CN117761480B - A method for detecting arc faults with resistance to load fluctuation based on ratio-RMS - Google Patents
A method for detecting arc faults with resistance to load fluctuation based on ratio-RMS Download PDFInfo
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Abstract
The invention discloses a load fluctuation resistant fault arc detection method based on a ratio-root mean square, which comprises the steps of constructing a fault arc detection platform, calculating collected current signals through the ratio-root mean square method to obtain characteristic quantity Z representing the difference between each current period and adjacent current periods, judging that a fault arc is generated if the value of Z exceeds a threshold value, calculating the power and the power change rate of the current periods with larger Z value, and judging whether the fault arc is generated or not according to the number of the current periods with non-zero power change rate. According to the invention, quantized data is obtained by sampling and detecting a plurality of points at the same position in different periods, continuous monitoring of current waveforms is not needed, conversion between a time domain and other domains is not needed, the operation amount of the data is greatly reduced, and the purpose of greatly reducing fault judging time is further realized; the invention has simple structure, small calculated amount and easy realization in hardware, and can realize rapid identification of fault arc and simultaneously give consideration to accuracy.
Description
Technical Field
The invention relates to a fault arc detection method, in particular to a load fluctuation resistant fault arc detection method based on a ratio-root mean square.
Background
The fault arc is one of the causes of electrical fire, wherein the fault arc is difficult to identify by a conventional protection device because the characteristics of low voltage and high current are not accompanied in the process of generating the series fault arc, but if the fault arc is developed, the fault arc which continuously burns generates high temperature of 2000-4000 ℃ and can ignite surrounding objects to cause fire.
At present, three common fault arc identification methods are mainly used, namely, the first method is based on physical feature identification such as infrared, arc light and noise when the fault arc occurs, but the arc position is difficult to judge, a sensor is difficult to install according to the arc position, meanwhile, the physical feature shown by the arc is easy to be interfered by the surrounding environment, the second method is based on the feature of an electric signal when the fault arc occurs, such as the rising rate and average value of a current signal, zero-break phenomenon and higher harmonics and the like, whether the fault arc occurs is judged by a method of setting a threshold value in advance, but the randomness of the arc often cannot achieve an ideal effect, and the third method is based on fault arc identification based on deep learning, but experiments are generally only carried out in a single load, the accuracy is high, the calculated amount is large, and the fault arc detection needs to be completed within a specified period according to the requirement of UL1699, so that high requirements are put forward on hardware. By integrating the aspects, the fault arc detection method based on the electric signals is a more effective fault arc identification method in a multi-load loop at present.
However, the conventional method for judging the fault arc according to the electric signal often depends on a hard threshold or an empirical threshold, when the circuit environment changes or load switching is involved, the empirical threshold may change, and the current fault detection field lacks a public and authoritative fault arc sample data set, and the empirical threshold can only be determined according to the actual condition of the circuit load. Therefore, the traditional method for judging the fault arc according to the electric signal has certain limitations.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides the load fluctuation resistant fault arc detection method based on the ratio-root mean square, which is used for carrying out calculation analysis on the acquired data only by sampling at the time domain sampling points, reducing the fault judgment time, reducing the requirement on hardware and having high algorithm execution efficiency and accuracy.
In order to achieve the aim, the invention provides the technical scheme that the method for detecting the load fluctuation resistant fault arc based on the ratio-root mean square comprises the following steps of;
The method comprises the following steps of S1, constructing a fault arc detection platform, wherein the detection platform comprises a power supply, a fault arc generating device, a load, a current transformer, a data processing system, a voltmeter and an oscilloscope, the power supply, the current transformer, the fault arc generating device and the load are sequentially connected in series, the voltmeter is connected with the fault arc generating device in parallel, and the current transformer, the voltmeter and the oscilloscope are respectively connected with the data processing system;
S2, calculating the collected current signals in a data processing system through a ratio-root mean square method to obtain a characteristic quantity Z representing the difference between each current period and adjacent current periods, and judging that a fault arc is generated if the value of Z exceeds a set threshold Z b;
And S3, for the current period with larger Z value, calculating the power and the power change rate delta of the collected current and voltage signals in the data processing system, judging whether a fault arc occurs according to the number of current periods with the power change rate delta not being zero, judging that the fault arc exists by the power change rate delta of 3 continuous periods with the randomness, and otherwise, judging that the fault arc is caused by load fluctuation by the power change rate delta of 2 periods with the power change rate delta not being zero.
Further, the fault arc generating device in S1 adopts a Cassie arc model.
Further, the judging method of whether the fault arc is generated in the step S2 is as follows:
detecting current amplitudes of points acquired in three adjacent current periods, wherein three points are acquired in each current period;
The numerical values of the current amplitudes of the three points collected in each current period are added to obtain algebraic sums S n-1、Sn、Sn+1 of the three points of each current period, and then the ratio of the next current period to the previous current period is calculated to obtain D n-1 and D n;
the calculation formula of the characteristic quantity Z is obtained by utilizing the root mean square formula of all the calculated ratios,
When the value of Z exceeds the set threshold value Z b, the occurrence of the series fault arc is judged.
Further, the power change rate delta calculation method in the step S3 is as follows;
According to the active power calculation formula Calculating the change in average power per current cycle, for the collected discrete power data P 1,P2,P3, corresponding to time t 1,t2,t3, the discrete power data P i corresponding to time t i, wherein i=1, 2,3,..:
furthermore, the plurality of loads are connected in parallel, and each load is connected into the detection platform through a switch.
Furthermore, the electrode of the fault arc generating device adopts a copper carbon electrode.
Compared with the prior art, the invention has the advantages that quantized data is obtained by sampling and detecting a plurality of points at the same position in different periods, continuous monitoring of current waveforms is not needed, conversion of time domain and other domains is not needed, the operation amount of the data is greatly reduced, the purpose of greatly reducing fault judgment time is further realized, the requirement on hardware is also reduced, the cost is greatly reduced, the invention has simple structure and small calculation amount, the realization is easy in hardware, the time of fault occurrence can be timely obtained in the time domain, and the accuracy is simultaneously realized while the rapid identification of fault arcs can be realized.
Drawings
FIG. 1 is a circuit diagram of a fault arc detection platform of the present invention;
FIG. 2 is a flow chart of fault arc detection in accordance with the present invention;
FIG. 3 is a graph showing the variation of the value of characteristic Z with current period according to the present invention;
FIG. 4 is a graph of the rate of change of power in a fault arc line of the present invention;
In the figure, 1, a power supply, 2, a fault arc generating device, 3, a load, 4, a current transformer, 5, a data processing system, 6, a voltmeter, 7 and an oscilloscope.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 2, the present invention provides a technical solution, which includes the following steps.
The method comprises the steps of S1, constructing a fault arc detection platform according to actual household electricity consumption conditions of residential areas, wherein the detection platform comprises a power supply 1, a fault arc generating device 2, a load 3, a current transformer 4, a data processing system 5, a voltmeter 6 and an oscilloscope 7, the power supply 1 adopts single-phase electricity with the voltage level of 220V and the frequency of 50Hz, an electrode of the fault arc generating device 2 adopts a copper carbon electrode, an arc formed by simulated carbonization is ensured to be stable in arcing, the data processing system 5 adopts a computer provided with MATLAB, the power supply 1, the current transformer 4, the fault arc generating device 2 and the load 3 are sequentially connected in series, the simulated series fault arc is connected with the voltmeter 6 in parallel with the fault arc generating device 2, the current transformer 4, the voltmeter 6 and the oscilloscope 7 are respectively connected with the data processing system 5, then the change of fault current in a line is monitored through the oscilloscope 7, the load 3 is multiple, the loads 3 are connected in parallel, each load 3 is respectively connected into the detection platform through a switch, the connection condition of the load 3 is realized through the opening and closing of the switch, and the connection of the load 3 is realized, and the connection of the single load 3 is also realized.
The fault arc generating device 2 adopts a Cassie arc model, the model considers that the shape of an arc is approximate to a cylinder, a middle channel of the arc is formed by gas, the arc has uniform temperature distribution on the section of the arc, and the temperature of the arc is constant in time and space. From the energy balance principle it is possible to:
In the formula (1), dq/dt is the change of the stored energy in the arc column of the arc in unit length, eXi h is the input power of the arc in unit length, i h is the instantaneous value of the arc current, e is the electric field intensity in the arc column, and P loss is the power loss in unit arc length. Because the arc resistance value is smaller, the conversion of the arc model into a form of conductance can be more intuitively analyzed:
in the formula (2), g is arc conductance if the following is given And the arc length is L, the second formula in formula (2) can be rewritten as:
Arc voltage u h =l×e, power loss of arc column Bringing it into formula (3) yields:
From this, the dynamic arc equation of Cassie is as follows:
In the formula (5), T is a time constant in a Cassie arc equation, and u c is an arc voltage constant. A Cassie arc model is built up according to equation (5) in MATLAB in the data processing system (5) for simulation calculations.
S2, calculating current data acquired by the current transformer 4 in MATLAB through a ratio-root mean square method to obtain a characteristic quantity Z representing the difference between each current period and an adjacent current period, judging the similarity between the current period and the adjacent current period according to whether the characteristic quantity Z is a value close to 0, wherein the closer the value of the characteristic quantity Z is to 0, the higher the similarity is represented, otherwise, the more the value of the characteristic quantity Z is, the less the similarity is represented, namely the more the difference is, and if the value of the characteristic quantity Z exceeds a set threshold Z b, the fault arc is judged to be generated, wherein the specific judging method is as follows:
Collecting current amplitude values of random three points in the nth current period to be detected, adding to obtain algebraic sum representing current characteristics of the nth current period as S n, correspondingly selecting three points at the same time position as the nth current period for the previous current period n-1 and the next current period n+1 of the selected nth current period, obtaining algebraic sum of current amplitude values to obtain S n-1, S n+1,Sn-1 and S n+1 which respectively represent the previous current period n-1 and the next current period n+1, and calculating ratio of the next current period to the previous current period to obtain All calculated ratios are calculated according to the root mean square formulaThe calculation formula for obtaining the value of the characteristic quantity Z, i.eIn S, D and Z, the value of D and the value of Z play a role in determining, are irrelevant to the value of S and are irrelevant to the selection moment in one current period, and meanwhile, the S takes three current amplitude sums, so that errors can be reduced, the difference between fault and non-fault signals is amplified, identification of fault arcs is facilitated, the value of D is relevant to the relative value of a circuit, is irrelevant to the absolute value of the circuit amplitude, namely only the difference between different current periods of the circuit is concerned, particularly the difference between the normal current period and the fault current period is amplified, and the calculation method of the value of Z has the effect of reducing the errors and avoids misoperation caused by overhigh sensitivity of hardware. Through multiple experiments, when the line fails, the value of Z is maintained near a lower value, when a series fault arc exists, the value of Z is obviously increased, and then current signals with nine current periods are selected for verification.
TABLE 1
As can be seen from table 1, the value of each Z contains information of three current periods, from several consecutive current periods, the sudden change in the circuit fault current value occurs within 1 current period, after which the value of S tends to be smooth, but this change is mapped to the value of Z, which appears as a significant increase in the value of 2Z, multiple experiments show that the probability of occurrence of a fault arc exceeds eighty when the value of Z exceeds 5, and it can be noted that the value of Z approaches 0 after no series fault arc and stable combustion of the arc occurs. For distinguishing fault arc by means of a time domain method, although the time of occurrence of faults can be obtained, the influence caused by time domain load fluctuation cannot be eliminated, and the accuracy of judgment can be improved by the power change rate.
S3, for the fault arc detection method adopting a time domain, the problem of load disturbance in the detection process is easy to occur, for the current period with larger Z value, a power method is adopted to calculate in MATLAB whether fault arc occurs or not, and for the collected current and voltage signals, the power and the power change rate delta are calculated in MATLAB, wherein the power change rate delta calculation method is as follows, according to an active power calculation formulaCalculating the change in average power per current cycle, for the collected discrete power data P 1,P2,P3, corresponding to time t 1,t2,t3, the discrete power data P i corresponding to time t i, wherein i=1, 2,3,..: judging whether a fault arc occurs according to the number of current periods in which the power change rate delta is not zero, wherein the power change rate delta values of the continuous 3 periods are not zero and have larger randomness, so that the fault arc can be judged, and otherwise, judging that the power change rate delta values in the 2 periods are not zero and are caused by load fluctuation.
Fig. 3 is a smooth curve formed according to the data in table 1, and it can be intuitively seen that there is a large jump in the value from the 4 th current period to the 5 th current period Z, and a good effect can be obtained by judging the series fault arc according to the value Z.
Fig. 4 shows the power change rate of the series fault arc branch, and it can be seen that, due to the randomness of the fault arc, compared with the sudden load change, the fault arc branch power can appear in a plurality of cases of non-zero sudden change, and each case is not identical, while under normal conditions, the power change caused by the load change usually only appears in a situation that the power change rate is not zero in one place or a shorter time span, and according to this feature, the fault arc identification with load disturbance resistance in the time domain can be realized.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the invention, but any minor modifications, equivalents, and improvements made to the above embodiments according to the technical principles of the present invention should be included in the scope of the technical solutions of the present invention.
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| RU2232456C1 (en) * | 2002-11-10 | 2004-07-10 | Новосибирский государственный технический университет | Method for detecting single-phase arcing-ground fault and phase failure in resonance-grounded-neutral distribution mains |
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| US11070049B2 (en) * | 2017-11-08 | 2021-07-20 | Eaton Intelligent Power Limited | System, method, and apparatus for power distribution in an electric mobile application using a combined breaker and relay |
| US10803733B2 (en) * | 2018-07-06 | 2020-10-13 | Schneider Electric USA, Inc. | Systems and methods for managing voltage event alarms in an electrical system |
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| CN104678265A (en) * | 2015-01-30 | 2015-06-03 | 广东雅达电子股份有限公司 | Detection device and detection method for series arc faults |
| CN111933459A (en) * | 2020-07-20 | 2020-11-13 | 西安热工研究院有限公司 | Method for detecting electrical wear state of breaker contact by utilizing arc power |
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