CN117517897A - Arc fault detection method based on edge calculation - Google Patents
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The invention belongs to the field of fault arc detection of aviation power distribution systems, and relates to an arc fault detection method based on edge calculation, which comprises the following steps: the electric arc detection terminal collects current information in the circuit in real time, judges whether various current characteristics exceed respective current characteristic thresholds for each current period, and if so, the current period is an electric arc period. And judging whether the accumulated arc period in one moving time window exceeds a set threshold value, if so, generating an arc fault, and reporting an arc fault alarm and current data in one moving time window to an arc detection far end. And the arc detection far end performs arc identification, if the arc fault is identified, the arc fault alarm information is reported to the upper computer, and an arc protection command is issued to the arc detection terminal. And the arc detection terminal performs arc fault protection. The technical scheme of the invention reduces the interference of nonlinear loads and the like and reduces the false alarm rate while having higher arc detection rate.
Description
Technical Field
The invention belongs to the technical field of aviation power supply and distribution systems and electric safety detection, and particularly relates to an arc fault detection method based on edge calculation.
Background
The arc is a gas free discharge phenomenon in which gas has good insulation in a normal state, but when a sufficiently large electric field is applied to both ends of a gas gap, the gas free discharge occurs, and an arc is generated. The arc is characterized by high energy and high temperature, which can reach thousands of degrees celsius in a few microseconds. The electric arc generates a large amount of heat, so that the aging and damage of the insulation layers of surrounding cables are accelerated, the contact and the insulation materials are burnt, and the safety of circuits and equipment is endangered; in severe cases, cables and equipment of the electrical system may ignite, causing fires and explosions.
The electric arc brings great threat to the safe operation of the aircraft, and students at home and abroad conduct a great deal of research on an electric arc fault detection method, for example, in the patent 'an aircraft fault electric arc detection method', db3 wavelet which adopts an energy ratio method to select the characteristics of the aircraft fault electric arc is used as a basic wavelet of a decomposition signal, the acquired current signal is subjected to wavelet decomposition, d2 signal is extracted, and the standard deviation of the cycle of the wavelet high-frequency component is used as the identification characteristics of the aviation fault electric arc, so that electric arc identification is conducted.
In the existing arc fault identification, the actual influence of complex load types and different current sizes on the arc faults is often ignored, so that the arc fault identification method is only suitable for arc types of individual types, the interference of complex loads cannot be eliminated, the existing aircraft arc fault detection terminal has limited calculation, the existing arc fault detection method is low in detection accuracy, and the detection false alarm rate is high.
Disclosure of Invention
The invention solves the technical problems that in aviation arc fault detection, the types of nonlinear loads are more, the types of arcs are complex, fault identification is seriously interfered, the adopted arc detection algorithm has limited detection capability, and the false alarm rate is high, and provides an arc fault detection method based on edge calculation.
The aim of the invention can be achieved by the following technical scheme, which comprises the following steps:
the invention provides an arc fault arc detection method based on edge calculation, which comprises the following steps of: an arc detection distal end and a plurality of arc detection terminals, said method comprising the steps of:
s1, any arc detection terminal collects current in a main circuit of a power supply and distribution network in real time at a specific sampling frequency, and performs validity check, data preprocessing and current frequency detection on collected current data;
s2, the arc detection terminal determines an ADC sampling rate according to a current frequency detection result, performs ADC sampling according to the ADC sampling rate, and performs various current characteristic extraction on each current period;
s3, the arc detection terminal judges whether the current characteristic of each current period exceeds the respective characteristic threshold value, if so, the current period is considered as an arc period, and the accumulated quantity of the arc period is added by 1;
s4, in a preset moving time window, the arc detection terminal judges whether the arc period number exceeds a set first threshold value of the arc period accumulation amount, if yes, the arc fault is considered to be detected, the arc period accumulation amount is cleared, and otherwise, the detection of the next current period in the moving time window is continued according to S3;
s5, the arc detection terminal detects that an arc fault exists in the circuit, and reports an arc fault alarm and all current data in the moving time window to an arc detection far end;
s6, the arc detection far end receives arc fault alarm information reported by the arc detection terminal and all current data in the movement detection time window;
s7, the electric arc detection far end performs feature extraction on all current data in the moving detection time window according to the same current period as the electric arc detection terminal, and extracts various current features;
s8, the arc detection far end takes the extracted various current characteristics as input, and inputs the extracted various current characteristics into an arc fault neural network identifier to identify an arc fault;
s9, if the arc fault neural network identifier judges that the section of current data has an arc fault, the arc detection remote end reports arc fault alarm information to the upper computer, stores the section of current data and sends an arc protection command to the arc detection terminal; otherwise, if the arc fault neural network identifier identifies that the segment of data does not have arc faults, the arc detection remote end reports arc fault false alarm information to the upper computer and stores the segment of current data;
s10, the arc detection terminal receives an arc protection command issued by the arc detection remote end, and performs arc protection.
Further, the arc detection terminal data is preprocessed to perform data validity verification, specifically: comparing the acquired current signal with a preset filtering threshold value, if the acquired current signal is larger than the filtering threshold value, considering the acquired current data as effective data, and transferring to the next step; and if the current data is lower than the filtering threshold value, the acquired current data is considered to be invalid, and current sampling is repeated.
Further, the current frequency detection of the arc detection terminal determines whether the frequency of the current is direct current or alternating current according to the collected current data, selects the ADC sampling rate based on the current frequency, and selects the ADC sampling rate based on the frequency of 400HZ if the current is direct current.
Further, the current characteristic extraction performed by the arc detection terminal per current period specifically includes: carrying out multiple current characteristic extraction on the cache current data sampled by each ADC, calculating a current characteristic value of each current period, wherein the calculated characteristic value is numbered as current characteristic 1 and current characteristic 2, current characteristics n and n are positive integers larger than 2, and the current characteristics are as follows: the current average value, the current peak value, the current standard deviation, the current two-period average amplitude difference, the current uniformity, the current change rate average value, the current bias state, the current cosine similarity, the current front n times harmonic wave and/or the pearson correlation coefficient of each frequency component of the current front window and the current rear window; the arc detection terminal judges whether each current period is an arc period or not, specifically: and the arc detection terminal judges whether each current characteristic i exceeds the current characteristic i identification threshold of the terminal, if all the current characteristics i exceed the current characteristic i identification threshold of the terminal, wherein i=1, 2,..n, the current period is judged to be the arc period, and the arc period accumulation amount is added by 1.
Further, the arc detection remote end calculates current characteristics 1,2, and m of each current period according to the current period, wherein m is a positive integer greater than or equal to 4, and the calculated current characteristics 1,2, and m of each current period are led into a trained neural network arc fault identifier to perform arc fault identification.
Further, the current characteristics are selected by respectively extracting current characteristics from arc current data, normal current data, interference current data and load current data to obtain arc current characteristics, normal current characteristics, interference current characteristics and load current characteristics, analyzing the obtained characteristics in a characteristic value range, determining the maximum value and the minimum value of the characteristics to obtain characteristic value ranges [ characteristic minimum value, characteristic maximum value ], comparing the arc current characteristic value range with the normal current characteristic value range, the interference current characteristic value range and the load current characteristic value range, and if the coincidence degree of the arc current characteristic value range and other characteristic value ranges is smaller than a preset characteristic value range coincidence threshold, identifying an arc fault by the current characteristics; if the arc current characteristic value range and other value ranges do not overlap, the characteristic value range overlap ratio is zero, and the current characteristic is preferably used for arc identification.
Further, the window length of the arc detection moving window is determined according to an arc detection standard and a current system;
the arc detection moving window length and the arc current period have the following relationship:
arc detection moving window length = current period number that arc detection needs to identify;
the current period is determined by the voltage frequency f, the current period = 1/voltage frequency f; in the case of a DC power supply, the current period can be selected as required, and can be selected to be 2.5ms.
Further, the neural network arc fault identifier is a pre-trained BP neural network:
(1) Setting nodes of an input layer, a hidden layer and an output layer of the neural network, and setting learning rate and an activation function;
(2) And performing feature extraction through pre-acquired arc data, normal data, load data and interference data, respectively extracting corresponding features of four types of data of the feature i to obtain corresponding arc current feature i, normal current feature i, load current feature i and interference current feature i, and performing neural network training by taking an array formed by the four types of features as a training set, wherein the output result of the neural network is arc fault and non-arc fault.
By adopting the technical scheme, the invention has the following advantages:
according to the scheme, the arc detection terminal and the arc detection far end are combined, and the high-sensitivity rapid detection of the terminal and the high-accuracy arc detection secondary rechecking of the far end are combined, so that the accuracy of the arc detection is improved, and the false alarm rate is reduced. The arc detection far end adopts an intelligent algorithm, and arc characteristic threshold value setting is not needed, so that the restriction of a single arc characteristic threshold value on the arc detection rate is avoided, the false alarm rate is reduced, and the detection rate is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that it is within the scope of the invention to obtain other drawings from these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of an arc detection method identification based on edge-based computation;
FIG. 2 is a flowchart of an identification of an arc detection terminal based on edge-based calculations;
FIG. 3 is a flowchart of an arc detection method identification based on edge calculation according to an embodiment of the present invention;
fig. 4 is a flowchart of the identification of an arc detection terminal based on edge calculation according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments of the present invention and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. 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.
The basic principle of the technical scheme of the invention is shown in fig. 1 and 2.
Firstly, arc current data is acquired through an arc detection terminal conditioning circuit, and the acquired arc current data is subjected to data preprocessing, so that weak signals are eliminated, and the noise disturbance influence when no current exists in a line is eliminated. Frequency detection is carried out, whether alternating current or direct current is carried out when current in the main circuit is determined, and if the alternating current or the direct current is the direct current, data sampling is carried out according to a preset sampling rate; if the alternating current is the alternating current, corresponding periodic sampling is carried out according to the frequency of the alternating current. If the arc characteristics are selected to have the frequency domain characteristics, the corresponding sampling frequency needs to contain the characteristic frequency band of the arc frequency domain.
And after data sampling, extracting current characteristics, and calculating each current characteristic of each sampling period. Judging whether all the current characteristics exceed respective current characteristic thresholds, if all the current characteristics exceed respective current characteristic thresholds, judging the current period as an arc period, and carrying out arc period accumulation quantity +1.
If the accumulated arc period amount in a certain moving time window is larger than a preset threshold value, judging that an arc occurs in the line, and alarming, wherein the arc detection terminal uploads arc fault alarming information, stored current data and current characteristics in one moving time window to an arc detection far end; otherwise, judging that no arc fault occurs, and continuing to perform arc detection.
After receiving alarm information and current data transmitted by an arc detection terminal, an arc detection far end processes the current data, extracts current characteristics according to a current period, inputs the extracted current characteristics as input to a trained arc fault neural network identifier for arc identification, reports the arc fault alarm information to an upper computer by the arc detection far end if the current data is identified as arc data, stores arc fault current data, and generates an arc protection command to the arc protection terminal, and the arc detection terminal receives the arc detection far end command for arc fault protection. Otherwise, the false detection information and the current data are recorded, and the false detection early warning information is reported to the upper computer.
Step one: the arc detection terminal collects and processes circuit current data in real time and detects whether an arc fault exists in a circuit or not, as shown in fig. 4.
(1) The arc detection terminal collects current signals in a main circuit of the power supply and distribution network through a sampling circuit (such as a low-pass filtering sampling circuit) in a specific sampling frequency and uninterrupted sampling mode.
(2) And preprocessing the data of the arc detection terminal, and checking the validity of the data. Comparing the collected current signal with a preset filtering threshold value, and if the current signal is larger than the filtering threshold value, considering the collected current data as effective data, and transferring to the next step. And if the current data is lower than the filtering threshold value, the acquired current data is considered to be invalid, and current sampling is repeated. This process is mainly for filtering the effects of unwanted small signal noise.
(3) Arc detection terminal current frequency detection, the frequency of the current is determined by the collected current data, whether the current is direct current or alternating current (such as 400Hz, 360Hz, 800Hz or variable frequency). The ADC sampling rate is selected based on the current frequency, and if dc, the ADC sampling rate is selected based on a frequency of 400HZ.
(4) And the arc detection terminal performs periodic ADC sampling according to the calculated sampling rate and caches the current data.
(5) The arc detection terminal performs current feature extraction per current period. And (3) carrying out various current characteristic extraction on the buffer current data sampled by each ADC, and calculating a current characteristic value of each current period, wherein the calculated characteristic value is numbered as current characteristic 1 and current characteristic 2.
(6) The arc detection terminal judges whether each current period is an arc period. The arc detection terminal judges whether each current characteristic i exceeds the current characteristic i identification threshold of the arc detection terminal itself. If all current characteristics i exceed respective current characteristic i identification thresholds (where i=1, 2,..n), then the current period is determined to be an arc period, and an arc period accumulation amount +1 is performed.
(7) In an arc detection moving window, the arc detection terminal judges whether the arc period accumulation amount exceeds a preset first threshold value of the arc period accumulation amount, if so, the arc detection terminal considers that an arc fault is generated, carries out fault alarm, and clears the arc period accumulation amount. If not, repeating the steps (4) - (6).
Step two: the arc detection terminal detects the occurrence of arc faults and transmits stored arc current data, current characteristics and arc warning information in an arc detection moving window to the arc fault detection remote equipment through a data bus.
Step three: and the arc detection far end receives and stores arc alarm information reported by the arc detection terminal, arc current data and arc current characteristics in a window, and performs secondary detection on the arc current data.
(1) The arc detection remote end divides arc current data into a plurality of current periods according to the current periods, and calculates current characteristics 1,2 and m (m is a positive integer greater than or equal to 4) of each current period according to the current periods.
(2) And (3) leading the calculated current cycle current characteristics 1, the current characteristics 2, the current characteristics m into a trained neural network arc fault identifier to identify arc faults.
Step four: if the arc detection remote end judges that the reported current data detects an arc fault, the arc detection remote end considers that the arc fault exists in the main circuit, and issues an arc fault protection command to an arc fault detection terminal, and the arc fault detection terminal protects the main circuit.
Step five: if the arc detection remote end judges that the reported current data does not detect the arc fault, the detection result is recorded, and the suspected false alarm information of the arc detection remote end is reported to the upper computer system.
In step one, the arc current characteristics herein refer to current characteristics that have a distinct distinction between arc faults and non-arc faults. The current characteristics are selected by respectively extracting current characteristics from arc current data, normal current data, interference current data and load current data to obtain arc current characteristics, normal current characteristics, interference current characteristics and load current characteristics, analyzing the obtained characteristics in characteristic value fields, determining the maximum value and the minimum value of the respective characteristics to obtain characteristic value fields [ characteristic minimum value, characteristic maximum value ], comparing the arc current characteristic value fields with normal current characteristic value fields, interference current characteristic value fields and load current characteristic value fields, and if the coincidence ratio of the arc current characteristic value fields and other characteristic value fields is smaller than a preset characteristic value field coincidence threshold, the current characteristics can be used for arc fault identification. If the characteristic value range of the arc current is not overlapped with other value ranges, the overlapping degree of the characteristic value range is zero, and the current characteristic is used for the arc identification effect to be optimal.
In the first step, the current characteristic may be any characteristic, such as a mean value, a peak-to-peak value, a standard deviation, a two-period average amplitude difference, a current uniformity, a current change rate mean value, a bias state, a cosine similarity, a first n-order harmonic sum, and pearson correlation coefficients of frequency components of front and rear windows.
In the first step, the arc detection terminal may determine whether the current characteristic i exceeds the current characteristic i threshold value when arc fault recognition is performed, and if so, consider the current period as an arc period.
In the first step, the current characteristic threshold of the arc detection terminal is set to detect as many arc faults as possible, and the reduction of the false detection rate can be further verified through the arc detection far end.
In step one, the arc detection moving window may be set by: the window length of the arc detection moving window is determined according to the arc detection standard and the current system. The arc detection moving window length and the arc current period have the following relationship:
arc detection moving window length = current period number of cycles arc detection needs to identify.
The current period is determined by the voltage frequency f, current period=1/voltage frequency f. As for 230VAC/800Hz, the current period=1.25 ms. In the case of a DC power supply, the current period can be selected as required, and is usually 2.5ms.
If the single current period is 2.5ms and the arc detection time is required to be 100ms for the 115VAC/400HZ voltage system, the arc detection moving window length is selected to be 100ms, and 40 current periods are included.
In step one, it may be determined whether the current period is an arc period by determining whether the arc characteristic or characteristics exceeds an arc characteristic threshold. Whether the current period is an arc period may also be determined synthetically by determining whether the arc characteristic or characteristics exceed an arc characteristic threshold, and determining whether the arc characteristic or characteristics exceed a combination of certain thresholds.
In the first step, the preset first threshold value of the arc period accumulation amount may be determined according to an industry arc determination standard, or may be determined according to a specific protection requirement, for example, the arc detection moving window may be set to 100ms, or may be other reasonable duration.
In step three, the neural network arc fault identifier is a pre-trained BP neural network:
(1) Setting the input layer, hidden layer and output layer nodes of the neural network, and setting learning rate, activation function and other parameters.
(2) And performing feature extraction through pre-acquired arc data, normal data, load data and interference data, respectively extracting corresponding features of four types of data of a feature i (i is a positive integer greater than or equal to 4), obtaining corresponding arc current features i, normal current features i, load current features i and interference current features i, performing neural network training by taking an array formed by the four types of features as a training set, and outputting the results of the neural network as arc faults and non-arc faults.
To verify the feasibility of the present invention, the specific example provided by the present invention is to use arc data collected under the power supply of 115v 400hz ac power as an input signal, and process the arc data according to the identification flow of fig. 3.
The first step, arc current data are collected through low-pass filtering, high-frequency noise signals are filtered through low-pass filtering, and the influence of high-frequency interference signals on arc characteristic signals is discharged. Since the arc fault characteristic frequency band is concentrated in the range of tens of KHz to 50KHz, the cut-off frequency of 50KHz low-pass filtering can be selected, and other cut-off frequencies can be selected.
And secondly, data preprocessing is carried out, the collected current signals are compared with a filtering threshold value 1, and when the current data is larger than the filtering threshold value 1, the sampling filtering super-threshold value count accumulation amount Ncount is accumulated. And in one sampling period, if the filtering super-threshold counting accumulation amount Ncount exceeds the filtering threshold 2, the acquired current data is considered to be effective data and data caching is performed, otherwise, the periodic current data is considered to be invalid data, the periodic data is removed, and the next periodic current data is acquired again.
And thirdly, detecting the frequency of the current signal, if the current signal can be subjected to zero crossing detection, judging whether the current is alternating current or direct current, if the current is alternating current, further judging the frequency of the alternating current, such as aviation 115V 400Hz.
Fourth, setting ADC sampling rate based on current frequency, wherein the principle is that the sampling rate setting can periodically and completely collect the data of current period. If the frequency is 400Hz and the sampling rate is 80K, one current period is 2.5ms, namely 200 times of sampling is carried out every 2.5ms, and data of one complete current period can be acquired.
And fifthly, sampling the periodic ADC according to the calculated period and storing data. The method can be used for sampling and calculating simultaneously, and the calculation result is stored; or can be stored and calculated.
A sixth step of starting a moving window, which may be determined according to arc criteria or according to other criteria, where the moving window is given 100ms, each current period being 2.5ms, i.e. one arc detection moving window being 40 current periods.
And seventhly, calculating the characteristics of each current period, and calculating the current uniformity and the frequency band amplitude mean value of each current period.
Eighth, arc determination is performed for each current cycle. Firstly judging whether the current uniformity exceeds a current uniformity threshold, and if so, considering that the first condition is satisfied.
And ninth, if the first condition is met, judging whether the current frequency band amplitude average value exceeds a current frequency band amplitude average value threshold value, and if the current frequency band amplitude average value exceeds the current frequency band amplitude average value threshold value, judging that the second condition is met.
And tenth, if the first condition and the second condition are satisfied at the same time, the current period is considered as an arc period, and the arc period accumulation amount +1 is performed.
And eleventh step, judging whether the arc period accumulated quantity exceeds a preset arc judgment threshold value, if so, judging that an arc occurs, giving out fault alarm, resetting the arc period accumulated quantity, and reporting arc fault alarm information, current data in a moving window and current characteristics to an arc detection far end.
If not, further judging whether the current mobile recognition window is finished, and if so, starting the next mobile recognition window; if the current movement identification window is not finished, the characteristic calculation and the judgment of the next current period are continued.
Twelfth, the arc detection far end receives arc fault information and current data reported by an arc detection terminal, the arc current data is extracted according to a period of 2.5ms, and current peak-to-peak values, current standard deviation, current change rate mean value, alternating current component effective value, first 20 harmonic sums, high-low frequency difference mean value and frequency spectrum standard deviation of all the data are extracted.
And thirteenth, the electric arc detection far end takes the extracted current peak-to-peak value, current standard deviation, current change rate mean value, alternating current component effective value, the first 20 times of harmonic sum, high-low frequency difference mean value and frequency spectrum standard deviation as input, and inputs the input into the trained electric arc fault neural network identifier for electric arc fault identification.
And fourteenth step, if the arc detection far end identifies an arc fault, the arc detection far end reports arc fault information to the upper computer, stores the arc fault information and current data, and generates an arc protection command to the arc detection terminal. Otherwise, the arc detection remote end reports the arc false detection information to the upper computer and stores the false detection information and current data.
Fifteenth, the arc detection terminal receives an arc fault protection command issued by the arc detection remote end to perform arc protection.
The technical scheme of the invention provides an arc fault detection method based on edge calculation, which comprises the following steps: the arc detection terminal collects current information in a circuit in real time at a specific frequency, performs current data preprocessing and current frequency detection, determines a current period according to the detected current frequency, performs ADC period sampling, extracts various current characteristics for each current period, judges whether the various current characteristics exceed respective current characteristic thresholds, considers the current period as an arc period if the current characteristics exceed respective current characteristic thresholds, and performs arc period accumulation +1. After the arc detection terminal judges the arc period of each current period, judging whether the accumulated amount of the arc period in one moving time window exceeds a set threshold value, if so, considering that an arc fault occurs, and reporting an arc fault alarm and current data in one moving time window to an arc detection far end. The arc detection remote end receives arc fault alarm information and current data of the arc detection terminal, extracts various current characteristics according to a current period, inputs the extracted current characteristics into the arc fault neural network identifier for arc identification, reports the arc fault alarm information to the upper computer if the arc fault exists in the identified current data, stores the arc fault alarm information and the current data to the local, and sends an arc protection command to the arc detection terminal. And the arc detection terminal receives an arc protection command issued by the arc detection remote end and performs arc fault protection. The fault arc identification method can be applied to fault arc online detection of various types of arcs and loads, and has high arc detection rate, meanwhile, the interference of nonlinear loads and the like is reduced, and the false alarm rate is reduced due to the adoption of multi-feature fusion detection and intelligent algorithm.
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