CN115545464A - Power distribution network fastener health state assessment method based on multi-source data fusion - Google Patents
Power distribution network fastener health state assessment method based on multi-source data fusion Download PDFInfo
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- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- 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
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/33—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using ultraviolet light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- 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
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
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- 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
- G01R31/1209—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 acoustic measurements
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- 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
- G01R31/1218—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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- G—PHYSICS
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- G—PHYSICS
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a health state assessment method of a power distribution network fastener based on multi-source data fusion, and relates to a non-contact joint diagnosis technology adopting ultrasonic wave, infrared and ultraviolet detection and visible light detection. The invention can more intuitively and objectively reflect the insulation state of the distribution network equipment, realize fault early warning and asset management of the distribution network equipment and realize intelligent comprehensive application of distribution network fastener management.
Description
Technical Field
The invention relates to the field of power distribution network fasteners, in particular to a power distribution network fastener health state assessment method based on multi-source data fusion.
Background
With the rapid development of the scale of the power grid, the number of electrical devices is rapidly increasing. Because most of the network equipment is arranged outdoors, the environment influence is large. Before the outdoor distribution network equipment breaks down, partial discharge often occurs, and the discharge source can produce sound, electricity and chemical effect. Experienced operating personnel can often utilize supersound, ultraviolet and infrared equipment, discover equipment hidden danger in advance and stop the emergence of accident. However, in a working site, various environmental noises often interfere with judgment of maintainers, and the fault detection efficiency is reduced. The ultrasonic, ultraviolet and infrared signal detection effects have strong dependence on experience of maintainers, and have certain difficulty in learning and inheritance in a short time under the current maintenance system, and large artificial uncontrollable factors. On the other hand, the fastener of the distribution network part is connected with the insulating equipment, no current flows through, and the defects of loosening, aging and the like are more difficult to find. Therefore, how to overcome the environmental noise interference, reduce human factors and help a maintainer to find a fault position in advance and judge the fault type in a more intuitive and effective manner is one of the key problems to be solved in equipment management.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a power distribution network fastener health state assessment method based on multi-source data fusion, which is used for accurately and efficiently assessing the power distribution network fastener health state.
One technical scheme for achieving the above purpose is as follows: a health state evaluation method for a power distribution network fastener based on multi-source data fusion comprises the following steps:
acquiring ultrasonic signals, ultraviolet signals, infrared signals and visible light signals of a power distribution network fastener through a sensor module; the signal processing module processes the acquired signals, the obtained data is input into the integrated module through a data interface, the integrated module realizes the positioning and monitoring of the discharge defects through an algorithm, and realizes the image fusion of the three non-electric quantity detection modules and the visible light inspection image, and the ultrasonic, infrared and ultraviolet combined detection based on image synthesis;
collecting multi-source images through ultrasonic, infrared and ultraviolet of the power distribution network fastener, expanding the small sample images by using a countermeasure generation network, identifying the defect type of the power distribution network fastener by using a convolutional neural network, and performing identification training on mass polling images of the power distribution network fastener to realize pixel-level segmentation on the outline of the defect;
and step three, performing electronic data warehousing on the routing inspection images processed by the example segmentation model, and establishing a defect case library, wherein the map comprises defect images, defect types and complete defect outline information, so that the precision of the routing inspection images on the fault detection of the power transmission equipment is ensured, and the lean and intelligent operation and maintenance management of the power distribution network fastener assets is realized.
Further, in the step one, the image fusion is to respectively fuse each module signal with the visible light video for pairwise registration, and then respectively correct the image registration result according to the manual calibration result, so as to realize the real-time fusion of infrared, ultraviolet and ultrasound.
And step two, according to the infrared image data, the ultrasonic image data, the ultraviolet image data and the visible light image data which are collected in the step one, positioning the data feature points by defining a feature point set, detecting and positioning the collected data based on multi-feature and projection histogram analysis, combining iteration based on a confidence coefficient function and template matching, and extracting the feature point set of the data.
And step three, collecting not less than 2 thousands of pictures, classifying according to different types of power distribution network fasteners, shooting real-time pictures by using a mobile operation terminal of a maintainer, and automatically comparing the pictures with various outlines of the equipment in a fault type library through an intelligent diagnosis application module of the power distribution network fastener fault types to determine the fault types.
The method for evaluating the health state of the power distribution network fastener based on multi-source data fusion has the following beneficial effects: the multi-source combined detection system has the advantages of visualization, high accuracy and intellectualization, and the provided combined diagnosis method can more intuitively and objectively reflect the insulation state of distribution network equipment, realize fault early warning and asset management of the distribution network equipment and realize intelligent comprehensive application of distribution network fastener management.
Drawings
Fig. 1 is a flowchart of a health state evaluation method of a power distribution network fastener based on multi-source data fusion in the invention.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is given by specific examples:
referring to fig. 1, a method for evaluating the health status of a fastener of a power distribution network based on multi-source data fusion according to the present invention includes,
the method comprises the following steps that firstly, ultrasonic signals, ultraviolet signals, infrared signals and visible light signals of the power distribution network fastener are collected through all sensor modules. The modules respectively process the acquired signals, the obtained data is input into the integrated module through a data interface, the integrated module realizes the positioning and monitoring of defects such as discharge and the like through a certain algorithm, and realizes the image fusion of the three non-electric quantity detection modules and the visible light inspection image, and the ultrasonic, infrared and ultraviolet combined detection based on image synthesis is realized,
collecting multi-source images through ultrasound, infrared and ultraviolet of the power distribution network fastener, expanding the small sample images by using a countermeasure generation network, identifying the defect types of the power distribution network fastener by using a convolutional neural network, identifying and training massive routing inspection images of the power distribution network fastener, realizing pixel-level segmentation of the outline of the defect,
and step three, performing electronic data warehousing on the routing inspection image processed by the case segmentation model, and establishing a defect case library, wherein the map contains important information such as a defect image, a defect type, a complete defect outline and the like, so that the precision of the routing inspection image on the fault detection of the power transmission equipment is ensured, and lean and intelligent operation and maintenance management of the power distribution network fastener assets is realized.
In the preferred embodiment of the health state evaluation method of the power distribution network fastener based on the multi-source data fusion, in the step one, the image fusion is to respectively perform pairwise registration on signals of each module and visible light video fusion, and then respectively correct image registration results according to manual calibration results, so that real-time fusion of infrared, ultraviolet and ultrasound is realized.
In the preferred embodiment of the method for evaluating the health state of the power distribution network fastener based on multi-source data fusion, in the second step, according to the infrared, ultrasonic, ultraviolet and visible light image data collected in the first step, a plurality of feature point sets are defined to position data feature points, the collected data are detected and positioned based on multi-feature and projection histogram analysis, iteration based on a confidence coefficient function and template matching, and the feature point sets of the data are extracted.
In the preferred embodiment of the method for evaluating the health state of the power distribution network fastener based on multi-source data fusion, in the third step, at least 2 ten thousand pictures are collected and classified according to different types of power distribution network fasteners, a mobile operation terminal of a maintainer is used for shooting real-time pictures, and the fault type is determined by automatically comparing various outlines of the equipment in a fault type library through an intelligent diagnosis application module of the fault type of the power distribution network fastener.
According to the method, the health state evaluation of the power distribution network fastener based on multi-source data fusion is considered, and the detection efficiency and the detection precision of the power distribution network fastener can be greatly improved through a non-contact detection technology, a multi-source information fusion technology and a digital diagnosis technology. Meanwhile, other power distribution network equipment detection technologies can be further popularized and enriched.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.
Claims (4)
1. A health state evaluation method for a power distribution network fastener based on multi-source data fusion is characterized by comprising the following steps:
acquiring ultrasonic signals, ultraviolet signals, infrared signals and visible light signals of a power distribution network fastener through a sensor module; the signal processing module processes the acquired signals, the obtained data is input into the integrated module through a data interface, the integrated module realizes the positioning and monitoring of the discharge defects through an algorithm, the image fusion of the three non-electric quantity detection modules and the visible light inspection image is realized, and the ultrasonic, infrared and ultraviolet combined detection based on image synthesis is realized;
acquiring multi-source images through ultrasonic, infrared and ultraviolet of the power distribution network fastener, expanding the small sample images by using a countermeasure generation network, identifying the defect type of the power distribution network fastener by using a convolutional neural network, and performing identification training on a mass inspection image of the power distribution network fastener to realize pixel-level segmentation on the outline of the defect;
and step three, performing electronic data warehousing on the routing inspection image processed by the example segmentation model, and establishing a defect case library, wherein the map comprises a defect image, a defect type and complete defect outline information, so that the precision of the routing inspection image on the fault detection of the power transmission equipment is ensured, and the lean and intelligent operation and maintenance management of the power distribution network fastener assets is realized.
2. The method for assessing the health state of the power distribution network fastener based on the multi-source data fusion as claimed in claim 1, wherein in the step one, the image fusion is to respectively fuse each module signal with visible light video for pairwise registration, and then respectively correct the image registration result according to the manual calibration result, so that the real-time fusion of infrared, ultraviolet and ultrasound is realized.
3. The method for assessing the health state of the power distribution network fastener based on the multi-source data fusion is characterized in that in the second step, according to the infrared, ultrasonic, ultraviolet and visible light image data collected in the first step, feature points of the data are located by defining a feature point set, the collected data are detected and located based on multi-feature and projection histogram analysis and combination of iteration based on a confidence coefficient function and template matching, and the feature point set of the data is extracted.
4. The method for evaluating the health state of the power distribution network fastener based on the multi-source data fusion is characterized in that in the third step, not less than 2 thousands of pictures are collected and classified according to different types of power distribution network fasteners, a mobile operation terminal of a maintainer is used for shooting real-time pictures, and the fault type is determined by automatically comparing a fault type intelligent diagnosis application module of the power distribution network fastener with various profiles of the equipment in a fault type library.
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