CN116699335B - Partial discharge monitoring method and system for high-voltage power equipment - Google Patents
Partial discharge monitoring method and system for high-voltage power equipment Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 486
- 238000000034 method Methods 0.000 title claims abstract description 51
- 230000002159 abnormal effect Effects 0.000 claims abstract description 103
- 238000012098 association analyses Methods 0.000 claims abstract description 9
- 238000012549 training Methods 0.000 claims description 33
- 230000005856 abnormality Effects 0.000 claims description 32
- 238000012795 verification Methods 0.000 claims description 18
- 230000008859 change Effects 0.000 claims description 17
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- 238000013528 artificial neural network Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 abstract description 6
- 238000012545 processing Methods 0.000 abstract description 3
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- 230000015556 catabolic process Effects 0.000 description 2
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
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- 238000013075 data extraction Methods 0.000 description 1
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- 239000012774 insulation material Substances 0.000 description 1
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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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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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 partial discharge monitoring method and a system of high-voltage power equipment, which relate to the technical field of data processing, and the method comprises the following steps: acquiring power equipment distribution information of a target area; obtaining N monitoring data sets; performing association matching according to the monitoring ranges of the N monitoring points and the distribution information of the power equipment to obtain N power equipment sets; inputting the partial discharge characteristic monitoring model, and outputting N abnormal monitoring characteristic sets; inputting the N abnormal monitoring feature sets into a three-dimensional feature analysis chart for feature analysis, and outputting a first monitoring result set; performing association analysis based on the first monitoring result set and the power equipment line information to obtain a second monitoring result; and obtaining a partial discharge monitoring result according to the first monitoring result set and the second monitoring result. The invention solves the technical problems of low accuracy and low intelligent degree of partial discharge monitoring in the prior art, and achieves the technical effect of improving the reliability of the partial discharge monitoring result.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a partial discharge monitoring method and system of high-voltage power equipment.
Background
The partial discharge of the high-voltage power equipment mainly comprises the discharge of the internal insulation of equipment such as a transformer, a high-voltage cable and the like under the action of high voltage. Although the partial discharge is weak, it may damage the insulation of the device, resulting in the breakdown of the device insulation. The current monitoring result is often analyzed by technicians, the feedback period is long, and the accuracy cannot be ensured. In the prior art, the technical problems of low accuracy and low intelligent degree of partial discharge monitoring exist.
Disclosure of Invention
The application provides a partial discharge monitoring method and a system of high-voltage power equipment, which are used for solving the technical problems of low accuracy and low intelligent degree of partial discharge monitoring in the prior art.
In view of the above, the present application provides a method and a system for monitoring partial discharge of a high-voltage power device.
In a first aspect of the present application, there is provided a method for monitoring partial discharge of a high voltage power device, wherein the method is applied to a partial discharge monitoring system, the method comprising:
acquiring power equipment distribution information of a target area, wherein the power equipment distribution information comprises power equipment line information;
collecting monitoring data of N monitoring points in a target area in a preset time window to obtain N monitoring data sets;
performing association matching according to the monitoring ranges of the N monitoring points and the power equipment distribution information to obtain N power equipment sets, wherein the N power equipment sets are provided with monitoring point identifiers;
inputting the N monitoring data sets and the N power equipment sets into a partial discharge characteristic monitoring model, and outputting N abnormal monitoring characteristic sets, wherein each abnormal monitoring characteristic has a corresponding power equipment identifier;
inputting the N abnormal monitoring feature sets into a three-dimensional feature analysis chart for feature analysis, and outputting a first monitoring result set;
performing association analysis based on the first monitoring result set and the power equipment line information to obtain a second monitoring result;
and obtaining a partial discharge monitoring result according to the first monitoring result set and the second monitoring result.
In a second aspect of the present application, there is provided a partial discharge monitoring system for a high voltage power device, the system comprising:
the device distribution information acquisition module is used for acquiring power device distribution information of a target area, wherein the power device distribution information comprises power device line information;
the monitoring data acquisition module is used for acquiring monitoring data of N monitoring points in a target area in a preset time window to acquire N monitoring data sets;
the power equipment acquisition module is used for carrying out association matching on the monitoring ranges of the N monitoring points and the power equipment distribution information to obtain N power equipment sets, wherein the N power equipment sets are provided with monitoring point identifiers;
the monitoring feature acquisition module is used for inputting the N monitoring data sets and the N power equipment sets into a partial discharge feature monitoring model and outputting N abnormal monitoring feature sets, wherein each abnormal monitoring feature has a corresponding power equipment identifier;
the first monitoring result obtaining module is used for inputting the N abnormal monitoring feature sets into a three-dimensional feature analysis chart for feature analysis and outputting a first monitoring result set;
the second monitoring result obtaining module is used for carrying out association analysis based on the first monitoring result set and the power equipment line information to obtain a second monitoring result;
and the discharge monitoring result obtaining module is used for obtaining partial discharge monitoring results according to the first monitoring result set and the second monitoring result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps of obtaining power equipment distribution information of a target area, wherein the power equipment distribution information comprises power equipment line information; collecting monitoring data of N monitoring points in a target area in a preset time window to obtain N monitoring data sets, performing association matching on the N monitoring points and power equipment distribution information according to the monitoring ranges of the N monitoring points to obtain N power equipment sets, wherein the N power equipment sets are provided with monitoring point identifiers, inputting the N monitoring data sets and the N power equipment sets into a partial discharge characteristic monitoring model, outputting N abnormal monitoring characteristic sets, wherein each abnormal monitoring characteristic is provided with a corresponding power equipment identifier, inputting the N abnormal monitoring characteristic sets into a three-dimensional characteristic analysis graph for characteristic analysis, and outputting a first monitoring result set; and carrying out association analysis based on the first monitoring result set and the power equipment line information to obtain a second monitoring result, and obtaining a partial discharge monitoring result according to the first monitoring result set and the second monitoring result. The technical effects of improving the accuracy and the monitoring efficiency of partial discharge monitoring are achieved.
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 some 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 flow chart of a partial discharge monitoring method of a high-voltage power device according to an embodiment of the present application;
fig. 2 is a schematic flow chart of sending monitoring reminding information to staff in a partial discharge monitoring method of high-voltage power equipment according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a three-dimensional feature analysis chart obtained by a partial discharge monitoring method of high-voltage power equipment according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a partial discharge monitoring system of a high-voltage power device according to an embodiment of the present application.
Reference numerals illustrate: the device distribution information acquisition module 11, the monitoring data acquisition module 12, the power device acquisition module 13, the monitoring feature acquisition module 14, the first monitoring result acquisition module 15, the second monitoring result acquisition module 16 and the discharge monitoring result acquisition module 17.
Detailed Description
The application provides a partial discharge monitoring method and a system of high-voltage power equipment, which are used for solving the technical problems of low accuracy and low intelligent degree of partial discharge monitoring in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a method for monitoring partial discharge of a high-voltage power device, wherein the method is applied to a partial discharge monitoring system, and the method includes:
step S100: acquiring power equipment distribution information of a target area, wherein the power equipment distribution information comprises power equipment line information;
in one possible embodiment, the target area is an area where partial discharge monitoring is required. The power equipment distribution information is information describing power equipment line configuration conditions and equipment configuration conditions in the target area, and includes power equipment line information. The power equipment is high-voltage power equipment and comprises a high-voltage transformer, a high-voltage cable, a high-voltage control cabinet and the like. The power equipment line information is information describing the line connection condition of high-voltage power equipment in a target area, and comprises a power equipment parallel line and a power equipment serial line.
Step S200: collecting monitoring data of N monitoring points in a target area in a preset time window to obtain N monitoring data sets;
in one embodiment, the preset time window is a preset time period for monitoring partial discharge, and optionally, the preset time window is set by a person skilled in the art, and may be, without limitation, half a month, one month, or the like. N monitoring points are distributed in the target area, and the N monitoring data sets are obtained by collecting monitoring data of the N monitoring points in a preset time window. The N monitoring points are position points for monitoring the partial discharge condition of the high-voltage power equipment in the target area. The N monitoring data sets reflect partial discharge conditions of the high-voltage power equipment in a preset time window. The technical effect of providing basic analysis data for subsequent analysis is achieved.
Step S300: performing association matching according to the monitoring ranges of the N monitoring points and the power equipment distribution information to obtain N power equipment sets, wherein the N power equipment sets are provided with monitoring point identifiers;
further, as shown in fig. 2, according to the correlation matching between the monitoring ranges of the N monitoring points and the power equipment distribution information, N power equipment sets are obtained, and step S300 in this embodiment of the present application further includes:
step S310: according to the positions of N monitoring points in the target area and the monitoring ranges of the N monitoring points, matching with the power equipment distribution information of the target area to obtain N power equipment sets;
step S320: extracting M pieces of electric equipment based on the electric equipment distribution information, respectively matching N pieces of electric equipment sets according to the M pieces of electric equipment, judging whether a plurality of matching results exist, and if so, mapping a plurality of matching monitoring points according to the plurality of matching results to obtain P pieces of technical repeated monitoring point sets, wherein P is an integer greater than or equal to 1 and less than M;
step S330: acquiring historical monitoring data of a target area in a historical time period, and repeating a monitoring point set and P pieces of power equipment according to P technologies to obtain P monitoring time domain difference value sets;
step S340: verifying the partial discharge monitoring result according to the P monitoring time domain difference value sets and the N monitoring data sets to obtain a verification result;
step S350: and obtaining monitoring reminding information according to the verification result, and sending the monitoring reminding information to staff.
In one embodiment, the monitoring range of the N monitoring points is a monitoring area determined according to the monitoring device capabilities of the N monitoring points. The N power equipment sets respectively reflect the power equipment which can be monitored by the N monitoring points. And matching the positions of the N monitoring points in the target area and the monitoring ranges of the N monitoring points with the positions of the power equipment described in the power equipment distribution information of the target area to obtain the N power equipment sets. Therefore, the aim of laying the partial discharge source is achieved for the identification of the subsequent abnormal monitoring characteristics.
In one embodiment, in order to ensure that the power devices in the target area all have corresponding monitoring points, when the monitoring points are set, the situation that two or more monitoring points monitor the same power device often occurs, at this time, because the distances between different monitoring points and the power device are different, the monitoring data of the power device from different monitoring points are affected by signal transmission at the same time node, and a time domain difference exists between the corresponding monitoring data. When abnormal partial discharge is detected, judging whether the difference value between the monitoring data of the power equipment and different monitoring points in the monitoring data set meets the monitoring time domain difference value according to the monitoring time domain difference value of the power equipment with two or more monitoring points, if so, verifying that the partial discharge monitoring result passes, and if not, indicating that the monitoring result is inaccurate, and if not, verifying that the corresponding verifying result does not pass.
In one embodiment, the power devices arranged at different positions in the target area, that is, the M power devices, are obtained from the power device distribution information. And M pieces of power equipment are used as indexes to be respectively matched with N pieces of power equipment sets, whether the power equipment has a plurality of matching results is judged, if so, a plurality of matching monitoring points are obtained according to the mapping relation between the power equipment sets and the monitoring points in the plurality of matching results, and the plurality of matching monitoring points corresponding to the power equipment with the plurality of matching results are used as technical repeated monitoring point sets, so that P technical repeated monitoring point sets are obtained. P is the number of power devices for which there are multiple matching results among the M power devices.
Specifically, historical monitoring data of a target area in a historical time period are obtained, a monitoring point set and P pieces of electric equipment are repeated according to P technologies, the historical monitoring data are subjected to data extraction, a corresponding monitoring time domain difference value set is obtained according to the difference value between the technical repeated monitoring point set of each electric equipment and the monitoring data mean value in the extraction result, and P monitoring time domain difference value sets are obtained after summarization. And verifying the partial discharge monitoring result according to the P monitoring time domain difference value sets and the N monitoring data sets to obtain a verification result. The monitoring reminding information comprises a partial discharge monitoring result which is not verified, and the monitoring reminding information is sent to the staff, so that the staff is reminded to manually verify the partial discharge monitoring result.
Step S400: inputting the N monitoring data sets and the N power equipment sets into a partial discharge characteristic monitoring model, and outputting N abnormal monitoring characteristic sets, wherein each abnormal monitoring characteristic has a corresponding power equipment identifier;
further, the inputting the N monitoring data sets and the N power equipment sets into the partial discharge feature monitoring model, step S400 of the embodiment of the present application further includes:
step S410: acquiring a plurality of sample monitoring data sets, a plurality of sample power equipment sets and a plurality of sample abnormality monitoring feature sets with data identifiers as construction data sets;
step S420: dividing the constructed data set into a training set and a verification set according to a preset dividing proportion;
step S430: training a framework constructed based on the BP neural network according to the training set, supervising the training process by utilizing a plurality of sample abnormal monitoring feature sets with data marks in the training set until the training reaches convergence, verifying a local discharge feature monitoring model by utilizing a verification set, and obtaining the local discharge feature monitoring model after the training is completed if the requirement is met;
step S440: and inputting the N monitoring data sets and the N power equipment sets into a partial discharge characteristic monitoring model, and outputting N abnormal monitoring characteristic sets.
Further, step S410 of the embodiment of the present application further includes:
step S411: acquiring temperature change characteristics of a plurality of sample devices, and taking the temperature change characteristics as a plurality of first sample abnormality monitoring characteristics;
step S412: acquiring equipment appearance characteristics of a plurality of sample equipment, and taking the equipment appearance characteristics as a plurality of second sample abnormality monitoring characteristics, wherein the equipment appearance characteristics comprise surface integrity, cracks and pits;
step S413: acquiring equipment stress characteristics of a plurality of sample equipment, and taking the equipment stress characteristics as a plurality of third sample abnormality monitoring characteristics;
step S414: and obtaining a plurality of sample abnormality monitoring feature sets according to the first sample abnormality monitoring feature, the second sample abnormality monitoring feature and the third sample abnormality monitoring feature.
In one embodiment, the partial discharge feature monitoring model is a functional model for performing abnormal conditions on monitoring data of a target area, that is, performing intelligent analysis on data features of partial discharge, where input data is N monitoring data sets, N power equipment sets, and output data is N abnormal monitoring feature sets. According to the N power equipment sets, the source of the abnormality monitoring characteristics in the N abnormality monitoring characteristics sets, namely the power equipment with partial discharge, can be determined, and then the corresponding power equipment identification is carried out on the abnormality monitoring characteristics. The N abnormal monitoring feature sets reflect the features of monitoring data corresponding to the power equipment with partial discharge in the target area.
In one embodiment, when the temperature of the power device is too high, the temperature of the insulating material is greatly increased, so that thermal breakdown is caused when the damage of thermal stability is serious, and thus, the temperature change characteristics of a plurality of sample devices are obtained and used as first sample abnormality monitoring characteristics. The temperature change characteristics are characteristics for describing the temperature change condition of the sample equipment, and include characteristics of temperature change speed, temperature extreme value and the like. The insulation material of the electric equipment is cracked under the action of mechanical force and wire tension, the insulation electrical performance is reduced, and partial discharge is generated, so that the equipment appearance characteristics of the plurality of sample equipment are taken as second sample abnormality monitoring characteristics. Wherein the device appearance features include surface integrity, cracks, depressions. When a large stress is generated in the insulating material or at the junction surface of the insulating material and the insulating material, insulation damage is also caused, and partial discharge is generated, and therefore, the device stress characteristics of a plurality of sample devices are taken as third sample abnormality monitoring characteristics. And summarizing the first sample abnormal monitoring feature, the second sample abnormal monitoring feature and the third sample abnormal monitoring feature to obtain a plurality of sample abnormal monitoring feature sets. And simultaneously, based on a monitoring database of the target area, a plurality of sample monitoring data sets and a plurality of sample power equipment sets are called, and meanwhile, data identification is carried out on the obtained plurality of sample abnormal monitoring feature sets, so that the construction data set is obtained.
In one possible embodiment, the preset division ratio is a division ratio set by a person skilled in the art, and may be 2:1. and training a framework constructed based on the BP neural network by using the training set, and supervising the training process by using a plurality of sample abnormal monitoring feature sets with data identifiers in the training set until the training is carried out until the output reaches convergence. And inputting the plurality of sample monitoring data sets and the plurality of sample power equipment sets in the verification set into a converged partial discharge feature monitoring model to obtain a plurality of verification sample abnormal monitoring feature sets. Comparing the plurality of verification sample abnormal monitoring feature sets with the plurality of sample abnormal monitoring feature sets, taking the proportion of the feature sets which are successfully compared to the plurality of sample abnormal monitoring feature sets as accuracy, and obtaining the trained partial discharge feature monitoring model when the accuracy meets the preset accuracy. And then, inputting the N monitoring data sets and the N power equipment sets into a partial discharge characteristic monitoring model, and outputting N abnormal monitoring characteristic sets.
Step S500: inputting the N abnormal monitoring feature sets into a three-dimensional feature analysis chart for feature analysis, and outputting a first monitoring result set;
further, as shown in fig. 3, the inputting the N abnormality monitoring feature sets into the three-dimensional feature analysis chart for feature analysis, step S500 in the embodiment of the present application further includes:
step S510: taking the temperature change characteristic as an X axis, taking the appearance characteristic of equipment as a Y axis and taking the stress characteristic of the equipment as a Z axis, and constructing a framework of a three-dimensional characteristic analysis chart;
step S520: inputting the plurality of sample abnormal monitoring feature sets into a three-dimensional feature analysis chart to obtain a plurality of sample coordinate points;
step S530: marking a plurality of sample coordinate points according to a plurality of sample first monitoring results corresponding to the plurality of sample abnormal monitoring feature sets to obtain a plurality of sample marking results;
step S540: and obtaining the three-dimensional characteristic analysis graph according to the framework of the three-dimensional characteristic analysis graph, the plurality of sample coordinate points and the plurality of sample marking results.
Further, step S500 in the embodiment of the present application further includes:
step S550: inputting the N abnormal monitoring feature sets into the three-dimensional feature analysis graph to obtain N abnormal monitoring coordinate point sets;
step S560: randomly selecting one abnormal monitoring coordinate point set from N abnormal monitoring coordinate point sets as a first abnormal monitoring coordinate point;
step S570: obtaining L sample first monitoring results corresponding to L sample coordinate points nearest to the first abnormal monitoring coordinate point, wherein L is an integer greater than or equal to 3;
step S580: calculating the average value of the first monitoring results of the L samples to obtain a first abnormal monitoring result;
step S590: and obtaining a first monitoring result set according to the N abnormal monitoring coordinate point sets.
In one possible embodiment, the three-dimensional feature analysis chart is an analysis chart for performing feature analysis on the anomaly monitoring feature set from three dimensions, so as to intelligently determine the partial discharge cause, wherein the analysis chart takes a temperature change feature as an X axis, takes a device appearance feature as a Y axis, and takes a device stress feature as a Z axis. A three-dimensional feature analysis map is generated by monitoring the feature set from the plurality of samples and the first monitoring result from the plurality of samples. Inputting the plurality of sample abnormal monitoring feature sets into a three-dimensional feature analysis chart, and obtaining a plurality of sample coordinate points according to the plurality of first sample abnormal monitoring features, the plurality of second sample abnormal monitoring features and the plurality of third sample abnormal monitoring features in the plurality of sample abnormal monitoring feature sets. Marking a plurality of sample coordinate points according to a plurality of sample first monitoring results corresponding to the plurality of sample abnormal monitoring feature sets, and determining a sample first monitoring result corresponding to each sample coordinate point, so that a plurality of sample marking results are obtained. Further, the three-dimensional feature analysis chart is composed of a frame of the three-dimensional feature analysis chart, a plurality of sample coordinate points, and a plurality of sample marking results.
In one embodiment, the staff member inputs the N abnormal monitoring feature sets into the three-dimensional feature analysis graph to obtain N abnormal monitoring coordinate point sets, randomly selects one abnormal monitoring coordinate point set from the N abnormal monitoring coordinate point sets to serve as a first abnormal monitoring coordinate point, and obtains a first monitoring result corresponding to the first abnormal monitoring coordinate point through the three-dimensional feature analysis graph. And acquiring L sample first monitoring results corresponding to L sample coordinate points nearest to the first abnormal monitoring coordinate point, wherein L is a numerical value set by a person skilled in the art. And taking the average value of the first monitoring results of the L samples as a first abnormal monitoring result. Based on the same principle, a first monitoring result set is obtained according to the N abnormal monitoring coordinate point sets and the three-dimensional characteristic analysis graph. The first monitoring result is used for describing the generation reasons of partial discharge phenomena corresponding to the abnormal monitoring feature set, and the reasons comprise mechanical vibration, overload of the power equipment, corrosion of the power equipment materials caused by natural environment and the like.
Step S600: performing association analysis based on the first monitoring result set and the power equipment line information to obtain a second monitoring result;
further, step S600 in the embodiment of the present application further includes:
step S610: y power equipment lines are obtained from the power equipment line information and are respectively matched with a first monitoring result set to obtain Y matching results;
step S620: and calculating damage coefficients of Y power equipment lines according to the Y matching results, obtaining Y line damage coefficients, and taking the Y line damage coefficients as a second monitoring result.
In one possible embodiment, after the first monitoring result is obtained, by performing association analysis in combination with the power equipment line information, determining line damage caused by partial discharge of each line in the target area, and obtaining a second monitoring result. And acquiring Y power equipment lines from the power equipment line information, matching the Y power equipment lines with the first monitoring result sets, determining the first monitoring result set corresponding to each power equipment line, and acquiring Y matching results. And further, calculating a line damage coefficient according to the number of the first monitoring results and the types of the first monitoring results in the Y matching results. The more the number of the first monitoring results is, the larger the line damage is caused by the types of the first monitoring results, and the larger the corresponding line damage coefficient is.
Step S700: and obtaining a partial discharge monitoring result according to the first monitoring result set and the second monitoring result.
Specifically, summarizing is performed according to the first monitoring result set and the second monitoring result set, and the partial discharge monitoring result is obtained. The partial discharge monitoring result is information describing the source, the generation reason and the damage condition of the line of the partial discharge in the target area.
In summary, the embodiments of the present application have at least the following technical effects:
according to the method, the distribution condition of the power equipment in the target area and the monitoring data of the monitoring points are analyzed, N abnormal monitoring feature sets in a preset time window are determined, the reason of the abnormality is determined by utilizing the three-dimensional feature analysis chart, namely the first monitoring result set, and then the correlation analysis is carried out by combining the line information of the power equipment, so that a second monitoring result is obtained, and the partial discharge monitoring result is obtained. The technical effects of improving the accuracy and the intelligent degree of the partial discharge monitoring are achieved.
Example two
Based on the same inventive concept as the partial discharge monitoring method of a high-voltage power device in the foregoing embodiments, as shown in fig. 4, the present application provides a partial discharge monitoring system of a high-voltage power device, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
the device distribution information acquisition module 11, wherein the device distribution information acquisition module 11 is configured to acquire power device distribution information of a target area, and the power device distribution information includes power device line information;
the monitoring data acquisition module 12 is used for acquiring monitoring data of N monitoring points in a target area in a preset time window to acquire N monitoring data sets;
the power equipment obtaining module 13 is used for carrying out association matching on the power equipment distribution information according to the monitoring ranges of the N monitoring points to obtain N power equipment sets, wherein the N power equipment sets are provided with monitoring point identifiers;
the monitoring feature obtaining module 14 is configured to input the N monitoring data sets and the N power equipment sets into a partial discharge feature monitoring model, and output N abnormal monitoring feature sets, where each abnormal monitoring feature has a corresponding power equipment identifier;
the first monitoring result obtaining module 15 is configured to input the N abnormal monitoring feature sets into a three-dimensional feature analysis chart for feature analysis, and output a first monitoring result set;
the second monitoring result obtaining module 16, where the second monitoring result obtaining module 16 is configured to perform association analysis based on the first monitoring result set and the power equipment line information to obtain a second monitoring result;
the discharge monitoring result obtaining module 17 is configured to obtain a partial discharge monitoring result according to the first monitoring result set and the second monitoring result, where the discharge monitoring result obtaining module 17 is configured to obtain a partial discharge monitoring result according to the first monitoring result set and the second monitoring result.
Further, the power device obtaining module 13 is configured to perform the following method:
according to the positions of N monitoring points in the target area and the monitoring ranges of the N monitoring points, matching with the power equipment distribution information of the target area to obtain N power equipment sets;
extracting M pieces of electric equipment based on the electric equipment distribution information, respectively matching N pieces of electric equipment sets according to the M pieces of electric equipment, judging whether a plurality of matching results exist, and if so, mapping a plurality of matching monitoring points according to the plurality of matching results to obtain P pieces of technical repeated monitoring point sets, wherein P is an integer greater than or equal to 1 and less than M;
acquiring historical monitoring data of a target area in a historical time period, and repeating a monitoring point set and P pieces of power equipment according to P technologies to obtain P monitoring time domain difference value sets;
verifying the partial discharge monitoring result according to the P monitoring time domain difference value sets and the N monitoring data sets to obtain a verification result;
and obtaining monitoring reminding information according to the verification result, and sending the monitoring reminding information to staff.
Further, the monitoring feature obtaining module 14 is configured to perform the following method:
acquiring a plurality of sample monitoring data sets, a plurality of sample power equipment sets and a plurality of sample abnormality monitoring feature sets with data identifiers as construction data sets;
dividing the constructed data set into a training set and a verification set according to a preset dividing proportion;
training a framework constructed based on the BP neural network according to the training set, supervising the training process by utilizing a plurality of sample abnormal monitoring feature sets with data marks in the training set until the training reaches convergence, verifying a local discharge feature monitoring model by utilizing a verification set, and obtaining the local discharge feature monitoring model after the training is completed if the requirement is met;
and inputting the N monitoring data sets and the N power equipment sets into a partial discharge characteristic monitoring model, and outputting N abnormal monitoring characteristic sets.
Further, the monitoring feature obtaining module 14 is configured to perform the following method:
acquiring temperature change characteristics of a plurality of sample devices, and taking the temperature change characteristics as a plurality of first sample abnormality monitoring characteristics;
acquiring equipment appearance characteristics of a plurality of sample equipment, and taking the equipment appearance characteristics as a plurality of second sample abnormality monitoring characteristics, wherein the equipment appearance characteristics comprise surface integrity, cracks and pits;
acquiring equipment stress characteristics of a plurality of sample equipment, and taking the equipment stress characteristics as a plurality of third sample abnormality monitoring characteristics;
and obtaining a plurality of sample abnormality monitoring feature sets according to the first sample abnormality monitoring feature, the second sample abnormality monitoring feature and the third sample abnormality monitoring feature.
Further, the first monitoring result obtaining module 15 is configured to perform the following method:
taking the temperature change characteristic as an X axis, taking the appearance characteristic of equipment as a Y axis and taking the stress characteristic of the equipment as a Z axis, and constructing a framework of a three-dimensional characteristic analysis chart;
inputting the plurality of sample abnormal monitoring feature sets into a three-dimensional feature analysis chart to obtain a plurality of sample coordinate points;
marking a plurality of sample coordinate points according to a plurality of sample first monitoring results corresponding to the plurality of sample abnormal monitoring feature sets to obtain a plurality of sample marking results;
and obtaining the three-dimensional characteristic analysis graph according to the framework of the three-dimensional characteristic analysis graph, the plurality of sample coordinate points and the plurality of sample marking results.
Further, the first monitoring result obtaining module 15 is configured to perform the following method:
inputting the N abnormal monitoring feature sets into the three-dimensional feature analysis graph to obtain N abnormal monitoring coordinate point sets;
randomly selecting one abnormal monitoring coordinate point set from N abnormal monitoring coordinate point sets as a first abnormal monitoring coordinate point;
obtaining L sample first monitoring results corresponding to L sample coordinate points nearest to the first abnormal monitoring coordinate point, wherein L is an integer greater than or equal to 3;
calculating the average value of the first monitoring results of the L samples to obtain a first abnormal monitoring result;
and obtaining a first monitoring result set according to the N abnormal monitoring coordinate point sets.
Further, the second monitoring result obtaining module 16 is configured to perform the following method:
y power equipment lines are obtained from the power equipment line information and are respectively matched with a first monitoring result set to obtain Y matching results;
and calculating damage coefficients of Y power equipment lines according to the Y matching results, obtaining Y line damage coefficients, and taking the Y line damage coefficients as a second monitoring result.
It should be noted that the sequence of the embodiments of the present application is merely for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the present application is not intended to limit the invention to the particular embodiments of the present application, but to limit the scope of the invention to the particular embodiments of the present application.
The specification and drawings are merely exemplary of the application and are to be regarded as covering any and all modifications, variations, combinations, or equivalents that are within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (4)
1. A method of partial discharge monitoring of a high voltage power plant, the method being applied to a partial discharge monitoring system, the method comprising:
acquiring power equipment distribution information of a target area, wherein the power equipment distribution information comprises power equipment line information;
collecting monitoring data of N monitoring points in a target area in a preset time window to obtain N monitoring data sets;
performing association matching according to the monitoring ranges of the N monitoring points and the power equipment distribution information to obtain N power equipment sets, wherein the N power equipment sets are provided with monitoring point identifiers;
inputting the N monitoring data sets and the N power equipment sets into a partial discharge feature monitoring model, and outputting N abnormal monitoring feature sets, wherein each abnormal monitoring feature has a corresponding power equipment identifier;
inputting the N abnormal monitoring feature sets into a three-dimensional feature analysis chart for feature analysis, and outputting a first monitoring result set;
performing association analysis based on the first monitoring result set and the power equipment line information to obtain a second monitoring result;
obtaining a partial discharge monitoring result according to the first monitoring result set and the second monitoring result;
the N monitoring data sets and N power equipment sets are input into a partial discharge characteristic monitoring model, and the method comprises the following steps:
acquiring a plurality of sample monitoring data sets, a plurality of sample power equipment sets and a plurality of sample abnormality monitoring feature sets with data identifiers as construction data sets;
dividing the constructed data set into a training set and a verification set according to a preset dividing proportion;
training a framework constructed based on the BP neural network according to the training set, supervising the training process by utilizing a plurality of sample abnormal monitoring feature sets with data marks in the training set until the training reaches convergence, verifying a local discharge feature monitoring model by utilizing a verification set, and obtaining the local discharge feature monitoring model after the training is completed if the requirement is met;
acquiring temperature change characteristics of a plurality of sample devices, and taking the temperature change characteristics as a plurality of first sample abnormality monitoring characteristics;
acquiring equipment appearance characteristics of a plurality of sample equipment, and taking the equipment appearance characteristics as a plurality of second sample abnormality monitoring characteristics, wherein the equipment appearance characteristics comprise surface integrity, cracks and pits;
acquiring equipment stress characteristics of a plurality of sample equipment, and taking the equipment stress characteristics as a plurality of third sample abnormality monitoring characteristics;
obtaining a plurality of sample anomaly monitoring feature sets according to the first sample anomaly monitoring feature, the second sample anomaly monitoring feature and the third sample anomaly monitoring feature;
the method includes the steps of inputting the N abnormal monitoring feature sets into a three-dimensional feature analysis chart for feature analysis, and the method includes the steps of:
taking the temperature change characteristic as an X axis, taking the appearance characteristic of equipment as a Y axis and taking the stress characteristic of the equipment as a Z axis, and constructing a framework of a three-dimensional characteristic analysis chart;
inputting the plurality of sample abnormal monitoring feature sets into a three-dimensional feature analysis chart to obtain a plurality of sample coordinate points;
marking a plurality of sample coordinate points according to a plurality of sample first monitoring results corresponding to the plurality of sample abnormal monitoring feature sets to obtain a plurality of sample marking results;
obtaining a three-dimensional characteristic analysis chart according to a framework of the three-dimensional characteristic analysis chart, a plurality of sample coordinate points and a plurality of sample marking results;
inputting the N abnormal monitoring feature sets into the three-dimensional feature analysis graph to obtain N abnormal monitoring coordinate point sets;
randomly selecting one abnormal monitoring coordinate point set from N abnormal monitoring coordinate point sets as a first abnormal monitoring coordinate point;
obtaining L sample first monitoring results corresponding to L sample coordinate points nearest to the first abnormal monitoring coordinate point, wherein L is an integer greater than or equal to 3;
calculating the average value of the first monitoring results of the L samples to obtain a first abnormal monitoring result;
and obtaining a first monitoring result set according to the N abnormal monitoring coordinate point sets.
2. The method of claim 1, wherein N sets of power devices are obtained from the correlation matching of the monitoring ranges of the N monitoring points with the power device distribution information, the method comprising:
according to the positions of N monitoring points in the target area and the monitoring ranges of the N monitoring points, matching with the power equipment distribution information of the target area to obtain N power equipment sets;
extracting M pieces of electric equipment based on the electric equipment distribution information, respectively matching N pieces of electric equipment sets according to the M pieces of electric equipment, judging whether a plurality of matching results exist, and if so, mapping a plurality of matching monitoring points according to the plurality of matching results to obtain P pieces of technical repeated monitoring point sets, wherein P is an integer greater than or equal to 1 and less than M;
acquiring historical monitoring data of a target area in a historical time period, and repeating a monitoring point set and P pieces of power equipment according to P technologies to obtain P monitoring time domain difference value sets;
verifying the partial discharge monitoring result according to the P monitoring time domain difference value sets and the N monitoring data sets to obtain a verification result;
and obtaining monitoring reminding information according to the verification result, and sending the monitoring reminding information to staff.
3. The method of claim 1, wherein the method comprises:
y power equipment lines are obtained from the power equipment line information and are respectively matched with a first monitoring result set to obtain Y matching results;
and calculating damage coefficients of Y power equipment lines according to the Y matching results, obtaining Y line damage coefficients, and taking the Y line damage coefficients as a second monitoring result.
4. A partial discharge monitoring system for a high voltage power plant, the system comprising:
the device distribution information acquisition module is used for acquiring power device distribution information of a target area, wherein the power device distribution information comprises power device line information;
the monitoring data acquisition module is used for acquiring monitoring data of N monitoring points in a target area in a preset time window to acquire N monitoring data sets;
the power equipment acquisition module is used for carrying out association matching on the monitoring ranges of the N monitoring points and the power equipment distribution information to obtain N power equipment sets, wherein the N power equipment sets are provided with monitoring point identifiers;
the monitoring feature acquisition module is used for inputting the N monitoring data sets and the N power equipment sets into a partial discharge feature monitoring model and outputting N abnormal monitoring feature sets, wherein each abnormal monitoring feature has a corresponding power equipment identifier;
the first monitoring result obtaining module is used for inputting the N abnormal monitoring feature sets into a three-dimensional feature analysis chart for feature analysis and outputting a first monitoring result set;
the second monitoring result obtaining module is used for carrying out association analysis based on the first monitoring result set and the power equipment line information to obtain a second monitoring result;
the discharge monitoring result obtaining module is used for obtaining partial discharge monitoring results according to the first monitoring result set and the second monitoring result;
the monitoring feature obtaining module is further configured to perform the following method:
acquiring a plurality of sample monitoring data sets, a plurality of sample power equipment sets and a plurality of sample abnormality monitoring feature sets with data identifiers as construction data sets;
dividing the constructed data set into a training set and a verification set according to a preset dividing proportion;
training a framework constructed based on the BP neural network according to the training set, supervising the training process by utilizing a plurality of sample abnormal monitoring feature sets with data marks in the training set until the training reaches convergence, verifying a local discharge feature monitoring model by utilizing a verification set, and obtaining the local discharge feature monitoring model after the training is completed if the requirement is met;
acquiring temperature change characteristics of a plurality of sample devices, and taking the temperature change characteristics as a plurality of first sample abnormality monitoring characteristics;
acquiring equipment appearance characteristics of a plurality of sample equipment, and taking the equipment appearance characteristics as a plurality of second sample abnormality monitoring characteristics, wherein the equipment appearance characteristics comprise surface integrity, cracks and pits;
acquiring equipment stress characteristics of a plurality of sample equipment, and taking the equipment stress characteristics as a plurality of third sample abnormality monitoring characteristics;
obtaining a plurality of sample anomaly monitoring feature sets according to the first sample anomaly monitoring feature, the second sample anomaly monitoring feature and the third sample anomaly monitoring feature;
the first monitoring result obtaining module is further configured to perform the following method:
taking the temperature change characteristic as an X axis, taking the appearance characteristic of equipment as a Y axis and taking the stress characteristic of the equipment as a Z axis, and constructing a framework of a three-dimensional characteristic analysis chart;
inputting the plurality of sample abnormal monitoring feature sets into a three-dimensional feature analysis chart to obtain a plurality of sample coordinate points;
marking a plurality of sample coordinate points according to a plurality of sample first monitoring results corresponding to the plurality of sample abnormal monitoring feature sets to obtain a plurality of sample marking results;
obtaining a three-dimensional characteristic analysis chart according to a framework of the three-dimensional characteristic analysis chart, a plurality of sample coordinate points and a plurality of sample marking results;
inputting the N abnormal monitoring feature sets into the three-dimensional feature analysis graph to obtain N abnormal monitoring coordinate point sets;
randomly selecting one abnormal monitoring coordinate point set from N abnormal monitoring coordinate point sets as a first abnormal monitoring coordinate point;
obtaining L sample first monitoring results corresponding to L sample coordinate points nearest to the first abnormal monitoring coordinate point, wherein L is an integer greater than or equal to 3;
calculating the average value of the first monitoring results of the L samples to obtain a first abnormal monitoring result;
and obtaining a first monitoring result set according to the N abnormal monitoring coordinate point sets.
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