CN116068347A

CN116068347A - Low-voltage line fault arc identification method and device

Info

Publication number: CN116068347A
Application number: CN202310171667.7A
Authority: CN
Inventors: 周扬; 疏学明; 张腾; 薛溟枫; 张雷
Original assignee: Tsinghua University; State Grid Jiangsu Electric Power Co Ltd
Current assignee: Tsinghua University; State Grid Jiangsu Electric Power Co Ltd
Priority date: 2023-02-27
Filing date: 2023-02-27
Publication date: 2023-05-05

Abstract

The invention discloses a low-voltage line fault arc identification method and a device, wherein the low-voltage line fault arc identification method comprises the following steps: acquiring a current signal of a low-voltage circuit where a load is located; converting the current signal from an analog form to a digital form; carrying out wavelet analysis on the digital current signal to obtain multi-band energy entropy; judging whether a fault arc occurs to the low-voltage line according to the multi-band energy entropy. The invention adopts the wavelet transformation method for extracting the characteristic information of the current signal, can simultaneously meet the high frequency domain resolution under the condition of relatively stable signals and the high time-frequency domain resolution under the condition of instantaneous time-varying signals, solves the problem that the time and frequency resolution in the Fourier transformation are difficult to realize the optimal solution at the same time, and improves the accuracy of fault arc detection.

Description

Low-voltage line fault arc identification method and device

Technical Field

The invention relates to the technical field of power electrical equipment, in particular to a low-voltage line fault arc identification method and device.

Background

For town resident low voltage lines, fault arcs are one of the important factors for initiating an electrical fire. The fault arc is a gas free discharge phenomenon caused by air breakdown due to the reasons of insulation aging damage, loose electrical connection, air humidity, rapid voltage and current rise and the like in an electrical circuit or equipment, when the series fault arc occurs, the center temperature of the series fault arc is as high as 3000-4000 ℃, and metal melt splash is accompanied, and because the arc per se has small current in an impedance circuit, the current is lower than a set value of a current protector widely installed in the field of a power system, particularly low-voltage power distribution, so that the fire disaster is extremely easy to occur. For this reason, it is necessary to perform fault arc detection.

The traditional fault arc detection mostly adopts the current amplitude waveform characteristic in the time domain range to judge, namely a zero-break detection method, but as the use types and the number of modern household electrical appliances are more and more, nonlinear load appliances are gradually increased except linear load appliances, the traditional detection and identification method based on the arc zero-break characteristic is difficult to judge the fault arc, and false alarm and missing alarm are easy to occur. According to the arc fault identification method based on short-time Fourier transform, windowing and cutting-off processing is carried out on a current non-stationary time-varying signal, then Fourier transform is carried out on the cut-off signal, and finally energy distribution of arc faults at different moments under different frequency bands is obtained, so that fault arc identification and detection are achieved, however, fourier transform does not have a time and frequency positioning function, and is not suitable for analysis of non-stationary signals. Meanwhile, the Fourier transform is also limited by the Hessenberg uncertainty principle, namely, the time and frequency resolution of the window function cannot realize the optimal solution at the same time.

Disclosure of Invention

The present invention aims to solve at least one of the technical problems in the related art to some extent. Therefore, an object of the present invention is to provide a method and apparatus for identifying a fault arc of a low voltage line, which uses a wavelet transformation method to extract characteristic information of a current signal, so as to simultaneously satisfy a high frequency domain resolution under a relatively stable signal condition and a high time-frequency domain resolution under an instantaneous time-varying signal condition, thereby improving accuracy of fault arc detection.

To achieve the above object, an embodiment of a first aspect of the present invention provides a method for identifying a fault arc of a low-voltage line, the method including: acquiring a current signal of a low-voltage circuit where a load is located; converting the current signal from an analog form to a digital form; carrying out wavelet analysis on the digital current signal to obtain multi-band energy entropy; judging whether the low-voltage line has fault arc or not according to the multi-frequency band energy entropy.

In addition, the low-voltage line fault arc identification method according to the embodiment of the invention may further have the following additional technical features:

according to one embodiment of the invention, the digital current signal is subjected to wavelet analysis by using a Mallat algorithm to obtain multi-band energy entropy.

According to one embodiment of the present invention, the performing wavelet analysis on the digital current signal by using the Mallat algorithm to obtain a multi-band energy entropy includes: performing M times of filtering operation on the digital current signal, wherein M is an integer greater than or equal to 2; for the first filtering operation, performing low-pass filtering on the digital current signal through a low-pass filter, and performing high-pass filtering on the digital current signal through a high-pass filter; for the ith filtering operation, carrying out low-pass filtering on the signal obtained by the last low-pass filtering through a low-pass filter, and carrying out high-pass filtering on the signal obtained by the last low-pass filtering through a high-pass filter, wherein i is an integer greater than or equal to 2 and less than or equal to M; and obtaining the multi-band energy entropy according to the M times of filtering operation results.

According to one embodiment of the invention, the following relationship exists between the low-pass filter and the high-pass filter in each filtering operation:

H ₀ []＝(-1) ⁿ H ₁ [2K-1-n]

wherein H is ₀ []Is a low-pass filter, H ₁ [2K-1-n]K is the length of the filter, and n is the nth sample point in the filter time series, for a high pass filter corresponding to the low pass filter.

According to one embodiment of the present invention, in each filtering operation, the upper limit frequency of the low pass filter is half of the upper limit frequency of the high pass filter, and the upper limit frequency of the high pass filter in the current filtering operation is the same as the upper limit frequency of the low pass filter in the previous filtering operation.

According to one embodiment of the present invention, the determining whether the low-voltage line is in a fault arc according to the multi-band energy entropy includes: determining a target frequency band, and comparing each energy entropy of the target frequency band with a preset energy entropy threshold value respectively; counting the number N1 of the energy entropies larger than the preset energy entropy threshold to obtain a first half-period high-frequency component count value N1 and a first half-period window count value N1, and counting the number N2 of the energy entropies smaller than or equal to the preset energy entropy threshold to obtain a second half-period window count value N1+N2; comparing the second half-period window count value with the total half-period number in the time domain range corresponding to the target frequency band; if the second half-period window count value is smaller than or equal to the corresponding total half-period window number, comparing the first half-period high-frequency component count value with a preset component count threshold value; if the first half-cycle high-frequency component count value is greater than the preset component count threshold, storing the corresponding energy entropy into N1 buffer areas, setting a high-frequency component attenuation value as a fixed percentage value expected by the energy entropy amplitude, and resetting the first half-cycle high-frequency component count for the high-frequency component count of the next time period; attenuating the energy entropy stored in the buffer area according to the set attenuation value of the high-frequency component, and counting the number of the high-frequency components in the buffer area in each time period when the energy entropy amplitude of the high-frequency component is attenuated to 0; and if the number of the buffer areas reduced in each time period is greater than 14, judging that the low-voltage line is in fault arc, otherwise, judging that no fault arc is generated, and continuing fault arc identification of the next period.

According to one embodiment of the invention, M has a value of 12.

According to one embodiment of the invention, the load comprises at least one of a resistive load, a half-wave load, a capacitive load, an inductive load.

According to the low-voltage line fault arc identification method, firstly, a current transformer collects current signals of a low-voltage line where a load is located, the current signals are converted from an analog form to a digital form by utilizing A/D conversion, then wavelet analysis is carried out on the current signals in the digital form to obtain multi-band energy entropy, and whether the low-voltage line is subjected to fault arc is judged according to the multi-band energy entropy. The invention adopts the wavelet transformation method for extracting the characteristic information of the current signal, can simultaneously meet the high frequency domain resolution under the condition of relatively stable signals and the high time-frequency domain resolution under the condition of instantaneous time-varying signals, solves the problem that the time and frequency resolution in the Fourier transformation are difficult to realize the optimal solution at the same time, and improves the accuracy of fault arc detection.

To achieve the above object, an embodiment of a second aspect of the present invention provides a low-voltage line fault arc identification device, including: the current transformer is used for collecting current signals of a low-voltage circuit where the load is located; a high frequency sampling mechanism for converting the current signal from an analog form to a digital form; the wavelet analysis mechanism is used for carrying out wavelet analysis on the digital current signal to obtain multi-band energy entropy; and the comprehensive judging mechanism is used for judging whether the low-voltage line has fault arc or not according to the multi-frequency band energy entropy.

According to one embodiment of the invention, the apparatus comprises: the switching mechanism is used for establishing connection between a power supply and the load; the comprehensive judging mechanism is also connected with the switching mechanism and is used for controlling the switching mechanism to be disconnected when the low-voltage line is judged to generate fault arc so as to disconnect the power supply from the load.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

FIG. 1 is a flow chart of a low voltage line fault arc identification method according to one embodiment of the present invention;

FIG. 2 is a flow chart of wavelet analysis of a current signal in digital form according to one embodiment of the present invention;

FIG. 3 is an exploded schematic view of a Mallat algorithm discrete wavelet transform according to one embodiment of the invention;

FIG. 4 is a flow chart of determining whether a fault arc has occurred in a low voltage line in accordance with one embodiment of the present invention;

FIG. 5 is a schematic diagram of fault arc analysis decision logic according to one embodiment of the present invention;

FIG. 6 (a) is a graph of a frequency spectrum of a non-faulty arc of a sterilizer (resistive) load according to one embodiment of the present invention;

FIG. 6 (b) is a graph of a decontamination cabinet (resistive) load fault arc spectrum in accordance with one embodiment of the present invention;

FIG. 7 (a) is a graph of a spectrum of a warmer (half wave) load fault-free arc in accordance with one embodiment of the present invention;

FIG. 7 (b) is a graph of a spectrum of a heater (half wave) loaded fault arc in accordance with one embodiment of the present invention;

FIG. 8 (a) is a graph of a computer (capacitive) load fault-free arc spectrum in accordance with one embodiment of the present invention;

FIG. 8 (b) is a graph of a computer (capacitive) load fault arc spectrum in accordance with one embodiment of the present invention;

FIG. 9 (a) is a spectral diagram of a motor (inductive) load fault-free arc in accordance with one embodiment of the present invention;

FIG. 9 (b) is a spectral diagram of a motor (inductive) load with fault arc in accordance with one embodiment of the present invention;

FIG. 10 is a graph showing energy entropy contrast for each frequency band in a sterilizer (resistive) load arcing and non-arcing state according to an embodiment of the present invention;

fig. 11 is a schematic structural view of a low-voltage line fault arc identifying apparatus according to an embodiment of the present invention.

Detailed Description

Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.

The low-voltage line fault arc identification method and device according to the embodiment of the invention are described in detail below with reference to the accompanying drawings.

Fig. 1 is a flow chart of a low voltage line fault arc identification method in accordance with one embodiment of the present invention.

In one embodiment of the present invention, as shown in fig. 1, the low-voltage line fault arc identification method includes:

s1, acquiring a current signal of a low-voltage circuit where a load is located.

Specifically, in order to judge whether a fault arc occurs in a low-voltage line, a current signal of the low-voltage line where a load is located needs to be acquired first. The current transformer transmits the collected power signals to the operational amplifier, amplifies the current signals, performs A/D conversion on the current signals, and converts the analog current signals into digital current signals so as to perform discrete wavelet transformation on the subsequent digital current signals.

S2, converting the current signal from an analog form to a digital form.

Specifically, since the a/D converter can only receive analog signals within a certain range, the current signal amplified by the operational amplifier cannot pass through the a/D converter completely, and thus the amplified current signal is converted into a standard signal after operations such as jitter elimination, filtering, level conversion, and the like, and then is converted into a digital current signal after a/D conversion with a high sampling frequency.

Further specifically, the invention carries out wavelet processing and analysis on the digital current signal converted by the A/D converter to obtain multi-band energy entropy, and judges whether the low-voltage line has fault arc or not according to the characteristics of the multi-band energy entropy.

S3, carrying out wavelet analysis on the digital current signal to obtain the multi-band energy entropy.

Specifically, the traditional detection and identification method based on the arc zero-break characteristic is difficult to judge the fault arc, and false alarm and missing report are easy to occur. And the arc fault identification method based on short-time Fourier transform does not have a time and frequency positioning function and is not suitable for analysis of non-stationary signals. The invention adopts wavelet transformation to current signals to overcome the limitations of the traditional identification method and the arc fault identification method of short-time Fourier transformation. The wavelet transformation can effectively extract signal characteristic information, the signals are subjected to multi-scale analysis by methods such as stretching and translation, the method is suitable for analysis of instantaneous time-varying signals, a high-frequency instantaneous time-varying signal is usually introduced by fault arcs under resistive, half-wave, capacitive and inductive loads, and the traditional zero-break detection and detection method based on Fourier transformation cannot meet the requirement of video signal analysis under the interference. The wavelet transformation can simultaneously meet the high frequency domain resolution under the condition that the signal is relatively stable and the high time-frequency domain resolution under the condition of the instantaneous time-varying signal, solves the problem that the Fourier transformation time and the frequency resolution are difficult to meet simultaneously, and also shows good performance and higher accuracy in an actual test environment.

In one embodiment of the invention, a Mallat algorithm is used to perform wavelet analysis on the current signal in digital form to obtain multi-band energy entropy.

Specifically, because random high-frequency interference current is generated when fault arcs are generated on the circuit, the frequency spectrum can reach 5MHz, and the noise currents are distributed in a Gaussian mode and have instantaneity and aperiodicity, the invention adopts a Mallat algorithm to carry out wavelet analysis on digital current signals, and the Mallat algorithm is used for extracting interference current characteristics so as to diagnose whether fault arcs occur. The Mallat algorithm is a multi-layer decomposition method, i.e., a first level wavelet transform is calculated, then the next level wavelet transform is calculated on this basis and repeated.

In one embodiment of the present invention, as shown in fig. 2, the performing wavelet analysis on the current signal in digital form by using the Mallat algorithm to obtain multi-band energy entropy includes:

s31, carrying out M times of filtering operation on the current signal in the digital form, wherein M is an integer greater than or equal to 2.

S32, for the first filtering operation, the digital current signal is subjected to low-pass filtering through a low-pass filter, and the digital current signal is subjected to high-pass filtering through a high-pass filter.

S33, for the ith filtering operation, performing low-pass filtering on the signal obtained by the last low-pass filtering through a low-pass filter, and performing high-pass filtering on the signal obtained by the last low-pass filtering through a high-pass filter, wherein i is an integer greater than or equal to 2 and less than or equal to M.

S34, obtaining the multi-band energy entropy according to the M times of filtering operation results.

Specifically, as shown in the schematic diagram of the Mallat discrete wavelet transform of FIG. 3, for discrete current signals in digital form

Performing M times of filtering operation, wherein the first filtering operation is equivalent to +.>

Half-decomposition, low-pass filtering the digital current signal by a low-pass filter, and high-pass filtering the digital current signal by a high-pass filter to obtain a discrete signal +_after one-time high-pass filtering>

And discrete signal after one low pass filtering +.>

Then the signal obtained by the last low-pass filtering is further subjected to half decomposition, and the low-frequency approximate signal obtained by the previous decomposition is subjected to half decompositionRepeating the operation, decomposing the high-frequency detail signals into low-frequency approximate and high-frequency detail signals through a series of filters, and sequentially decomposing the low-frequency approximate and high-frequency detail signals, wherein the high-frequency detail signals obtained through the decomposition of each step of iterative operation are not further decomposed. The filtering operation of the single discrete wavelet transform in fig. 3 can be expressed as:

0≤m<M

wherein, the liquid crystal display device comprises a liquid crystal display device,

is the low-pass discrete signal sequence after m+1st discrete wavelet transform, +.>

Is the high-pass discrete signal sequence after m+1st discrete wavelet transform, +.>

Is a low-pass approximate signal sequence after m-th discrete wavelet transformation, K is the length of a filter, and H is the length of the filter ₀ [k]And H ₁ [k]The expressions of the half-band low-pass filter and the half-band high-pass filter in the discrete domain are respectively, and M is the iteration number of discrete wavelet transformation.

Further specifically, for discrete current signals in digital form

After M times of filtering operation, M-order spectrum energy amplitude data of the input discrete current signals can be obtained, and the energy entropy of the current signals in each frequency band is calculated to obtain multi-frequency band energy entropy.

In one embodiment of the invention, the following relationship exists between the low-pass filter and the high-pass filter in each filtering operation:

H ₀ []＝(-1) ⁿ H ₁ [2K-1-n]

Specifically, in practical application, the low-pass filter and the high-pass filter are interrelated, and the relationship between them is shown in the formula ₀ []Is the expression of the low-pass filter in the discrete domain, H ₁ [2K-1-n]The expression of the high-pass filter corresponding to the low-pass filter in the discrete domain is given by K, which is the length of the filter, and n is the nth sampling point in the filter time sequence.

In one embodiment of the present invention, the upper limit frequency of the low pass filter is half of the upper limit frequency of the high pass filter in each filtering operation, and the upper limit frequency of the high pass filter in the current filtering operation is the same as the upper limit frequency of the low pass filter in the last filtering operation.

Specifically, when the discrete current signal is subjected to the filtering operation, the current signal is halved, that is, the upper limit frequency of the low-pass filter is set to be half of the upper limit frequency of the high-pass filter, and the upper limit frequency of the high-pass filter is set according to the frequency of the discrete signal before the sampling of the filter, that is, the upper limit frequency of the high-pass filter is set to be the same as the upper limit frequency of the low-pass filter in the previous filtering operation.

In one embodiment of the invention, M has a value of 12.

Specifically, the invention adopts 12 times of filtering treatment to the current signal to obtain 12-order spectrum energy amplitude data, calculates the 12-order spectrum energy amplitude data to obtain multi-frequency band current signal energy entropy, and analyzes the multi-frequency band energy entropy to judge whether the low-voltage line has fault arc or not.

And S4, judging whether a fault arc occurs in the low-voltage line according to the multi-band energy entropy.

Specifically, the invention analyzes parameters such as multi-band energy entropy, half-period window count, high-frequency component count, buffer area high-frequency component number and the like to judge whether the condition of low-voltage line fault arc is met, thereby determining whether the low-voltage line fault arc is generated.

In one embodiment of the present invention, as shown in fig. 4, determining whether a fault arc occurs in a low-voltage line according to multi-band energy entropy includes:

s41, determining a target frequency band, and comparing each energy entropy of the target frequency band with a preset energy entropy threshold.

S42, counting the number N1 of the energy entropies larger than a preset energy entropy threshold to obtain a first half-cycle high-frequency component count value N1 and a first half-cycle window count value N1, and counting the number N2 of the energy entropies smaller than or equal to the preset energy entropy threshold to obtain a second half-cycle window count value N1+N2.

S43, comparing the second half period window count value with the total half period number in the time domain range corresponding to the target frequency band.

S44, if the count value of the second half-period window is smaller than or equal to the corresponding total half-period window number, comparing the count value of the first half-period high-frequency component with a preset component count threshold value.

S45, if the counting value of the high-frequency component in the first half period is larger than a preset component counting threshold, storing the corresponding energy entropy into N1 buffer areas, setting the attenuation value of the high-frequency component as a fixed percentage value expected by the amplitude of the energy entropy, and resetting the counting value of the high-frequency component in the first half period for counting the high-frequency component in the next time period.

S46, attenuating the energy entropy stored in the buffer area according to the set attenuation value of the high-frequency component, and counting the number of the high-frequency components in the buffer area in each time period when the energy entropy amplitude of the high-frequency component is attenuated to 0 and the number of the high-frequency components in the buffer area is-1.

And S47, if the number of the buffer areas reduced in each time period is greater than 14, judging that the low-voltage line is in fault arc, otherwise, judging that no fault arc is generated, and continuing fault arc identification of the next period.

Specifically, the fault arc introduces a high-frequency component into the low-voltage circuit, so that a current signal high-frequency component can be used as a target frequency band, each energy entropy of the target frequency band is compared with a preset energy entropy threshold, as shown in a fault arc analysis and judgment logic diagram in fig. 5, the target frequency band can adopt the current signal high-frequency component output by Mallat discrete wavelet transformation as an input parameter, the energy entropy amplitude of the target frequency band needs to be compared with each time-domain half period of the preset energy entropy threshold in a time domain range, if the high-frequency component amplitude is greater than the preset threshold, the half period high-frequency component count is increased by one at the moment, a first half period high-frequency component count value N1 is obtained, meanwhile, the half period window count is increased by one, the first half period window count value N1 is obtained, the energy entropy number N2 smaller than or equal to the preset energy entropy threshold is counted, and if the high-frequency component amplitude is smaller than the preset threshold, only the half period window count is increased by one, and the second half period window count N1+N2 is obtained by accumulation.

And further specifically, comparing the accumulated half-period window count with the total half-period number in the time domain range corresponding to the target frequency band, if the second half-period window count is larger than the total half-period window number, reporting errors by the program at the moment, restarting the statistics, and if the second half-period window count is smaller than or equal to the total half-period window number, comparing the first half-period high-frequency component count with a preset component count threshold. If the first half-period high-frequency component count is smaller than or equal to a preset component count threshold, the first half-period high-frequency component count is cleared, if the half-period high-frequency component count is larger than the preset component count threshold, the half-period high-frequency component greater than the preset component count threshold is input into a buffer zone, the number of the high-frequency component buffer zones is increased by one, a high-frequency component attenuation value is set, the high-frequency component attenuation value is set to be a fixed percentage value expected by the energy entropy amplitude, meanwhile, the first half-period high-frequency component count is cleared and used for the high-frequency component count of the next time period, and the time period is set to be 1S, namely the high-frequency component count in unit time. After the counting of the high-frequency components in the first half period is cleared, the high-frequency components stored in the buffer area are required to be attenuated according to the set attenuation value of the high-frequency components, when the high-frequency components are attenuated to 0, the characteristic of the high-frequency components is not obvious, namely the number-1 of the high-frequency component buffer areas is determined, namely the high-frequency components are not included in the statistical range, then the number of the high-frequency components in the buffer areas in each time period is counted, if and only if the number of the high-frequency component buffer areas is smaller than or equal to 14, the fault arc is judged to not occur, the current signal in the next time period is continuously detected, if the number of the high-frequency components in the buffer areas in each time period is larger than 14, the fault arc is judged to occur, an alarm instruction is sent, the extraction of the characteristics of the fault arc signal in the electrical fault accident is realized, the problem of false alarm of the existing detection equipment in the nonlinear load environment is effectively solved, and the safety of low-voltage distribution circuit, electric equipment and personnel is ensured.

It should be noted that, according to GB14287.4, "electric fire monitoring system part 4: in the fault arc detector, when 14 or more half cycles of fault arcs occur in 1s of a tested line, the fault arc detector can emit an alarm signal within 30s, and an alarm indicator lamp is lighted to send the alarm signal to an electric fire monitoring device. The invention sets the threshold value of the high-frequency components of the buffer area in each time period as 14, and judges that the low-voltage line has fault arc when the number of the buffer areas reduced in each second time period is more than 14, and sends out an alarm instruction.

In one embodiment of the invention, the load comprises at least one of a resistive load, a half-wave load, a capacitive load, an inductive load.

Specifically, the invention builds the relevant experimental environment under the resistive, capacitive, inductive and half-wave environments aiming at the method, and the test result shows that the method can still realize the fault arc identification and detection accuracy rate of more than 96% under the complex power grid environment.

The invention also carries out comparative analysis on the current time domain and 12-order spectrum energy entropy under resistive, half-wave, capacitive and inductive loads under fault-free arc and fault arc, wherein the 12-order wavelet corresponding frequency band is shown in the following table 1:

TABLE 1

Fig. 6-9 are graphs of comparative analysis of the energy entropy of the current time domain and 12 th order spectrum under resistive, half-wave, capacitive and inductive loads, and the energy entropy of the a-band, which is the low frequency component after 12 th order wavelet transformation, under non-fault and fault arcs, respectively. As is evident from the analysis of the graph, the fault arc under four loads introduces significant amplitude increases in different frequency bands to the circuit current, especially in the high and low frequency bands.

Statistics are performed on the energy entropy distribution of each frequency band of the disinfection cabinet (resistive) load in fig. 6, as shown in the following table 2:

TABLE 2

The energy entropy of each frequency band of the (resistive) load of the disinfection cabinet shown in fig. 10 is compared with the energy entropy of the disinfection cabinet in fault arc and normal state. Compared with the state without arc, the fault arc under the resistive load can be introduced with high-frequency and low-frequency characteristics, wherein the frequency spectrum characteristics under 0-30kHz are easily interfered by external low-frequency noise, so that false alarm occurs, and the false alarm is not generally used as the basis for identifying and detecting the fault arc; the frequency range of 50-100KHz can be regarded as a low-frequency response area of a disinfection cabinet (resistive) load, and the frequency range of 2.5-5MHz is a high-frequency response area of the disinfection cabinet (resistive) load, so that the dual-section high-low frequency composite time-frequency analysis can extract more obvious energy entropy amplitude characteristics, the signal-to-noise ratio performance is better, various types of circuit loads can be compatible, and meanwhile, the interference caused by low-frequency signals can be eliminated.

The invention also provides a low-voltage line fault arc identification device.

In an embodiment of the present invention, as shown in fig. 11, a low-voltage line fault arc recognition apparatus includes: the current transformer 10 is used for collecting current signals of a low-voltage circuit where the load is located; a high frequency sampling mechanism 20 for converting the current signal from an analog form to a digital form; a wavelet analysis mechanism 30 for performing wavelet analysis on the digital current signal to obtain multi-band energy entropy; and the comprehensive judging mechanism 40 is used for judging whether the low-voltage line is in fault arc or not according to the multi-band energy entropy.

In some embodiments of the present invention, as shown in fig. 11, the low-voltage line fault arc identification apparatus further includes: a switching mechanism 60 for establishing connection between the power supply and the load; the comprehensive judging mechanism 40 is further connected to the switching mechanism 60, and is used for controlling the switching mechanism 60 to be disconnected to disconnect the power supply and the load when the fault arc of the low-voltage line is judged.

Specifically, as shown in fig. 11, the low-voltage line fault arc identification device can perform online real-time detection on parameters such as fault arcs, resistive residual currents, capacitive residual currents, harmonic currents, overcurrent and short circuits which are easy to cause electric fires, so that the characteristic universality of electric fault arc signals is extracted, and the safety of low-voltage distribution lines, electric equipment and personnel is effectively ensured. The current transformer 10 collects the current signal received by the load element or device and transmits it to the power amplifier 50, and the high-frequency sampling mechanism 20 includes an a/D converter, and since the a/D converter can only receive analog signals within a certain range, the amplified current signal needs to be converted into a standard signal after operations such as jitter elimination, filtering, level conversion, and the like, and then is converted into a digital signal through a/D conversion with a high sampling frequency. The converted digital signal is processed and analyzed by the FPGA module (namely the wavelet analysis mechanism 30) in a wavelet way, the obtained multi-band energy entropy calculation result is sent to the ARM processor (namely the comprehensive judgment mechanism 40), parameters including signal time-frequency characteristics, residual current, harmonic current and the like are comprehensively calculated and fused to alarm logic analysis, if the analysis result judges that the circuit has fire risk, the ARM processor immediately sends out trip signals, and the switch mechanism is controlled to complete current interruption, so that the risk of fault arc and the occurrence of fire accidents are avoided.

In the low-voltage line fault arc identification device provided by the embodiment of the invention, the current transformer collects the current signals received by the load element or equipment and transmits the current signals to the power amplifier, the amplified current signals are converted into standard signals after the operations of jitter elimination, filtering, level conversion and the like, and then the standard signals are converted into digital signals through A/D conversion with high sampling frequency. The converted digital signal is processed and analyzed by the FPGA module wavelet, the obtained multi-band energy entropy calculation result is sent to the ARM processor, parameters including signal time-frequency characteristics, residual current, harmonic current and the like are comprehensively calculated and fused to alarm logic analysis, if the analysis result judges that the circuit has fire risk, the ARM processor immediately sends out a tripping instruction to control the switching mechanism to complete cut-off, and therefore the risk of fault arc and the occurrence of fire accidents are avoided.

It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered as a ordered listing of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.

In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.

In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.

In the present invention, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.

While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.

Claims

1. A method for identifying a low voltage line fault arc, the method comprising:

acquiring a current signal of a low-voltage circuit where a load is located;

converting the current signal from an analog form to a digital form;

carrying out wavelet analysis on the digital current signal to obtain multi-band energy entropy;

judging whether the low-voltage line has fault arc or not according to the multi-frequency band energy entropy.

2. The method for identifying a fault arc of a low voltage line according to claim 1, wherein the digital current signal is subjected to wavelet analysis by using a Mallat algorithm to obtain multi-band energy entropy.

3. The method for identifying a fault arc of a low voltage line according to claim 2, wherein the performing wavelet analysis on the digital current signal by using a Mallat algorithm to obtain a multi-band energy entropy comprises:

performing M times of filtering operation on the digital current signal, wherein M is an integer greater than or equal to 2;

for the first filtering operation, performing low-pass filtering on the digital current signal through a low-pass filter, and performing high-pass filtering on the digital current signal through a high-pass filter;

for the ith filtering operation, carrying out low-pass filtering on the signal obtained by the last low-pass filtering through a low-pass filter, and carrying out high-pass filtering on the signal obtained by the last low-pass filtering through a high-pass filter, wherein i is an integer greater than or equal to 2 and less than or equal to M;

and obtaining the multi-band energy entropy according to the M times of filtering operation results.

4. A low-voltage line fault arc identification method as claimed in claim 3, wherein the following relation exists between the low-pass filter and the high-pass filter in each filtering operation:

H ₀ [n]＝(-1) ⁿ H ₁ [2K-1-n]

wherein H is ₀ [n]Is a low-pass filter, H ₁ [2K-1-n]K is the length of the filter, and n is the nth sample point in the filter time series, for a high pass filter corresponding to the low pass filter.

5. A low-voltage line fault arc identification method according to claim 3, wherein the upper limit frequency of the low-pass filter is half of the upper limit frequency of the high-pass filter in each filtering operation, and the upper limit frequency of the high-pass filter in the current filtering operation is the same as the upper limit frequency of the low-pass filter in the last filtering operation.

6. The method for identifying a fault arc of a low voltage line according to claim 3, wherein said determining whether the fault arc of the low voltage line occurs according to the multi-band energy entropy comprises:

determining a target frequency band, and comparing each energy entropy of the target frequency band with a preset energy entropy threshold value respectively;

counting the number N1 of the energy entropies larger than the preset energy entropy threshold to obtain a first half-period high-frequency component count value N1 and a first half-period window count value N1, and counting the number N2 of the energy entropies smaller than or equal to the preset energy entropy threshold to obtain a second half-period window count value N1+N2;

comparing the second half-period window count value with the total half-period number in the time domain range corresponding to the target frequency band;

if the second half-period window count value is smaller than or equal to the corresponding total half-period window number, comparing the first half-period high-frequency component count value with a preset component count threshold value;

if the first half-cycle high-frequency component count value is greater than the preset component count threshold, storing the corresponding energy entropy into N1 buffer areas, setting a high-frequency component attenuation value as a fixed percentage value expected by the energy entropy amplitude, and resetting the first half-cycle high-frequency component count for the high-frequency component count of the next time period;

attenuating the energy entropy stored in the buffer zone according to a set attenuation value of the high-frequency component, and counting the number of the high-frequency components of the buffer zone in each time period when the energy entropy amplitude of the high-frequency component is attenuated to 0, wherein the number of the buffer zones of the high-frequency component is-1;

and if the number of the buffer areas reduced in each time period is greater than 14, judging that the low-voltage line is in fault arc, otherwise, judging that no fault arc is generated, and continuing fault arc identification of the next period.

7. The method of claim 5, wherein M has a value of 12.

8. The low voltage line fault arc identification method of claim 3, wherein the load comprises at least one of a resistive load, a half wave load, a capacitive load, an inductive load.

9. A low voltage line fault arc identification device, the device comprising:

the current transformer is used for collecting current signals of a low-voltage circuit where the load is located;

a high frequency sampling mechanism for converting the current signal from an analog form to a digital form;

the wavelet analysis mechanism is used for carrying out wavelet analysis on the digital current signal to obtain multi-band energy entropy;

and the comprehensive judging mechanism is used for judging whether the low-voltage line has fault arc or not according to the multi-frequency band energy entropy.

10. The low voltage line fault arc identification device of claim 9, wherein the device comprises:

the switching mechanism is used for establishing connection between a power supply and the load;

the comprehensive judging mechanism is also connected with the switching mechanism and is used for controlling the switching mechanism to be disconnected when the low-voltage line is judged to generate fault arc so as to disconnect the power supply from the load.