in the bandfrom 0 to 10 kHz. We observe this phenomenon in nearly all measurements except for the measurements in the PVPlant in Biel.41See the website http://www.ul.com.2See the website http://www.pvtest.ch.3http://microlab.ti.bfh.ch/research/projects/afci-pv-systems/: Only a preliminary version with limited functionality isonline. The final version will be available by 2013.4See our database at http://microlab.ti.bfh.ch.1 2 3 4 5 6 7 8 9x 10400.20.40.60.81x 10−4VsInput AC Current Spectrum in Normal Operation1 2 3 4 5 6 7 8 9x 10400.0020.0040.0060.0080.01Input AC Current Spectrum with Arc FaultFrequency in HzVs(a) Amplitude density spectrum with battery as supplyDistribution Curve0.00%20.00%40.00%60.00%80.00%100.00%0.01 0.11 0.21 0.31 0.41 0.51 0.61 0.71 0.81 0.91NBielBurgdorf(b) Cumulated distribution function of N for two PV plants (fmin =0 kHz, fmax = 10 kHzFig. 2. Signal energy change—take care of the different scales—and our measure N for measurements in different PV plants.Figure 2(b) illustrates the cumulative distribution function of N for two different PV plants. One plant is located inBurgdorf on the roof of the Bern University of Applied Sciences building, and the other is the test plant from SputnikEngineering in Biel, next to a railroad. The situation for this second plant looks not very nice. The native noise inthis plant is much higher than in other ones. The calculated measure N is for the frequency band up to 10 kHz nearlyuniformly distributed. But the goal of our measure is to set a threshold at a defined level with all values above it beingdetected as arc fault. A possibility to improve the situation would be to increase the bandwidth, but it shows also thatonly supervising the spectrum is insufficient. We need some more indicators.B. Peak DetectionWe further have observed in all measurements at the beginning of an arc fault a clearly visible peak in iin. Interestingis the difference in the peak shape; in the arc faults provoked as dictated in the UL standard, the burning of the steelwool is clearly visible. In our generated cases with growing electrode distances, the peak has much less noise.C. Operating PointAn appearing arc fault in the system abruptly changes input power to the inverter. Fluctuations in irradiation of thesun are not as fast. However, we must distinguish between large and small plants. The case of an arc burning with40 W in a MW plant is not easy to detect at the inverter input. The abrupt change of the operating point can, howeverbe used to prevent faulty activation. For instance if some switching operation is running, power changes over 100 %are possible. Such a decrease/increase in power is probably (hopefully) not be provoked by an arc fault.IV. DETECTION ALGORITHMOut detection algorithm bases on the above three indicators. So we must convert them into a form that a controllereasily handles.A. SegmentationBecause we target a real time application, the signal has to be analyzed on the level of received samples. Thealgorithm calculates three different kind of segments. A segment is a property of the signal in a given interval. Thedifferent segments correspond to the three indicators found in the previous section III. There you have ’FrequencySpectrum’, ’Peak Detection’ and ’Operating Point’. The corresponding segments are ’Signal Energy’, ’Peak’ and ’DCPower’. Figure 3(a) illustrates these segmentations. On the top the input signal iin is modeled. The more red the signalis the more noise it contains.1) Signal Energy: In Section III we have determined the signal energy in the 10 kHz band via FFT. However thecomputation effort is to large for a controller. Therefore, we low-pass filter the signal and then compute an approximationfor the energyEx ≈b∑a|iin[k]|2 , (3)where the variables a and b are the borders of the segment and x is the segment number.2) DC Power: The Segment Px contains the input power to the inverter in the given time interval. This value canbe calculated with Iin and Vin.nn n-1n-1 n-2n-2 n-3n-3 99 88 77 66 55 44 33 22 11tSEGtGAP99 88 77 66 55 44 33 22 1199 88 77 66 55 44 33 22 11No Arc ArcInput SignalSignal EnergyDC PowerPeaktimet0tfiretbuf(a) Segmentation of input variablesDefuzzify FuzzyficationCascaded Arc DetectorRules DefuzzifyPeakevaluatorPoweranalyserWindowNearWindowWide(b) Cascaded fuzzy detectionFig. 3. Overview over input variables (segments) for our algorithm and the modular fuzzy logic detection.3) Peak: The measure of the peak size is calculated using the standard deviation of iin. When we assume that themean value of iin is 0, then the standard deviation can be approximately calculated with the help of Ex:σx ≈√√√√ 1b− a ·b∑a(iin[k])2=√1b− a · Ex. (4)We may define a threshold a τx = m · 3 · σx where m is an adjustable parameter. So the peak size in the particularsegment can be determined in a manner similar to the measure in (2).PKx =max (|iin[k]|)k=bk=a − τxmax (|iin[k]|)k=bk=a + τx(5)B. Cascaded Fuzzy DetectionWe detect changes in either the signal energy, or the operating point, or the peaks can be detected via differentprimary fuzzy detectors. The algorithm uses four different primary fuzzy detectors:1) Peakevaluator: Evaluates the peak size. Inputs are the peak size segment PK5 and relations of the signal energysegments E3...7. The peak is identified as large, when it is in the interval of segment 5. So the algorithm has enoughdata to compare the segments before and after the peak.2) Window Near: Compares the signal energy before and after the peak similar to (2).3) Window Wide: If an arc is burning not immediately with its full power, the Window Near detector has no chanceto detect a growth in the noise energy. So the Window Wide detector supervises the changes of noise in a wider timeinterval.4) Poweranalyser: Observes continuously the input power to the inverter. In most cases of arc faults, the powerdoes not change too much. So this detector serves mainly to prevent faulty activation.The outputs of these four detectors are inputs for the master fuzzy detector, see figure 3(b). The advantage of sucha system is that it can be improved on the run due to its modularity. The presently used prototype of our Arc FaultDetector seconds all critical events, allowing to modify rules and/or to add new rules.V. RESULTSExperiments in the laboratory show that 100 % of the arc faults, with a given distance of the electrode, can bedetected. The algorithms detects all synthetic arc faults of the database with an output value larger than 0.8.VI. ACKNOWLEDGMENTWe like to thank to Sputnik Engineering AG, especially Joerg Marzoner and Norman Bayer for their assistance andfor providing different inverters. Furthermore we want to thank to the PV Lab in Burgdorf, especially Daniel Gfellerfor the measurements, and Prof. Heinrich Haeberlin and Prof. Urs Muntwyler for their support; and Marcel Jost andProf. Roger Weber from the Institute for Mobile Communications, research group embedded systems, for the futureimplementation.REFERENCES[1] U.S. Fire Administrations (USFAs) National Fire Incident Reporting System, December 2011.[2] H. Haeberlin, Arc Detector as an External Accessory Device for PV Inverters for Remote Detection of Dangerous Arcs on the DC Side of PVPlants, World Conference on Photovoltaic Energy Conversion, Valencia, Spain, 2010.[3] Standard UL1699B - PHOTOVOLTAIC (PV) DC ARC-FAULT CIRCUIT PROTECTION, Underwriter Laboratories, http://www.ul.com.[4] National Electrical Code 2011., NFPA, ISBN 978-087765914-3, 690.11 Arc-Fault Circuit Protection (Direct Current), p. 70-599.

Cascaded Fuzzy Logic based Arc Fault Detection in Photovoltaic Applications

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Cascaded Fuzzy Logic based Arc Fault Detection in Photovoltaic Applications

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