CN114167234B - Insulator aging detection method based on hyperspectrum of unmanned aerial vehicle - Google Patents
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- 239000012212 insulator Substances 0.000 title claims abstract description 59
- 238000001514 detection method Methods 0.000 title claims abstract description 50
- 230000032683 aging Effects 0.000 title claims abstract description 35
- 238000000701 chemical imaging Methods 0.000 claims abstract description 17
- 238000001228 spectrum Methods 0.000 claims description 45
- 238000000034 method Methods 0.000 claims description 16
- 238000003491 array Methods 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 10
- 101100442582 Neurospora crassa (strain ATCC 24698 / 74-OR23-1A / CBS 708.71 / DSM 1257 / FGSC 987) spe-1 gene Proteins 0.000 claims description 4
- 238000003384 imaging method Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 3
- 239000002131 composite material Substances 0.000 description 3
- 229920002379 silicone rubber Polymers 0.000 description 3
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- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000004945 silicone rubber Substances 0.000 description 1
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1218—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing using optical methods; using charged particle, e.g. electron, beams or X-rays
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
- G01R31/1245—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of line insulators or spacers, e.g. ceramic overhead line cap insulators; of insulators in HV bushings
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Abstract
The invention provides an insulator aging detection method based on unmanned aerial vehicle hyperspectrum, which is applied to an unmanned aerial vehicle cruise detection system, wherein the system comprises an unmanned aerial vehicle module, a hyperspectral imaging module and a control computer module, the hyperspectral imaging module is arranged on the unmanned aerial vehicle module, and the unmanned aerial vehicle module is in data intercommunication with the control computer module.
Description
Technical Field
The invention relates to the technical field of optical detection, in particular to an insulator aging detection method based on hyperspectrum of an unmanned aerial vehicle.
Background
In the power industry, the silicon rubber composite insulator has the advantages of good pollution resistance, light weight, high mechanical strength, convenient installation and maintenance and the like, and is widely applied. The hyperspectral technology is originally developed for remote sensing design, is currently commonly applied to astronomy, agriculture, pharmacy, medicine and other fields, and adopts the hyperspectral imaging technology to carry out optical diagnosis on the aging degree of a silicone rubber composite insulator in public works, so that the hyperspectral technology is a reasonable monitoring method. However, the surface of the silicon rubber composite insulator can generate pollution in the long-term working process, and the insulator aging and the insulator pollution are not easy to distinguish, so that the detection result is not accurate enough, and no better method can solve the problem at present.
Disclosure of Invention
In view of the above, the present invention is directed to an insulator aging detection method based on hyperspectrum of an unmanned aerial vehicle, so as to overcome or at least partially solve the above-mentioned problems of the prior art.
In order to achieve the above-mentioned aim, the invention provides an insulator aging detection method based on hyperspectral of an unmanned aerial vehicle, the method is applied to an unmanned aerial vehicle cruise detection system, the system comprises an unmanned aerial vehicle module, a hyperspectral imaging module and a control computer module, the hyperspectral imaging module is arranged on the unmanned aerial vehicle module, the unmanned aerial vehicle module is in data intercommunication with the control computer module, and the method specifically comprises the following steps:
the control computer module controls the unmanned aerial vehicle module to respectively carry out hyperspectral cruising detection before and after rainfall;
The unmanned aerial vehicle module performs hyperspectral scanning imaging on the insulators before and after rainfall to generate insulator hyperspectral data, and controls the computer module to acquire and store two groups of insulator hyperspectral data before and after rainfall respectively;
And the control computer module extracts the spatial position offset of the insulator by using an unmanned aerial vehicle hyperspectral image matching algorithm, and performs difference value on hyperspectral data corrected with the spatial position offset to perform insulator aging detection.
Further, when the unmanned aerial vehicle module carries out hyperspectral cruising detection, the control computer module also records flight data of the unmanned aerial vehicle module, wherein the flight data comprises global positioning information, altitude information, speed information and unmanned aerial vehicle attitude information.
Further, the hyperspectral data of the insulator are three-dimensional arrays Img0[ m, g and k ], wherein m and g are integers larger than 0, the first dimension m is a horizontal space dimension, the second dimension g is a spectrum dimension, the third dimension k is a discrete time sequence dimension, a time gap of the discrete time sequence dimension is t seconds, and a vertical space dimension interval corresponding to two adjacent serial numbers of the third dimension k is vt.
Further, when carrying out hyperspectral cruising detection, the unmanned aerial vehicle module is perpendicular with the slit direction in the hyperspectral imaging module in the direction of operation of unmanned aerial vehicle module.
Further, the control computer module controls the unmanned aerial vehicle module to respectively develop hyperspectral cruising detection before and after rainfall, and specifically comprises the following steps:
The control computer module controls the unmanned aerial vehicle module to carry out hyperspectral cruise detection before rainfall, acquires a three-dimensional array Img0[ m, g, k ] b of hyperspectral data before rainfall, and records flight data of the unmanned aerial vehicle module at the same time;
and in T hours after rainfall, the control computer module controls the unmanned aerial vehicle module to carry out hyperspectral cruise detection again according to flight data when the unmanned aerial vehicle module carries out hyperspectral cruise detection before rainfall, and hyperspectral data Img0[ m, g, k ] _a after rainfall is obtained.
Further, the method for extracting the spatial position offset of the insulator by using the unmanned aerial vehicle hyperspectral image matching algorithm specifically comprises the following steps:
Under the same flight data recording parameters, hyperspectral three-dimensional arrays Img0[ m, g, k ] b and Img0[ m, g, k ] a before and after rainfall are obtained;
Extracting spatial image data Img [ m, k ] b=Σ j:1→g Img0[ m, j, k ] b before rainfall, extracting spatial image data Img [ m, k ] a=Σ j:1→g Img0[ m, j, k ] a after rainfall;
And extracting spatial offset by using a scale-invariant feature transform algorithm for the Img [ m, k ] b and the Img [ m, k ] a to obtain a spatial offset value [ xs, ys ].
Further, the difference value of the hyperspectral data corrected for the spatial position shift specifically includes the following steps:
After the space deviation value [ xs, ys ] is obtained, correcting the hyperspectral three-dimensional array Img0[ m, g, k ] a after rainfall into Img0[ m-xs, g, k-ys ] a;
Carrying out differential calculation on the hyperspectral three-dimensional arrays before and after rainfall, wherein the calculation formula is as follows: img0[ m-xs, g, k-ys ] _a-img0[ m, g, k ] _a, and obtaining differential spectra Sp0[ g ] of all spatial points in sequence;
The spectrum of [ Σ j:g/2→g(Sp0[j])]/[Σj:1→g/2 (Sp 0[ j ]) ] 2 in the differential spectrum is defined as an insulator spectrum array SSp [ g ].
Further, the insulator aging detection specifically includes the following steps:
and carrying out difference square sum calculation on the insulator spectrum array SSp [ g ] and q spectrums in an offline standard spectrum database to obtain q difference arrays dif [ q ], wherein an index number k corresponding to a minimum value in the difference arrays represents the ageing degree corresponding to the insulator spectrum array SSp [ g ] as a kth level, and k and i are integers with values ranging from 1 to q.
Further, the building of the offline standard spectrum database specifically includes: calibrating the aging of the insulator, distinguishing the aging degree of q levels, collecting u samples at each level, respectively carrying out spectrum detection on the samples at each aging level, obtaining spectrum data, and sequentially marking the spectrum data as Spe_1[ g ], spe_2[g ],. Spe_u [ g ], carrying out weighted average on the u spectrum data at each aging level, wherein g represents spectrum dimension, and q, u and g are integers larger than 1.
Compared with the prior art, the invention has the beneficial effects that:
according to the insulator aging detection method based on the hyperspectral of the unmanned aerial vehicle, hyperspectral cruise detection is carried out on the insulator through the unmanned aerial vehicle module carrying the hyperspectral imaging module, hyperspectral data of the insulator can be conveniently collected to detect the aging condition of the insulator, meanwhile, the situation that the pollution of the insulator influences the detection result is considered, and the storm can wash the pollution on the surface of the insulator is considered.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only preferred embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of the overall structure of an unmanned aerial vehicle cruise detection system according to an embodiment of the present invention.
Fig. 2 is a schematic overall flow chart of an insulator aging detection method according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a high-spectrum cruise detection flow of an unmanned aerial vehicle module according to an embodiment of the present invention.
In the figure, 1 is unmanned aerial vehicle module, 2 is hyperspectral imaging module, and 3 is control computer module.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the illustrated embodiments are provided for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
Referring to fig. 1 and 2, the embodiment provides an insulator aging detection method based on hyperspectral of an unmanned aerial vehicle, the method is applied to an unmanned aerial vehicle cruise detection system, the system comprises an unmanned aerial vehicle module 1, a hyperspectral imaging module 2 and a control computer module 3, the hyperspectral imaging module 2 is arranged on the unmanned aerial vehicle module 1, the unmanned aerial vehicle module 1 and the control computer module 3 are in data intercommunication, and the method specifically comprises the following steps:
s101, controlling the unmanned aerial vehicle module 1 to respectively carry out hyperspectral cruising detection before and after rainfall by the control computer module 3. The hyperspectral cruise detection is that the hyperspectral imaging module 2 is carried on the unmanned aerial vehicle module 1 to collect hyperspectral data of an insulator in the air.
S102, respectively carrying out hyperspectral scanning imaging on insulators before and after rainfall by the unmanned aerial vehicle module to generate insulator hyperspectral data, and respectively acquiring and storing two groups of insulator hyperspectral data before and after rainfall by the control computer module;
S103, the control computer module extracts the spatial position offset of the insulator by using an unmanned aerial vehicle hyperspectral image matching algorithm, and performs difference value on hyperspectral data corrected for the spatial position offset to perform insulator aging detection.
Specifically, when the unmanned aerial vehicle module 1 carries out hyperspectral cruise detection, the control computer 3 synchronously records flight data of the unmanned aerial vehicle module 1, wherein the flight data comprises global positioning information, altitude information, speed information and unmanned aerial vehicle attitude information.
In step S102, the unmanned aerial vehicle module 1 performs hyperspectral scanning imaging through the hyperspectral imaging module 2, and the running direction of the unmanned aerial vehicle module 1 is perpendicular to the slit direction in the hyperspectral imaging module 2. The insulator hyperspectral data generated by the hyperspectral imaging module 2 are three-dimensional arrays Img0[ m, g and k ], wherein m and g are integers larger than 0, the first dimension m is a horizontal space dimension, the second dimension g is a spectrum dimension, the third dimension k is a discrete time sequence dimension, the time gap of the discrete time sequence dimension is t seconds, and the vertical space dimension interval corresponding to two adjacent serial numbers of the third dimension k is vt.
As a preferred example, referring to fig. 3, the control computer module 3 controls the unmanned aerial vehicle module 1 to perform hyperspectral cruise detection before and after rainfall, specifically including the following steps:
s201, controlling the unmanned aerial vehicle module to conduct hyperspectral cruise detection before rainfall, obtaining a three-dimensional array Img0[ m, g, k ] _b of hyperspectral data before rainfall, and simultaneously recording flight data when the unmanned aerial vehicle module conducts hyperspectral cruise detection before rainfall.
S202, controlling the unmanned aerial vehicle module to perform hyperspectral cruise detection again by the control computer module according to flight data when the unmanned aerial vehicle module performs hyperspectral cruise detection before rainfall within T hours after rainfall, and obtaining hyperspectral data Img0[ m, g, k ] _a after rainfall.
On this basis, in step S103, the method for extracting the spatial position offset of the insulator by using the unmanned aerial vehicle hyperspectral image matching algorithm specifically includes the following steps:
s301, acquiring hyperspectral three-dimensional arrays Img0[ m, g, k ] b and Img0[ m, g, k ] a before and after rainfall under the same flight data recording parameters.
S302, extracting spatial image data Img [ m, k ] b=Σ j:1→g Img0[ m, j, k ] b before rainfall, and extracting spatial image data Img [ m, k ] a=Σ j:1→g Img0[ m, j, k ] a after rainfall.
S303, extracting spatial offset by using a scale-invariant feature transform algorithm for the Img [ m, k ] b and the Img [ m, k ] a to obtain a spatial offset value [ xs, ys ].
After extracting the spatial position offset of the insulator, using a spectral data difference method to perform difference on the hyperspectral data corrected for the spatial position offset, specifically comprising the following steps:
After the space deviation value [ xs, ys ] is obtained, the hyperspectral three-dimensional array Img0[ m, g, k ] a after rainfall is corrected to be Img0[ m-xs, g, k-ys ] a.
Carrying out differential calculation on hyperspectral three-dimensional data before and after rainfall, wherein the calculation formula is as follows:
Img0[m-xs,g,k-ys]_a-Img0[m,g,k]_a
thereby obtaining the differential spectrum Sp 0g of all the space points in turn.
The spectrum of [ Σ j:g/2→g(Sp0[j])]/[Σj:1→g/2 (Sp 0[ j ]) ] 2 in the differential spectrum is defined as an insulator spectrum array SSp [ g ].
As a preferred example, the insulator aging detection specifically includes the following steps: and carrying out difference square sum calculation on the insulator spectrum array SSp [ g ] and q spectrums in an off-line standard spectrum database, wherein the calculation formula is as follows:
Σj:1→g(SSp[j]-Spe[i,j])2
Q differential value arrays dif [ q ] are obtained, wherein an index number k corresponding to the minimum value in the differential value arrays represents the aging degree corresponding to the insulator spectrum array SSp [ g ] as a kth stage, and k and i are integers with values ranging from 1 to q.
The establishment of the offline standard spectrum database specifically comprises the following steps: calibrating the aging of the insulator, distinguishing the aging degree of q levels, collecting u samples at each level, respectively carrying out spectrum detection on the samples at each aging level, obtaining spectrum data and marking the spectrum data as Spe_1[ g ], spe_2[g ],. Spe_u [ g ], and carrying out weighted average on the u spectrum data at each aging level, wherein the weighted average is shown in the following formula:
(Spe_1[g]+Spe_2[g]+...+Spe_u[g])/u
wherein g represents a spectral dimension, and q, u and g are integers greater than 1.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (3)
1. The method is characterized by being applied to an unmanned aerial vehicle cruise detection system, the system comprises an unmanned aerial vehicle module, a hyperspectral imaging module and a control computer module, the hyperspectral imaging module is arranged on the unmanned aerial vehicle module, the unmanned aerial vehicle module is in data intercommunication with the control computer module, and the method specifically comprises the following steps:
the control computer module controls the unmanned aerial vehicle module to respectively carry out hyperspectral cruising detection before and after rainfall;
The unmanned aerial vehicle module performs hyperspectral scanning imaging on the insulators before and after rainfall to generate insulator hyperspectral data, and controls the computer module to acquire and store two groups of insulator hyperspectral data before and after rainfall respectively;
The control computer module extracts the spatial position offset of the insulator by using an unmanned aerial vehicle hyperspectral image matching algorithm, and performs difference value on hyperspectral data corrected with the spatial position offset to perform insulator aging detection;
the hyperspectral data of the insulator are three-dimensional arrays Img0[ m, g and k ], wherein m and g are integers larger than 0, the first dimension m is a horizontal space dimension, the second dimension g is a spectrum dimension, the third dimension k is a discrete time sequence dimension, the time gap of the discrete time sequence dimension is t seconds, and the vertical space dimension interval corresponding to two adjacent serial numbers of the third dimension k is vt;
The control computer module controls the unmanned aerial vehicle module to respectively develop hyperspectral cruising detection before and after rainfall, and specifically comprises the following steps:
The control computer module controls the unmanned aerial vehicle module to carry out hyperspectral cruise detection before rainfall, acquires a three-dimensional array Img0[ m, g, k ] b of hyperspectral data before rainfall, and records flight data of the unmanned aerial vehicle module at the same time;
in T hours after rainfall, the control computer module controls the unmanned aerial vehicle module to carry out hyperspectral cruise detection again according to flight data when the unmanned aerial vehicle module carries out hyperspectral cruise detection before rainfall, and hyperspectral data Img0[ m, g, k ] _a after rainfall is obtained;
The method for extracting the spatial position offset of the insulator by using the hyperspectral image matching algorithm of the unmanned aerial vehicle specifically comprises the following steps:
Under the same flight data recording parameters, hyperspectral three-dimensional arrays Img0[ m, g, k ] b and Img0[ m, g, k ] a before and after rainfall are obtained;
Extracting spatial image data Img [ m, k ] b=Σ j:1→g Img0[ m, j, k ] b before rainfall, extracting spatial image data Img [ m, k ] a=Σ j:1→g Img0[ m, j, k ] a after rainfall;
extracting spatial offset by using a scale-invariant feature transform algorithm for Img [ m, k ] b and Img [ m, k ] a to obtain a spatial offset value [ xs, ys ];
The method for carrying out difference on the hyperspectral data corrected for the spatial position offset specifically comprises the following steps:
After the space deviation value [ xs, ys ] is obtained, correcting the hyperspectral three-dimensional array Img0[ m, g, k ] a after rainfall into Img0[ m-xs, g, k-ys ] a;
Carrying out differential calculation on the hyperspectral three-dimensional arrays before and after rainfall, wherein the calculation formula is as follows: img0[ m-xs, g, k-ys ] _a-img0[ m, g, k ] _a, and obtaining differential spectra Sp0[ g ] of all spatial points in sequence;
Defining a spectrum [ Σ j:g/2→g(Sp0[j])]/[Σj:1→g/2 (Sp 0[ j ]) ] 2 in the differential spectrum as an insulator spectrum array SSp [ g ];
the insulator aging detection method specifically comprises the following steps of:
Carrying out difference square sum calculation on an insulator spectrum array SSp [ g ] and q spectrums in an offline standard spectrum database to obtain q difference arrays dif [ q ], wherein an index number k corresponding to a minimum value in the difference arrays represents the ageing degree corresponding to the insulator spectrum array SSp [ g ] as a kth level, and k and i are integers with values ranging from 1 to q;
The establishment of the offline standard spectrum database specifically comprises the following steps: calibrating the aging of the insulator, distinguishing the aging degree of q levels, collecting u samples at each level, respectively carrying out spectrum detection on the samples at each aging level, obtaining spectrum data, and sequentially marking the spectrum data as Spe_1[ g ], spe_2[g ],. Spe_u [ g ], carrying out weighted average on the u spectrum data at each aging level, wherein g represents spectrum dimension, and q, u and g are integers larger than 1.
2. The method for detecting the aging of the insulator based on the hyperspectral of the unmanned aerial vehicle according to claim 1, wherein the control computer module further records flight data of the unmanned aerial vehicle module when the hyperspectral cruise detection is carried out by the unmanned aerial vehicle module, and the flight data comprises global positioning information, altitude information, speed information and unmanned aerial vehicle posture information.
3. The method for detecting the aging of the insulator based on the hyperspectral of the unmanned aerial vehicle according to claim 1, wherein the running direction of the unmanned aerial vehicle module is perpendicular to the slit direction in the hyperspectral imaging module when the hyperspectral cruising detection is carried out by the unmanned aerial vehicle module.
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