CN111929489A - Fault arc current detection method and system - Google Patents
Fault arc current detection method and system Download PDFInfo
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- CN111929489A CN111929489A CN202010832492.6A CN202010832492A CN111929489A CN 111929489 A CN111929489 A CN 111929489A CN 202010832492 A CN202010832492 A CN 202010832492A CN 111929489 A CN111929489 A CN 111929489A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
- G01R19/16566—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533
- G01R19/16571—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533 comparing AC or DC current with one threshold, e.g. load current, over-current, surge current or fault current
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
- G01R19/16533—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values characterised by the application
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Abstract
The invention discloses a method for detecting fault arc current, which comprises the steps of firstly, acquiring actually measured current data; subtracting two adjacent periods to obtain a differential current signal; carrying out autocorrelation operation on the differential current signals to obtain autocorrelation coefficients; then, carrying out first classification on the actually measured current data through an autocorrelation coefficient to obtain a normal current signal; dividing the normal differential current signal into a plurality of cell intervals, calculating the variance of each cell interval, and finally carrying out secondary classification according to the variance; if the difference value of the maximum variance and the minimum variance is larger than the preset multiplying factor value of the minimum variance, the fault arc current is judged; the method avoids the situation that proper judgment standards cannot be made due to overlarge current signal difference caused by different processing loads and circuit loops. The preceding and following periods are subtracted, and after the period component is removed, only the differential current signal needs to be judged. The requirement that a large number of reliable samples are needed for training is avoided, and any current signal can be detected.
Description
Technical Field
The invention relates to the technical field of fault current detection, in particular to a fault arc current detection method and system.
Background
In order to realize fault arc detection, wavelet decomposition, neural networks, support vector machines and other methods are used conventionally. When wavelet decomposition is used, a good detection effect can be achieved only by determining a proper parent wave and the number of decomposition layers, and when a neural network and a support vector machine are used, a large amount of measured data is needed for training the neural network and the support vector machine. In order to train a network, a large amount of arc current needs to be given a normal or fault label in advance, and an expert in the field needs to assist in marking when a high-quality label is required. In the case of a large number, this is a very difficult task.
Disclosure of Invention
In view of the above, the present invention provides a method for detecting a fault arc current, which uses the periodicity and stationarity of a normal current to detect the fault arc current.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention provides a method for detecting fault arc current, which comprises the following steps:
acquiring measured current data;
subtracting two adjacent periods of the actually measured current data to obtain a differential current signal;
carrying out autocorrelation operation on the differential current signals to obtain autocorrelation coefficients;
carrying out first classification on the measured current data through an autocorrelation coefficient: judging an autocorrelation coefficient, and if the autocorrelation coefficient is larger than a correlation threshold when t is 1, determining that the actually measured current is a fault arc current signal; otherwise, the signal is a normal current signal;
acquiring all normal current signals in the first classification;
dividing the normal differential current signal into a plurality of small intervals, calculating the variance of each small interval,
performing second classification according to the variance; judging whether the difference value of the maximum variance and the minimum variance is larger than a preset multiplying factor value of the minimum variance, if so, judging the difference value to be a fault arc current; if not, the signal is a normal current signal.
Further, the correlation threshold is 0.2-0.5.
Further, the preset multiplying power value is 2-5.
Further, the subtraction of adjacent periods of the measured current data is performed according to the following formula:
S(K,n)＝X(KNT+n)-X((K-1)NT+n) (1)
wherein S (-) represents a differential current signal, X (-) is a measured current signal, N represents a time within a cycle, NTThe number of sampling points in one period is K, and K is a positive integer.
Further, the autocorrelation coefficient of the differential current signal is calculated according to the following formula:
wherein r iskIn order to be a function of the auto-correlation,
is the average of the differential current signal for one cycle.
Further, the normal current signal is divided into 8 small intervals, and the variance of each interval is calculated.
The invention also provides a fault arc current detection system, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the following steps:
acquiring measured current data;
subtracting two adjacent periods of the actually measured current data to obtain a differential current signal;
carrying out autocorrelation operation on the differential current signals to obtain autocorrelation coefficients;
carrying out first classification on the measured current data through an autocorrelation coefficient: judging an autocorrelation coefficient, and if the autocorrelation coefficient is larger than a correlation threshold when t is 1, determining that the actually measured current is a fault arc current signal; otherwise, the signal is a normal current signal;
acquiring all normal current signals in the first classification;
dividing the normal differential current signal into a plurality of small intervals, calculating the variance of each small interval,
performing second classification according to the variance; judging whether the difference value of the maximum variance and the minimum variance is larger than a preset multiplying factor value of the minimum variance, if so, judging the difference value to be a fault arc current; if not, the signal is a normal current signal.
Further, the correlation threshold is 0.2-0.5.
Further, the preset multiplying power value is 2-5.
Further, the normal current signal is divided into 8 small intervals, and the variance of each interval is calculated.
The invention has the beneficial effects that:
compared with the traditional fault arc detection method, the fault arc current detection method provided by the invention avoids the problem that proper judgment standards cannot be formulated due to overlarge current signal difference caused by different processing of various loads and circuit loops. The preceding and following periods are subtracted, and after the period component is removed, only the differential current signal needs to be judged.
Compared with a neural network, the method for detecting the fault arc current avoids the requirement that a large number of reliable samples are needed for training, and any current signal can be detected.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
fig. 1 is a normal current raw signal.
Fig. 2 is a raw signal of the fault arc current.
Fig. 3 is a normal differential current signal.
Fig. 4 is a fault arc differential current signal.
Fig. 5 is an autocorrelation coefficient of a normal differential current signal.
Fig. 6 is an autocorrelation coefficient of a fault arc differential current signal.
Fig. 7 is an algorithm flow chart.
Detailed Description
The present invention is further described with reference to the following drawings and specific examples so that those skilled in the art can better understand the present invention and can practice the present invention, but the examples are not intended to limit the present invention.
Example 1
As shown in fig. 1, the method for detecting a fault arc current provided by this embodiment includes the following steps:
acquiring measured current data;
subtracting two adjacent periods of the actually measured current data to obtain a differential current signal;
carrying out autocorrelation operation on the differential current signals to obtain autocorrelation coefficients;
carrying out first classification on the measured current data through an autocorrelation coefficient: judging an autocorrelation coefficient, and if the autocorrelation coefficient is more than 0.3 when t is 1, determining that the actually measured current is a fault arc current signal; otherwise, the signal is a normal current signal;
selecting all differential current signals which are judged to be normal in the first classification;
the normal differential current signal is divided into 8 cells, the variance between each cell is calculated,
performing second classification according to the variance; judging whether the difference value of the maximum variance and the minimum variance is more than 3 times of the minimum variance, if so, judging the difference value to be fault arc current; if not, the signal is a normal current signal.
The subtraction of the measured current data provided by this embodiment from the adjacent periods is performed according to the following formula:
S(K,n)＝X(KNT+n)-X((K-1)NT+n) (1)
wherein S (-) represents a differential current signal and X (-) isMeasured current signal, N representing a certain time within a cycle, NTThe number of sampling points in one period is K, and K is a positive integer.
The timing diagrams of the differential current signals provided by the present embodiment are shown in fig. 3 and 4.
The autocorrelation operation of the differential current signal provided in this embodiment is calculated according to the following formula:
wherein r iskIn order to be a function of the auto-correlation,
is the average of the differential current signal for one cycle,
the self-correlation coefficient is the correlation coefficient of the t-th data and the t-k number in the original time sequence,
the autocorrelation coefficient graphs of the differential current signals provided by the present embodiment are shown in fig. 5 and 6.
The specific process of classifying by correlation coefficients provided in this embodiment is as follows:
the normal current signal is periodic, and after the subtraction of two adjacent periods, the obtained differential current signal should be white noise. The white noise autocorrelation coefficient is characterized in that: only when t is 0, the value is 1, and all the others are 0. In real life, since other noise having a small influence other than white noise is present in the current, 0.3 is set as a threshold of the autocorrelation coefficient, and the autocorrelation coefficient when t is 1 is detected by the threshold. If t is 1, the autocorrelation coefficient is larger than 0.3, the signal is judged to be a fault arc current signal, otherwise, the signal is a normal current signal.
The specific process of classifying by variance provided in this embodiment is as follows:
the period component is subtracted from the normal current signal, and the rest is only noise, so that all the normal current signals can be judged according to the characteristics of a white noise autocorrelation function. However, after a small portion of the fault arc current signal has the removed periodic component, the differential current signal is similar to white noise, and may be erroneously determined as a normal current signal in the first step, so that further processing is required.
The differential current signal judged to be normal by the autocorrelation coefficient is selected, divided into 8 small intervals, and the variance of each interval is calculated. And obtaining 8 variance values, and if the difference value between the maximum variance and the minimum variance is more than 3 times of the minimum variance, judging the fault arc current.
Since the variance of the signal represents the energy, the energy of the normal current signal does not change so drastically during a cycle. The final classification accuracy reaches 99.09%.
The fault arc detection method provided by the embodiment avoids the situation that proper judgment standards cannot be formulated due to overlarge current signal difference caused by different processing of various loads and circuit loops.
The preceding and following periods are subtracted, and after the period component is removed, only the differential current signal needs to be judged.
Compared with a neural network, the fault arc detection method provided by the embodiment avoids the requirement that a large number of reliable samples are required for training, and any current signal can be detected.
Example 2
The system for detecting a fault arc current provided by this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the following steps:
acquiring measured current data;
subtracting two adjacent periods of the actually measured current data to obtain a differential current signal;
carrying out autocorrelation operation on the differential current signals to obtain autocorrelation coefficients;
carrying out first classification on the measured current data through an autocorrelation coefficient: judging an autocorrelation coefficient, and if the autocorrelation coefficient is larger than a correlation threshold when t is 1, determining that the actually measured current is a fault arc current signal; otherwise, the signal is a normal current signal;
acquiring all normal current signals in the first classification;
dividing the normal differential current signal into a plurality of small intervals, calculating the variance of each small interval,
performing second classification according to the variance; judging whether the difference value of the maximum variance and the minimum variance is larger than a preset multiplying factor value of the minimum variance, if so, judging the difference value to be a fault arc current; if not, the signal is a normal current signal.
The correlation threshold is 0.2-0.5.
The preset multiplying power value is 2-5.
The normal current signal is divided into 8 small intervals, and the variance of each interval is calculated.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. The method for detecting the fault arc current is characterized in that: the method comprises the following steps:
acquiring measured current data;
subtracting two adjacent periods of the actually measured current data to obtain a differential current signal;
carrying out autocorrelation operation on the differential current signals to obtain autocorrelation coefficients;
carrying out first classification on the measured current data through an autocorrelation coefficient: judging an autocorrelation coefficient, and if the autocorrelation coefficient is larger than a correlation threshold when n is 1, determining that the actually measured current is a fault arc current signal; otherwise, the signal is a normal current signal;
acquiring all normal current signals in the first classification;
dividing the normal differential current signal into a plurality of small intervals, calculating the variance of each small interval,
performing second classification according to the variance; judging whether the difference value of the maximum variance and the minimum variance is larger than a preset multiplying factor value of the minimum variance, if so, judging the difference value to be a fault arc current; if not, the signal is a normal current signal.
2. The method of claim 1, wherein: the correlation threshold is 0.2-0.5.
3. The method of claim 1, wherein: the preset multiplying power value is 2-5.
4. The method of claim 1, wherein: subtracting adjacent periods of the current measurement data according to the following formula:
S(K,n)＝X(KNT+n)-X((K-1)NT+n)
wherein S (-) represents a differential current signal, X (-) is a measured current signal, N represents a time within a cycle, NTThe number of sampling points in one period is K, and K is a positive integer.
5. The method of claim 1, wherein: the autocorrelation coefficient of the differential current signal is calculated according to the following formula:
wherein the content of the first and second substances,
rkis an autocorrelation coefficient;
Sna value representing the differential current signal at time n;
Sn-ka value representing the differential current signal at time n-k;
NTrepresenting the number of sampling points in a period;
k represents the number of shifts of the time series;
n denotes a certain time within one period.
6. The method of claim 1, wherein: the normal current signal is divided into 8 small intervals, and the variance of each interval is calculated.
7. A system for detecting a fault arc current, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform the steps of:
acquiring measured current data;
subtracting two adjacent periods of the actually measured current data to obtain a differential current signal;
carrying out autocorrelation operation on the differential current signals to obtain autocorrelation coefficients;
carrying out first classification on the measured current data through an autocorrelation coefficient: judging an autocorrelation coefficient, and if the autocorrelation coefficient is larger than a correlation threshold when t is 1, determining that the actually measured current is a fault arc current signal; otherwise, the signal is a normal current signal;
acquiring all normal current signals in the first classification;
dividing the normal differential current signal into a plurality of small intervals, calculating the variance of each small interval,
performing second classification according to the variance; judging whether the difference value of the maximum variance and the minimum variance is larger than a preset multiplying factor value of the minimum variance, if so, judging the difference value to be a fault arc current; if not, the signal is a normal current signal.
8. The system of claim 7, wherein: the correlation threshold is 0.2-0.5.
9. The system of claim 7, wherein: the preset multiplying power value is 2-5.
10. The system of claim 7, wherein: the normal current signal is divided into 8 small intervals, and the variance of each interval is calculated.
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