CN116559599A - Distribution network cable fault early warning method and system based on big data - Google Patents
Distribution network cable fault early warning method and system based on big data Download PDFInfo
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Abstract
The invention relates to the field of distribution network cable fault analysis and early warning, and particularly discloses a distribution network cable fault early warning method and system based on big data. The method comprises the steps of obtaining the damage coefficient and the abnormal temperature coefficient of a protective layer of each cable in each section of underground pipe gallery, judging whether each cable in each section of underground pipe gallery has insulation ageing hidden danger or not, and preventing insulation ageing from developing into faults; acquiring indoor humidity coefficients, overall frame collapse coefficients and cable support stability coefficients of each section of underground pipe gallery, and judging whether potential safety hazards exist in the laying environment of cables in each section of underground pipe gallery; hidden danger and problems can be found in time, the number of faults and repair time are reduced, the reliability, stability and safety of power supply are improved, and the accident risk is reduced.
Description
Technical Field
The invention relates to the field of distribution network cable fault analysis and early warning, in particular to a distribution network cable fault early warning method and system based on big data.
Background
The safety of the distribution network underground cable is an important component of the safety of the power system, and the safety condition of the distribution network underground cable is not only related to the reliability of power supply, but also related to the life and property safety of people. The safety of the underground cable of the distribution network is analyzed and early-warned, which is an essential link of the operation of the power system, can find hidden danger and problems, reduce the times of faults and repair time, improve the reliability, stability and safety of power supply, and reduce the accident risk. Therefore, the method has practical significance in analyzing and early warning the safety of the distribution network underground cable.
The existing distribution network underground cable safety analysis and early warning method has some defects: on the one hand, when the existing method is used for analyzing whether the underground cable of the distribution network has potential safety hazards or not, the surface information of the cable, such as breakage, scratch, crack, deformation and depression, is mainly used for evaluation, the analyzed indexes are single and shallow, the bending degree of the cable paved and the surface temperature of the cable are not further analyzed, the bending degree of the cable is overlarge, the power transmission efficiency of the cable is reduced, the risk of mechanical damage of the cable is increased, the surface temperature of the cable is overhigh, the cable is aged and deteriorated, cracking or perforation occurs, and faults are caused.
On the other hand, the existing method lacks the analysis of underground cable laying environmental safety, namely underground pipe gallery safety, if underground pipe gallery cracks or is moist, underground pipe gallery has the risk of collapsing, and then brings the potential safety hazard for underground cable, causes the incident, simultaneously, if cable support in the underground pipe gallery drops from the underground pipe gallery inner wall because of deformation, corrosion etc. lead to cable support bearing capacity decline, can bring the damage for the cable.
Disclosure of Invention
In view of this, in order to solve the problems set forth in the background art, a distribution network cable fault early warning method and system based on big data are provided.
The technical scheme adopted for solving the technical problems is as follows: in a first aspect, the invention provides a distribution network cable fault early warning method based on big data, which comprises the following steps: step one, dividing underground pipe gallery: dividing the target underground pipe gallery according to a preset equal length principle to obtain each section of underground pipe gallery.
Step two, obtaining cable appearance information: appearance information of each cable in each section of underground pipe gallery is obtained, wherein the appearance information comprises deformation degree coefficients and bending degree coefficients.
Thirdly, checking mechanical damage of the cable: according to appearance information of each cable in each section of underground pipe gallery, analyzing appearance coincidence coefficients of each cable in each section of underground pipe gallery, judging whether mechanical damage hidden danger exists in each cable in each section of underground pipe gallery, and acquiring hidden danger point sets corresponding to the mechanical damage hidden danger of the cable.
Step four, obtaining basic information of the cable: basic information of each cable in each section of underground pipe gallery is obtained, wherein the basic information comprises a sheath breakage coefficient and a temperature anomaly coefficient.
Fifthly, cable insulation aging investigation: according to the basic information of each cable in each section of underground pipe gallery, the insulation aging coefficient of each cable in each section of underground pipe gallery is analyzed, whether each cable in each section of underground pipe gallery has insulation aging hidden danger or not is judged, and a hidden danger point set corresponding to the cable insulation aging hidden danger is obtained.
Step six, monitoring the safety of the cable laying environment: environmental information and structural information of each section of underground pipe gallery are acquired, indoor humidity coefficients, overall frame collapse coefficients and cable support stability coefficients of each section of underground pipe gallery are analyzed, safety indexes of each section of underground pipe gallery are comprehensively obtained, whether potential safety hazards exist in the laying environment of cables in each section of underground pipe gallery or not is judged, and a hidden danger point set corresponding to the potential hazards of the cable laying environment is acquired.
Step seven, cable fault early warning feedback: and the hidden danger points corresponding to the hidden danger of mechanical damage of the cable, the hidden danger of insulation aging of the cable and the hidden danger of the cable laying environment are transmitted to a cable safety management department.
In one possible design, the specific analysis process of the second step includes: acquiring the depression depth of each depression point on the surface of each cable in each section of underground pipe gallery, and marking the depression depth as，/>Indicate->Number of section underground pipe gallery>，Indicating the number of underground pipe galleries>Indicate->Number of the cable, ">，/>Indicating the number of cables>Indicate->Number of concave points->，/>Indicating the number of pit points.
By analysis of formulasObtaining the deformation degree coefficient of each cable in each section of underground pipe gallery>Wherein->Representing a preset depression depth threshold.
In one possible design, the stepsThe specific analysis process of II also comprises the following steps: the method comprises the steps of obtaining contour lines of cables in each section of underground pipe gallery, comparing the contour lines of the cables in each section of underground pipe gallery with a reference straight line to obtain bending points of the cables in each section of underground pipe gallery, making inscribed circles of the contour lines of the cables at the bending points of the cables in each section of underground pipe gallery, marking the inscribed circles as the curvature circles of the bending points of the cables in each section of underground pipe gallery, obtaining radiuses of the curvature circles corresponding to the bending points of the cables in each section of underground pipe gallery, and marking the inscribed circles as the radiuses of the curvature circles，/>Indicate->The number of the bending points is given by,，/>indicating the number of bending points.
By analysis of formulasObtaining the bending degree coefficient of each cable in each section of underground pipe gallery>Wherein->Indicate->The underground pipe gallery of the section is->The (th) of the cable>The bending points correspond to the curvature circlesRadius of>A threshold value representing the radius of the curvature circle corresponding to the preset bending point.
In one possible design, the step three is to analyze the appearance coincidence coefficient of each cable in each section of underground pipe gallery, and the specific process is as follows: by analysis of formulasObtaining the appearance coincidence coefficient of each cable in each section of underground pipe gallery>Wherein->Representing natural constant->、/>Threshold values respectively representing a preset deformation degree coefficient and a bending degree coefficient, < >>、/>Respectively representing the weights of the preset deformation degree coefficient and the bending degree coefficient.
In one possible design, the specific process of the fourth step is as follows: obtaining the scratch lengths and the crack lengths of the surfaces of the cable sheath layers in each section of underground pipe gallery, and respectively marking the scratch lengths and the crack lengths asAnd->，/>Indicate->Number of scratch>，/>Indicating the number of scratches>Indicate->Number of strip slit>，/>Indicating the number of cracks.
By analysis of formulasObtaining the damage coefficient of the protective layer of each cable in each section of underground pipe gallery>Wherein->、/>Respectively representing the influence factors corresponding to the preset unit scratch length and the unit crack length.
Acquiring infrared thermal imaging images of each cable in each section of underground pipe gallery, obtaining each temperature value of the surface of each cable in each section of underground pipe gallery, analyzing the area and the temperature of each abnormal temperature region in each cable in each section of underground pipe gallery, and respectively marking the areas and the temperatures as、/>，/>Indicate->Number of individual temperature anomaly region, +.>，/>Indicating the number of temperature anomaly regions.
By analysis of formulasObtaining the temperature anomaly coefficient of each cable in each section of underground pipe gallery>Wherein->、/>Respectively representing a preset temperature abnormal region area threshold value and a cable temperature early warning value, +>、/>Respectively representing the area of the preset temperature anomaly area and the weight of the temperature.
In one possible design, the specific analysis process in the fifth step is as follows: by analysis of formulasObtaining insulation aging coefficient of each cable in each section of underground pipe gallery>。
And comparing the insulation ageing coefficient of each cable in each section of underground pipe gallery with a preset insulation ageing coefficient threshold value, if the insulation ageing coefficient of a certain cable in a section of underground pipe gallery is larger than the preset insulation ageing coefficient threshold value, then the insulation ageing hidden danger exists in the cable in the section of underground pipe gallery, and recording the cable in the section of underground pipe gallery as hidden danger points corresponding to the cable insulation ageing hidden danger, and counting to obtain a hidden danger point set corresponding to the cable insulation ageing hidden danger.
In one possible design, the specific analysis in the step six includes: acquiring the humidity of each detection point in each section of underground pipe gallery, analyzing the area of each corresponding wet area of each wet detection point in each section of underground pipe gallery, and marking the area as，/>Indicate->Number of moist detection points->，/>Indicating the number of wetness detecting points.
By analysis of formulasObtaining the indoor humidity coefficient of each section of underground pipe gallery>Wherein->And representing the influence factor corresponding to the preset unit wet area.
Acquiring images of the inner walls of all sections of underground pipe gallery, and acquiring all cracks in the inner walls of all sections of underground pipe galleryIs described as the length of,/>Indicating the inner wall of the underground pipe gallery>Number of strip slit>。
By analysis of formulasObtaining the collapse coefficient of the integral frame of each section of underground pipe gallery +.>Wherein->And representing a preset underground pipe gallery inner wall crack length threshold.
Acquiring images of each cable support in each section of underground pipe gallery, analyzing the shape fitness of each cable support in each section of underground pipe gallery, and representing the shape fitness as,/>Indicate->Number of the cable support, ->，/>Indicating the number of cable supports.
According to the images of the cable brackets in each section of underground pipe gallery, the corrosion of the cable brackets in each section of underground pipe gallery is obtainedArea, which is denoted as。
By analysis of formulasObtaining the cable support stability coefficient of each section of underground pipe gallery>Wherein->Indicating the number of cable supports in the underground piping lane, +.>、/>Threshold values of the shape fitness and the rust area of the preset cable support are respectively shown, and the threshold values are ++>、/>Respectively representing the shape fitness and the weight of the rust area of the preset cable support.
In one possible design, the step six obtains the safety index of each section of underground pipe gallery, and the specific process is as follows: by analysis of formulasObtaining the safety index of each section of underground pipe gallery>Wherein->、/>、/>Respectively representing weights of preset cable support stability coefficient, indoor humidity coefficient and integral frame collapse coefficient.
In a second aspect, the present invention further provides a distribution network cable fault early warning system based on big data, including: underground pipe gallery dividing module: the method is used for dividing the target underground pipe gallery according to a preset equal length principle to obtain each section of underground pipe gallery.
The cable appearance information acquisition module is used for: the method is used for acquiring appearance information of each cable in each section of underground pipe gallery, wherein the appearance information comprises deformation degree coefficients and bending degree coefficients.
And the cable mechanical damage investigation module is as follows: the method is used for analyzing appearance coincidence coefficients of each cable in each section of underground pipe gallery according to appearance information of each cable in each section of underground pipe gallery, judging whether mechanical damage hidden danger exists in each cable in each section of underground pipe gallery, and acquiring hidden danger point sets corresponding to the mechanical damage hidden danger of the cable.
The cable basic information acquisition module: the method is used for acquiring basic information of each cable in each section of underground pipe gallery, wherein the basic information comprises a sheath breakage coefficient and a temperature anomaly coefficient.
Cable insulation aging checking module: and the device is used for analyzing the insulation aging coefficients of each cable in each section of underground pipe gallery according to the basic information of each cable in each section of underground pipe gallery, judging whether each cable in each section of underground pipe gallery has insulation aging hidden dangers, and acquiring hidden danger point sets corresponding to the cable insulation aging hidden dangers.
The cable laying environment safety monitoring module comprises: the method is used for acquiring environment information and structure information of each section of underground pipe gallery, analyzing indoor humidity coefficient, integral frame collapse coefficient and cable support stability coefficient of each section of underground pipe gallery, comprehensively obtaining the safety index of each section of underground pipe gallery, judging whether potential safety hazards exist in the laying environment of the cable in each section of underground pipe gallery, and acquiring a hidden danger point set corresponding to the potential hazards of the cable laying environment.
And the cable fault early warning feedback module: the method is used for sending hidden danger points corresponding to the hidden danger of mechanical damage of the cable, hidden danger of insulation and aging of the cable and hidden danger of the cable laying environment to a cable safety management department.
Database: a standard space model for storing individual cables in an underground piping lane and a standard space model for individual cable holders in an underground piping lane.
Compared with the prior art, the invention has the following beneficial effects: 1. according to the invention, by acquiring the deformation degree coefficient and the bending degree coefficient of each cable in each section of underground pipe gallery, whether each cable in each section of underground pipe gallery has mechanical damage hidden danger or not is judged, the mechanical damage hidden danger of the cable is found out in time and early warning is carried out, and the mechanical damage is prevented from developing into a fault.
2. According to the invention, the damage coefficient and the abnormal temperature coefficient of the protective layer of each cable in each section of underground pipe gallery are obtained, the insulation aging coefficient of each cable in each section of underground pipe gallery is analyzed, whether each cable in each section of underground pipe gallery has insulation aging hidden danger is judged, the insulation aging hidden danger of the cable is found in time, and early warning is carried out, so that the insulation aging is prevented from developing into faults.
3. According to the invention, the indoor humidity coefficient, the integral frame collapse coefficient and the cable support stability coefficient of each section of underground pipe gallery are obtained, so that the safety index of each section of underground pipe gallery is comprehensively obtained, whether the potential safety hazard exists in the laying environment of the cable in each section of underground pipe gallery is judged, the safety accidents caused by collapse of the pipe gallery, insecurity of the cable support and the like are prevented, and the underground cable is damaged.
4. According to the invention, hidden danger points corresponding to the hidden danger of mechanical damage of the cable, hidden danger of insulation and aging of the cable and hidden danger of the cable laying environment are transmitted to a cable safety management department for early warning, hidden danger and problems can be found in time, the number of faults and repair time are reduced, the reliability, stability and safety of power supply are improved, the accident risk is reduced, the safety performance and long-term stability of a power distribution system are improved, and the reliability of power supply is ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a system module connection diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
Referring to fig. 1, a first aspect of the present invention provides a distribution network cable fault early warning method based on big data, including the following steps: step one, dividing underground pipe gallery: dividing the target underground pipe gallery according to a preset equal length principle to obtain each section of underground pipe gallery.
As a preferred scheme, the specific analysis process of the first step is as follows: and obtaining a layout route of the target underground pipe gallery, and dividing the target underground pipe gallery according to a preset equal length principle to obtain each section of underground pipe gallery.
Step two, obtaining cable appearance information: appearance information of each cable in each section of underground pipe gallery is obtained, wherein the appearance information comprises deformation degree coefficients and bending degree coefficients.
Illustratively, the specific analysis procedure of the second step includes: acquiring the depression depth of each depression point on the surface of each cable in each section of underground pipe gallery, and marking the depression depth as，/>Indicate->Number of section underground pipe gallery>，/>Indicating the number of underground pipe galleries>Indicate->Number of the cable, ">，/>Indicating the number of cables>Represent the firstNumber of concave points->，/>Indicating the number of pit points.
As a preferable scheme, the method for obtaining the depression depth of each depression point on the surface of each cable in each section of underground pipe gallery comprises the following specific processes: and obtaining live-action images of each cable in each section of underground pipe gallery, and constructing a space model of each cable in each section of underground pipe gallery.
And extracting a standard space model of each cable in the underground pipe gallery stored in the database, comparing the space model of each cable in each section of underground pipe gallery with the corresponding standard space model to obtain each concave point on the surface of each cable in each section of underground pipe gallery, and obtaining the concave depth of each concave point on the surface of each cable in each section of underground pipe gallery.
By analysis of formulasObtaining the deformation degree of each cable in each section of underground pipe gallery>Coefficients, wherein->Representing a preset depression depth threshold.
As a preferable scheme, the method for acquiring the live-action image of each cable in each section of underground pipe gallery comprises the following steps: and obtaining each angle image of each cable in each section of underground pipe gallery through a high-definition camera, and further splicing to obtain the live-action image of each cable in each section of underground pipe gallery.
As a preferable scheme, the high-definition camera is installed by adopting a sliding rail type or a roller type, and can move along the underground pipe gallery.
Illustratively, the specific analysis process of the second step further includes: the method comprises the steps of obtaining contour lines of cables in each section of underground pipe gallery, comparing the contour lines of the cables in each section of underground pipe gallery with a reference straight line to obtain bending points of the cables in each section of underground pipe gallery, making inscribed circles of the contour lines of the cables at the bending points of the cables in each section of underground pipe gallery, marking the inscribed circles as the curvature circles of the bending points of the cables in each section of underground pipe gallery, obtaining radiuses of the curvature circles corresponding to the bending points of the cables in each section of underground pipe gallery, and marking the inscribed circles as the radiuses of the curvature circles，/>Indicate->Number of bending points>，/>Indicating the number of bending points.
By analysis of formulasObtaining the bending degree coefficient of each cable in each section of underground pipe gallery>Wherein->Indicate->The underground pipe gallery of the section is->The (th) of the cable>The bending points correspond to the radius of the curvature circle, +.>A threshold value representing the radius of the curvature circle corresponding to the preset bending point.
Thirdly, checking mechanical damage of the cable: according to appearance information of each cable in each section of underground pipe gallery, analyzing appearance coincidence coefficients of each cable in each section of underground pipe gallery, judging whether mechanical damage hidden danger exists in each cable in each section of underground pipe gallery, and acquiring hidden danger point sets corresponding to the mechanical damage hidden danger of the cable.
The third step is to analyze the appearance coincidence coefficient of each cable in each section of underground pipe gallery, and the specific process is as follows: by analysis of formulasObtaining the appearance coincidence coefficient of each cable in each section of underground pipe gallery>Wherein->Representing natural constant->、/>Threshold values respectively representing a preset deformation degree coefficient and a bending degree coefficient, < >>、/>Respectively representing the weights of the preset deformation degree coefficient and the bending degree coefficient.
As a preferred scheme, the specific process of the third step further includes: and comparing the appearance coincidence coefficient of each cable in each section of underground pipe gallery with a preset appearance coincidence coefficient threshold value, if the appearance coincidence coefficient of a certain cable in a certain section of underground pipe gallery is smaller than the preset appearance coincidence coefficient threshold value, then the hidden danger of mechanical damage exists in the cable in the section of underground pipe gallery, and recording the cable in the section of underground pipe gallery as hidden danger points corresponding to the hidden danger of mechanical damage of the cable, and counting to obtain a hidden danger point set corresponding to the hidden danger of mechanical damage of the cable.
In the embodiment, the deformation degree coefficient and the bending degree coefficient of each cable in each section of underground pipe gallery are obtained, whether mechanical damage hidden danger exists in each cable in each section of underground pipe gallery is judged, the mechanical damage hidden danger existing in the cable is found in time, early warning is carried out, and the mechanical damage is prevented from developing into faults.
Step four, obtaining basic information of the cable: basic information of each cable in each section of underground pipe gallery is obtained, wherein the basic information comprises a sheath breakage coefficient and a temperature anomaly coefficient.
The specific process of the fourth step is as follows: obtaining the scratch lengths and the crack lengths of the surfaces of the cable sheath layers in each section of underground pipe gallery, and respectively marking the scratch lengths and the crack lengths asAnd->，/>Indicate->The number of the scratch of the strip,，/>indicating the number of scratches>Indicate->Number of strip slit>，/>Indicating the number of cracks.
As a preferable scheme, the scratch length and the crack length of each cable sheath surface in each section of underground pipe gallery are obtained, and the specific method is as follows: the method comprises the steps of obtaining live-action images of all cables in all sections of underground pipe racks, respectively comparing the live-action images of all cables in all sections of underground pipe racks with preset scratch images and crack images on the surfaces of the cable jackets to obtain scratch areas and crack areas on the surfaces of all the cable jackets in all sections of underground pipe racks, and further obtaining scratch lengths and crack lengths on the surfaces of all the cable jackets in all the sections of underground pipe racks.
By analysis of formulasObtaining each underground pipe gallery of each sectionSheath breakage coefficient of strip cable>Wherein->、/>Respectively representing the influence factors corresponding to the preset unit scratch length and the unit crack length.
Acquiring infrared thermal imaging images of each cable in each section of underground pipe gallery, obtaining each temperature value of the surface of each cable in each section of underground pipe gallery, analyzing the area and the temperature of each abnormal temperature region in each cable in each section of underground pipe gallery, and respectively marking the areas and the temperatures as、/>，/>Indicate->Number of individual temperature anomaly region, +.>，/>Indicating the number of temperature anomaly regions.
As a preferable scheme, the area and the temperature of each abnormal temperature region in each cable in each section of underground pipe gallery are analyzed, and the specific process is as follows: comparing each temperature value of each cable surface in each section of underground pipe gallery with a preset cable temperature early warning value, if a certain temperature value of a certain cable surface in a certain section of underground pipe gallery is larger than or equal to the preset cable temperature early warning value, marking the temperature value as an abnormal temperature value, counting each abnormal temperature value of each cable surface in each section of underground pipe gallery, obtaining the corresponding area of each abnormal temperature value of each cable surface in each section of underground pipe gallery, marking the corresponding area as each abnormal temperature area of each cable in each section of underground pipe gallery, and obtaining the area and the temperature of each abnormal temperature area of each cable in each section of underground pipe gallery.
As a preferable scheme, the infrared thermal imaging image of the cable can display the corresponding image of each temperature region, so as to obtain the area of each temperature region.
By analysis of formulasObtaining the temperature anomaly coefficient of each cable in each section of underground pipe gallery>Wherein->、/>Respectively representing a preset temperature abnormal region area threshold value and a cable temperature early warning value, +>、/>Respectively representing the area of the preset temperature anomaly area and the weight of the temperature.
Fifthly, cable insulation aging investigation: according to the basic information of each cable in each section of underground pipe gallery, the insulation aging coefficient of each cable in each section of underground pipe gallery is analyzed, whether each cable in each section of underground pipe gallery has insulation aging hidden danger or not is judged, and a hidden danger point set corresponding to the cable insulation aging hidden danger is obtained.
The specific analysis process of the fifth step is as follows: by analysis of formulasObtaining insulation aging coefficient of each cable in each section of underground pipe gallery>。
And comparing the insulation ageing coefficient of each cable in each section of underground pipe gallery with a preset insulation ageing coefficient threshold value, if the insulation ageing coefficient of a certain cable in a section of underground pipe gallery is larger than the preset insulation ageing coefficient threshold value, then the insulation ageing hidden danger exists in the cable in the section of underground pipe gallery, and recording the cable in the section of underground pipe gallery as hidden danger points corresponding to the cable insulation ageing hidden danger, and counting to obtain a hidden danger point set corresponding to the cable insulation ageing hidden danger.
In the embodiment, the insulation aging coefficients of the cables in each section of underground pipe gallery are analyzed by acquiring the damage coefficients and the temperature anomaly coefficients of the protective layer of each cable in each section of underground pipe gallery, whether the insulation aging hidden danger exists in each cable in each section of underground pipe gallery is judged, the insulation aging hidden danger existing in the cable is found in time, early warning is carried out, and the insulation aging is prevented from developing into faults.
Step six, monitoring the safety of the cable laying environment: environmental information and structural information of each section of underground pipe gallery are acquired, indoor humidity coefficients, overall frame collapse coefficients and cable support stability coefficients of each section of underground pipe gallery are analyzed, safety indexes of each section of underground pipe gallery are comprehensively obtained, whether potential safety hazards exist in the laying environment of cables in each section of underground pipe gallery or not is judged, and a hidden danger point set corresponding to the potential hazards of the cable laying environment is acquired.
Illustratively, the specific analysis procedure in the sixth step includes: acquiring the humidity of each detection point in each section of underground pipe gallery, analyzing the area of each corresponding wet area of each wet detection point in each section of underground pipe gallery, and marking the area as，/>Indicate->Number of moist detection points->，/>Indicating the number of wetness detecting points.
As a preferable scheme, the area of each damp detection point corresponding to the damp area in each section of underground pipe gallery is analyzed, and the specific process is as follows: and arranging detection points in each section of underground pipe gallery according to a preset principle, acquiring the humidity of each detection point in each section of underground pipe gallery, comparing the humidity of each detection point in each section of underground pipe gallery with a preset humidity threshold, if the humidity of a detection point in a certain section of underground pipe gallery is greater than the preset humidity threshold, marking the detection point as a wet detection point, counting the humidity of each wet detection point in each section of underground pipe gallery, detecting the humidity of the area near each wet detection point in each section of underground pipe gallery by a hygrometer, marking the boundary line of each wet detection point corresponding to the wet area in each section of underground pipe gallery, and further acquiring the area of each wet detection point corresponding to the wet area in each section of underground pipe gallery.
By analysis of formulasObtaining the indoor humidity coefficient of each section of underground pipe gallery>Wherein->And representing the influence factor corresponding to the preset unit wet area.
Acquiring images of the inner walls of all sections of underground pipe rack, acquiring the lengths of all cracks in the inner walls of all sections of underground pipe rack, and marking the lengths as the lengths of all cracks,/>Indicating the inner wall of the underground pipe gallery>Number of strip slit>。
By analysis of formulasObtaining the collapse coefficient of the integral frame of each section of underground pipe gallery +.>Wherein->And representing a preset underground pipe gallery inner wall crack length threshold.
Acquiring images of each cable support in each section of underground pipe gallery, analyzing the shape fitness of each cable support in each section of underground pipe gallery, and representing the shape fitness as,/>Indicate->Number of the cable support, ->，/>Indicating the number of cable supports.
As a preferable scheme, the shape fitness of each cable support in each section of underground pipe gallery is analyzed, and the specific process is as follows: the method comprises the steps of collecting images of all cable supports in all sections of underground pipe racks, constructing space models of all cable supports in all sections of underground pipe racks, extracting standard space models of all cable supports in the underground pipe racks stored in a database, comparing the space models of all cable supports in all sections of underground pipe racks with corresponding standard space models to obtain the coincidence degree of the space models of all cable supports in all sections of underground pipe racks and the corresponding standard space models, and recording the coincidence degree as the shape coincidence degree of all cable supports in all sections of underground pipe racks.
According to the images of the cable brackets in each section of underground pipe gallery, the rust area of each cable bracket in each section of underground pipe gallery is obtained and is recorded as。
By analysis of formulasObtaining the cable support stability coefficient of each section of underground pipe gallery>Wherein->Indicating the number of cable supports in the underground piping lane, +.>、/>Threshold values of the shape fitness and the rust area of the preset cable support are respectively shown, and the threshold values are ++>、/>Respectively representing the shape fitness and the weight of the rust area of the preset cable support.
The safety index of each section of underground pipe gallery is obtained in the step six, and the specific process is as follows: by analysis of formulasObtaining the safety index of each section of underground pipe gallery>Wherein->、/>、/>Respectively representing weights of preset cable support stability coefficient, indoor humidity coefficient and integral frame collapse coefficient.
As a preferred solution, the specific process of the step six further includes: and comparing the safety index of each section of underground pipe gallery with a preset safety index early warning value of the underground pipe gallery, if the safety index of a certain section of underground pipe gallery is smaller than the preset safety index early warning value of the underground pipe gallery, enabling potential safety hazards to exist in the cable laying environment of the section of underground pipe gallery, marking the section of underground pipe gallery as potential hazard points corresponding to the potential hazards of the cable laying environment, and counting to obtain a potential hazard point set corresponding to the potential hazards of the cable laying environment.
In the embodiment, the safety index of each section of underground pipe gallery is comprehensively obtained by acquiring the indoor humidity coefficient, the whole frame collapse coefficient and the cable support stability coefficient of each section of underground pipe gallery, whether potential safety hazards exist in the cable laying environment of each section of underground pipe gallery is judged, safety accidents caused by collapse of the pipe gallery, infirm cable support and the like are prevented, and the underground cable is damaged.
Step seven, cable fault early warning feedback: and the hidden danger points corresponding to the hidden danger of mechanical damage of the cable, the hidden danger of insulation aging of the cable and the hidden danger of the cable laying environment are transmitted to a cable safety management department.
In the embodiment, the hidden danger points corresponding to the hidden danger of mechanical damage, the hidden danger of insulation and aging of the cable and the hidden danger of the cable laying environment are transmitted to the cable safety management department in a gathering way for early warning, hidden danger and problems can be found in time, the number of faults and the repair time are reduced, the reliability, the stability and the safety of power supply are improved, the accident risk is reduced, the safety performance and the long-term stability of a power distribution system are improved, and the reliability of power supply is ensured.
In a second aspect, the invention also provides a distribution network cable fault early warning system based on big data, which comprises an underground pipe gallery dividing module, a cable appearance information acquisition module, a cable mechanical damage investigation module, a cable basic information acquisition module, a cable insulation aging investigation module, a cable laying environment safety monitoring module, a cable fault early warning feedback module and a database.
The underground pipe gallery dividing module is respectively connected with the cable appearance information acquisition module, the cable basic information acquisition module and the cable laying environment safety monitoring module, the cable appearance information acquisition module is connected with the cable mechanical damage investigation module, the cable basic information acquisition module is connected with the cable insulation aging investigation module, the cable fault early warning feedback module is respectively connected with the cable mechanical damage investigation module, the cable insulation aging investigation module and the cable laying environment safety monitoring module, and the database is respectively connected with the cable appearance information acquisition module and the cable laying environment safety monitoring module.
The underground pipe gallery dividing module is used for dividing the target underground pipe gallery according to a preset equal length principle to obtain each section of underground pipe gallery.
The cable appearance information acquisition module is used for acquiring appearance information of each cable in each section of underground pipe gallery, wherein the appearance information comprises a deformation degree coefficient and a bending degree coefficient.
The cable mechanical damage investigation module is used for analyzing appearance coincidence coefficients of each cable in each section of underground pipe gallery according to appearance information of each cable in each section of underground pipe gallery, judging whether mechanical damage hidden danger exists in each cable in each section of underground pipe gallery, and acquiring hidden danger point sets corresponding to the cable mechanical damage hidden danger.
The cable basic information acquisition module is used for acquiring basic information of each cable in each section of underground pipe gallery, wherein the basic information comprises a sheath breakage coefficient and a temperature anomaly coefficient.
The cable insulation aging investigation module is used for analyzing insulation aging coefficients of each cable in each section of underground pipe gallery according to basic information of each cable in each section of underground pipe gallery, judging whether insulation aging hidden dangers exist in each cable in each section of underground pipe gallery, and acquiring hidden danger point sets corresponding to the cable insulation aging hidden dangers.
The cable laying environment safety monitoring module is used for acquiring environment information and structure information of each section of underground pipe gallery, analyzing indoor humidity coefficient, integral frame collapse coefficient and cable support stability coefficient of each section of underground pipe gallery, comprehensively obtaining safety indexes of each section of underground pipe gallery, judging whether potential safety hazards exist in the laying environment of cables in each section of underground pipe gallery, and acquiring hidden danger point sets corresponding to the potential hazards of the cable laying environment.
The cable fault early warning feedback module is used for sending hidden danger points corresponding to the hidden danger of mechanical damage of the cable, hidden danger of insulation and aging of the cable and hidden danger of a cable laying environment to the cable safety management department.
The database is used for storing a standard space model of each cable in the underground pipe gallery and a standard space model of each cable support in the underground pipe gallery.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (9)
1. A distribution network cable fault early warning method based on big data is characterized by comprising the following steps:
step one, dividing underground pipe gallery: dividing the target underground pipe gallery according to a preset equal length principle to obtain each section of underground pipe gallery;
step two, obtaining cable appearance information: obtaining appearance information of each cable in each section of underground pipe gallery, wherein the appearance information comprises a deformation degree coefficient and a bending degree coefficient;
thirdly, checking mechanical damage of the cable: according to the appearance information of each cable in each section of underground pipe gallery, analyzing the appearance coincidence coefficient of each cable in each section of underground pipe gallery, judging whether each cable in each section of underground pipe gallery has mechanical damage hidden danger or not, and acquiring a hidden danger point set corresponding to the mechanical damage hidden danger of the cable;
step four, obtaining basic information of the cable: basic information of each cable in each section of underground pipe gallery is obtained, wherein the basic information comprises a sheath breakage coefficient and a temperature anomaly coefficient;
fifthly, cable insulation aging investigation: according to basic information of each cable in each section of underground pipe gallery, analyzing insulation ageing coefficients of each cable in each section of underground pipe gallery, judging whether each cable in each section of underground pipe gallery has insulation ageing hidden danger or not, and acquiring a hidden danger point set corresponding to the cable insulation ageing hidden danger;
step six, monitoring the safety of the cable laying environment: acquiring environment information and structure information of each section of underground pipe gallery, analyzing indoor humidity coefficients, overall frame collapse coefficients and cable support stability coefficients of each section of underground pipe gallery, comprehensively obtaining safety indexes of each section of underground pipe gallery, judging whether potential safety hazards exist in the laying environment of cables in each section of underground pipe gallery, and acquiring a hidden danger point set corresponding to the potential hazards of the cable laying environment;
step seven, cable fault early warning feedback: and the hidden danger points corresponding to the hidden danger of mechanical damage of the cable, the hidden danger of insulation aging of the cable and the hidden danger of the cable laying environment are transmitted to a cable safety management department.
2. The distribution network cable fault early warning method based on big data according to claim 1, wherein the method comprises the following steps: the specific analysis process of the second step comprises the following steps: acquiring the depression depth of each depression point on the surface of each cable in each section of underground pipe gallery, and marking the depression depth as，/>Indicate->Number of section underground pipe gallery>，/>Indicating the number of underground pipe galleries>Indicate->Number of the cable, ">，/>Indicating the number of cables>Indicate->The number of the concave points is given,，/>representing the number of pit points;
by analysis of formulasObtaining the deformation degree coefficient of each cable in each section of underground pipe gallery>Wherein->Representing a preset depression depth threshold.
3. The distribution network cable fault early warning method based on big data according to claim 2, wherein the method is characterized in that: the specific analysis process of the second step further comprises the following steps:
the method comprises the steps of obtaining contour lines of cables in each section of underground pipe gallery, comparing the contour lines of the cables in each section of underground pipe gallery with a reference straight line to obtain bending points of the cables in each section of underground pipe gallery, making inscribed circles of the contour lines of the cables at the bending points of the cables in each section of underground pipe gallery, marking the inscribed circles as the curvature circles of the bending points of the cables in each section of underground pipe gallery, obtaining radiuses of the curvature circles corresponding to the bending points of the cables in each section of underground pipe gallery, and marking the inscribed circles as the radiuses of the curvature circles，/>Represent the firstNumber of bending points>，/>Representing the number of bending points;
by analysis of formulasObtaining the bending degree coefficient of each cable in each section of underground pipe gallery>Wherein->Indicate->The underground pipe gallery of the section is->In the first cableThe bending points correspond to the radius of the curvature circle, +.>A threshold value representing the radius of the curvature circle corresponding to the preset bending point.
4. The distribution network cable fault early warning method based on big data according to claim 3, wherein the method comprises the following steps: in the third step, the appearance coincidence coefficient of each cable in each section of underground pipe gallery is analyzed, and the specific process is as follows:
by analysis of formulasObtaining the appearance coincidence coefficient of each cable in each section of underground pipe gallery>Wherein->Representing natural constant->、/>Threshold values respectively representing a preset deformation degree coefficient and a bending degree coefficient, < >>、/>Respectively representing the weights of the preset deformation degree coefficient and the bending degree coefficient.
5. The distribution network cable fault early warning method based on big data according to claim 2, wherein the method is characterized in that: the specific process of the fourth step is as follows:
obtaining the scratch lengths and the crack lengths of the surfaces of the cable sheath layers in each section of underground pipe gallery, and respectively marking the scratch lengths and the crack lengths asAnd->，/>Indicate->Number of scratch>，/>Indicating the number of scratches>Represent the firstNumber of strip slit>，/>Indicating the number of cracks; by analysis of formulasObtaining the damage coefficient of the protective layer of each cable in each section of underground pipe gallery>Wherein->、/>Respectively representing influence factors corresponding to the preset unit scratch length and the unit crack length;
acquiring infrared thermal imaging images of each cable in each section of underground pipe gallery, obtaining each temperature value of the surface of each cable in each section of underground pipe gallery, analyzing the area and the temperature of each abnormal temperature region in each cable in each section of underground pipe gallery, and respectively marking the areas and the temperatures as、/>，/>Indicate->Number of individual temperature anomaly region, +.>，/>Indicating the number of temperature anomaly areas;
by analysis of formulasObtaining the temperature anomaly coefficient of each cable in each section of underground pipe gallery>Wherein->、/>Respectively representing a preset temperature abnormal region area threshold value and a cable temperature early warning value, +>、/>Respectively representing the area of the preset temperature anomaly area and the weight of the temperature.
6. The distribution network cable fault early warning method based on big data according to claim 5, wherein the method comprises the following steps: the specific analysis process in the fifth step is as follows:
by analysis of formulasObtaining insulation aging coefficient of each cable in each section of underground pipe gallery>；
And comparing the insulation ageing coefficient of each cable in each section of underground pipe gallery with a preset insulation ageing coefficient threshold value, if the insulation ageing coefficient of a certain cable in a section of underground pipe gallery is larger than the preset insulation ageing coefficient threshold value, then the insulation ageing hidden danger exists in the cable in the section of underground pipe gallery, and recording the cable in the section of underground pipe gallery as hidden danger points corresponding to the cable insulation ageing hidden danger, and counting to obtain a hidden danger point set corresponding to the cable insulation ageing hidden danger.
7. The distribution network cable fault early warning method based on big data according to claim 1, wherein the method comprises the following steps: the specific analysis process in the step six comprises the following steps:
acquiring humidity of each detection point in each section of underground pipe gallery and analyzing each section of underground pipeThe area of each wet detection point in the corridor corresponding to the wet area is designated as，/>Indicate->Number of moist detection points->，/>Indicating the number of wetness detecting points;
by analysis of formulasObtaining the indoor humidity coefficient of each section of underground pipe galleryWherein->Representing an influence factor corresponding to the area of a preset unit wet area;
acquiring images of the inner walls of all sections of underground pipe rack, acquiring the lengths of all cracks in the inner walls of all sections of underground pipe rack, and marking the lengths as the lengths of all cracks,/>Indicating the inner wall of the underground pipe gallery>Number of strip slit>；
By analysis of formulasObtaining the collapse coefficient of the integral frame of each section of underground pipe galleryWherein->Representing a preset crack length threshold value of the inner wall of the underground pipe gallery;
acquiring images of each cable support in each section of underground pipe gallery, analyzing the shape fitness of each cable support in each section of underground pipe gallery, and representing the shape fitness as,/>Indicate->Number of the cable support, ->，/>Representing the number of cable supports;
according to the images of the cable brackets in each section of underground pipe gallery, the rust area of each cable bracket in each section of underground pipe gallery is obtained and is recorded as；
By analysis of formulasObtain cable support steady of each section underground pipe galleryFixation factor->Wherein->Indicating the number of cable supports in the underground piping lane, +.>、/>Threshold values of the shape fitness and the rust area of the preset cable support are respectively shown, and the threshold values are ++>、/>Respectively representing the shape fitness and the weight of the rust area of the preset cable support.
8. The distribution network cable fault early warning method based on big data according to claim 7, wherein the method comprises the following steps: in the sixth step, the safety index of each section of underground pipe gallery is obtained, and the specific process is as follows:
by analysis of formulasObtaining the safety index of each section of underground pipe galleryWherein->、/>、/>Respectively representing weights of preset cable support stability coefficient, indoor humidity coefficient and integral frame collapse coefficient.
9. The utility model provides a join in marriage net cable fault early warning system based on big data which characterized in that includes:
underground pipe gallery dividing module: the method comprises the steps of dividing a target underground pipe gallery according to a preset equal length principle to obtain each section of underground pipe gallery;
the cable appearance information acquisition module is used for: the method comprises the steps of obtaining appearance information of each cable in each section of underground pipe gallery, wherein the appearance information comprises a deformation degree coefficient and a bending degree coefficient;
and the cable mechanical damage investigation module is as follows: the method comprises the steps of analyzing appearance coincidence coefficients of each cable in each section of underground pipe gallery according to appearance information of each cable in each section of underground pipe gallery, judging whether mechanical damage hidden danger exists in each cable in each section of underground pipe gallery, and acquiring a hidden danger point set corresponding to the mechanical damage hidden danger of the cable;
the cable basic information acquisition module: the method comprises the steps of obtaining basic information of each cable in each section of underground pipe gallery, wherein the basic information comprises a sheath breakage coefficient and a temperature anomaly coefficient;
cable insulation aging checking module: the method comprises the steps of analyzing insulation ageing coefficients of each cable in each section of underground pipe gallery according to basic information of each cable in each section of underground pipe gallery, judging whether each cable in each section of underground pipe gallery has insulation ageing hidden danger or not, and acquiring hidden danger point sets corresponding to the cable insulation ageing hidden danger;
the cable laying environment safety monitoring module comprises: the method comprises the steps of obtaining environment information and structure information of each section of underground pipe gallery, analyzing indoor humidity coefficients, overall frame collapse coefficients and cable support stability coefficients of each section of underground pipe gallery, comprehensively obtaining safety indexes of each section of underground pipe gallery, judging whether potential safety hazards exist in the laying environment of cables in each section of underground pipe gallery, and obtaining hidden danger point sets corresponding to the potential hazards of the cable laying environment;
and the cable fault early warning feedback module: the method comprises the steps of sending hidden danger points corresponding to hidden danger of mechanical damage of a cable, hidden danger of insulation and aging of the cable and hidden danger of a cable laying environment to a cable safety management department;
database: a standard space model for storing individual cables in an underground piping lane and a standard space model for individual cable holders in an underground piping lane.
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