CN113311358A - Grounding grid fault monitoring method and device based on rapid imaging - Google Patents
Grounding grid fault monitoring method and device based on rapid imaging Download PDFInfo
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Abstract
A grounding grid fault monitoring method and device based on rapid imaging comprises the following steps: 1. injecting constant-amplitude different-frequency current into the grounding grid through a lower lead of the grounding grid; 2. collecting earth surface magnetic induction data and position data through a magnetic field collecting device, and sending the earth surface magnetic induction data and the position data to a data gateway; 3. the data gateway utilizes an improved wavelet transformation algorithm to filter the electromagnetic induction data to obtain a filtered electromagnetic induction signal sequence; 4. generating an electromagnetic induction data matrix and associating the electromagnetic induction data matrix with a corresponding position data matrix; 5. and drawing a magnetic induction intensity distribution stereogram of the grounding grid to show the condition of the grounding grid. The invention omits a complex data modeling process and a simulation link, and overcomes the problem that the structure of the grounding network needs to be known or assumed in advance in the traditional method. The device disclosed by the invention can directly judge whether the grounding grid conductor exists or not through the existence of electromagnetic induction data, and meanwhile, the grounding grid topological structure is obtained by combining position information.
Description
Technical Field
The invention relates to the technical field of grounding grid monitoring, in particular to a grounding grid fault monitoring method and device based on rapid imaging.
Background
The grounding grid is an important guarantee for the safe operation of the power facility, the grounding performance of the grounding grid is always emphasized by production and operation departments, faults and positions where the faults occur can be calibrated quickly and accurately, and the grounding grid has important engineering application value for improving the safety of field personnel and equipment. The method generally adopted in China is to carry out regular measurement on the grounding grid, a large amount of manpower and material resources are required to be input in the measurement process, the test time is long, the cost is high, the time interval of regular detection is very long, the operation condition of the grounding grid cannot be reflected in time, and the safety operation of a power system and the personal safety of workers are seriously threatened.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a grounding grid fault monitoring method and device based on rapid imaging, which do not depend on a grounding grid design drawing, collect magnetic induction information and position information by using a signal acquisition device deployed on the ground surface of the grounding grid, draw a signal intensity distribution stereo image, and visually find a fault by using image information, locate the fault occurrence position and guide the field maintenance work.
The invention adopts the following technical scheme:
a grounding grid fault monitoring method based on rapid imaging comprises the following steps:
step 1: injecting constant-amplitude different-frequency current into the grounding grid through a lower lead of the grounding grid;
step 2: collecting earth surface magnetic induction data and position data, and sending the earth surface magnetic induction data and the position data to a data gateway;
and step 3: the data gateway utilizes an improved wavelet transformation algorithm to filter the electromagnetic induction data to obtain a filtered electromagnetic induction signal sequence;
and 4, step 4: generating an electromagnetic induction data matrix by using the electromagnetic induction signal sequence generated in the step 3, and associating the electromagnetic induction data matrix with a corresponding position data matrix;
and 5: and (4) drawing a magnetic induction intensity distribution stereogram of the grounding grid by using the electromagnetic induction data matrix and the position data matrix in the step (4) to show the condition of the grounding grid.
In step 1, the preferred amplitude of the injected current is 10A-20A, and the frequency needs to be different from the power frequency.
In step 2, the surface induction data refers to electromagnetic induction data generated by the excitation of the current on the surface after the constant-amplitude different-frequency current is injected into the grounding grid in step 1, and the data contains doped noise signals;
the position data is position data of the acquisition device, corresponds to the acquired magnetic induction data, and comprises relative position data and absolute position data.
The relative position data is the relative position according to the selected reference object, and comprises direction and distance, namely angle and distance; setting the existing reference object around the grounding grid as an origin of the position data, and recording the relative position data of each measuring point and the origin; selecting a scene of relative position data acquisition as the on-line monitoring needing long-term and fixed point positions;
the absolute position data is physical coordinate data under a direct recording earth coordinate system and can be GPS positioning data; the scene of absolute position data acquisition is selected when the equipment needs to be moved frequently or periodic inspection short-term measurement is carried out.
The improved wavelet transform algorithm in step 3 includes decomposition and reconstruction of the signal.
The signal decomposition includes the following:
step 301.1: selecting coefficients of a high-pass filter g (n) and a low-pass filter h (n), and making x (n) h (n) -ig (n), wherein x (n) is the difference between the coefficients of the high-pass filter and the low-pass filter as a complex sequence; i represents an imaginary unit;
step 301.2: calculating the Fourier transform of x (n) by using the fast Fourier transform:
wherein DFT [ x (n) ] represents the fast Fourier transform of x (n); x (k) represents the result of fourier transform; n represents the signal length of an original signal sequence f (N), namely the number of samples, j represents a signal decomposition scale, the value of j is 1 when the signal is decomposed for the first time, and k represents a point corresponding to the sequence X (k) after the fast Fourier transform;
step 301.3: after fourier transform of the original signal sequence f (n) to obtain a transformed result f (k), y (k) is calculated as f (k) X*(k) Wherein X is*(k) Is the conjugate function of X (k), and Y (k) is the product of the Fourier transform result and the conjugate function;
step 301.4: calculating the fourier product of step 301.3 by using inverse fast fourier transform to obtain IDFT { y (k) } ═ c (n) + id (n);
wherein, c (n) and d (n) represent an approximation function and a detail function of IDFT { y (k) }, respectively, the approximation function c (n) represents a real part of IDFT { y (k) }, the detail function d (n) represents an imaginary part of IDFT { y (k) }, and IDFT { y (k) }representsa result of calculating y (k) by inverse fast fourier transform;
step 301.5: performing secondary sampling on the IDFT { Y (k) } in the step 301.4, and then respectively taking a real number part and an imaginary number part, wherein the real number part corresponds to an approximate sequence of the original signal sequence after the secondary sampling, and the imaginary number part corresponds to a detail sequence of the original signal sequence after the secondary sampling;
the two-sampling refers to a signal sequence formed by all signals extracted from an original signal sequence at intervals of every other signal;
step 301.6: if the size of the decomposition scale j reaches the decomposition scale threshold value at the moment, signal decomposition is finished; otherwise, the value of the decomposition scale j is added with 1, and then the steps 301.2 to 301.6 are repeated.
The decomposition scale threshold is 3.
The reconstruction of the signal comprises the following:
step 302.1: for a signal with decomposition scale j +1, the approximation sequence after two samples in step 301.5 is cj+1(n) the detail sequence after subsampling is dj+1(n); to cj+1(n) and dj+1(n) performing a binary interpolation to obtain cj+1' (n) and dj+1' (n) and constructing a complex sequence y (n) ═ cj+1′(n)+dj+1′(n)；
The method of binary interpolation is to insert a 0 between 2 samples of each function;
step 302.2: calculating y (n) by fast Fourier transform to obtain Fourier transform Y (k)' ═ DFT [ y (n) ]
Step 302.3: and performing fast convolution calculation on y (n) and x (n) ═ h (n) -ig (n), namely multiplying Y (k)' by X (k), and then performing inverse Fourier transform, wherein the real number part of the obtained result is the signal sequence after filtering.
In step 4, the gateway processes the position data, and automatically calculates the position of the 'acquisition point' in the coordinate system according to a certain rule, wherein the rule can be based on an X axis, and the acquisition points are sequentially stored in a three-dimensional position matrix from small to large and from negative to positive;
when the three-dimensional position matrix records a relative position, the data respectively recorded by the first dimension and the second dimension are a distance and an angle, and X, Y coordinate values of the data can be obtained through calculation;
when the three-dimensional position matrix records the absolute position, the data recorded by the first dimension and the second dimension are longitude and latitude respectively;
after the three-dimensional position matrix is arranged, searching magnetic induction data with the same marking information according to the marking information of the third dimension from the first element of the three-dimensional position matrix, and storing the magnetic induction data at a corresponding position of the magnetic induction data matrix; if the three-dimensional position matrix is of order M x N, the magnetic induction data matrix is also of order M x N.
In step 5, when the three-dimensional position matrix in step 4 records a "relative position", the "established reference point" is taken as the center of the image, i.e. the origin of the coordinates during drawing;
when the three-dimensional position matrix in step 4 records "absolute position", the X axis represents latitude and the Y axis represents longitude when drawing, and the latitude average value and the longitude average value of the data are taken as the image center.
The invention also discloses a grounding grid fault monitoring device based on rapid imaging, which comprises an electromagnetic induction acquisition module, a signal amplification module, a filtering module, a processing controller module, a power management module, an uploading module and a position sensing module, and is characterized in that:
the acquisition module is responsible for collecting an excitation electromagnetic induction signal generated by the earth surface of the grounding grid and inputting the signal to the signal amplification module;
the signal operational amplifier module is responsible for carrying out operational amplification on the signal and inputting the amplified signal to the filtering module;
the filtering module removes noise in the magnetic induction signals through a hardware filtering circuit and sends the magnetic induction signals with the noise removed to the uploading module;
the position sensing module is responsible for acquiring physical position information corresponding to the electromagnetic induction data and transmitting the information to the uploading module;
the processing controller module is responsible for coordinating and controlling the work of each module and collecting data;
the power supply management module is responsible for supplying power to the whole device;
and the uploading module is used for sending the acquired data to the gateway.
Compared with the prior art, the method has the advantages that in the data acquisition stage, the magnetic induction data and the position data are acquired simultaneously to form an incidence relation, and the data are used for an upper computer to automatically draw the electromagnetic induction intensity distribution map of the real-time grounding grid area. Compared with other wavelet transforms, the wavelet transform provided by the invention ensures the filtering effect, reduces the operation complexity and the time complexity, is suitable for being realized on devices with limited computing capacity, such as gateways, edge devices and the like, and has low requirement on the devices. The method omits a complex data modeling process and a simulation link, does not need to predict the topological structure of the grounding grid, and adopts a method of combining electromagnetic induction data and physical position information, thereby overcoming the problem that the grounding grid structure needs to be known or assumed in advance in the traditional method, simplifying the monitoring process, reducing the technical requirements on field personnel, simplifying the scheme operation, and being convenient for application and popularization. In the detection of the grounding grid of the actual transformer substation, especially for the old transformer substation which runs for more than 10 years, most of the construction drawings of the grounding grid are stored incompletely, and in addition, the actual grounding grid structure is difficult to master due to later transformation and the like. The device in this patent possesses the function that electromagnetic induction data and position data gathered simultaneously, can directly judge whether there is ground net conductor through electromagnetic induction data, combines position information simultaneously, can fix a position ground net conductor and detection device position to obtain ground net topological structure.
Drawings
FIG. 1 is a specific flowchart of a method for monitoring a fault of a grounding grid based on fast imaging according to the present disclosure;
fig. 2 is a structural diagram of a grounding grid fault monitoring device based on rapid imaging disclosed by the invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the method for monitoring a fault of a grounding grid based on fast imaging includes the steps of:
step 1: injecting constant-amplitude different-frequency current into the grounding grid through a lower lead of the grounding grid;
selecting current with required amplitude and frequency according to different scales of the actual grounding network; in this embodiment, the preferred amplitude of the injected current is 10A-20A, and the frequency needs to be different at power frequency.
Step 2: collecting earth surface magnetic induction data and position data, and sending the earth surface magnetic induction data and the position data to a data gateway;
the surface induction data refers to electromagnetic induction data generated by exciting current on the surface after injecting constant-amplitude pilot frequency current into the grounding grid in step 1, and the data contains doped noise signals.
The position data is position data of the acquisition device and corresponds to the acquired magnetic induction data.
According to the actual situation, the position data of the acquisition device comprises relative position data and absolute position data;
the relative position data is the relative position according to the selected reference object, and comprises direction and distance, namely angle and distance; selecting a proper reference object by utilizing the existing reference objects at the periphery of the grounding grid, setting the proper reference object as an origin of the position data, and recording the relative position data of each measuring point and the origin; the scene of relative position data acquisition is selected for the long-term on-line monitoring of fixed point positions. Relative position, i.e. the relative position of the acquisition device from a given reference point. When the image is drawn subsequently, the reference point is used as the origin of the coordinate system. The absolute position data is physical coordinate data under a direct recording earth coordinate system and can be GPS positioning data; the scene of absolute position data acquisition is selected when the equipment needs to be moved frequently or periodic inspection short-term measurement is carried out.
And step 3: and the data gateway utilizes an improved wavelet transformation algorithm to filter the electromagnetic induction data, removes noise in the data and obtains an electromagnetic induction signal sequence after filtering.
And 3, after receiving the data uploaded by the acquisition device, the gateway stores the position data and the electromagnetic induction data separately. The electromagnetic induction data is filtered by hardware of the acquisition device, although electromagnetic interference and noise signals can be removed, the acquired signals are likely to have part of low-frequency electromagnetic interference noise, and secondary filtering needs to be performed on the signals in order to improve monitoring accuracy. In the method, the improved wavelet transformation algorithm is used for filtering electromagnetic induction data in the gateway measurement, so that the subsequent calculation speed and the monitoring accuracy are improved. Compared with other improved wavelet calculation, the improved wavelet transform algorithm has certain reduction in operation complexity and is beneficial to being realized on equipment with limited calculation capacity.
The wavelet transformation algorithm of the invention comprises the decomposition and reconstruction of signals, and the signal decomposition steps are as follows:
step 301.1: the coefficients of the high-pass filter g (n) and the low-pass filter h (n) are selected, and the coefficients are selected according to the actual situation in the field and by combining the experience, so that x (n) ═ h (n) — ig (n) is required.
Wherein x (n) is the difference between the coefficients of the high-pass filter and the low-pass filter, and is a complex sequence; i represents an imaginary unit;
the high-pass filter g (n) and the low-pass filter h (n) coefficient selection usually need to be based on the actual situation in the field and combined with experience. In the present embodiment, the selection rule of the coefficients of the high-pass filter g (n) and the low-pass filter h (n) is:
∑g(n)＝0
step 301.2: calculating the Fourier transform of x (n) by using the fast Fourier transform:
wherein DFT [ x (n) ] represents the fast Fourier transform of x (n); x (k) represents the result of fourier transform; n represents the signal length of an original signal sequence f (N), namely the number of samples, j represents a signal decomposition scale, the value of j is 1 when the signal is decomposed for the first time, and k represents a point corresponding to the sequence X (k) after the fast Fourier transform;
step 301.3: fourier-transforming the original signal sequence f (n) to obtain a transformed result f (k), and then calculating y (k) ═ f (k) × X*(k) Wherein X is*(k) Is the conjugate function of X (k), and Y (k) is the product of the Fourier transform result and the conjugate function;
step 301.4: the product of step 301.3 is calculated using inverse fast fourier transform to obtain
IDFT{Y(k)}＝c(n)+id(n)
Wherein, c (n) and d (n) represent an approximation function and a detail function of IDFT { y (k) }, respectively, the approximation function c (n) represents a real part of IDFT { y (k) }, the detail function d (n) represents an imaginary part of IDFT { y (k) }, and IDFT { y (k) }representsa result of calculating y (k) by inverse fast fourier transform;
step 301.5: the IDFT { y (k) } of step 301.4 is subsampled, and then the real part and the imaginary part are respectively extracted, the real part corresponds to the approximate sequence of the original signal sequence after subsampling, and the imaginary part corresponds to the detail sequence of the original signal sequence after subsampling.
The two-sampling refers to a signal sequence formed by all signals extracted from an original signal sequence at intervals of every other signal;
step 301.6: if the size of the decomposition scale j reaches the decomposition scale threshold value at the moment, signal decomposition is finished; otherwise, the value of the decomposition scale j is added with 1, and then the steps 301.2 to 301.6 are repeated.
In this embodiment, the decomposition scale threshold is 3; one skilled in the art can set a higher decomposition scale threshold depending on the complexity of the signal.
The signal reconstruction steps are as follows:
step 302.1: for a signal with decomposition scale j +1, the approximation sequence after two samples in step 301.5 is cj+1(n) the detail sequence after subsampling is dj+1(n); to cj+1(n) and dj+1(n) performing a binary interpolation to obtain cj+1' (n) and dj+1' (n) and constructing a complex sequence y (n) ═ cj+1′(n)+dj+1′(n)；
The method of binary interpolation is to insert a 0 between 2 samples of each function;
step 302.2: calculating y (n) by fast Fourier transform to obtain Fourier transform Y (k)' ═ DFT [ y (n) ]
Step 302.3: and (3) performing fast convolution calculation on y (n) and x (n) ═ h (n) -ig (n), namely multiplying Y (k)' by X (k), and then performing inverse Fourier transform, wherein the real number part obtained as a result is the signal sequence after filtering.
So far, the wavelet transformation algorithm of the invention is completed.
And 4, step 4: and 3, generating an electromagnetic induction data matrix by using the electromagnetic induction signal sequence generated in the step 3, and associating the electromagnetic induction data matrix with a corresponding position data matrix.
The gateway automatically arranges the position data according to the natural spatial arrangement relation of the position data to form a position data matrix. Electromagnetic induction data subjected to wavelet transformation processing further form an electromagnetic induction data distribution matrix according to the arrangement rule of the position data matrix, elements in the matrix are electromagnetic induction data on each position, the two data matrices have a determined incidence relation, and finally, the gateway sends the data to an upper computer platform for image drawing.
Since the electromagnetic induction data and the position data are originated from the same device and naturally have relevance, the gateway can correlate the electromagnetic induction data and the position data uploaded by the same device when receiving the data. The gateway processes the position data, and automatically calculates the position of the acquisition point in the coordinate system according to a certain rule, wherein the rule can store the acquisition point in a three-dimensional position matrix in sequence from negative to positive according to the X axis from small to large.
After the three-dimensional position matrix is arranged, magnetic induction data with the same marking information is searched for from the first element according to the marking information of the third dimension, and the magnetic induction data is stored in the corresponding position of the magnetic induction data matrix. Assuming that the position matrix is of order M x N, the magnetic induction data matrix is also of order M x N.
Specifically, when the three-dimensional position matrix records "relative position", the data recorded in the first dimension and the data recorded in the second dimension are distance and angle, respectively, and X, Y coordinate values can be obtained through calculation.
When the three-dimensional position matrix records an 'absolute position', the data recorded by the matrix is latitude and longitude, and the matrix can be arranged from big to small by taking the latitude as a reference and the longitude.
And 5: the processed data are uploaded to an upper computer processing platform, and a magnetic induction data matrix and a position data matrix are utilized to directly draw a magnetic induction intensity distribution stereogram of the grounding grid, so that the condition of the grounding grid is visually displayed in a three-dimensional image form.
And 5, automatically drawing a three-dimensional image of the magnetic induction intensity distribution of the grounding grid by the upper computer platform according to the corresponding relation between the electromagnetic induction data matrix and the position data matrix.
When the three-dimensional position matrix in step 4 records "relative position", the "established reference point" is taken as the center of the image, i.e. the origin of the coordinates during drawing.
When the three-dimensional position matrix in step 4 records "absolute position", the X axis represents latitude and the Y axis represents longitude when drawing, and the latitude average value and the longitude average value of the data are taken as the image center.
Where the X-axis and Y-axis of the image are information of a position data matrix, demarcating the physical position of each data point. And decomposing the elements of the position data matrix into X, Y-axis corresponding coordinate information, thereby drawing a gridding distribution plan of the measuring point positions. The Z-axis of the image is data in the magnetic induction matrix, representing the magnitude of magnetic induction corresponding to each physical location. And decomposing and marking the magnetic induction data matrix to a corresponding coordinate point position by utilizing the corresponding relation between the magnetic induction data points and the position points, and finally connecting the dispersed magnetic induction data points through a smooth curved surface to form a magnetic induction intensity three-dimensional distribution pattern.
The magnetic induction data in the image, namely the Z-axis data, changes in real time along with the acquisition signals of the acquisition device, so that a magnetic induction intensity distribution curve also changes in real time, abnormal fluctuation of magnetic induction intensity can be found visually through a distribution map, the grounding grid fault can be found in time, and the site is guided to carry out operation and maintenance excavation by combining position information given in the image.
In step 2, in order to draw a signal intensity image of a grounding grid area quickly, relevant data including electromagnetic induction data, physical position data values and the like needs to be acquired in real time, the invention also discloses that a data acquisition device must include an electromagnetic induction acquisition module, a signal amplification module, a filtering module, a processing controller module, a power management module, an uploading module and a position sensing module, as shown in fig. 2.
The acquisition module is responsible for collecting an excitation electromagnetic induction signal generated by the earth surface of the grounding grid and inputting the signal to the signal amplification module; the signal operational amplifier module is responsible for carrying out operational amplification on the signal and inputting the amplified signal to the filtering module; the filtering module removes noise in the magnetic induction signals through a hardware filtering circuit and sends the magnetic induction signals with the noise removed to the uploading module; the processing controller module is responsible for coordinating and controlling the work of each module and collecting data; the power supply management module is responsible for supplying power to the whole device; and the uploading module is used for sending the acquired data to the gateway.
The device is characterized in that a position sensing module is additionally arranged in the device and is responsible for acquiring physical position information corresponding to electromagnetic induction data and sending the information to an uploading module.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (11)
1. The grounding grid fault monitoring method based on rapid imaging is characterized by comprising the following steps:
step 1: injecting constant-amplitude different-frequency current into the grounding grid through a lower lead of the grounding grid;
step 2: collecting earth surface magnetic induction data and position data, and sending the earth surface magnetic induction data and the position data to a data gateway;
and step 3: the data gateway utilizes an improved wavelet transformation algorithm to filter the electromagnetic induction data to obtain a filtered electromagnetic induction signal sequence;
and 4, step 4: generating an electromagnetic induction data matrix by using the electromagnetic induction signal sequence generated in the step 3, and associating the electromagnetic induction data matrix with a corresponding position data matrix;
and 5: and (4) drawing a magnetic induction intensity distribution stereogram of the grounding grid by using the electromagnetic induction data matrix and the position data matrix in the step (4) to show the condition of the grounding grid.
2. The grounding grid fault monitoring method based on rapid imaging according to claim 1, characterized in that:
in the step 1, the preferred amplitude of the injected current is 10A-20A, and the frequency is different from the power frequency.
3. The ground grid fault monitoring method based on rapid imaging according to claim 1 or 2, characterized in that:
in the step 2, the surface induction data refers to electromagnetic induction data generated by the excitation of the current on the surface after the constant-amplitude pilot frequency current is injected into the grounding grid in the step 1, and the data contains doped noise signals;
the position data are position data of the acquisition device, correspond to the acquired magnetic induction data and comprise relative position data and absolute position data.
4. The ground net fault monitoring method based on rapid imaging according to claim 3, characterized in that:
the relative position data is the relative position according to the selected reference object, and comprises direction and distance, namely angle and distance; setting the existing reference object around the grounding grid as an origin of the position data, and recording the relative position data of each measuring point and the origin; selecting a scene of relative position data acquisition as the on-line monitoring needing long-term and fixed point positions;
the absolute position data is physical coordinate data under a direct recording earth coordinate system, and can be GPS positioning data; the scene of absolute position data acquisition is selected when the equipment needs to be moved frequently or periodic inspection short-term measurement is carried out.
5. The ground net fault monitoring method based on rapid imaging is characterized in that:
the improved wavelet transform algorithm in step 3 comprises decomposition and reconstruction of signals.
6. The ground net fault monitoring method based on rapid imaging according to claim 5, characterized in that:
the signal decomposition includes the following:
step 301.1: selecting coefficients of a high-pass filter g (n) and a low-pass filter h (n), and making x (n) h (n) -ig (n), wherein x (n) is the difference between the coefficients of the high-pass filter and the low-pass filter as a complex sequence; i represents an imaginary unit;
step 301.2: calculating the Fourier transform of x (n) by using the fast Fourier transform:
wherein DFT [ x (n) ] represents the fast Fourier transform of x (n); x (k) represents the result of fourier transform; n represents the signal length, i.e. the number of samples, of the original signal sequence f (N); j represents a signal decomposition scale, the value of j is 1 when the signal is decomposed for the first time, and k represents a point corresponding to an X (k) sequence after fast Fourier transform;
step 301.3: after fourier transform of the original signal sequence f (n) to obtain a transformed result f (k), y (k) is calculated as f (k) X*(k) Wherein X is*(k) Is the conjugate function of X (k), and Y (k) is the product of the Fourier transform result and the conjugate function;
step 301.4: calculating the fourier product of step 301.3 by using inverse fast fourier transform to obtain IDFT { y (k) } ═ c (n) + id (n);
wherein, c (n) and d (n) represent an approximation function and a detail function of IDFT { Y (k) }, respectively, the approximation function represents a real part of IDFT { Y (k) }, the detail function represents an imaginary part of IDFT { Y (k) }, and IDFT { Y (k) }representsa result of calculating Y (k) by using inverse fast Fourier transform;
step 301.5: performing two samples on IDFT { Y (k) } in the step 301.4, and then respectively taking a real number part and an imaginary number part, wherein the real number part corresponds to an approximation sequence c (n) of the original signal sequence after the two samples, and the imaginary number part corresponds to a detail sequence d (n) of the original signal sequence after the two samples;
the two-sampling refers to a signal sequence formed by all signals extracted from an original signal sequence at intervals of every other signal;
step 301.6: if the size of the decomposition scale j reaches the decomposition scale threshold value at the moment, signal decomposition is finished; otherwise, the value of the decomposition scale j is added with 1, and then the steps 301.2 to 301.6 are repeated.
7. The ground net fault monitoring method based on rapid imaging according to claim 6, characterized in that:
the decomposition scale threshold is 3.
8. The ground grid fault monitoring method based on rapid imaging according to claim 6 or 7, characterized in that:
the reconstruction of the signal comprises the following:
step 302.1: for a signal with decomposition scale j +1, the approximation sequence after two samples in step 301.5 is cj+1(n) the detail sequence after subsampling is dj+1(n); to cj+1(n) and dj+1(n) performing a binary interpolation to obtain cj+1' (n) and dj+1' (n) and constructing a complex sequence y (n) ═ cj+1′(n)+dj+1′(n)；
The method of binary interpolation is to insert a 0 between 2 samples of each function;
step 302.2: calculating y (n) by using fast Fourier transform to obtain Fourier transform Y (k)' ═ DFT [ y (n) ];
step 302.3: and performing fast convolution calculation on y (n) and x (n) ═ h (n) -ig (n), namely multiplying Y (k)' by X (k), and then performing inverse Fourier transform, wherein the real number part of the obtained result is the signal sequence after filtering.
9. The ground grid fault monitoring method based on rapid imaging according to claim 1 or 7, characterized in that:
in the step 4, the gateway processes the position data, and automatically calculates the position of the 'acquisition point' in the coordinate system according to a certain rule, wherein the rule is based on an X axis, the acquisition point is sequentially stored in the three-dimensional position matrix from small to large and from negative to positive;
when the three-dimensional position matrix records a relative position, the data respectively recorded by the first dimension and the second dimension are a distance and an angle, and X, Y coordinate values of the data can be obtained through calculation;
when the three-dimensional position matrix records the absolute position, the data recorded by the first dimension and the second dimension are longitude and latitude respectively;
after the three-dimensional position matrix is arranged, searching magnetic induction data with the same marking information according to the marking information of the third dimension from the first element of the three-dimensional position matrix, and storing the magnetic induction data at a corresponding position of the magnetic induction data matrix; if the three-dimensional position matrix is of order M x N, the magnetic induction data matrix is also of order M x N.
10. The ground net fault monitoring method based on rapid imaging of claim 9, characterized in that:
in the step 5, when the three-dimensional position matrix in the step 4 records a "relative position", the "established reference point" is taken as the center of the image, i.e. the origin of the coordinates during drawing;
when the three-dimensional position matrix in step 4 records "absolute position", the X axis represents latitude and the Y axis represents longitude when drawing, and the latitude average value and the longitude average value of the data are taken as the image center.
11. The monitoring device of the grounding grid fault monitoring method based on the rapid imaging according to any one of claims 1 to 10, comprising an electromagnetic induction acquisition module, a signal amplification module, a filtering module, a processing controller module, a power management module, an uploading module and a location awareness module, wherein:
the acquisition module is responsible for collecting an excitation electromagnetic induction signal generated by the earth surface of the grounding grid and inputting the signal to the signal amplification module;
the signal operational amplifier module is responsible for carrying out operational amplification on the signal and inputting the amplified signal to the filtering module;
the filtering module removes noise in the magnetic induction signals through a hardware filtering circuit and sends the magnetic induction signals with the noise removed to the uploading module;
the position sensing module is responsible for acquiring physical position information corresponding to the electromagnetic induction data and sending the information to the uploading module;
the processing controller module is responsible for coordinating and controlling the work of each module and collecting data;
the power supply management module is responsible for supplying power to the whole device;
and the uploading module is used for sending the acquired data to the gateway.
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