CN116660672B - Power grid equipment fault diagnosis method and system based on big data - Google Patents
Power grid equipment fault diagnosis method and system based on big data Download PDFInfo
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 95
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000002159 abnormal effect Effects 0.000 claims abstract description 121
- 229940029329 intrinsic factor Drugs 0.000 claims abstract description 114
- 238000012544 monitoring process Methods 0.000 claims abstract description 47
- AGVAZMGAQJOSFJ-WZHZPDAFSA-M cobalt(2+);[(2r,3s,4r,5s)-5-(5,6-dimethylbenzimidazol-1-yl)-4-hydroxy-2-(hydroxymethyl)oxolan-3-yl] [(2r)-1-[3-[(1r,2r,3r,4z,7s,9z,12s,13s,14z,17s,18s,19r)-2,13,18-tris(2-amino-2-oxoethyl)-7,12,17-tris(3-amino-3-oxopropyl)-3,5,8,8,13,15,18,19-octamethyl-2 Chemical compound [Co+2].N#[C-].[N-]([C@@H]1[C@H](CC(N)=O)[C@@]2(C)CCC(=O)NC[C@@H](C)OP(O)(=O)O[C@H]3[C@H]([C@H](O[C@@H]3CO)N3C4=CC(C)=C(C)C=C4N=C3)O)\C2=C(C)/C([C@H](C\2(C)C)CCC(N)=O)=N/C/2=C\C([C@H]([C@@]/2(CC(N)=O)C)CCC(N)=O)=N\C\2=C(C)/C2=N[C@]1(C)[C@@](C)(CC(N)=O)[C@@H]2CCC(N)=O AGVAZMGAQJOSFJ-WZHZPDAFSA-M 0.000 claims abstract description 6
- 238000012545 processing Methods 0.000 claims abstract description 6
- 238000001514 detection method Methods 0.000 claims description 57
- 238000011156 evaluation Methods 0.000 claims description 22
- 238000004364 calculation method Methods 0.000 claims description 21
- 230000035772 mutation Effects 0.000 claims description 18
- 230000002950 deficient Effects 0.000 claims description 12
- 238000010438 heat treatment Methods 0.000 claims description 10
- 230000007613 environmental effect Effects 0.000 claims description 6
- 230000005856 abnormality Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 2
- 238000000556 factor analysis Methods 0.000 abstract description 4
- 238000012423 maintenance Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 239000000779 smoke Substances 0.000 description 1
Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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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
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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 belongs to the technical field of power grid equipment supervision, in particular to a power grid equipment fault diagnosis method and system based on big data, wherein the power grid equipment fault diagnosis system comprises a server, a power grid equipment acquisition and marking module, an intrinsic factor data diagnosis and analysis module, an auxiliary data diagnosis and analysis module, an extrinsic factor data diagnosis and analysis module and an operation early warning supervision module; according to the invention, the corresponding power grid equipment in the corresponding associated equipment set is subjected to internal cause monitoring analysis to generate an internal cause normal signal or an internal cause abnormal signal, the corresponding power grid equipment is subjected to auxiliary data processing analysis to generate an auxiliary cause normal signal or an auxiliary cause abnormal signal in the generated internal cause normal signal, the environment condition in the management and control area to which the corresponding power grid equipment belongs is analyzed to generate an external cause normal signal or an external cause abnormal signal in the generated auxiliary cause normal signal, so that step-by-step multi-factor analysis is realized, the accuracy of fault diagnosis analysis results is remarkably improved, and the power grid equipment in the monitored area is effectively monitored.
Description
Technical Field
The invention relates to the technical field of power grid equipment supervision, in particular to a power grid equipment fault diagnosis method and system based on big data.
Background
The power grid equipment refers to the general terms of equipment such as a generator and a transformer in a power grid system, at present, when the power grid equipment is monitored, operation data of the power grid equipment are often collected and sent to a background terminal, and the background terminal judges whether the power grid equipment has faults or not based on the displayed monitoring data, so that effective monitoring and automatic fault diagnosis and analysis of all the power grid equipment in a monitoring area are difficult to realize, and effective monitoring of the power grid equipment in the monitoring area is not facilitated; the power grid equipment cannot be subjected to step-by-step multi-factor analysis, so that the accuracy of fault diagnosis analysis results is not improved, and the equipment is difficult to check and maintain in time and the safety, stability and continuous operation of the corresponding power grid equipment are ensured;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a power grid equipment fault diagnosis method and system based on big data, which solve the problems that the effective monitoring and automatic fault diagnosis analysis of all power grid equipment in a monitoring area are difficult to realize, the effective monitoring of the power grid equipment in the monitoring area is not facilitated, the step-by-step multi-factor analysis of the power grid equipment cannot be performed, the accuracy of a fault diagnosis analysis result is not facilitated to be improved, the equipment inspection and maintenance are difficult to be performed in time, the safety, stability, continuous operation and low intelligent degree of the corresponding power grid equipment are difficult to ensure in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the power grid equipment fault diagnosis method based on big data comprises the following steps:
step one, acquiring all power grid equipment in a monitoring area, dividing the same type of power grid equipment into corresponding associated equipment sets, and marking the corresponding power grid equipment in the corresponding associated equipment sets;
step two, carrying out intrinsic factor monitoring analysis on corresponding power grid equipment to generate an intrinsic factor normal signal or an intrinsic factor abnormal signal, carrying out step five when generating the intrinsic factor abnormal signal, and carrying out step three when generating the intrinsic factor normal signal;
step three, carrying out accessory data processing analysis on the corresponding power grid equipment to generate an accessory normal signal or an accessory abnormal signal, carrying out step five when the accessory abnormal signal is generated, and carrying out step four when the accessory normal signal is generated;
dividing the power grid equipment into a control area, analyzing the environmental conditions in the control area to generate an exogenous normal signal or an exogenous abnormal signal, and performing the next step;
and fifthly, when an external cause normal signal is generated, editing text information corresponding to safe and stable operation of power grid equipment, displaying the text information, and when an internal cause abnormal signal, an additional cause abnormal signal or an external cause abnormal signal is generated, editing corresponding abnormal text information, displaying the text information and sending corresponding abnormal early warning voice.
Furthermore, the invention also provides a power grid equipment fault diagnosis system based on big data, which comprises a server, a power grid equipment acquisition marking module, an internal cause data diagnosis and analysis module, an auxiliary data diagnosis and analysis module, an external cause data diagnosis and analysis module and an operation early warning supervision module; the power grid equipment acquisition and marking module is used for acquiring a supervision area, acquiring all power grid equipment in the supervision area, identifying and classifying all power grid equipment, dividing all power grid equipment of the same type into corresponding associated equipment sets, marking the corresponding power grid equipment in the corresponding associated equipment sets as i, wherein i is a natural number greater than or equal to 1;
the intrinsic factor data diagnosis and analysis module performs intrinsic factor monitoring analysis on the power grid equipment i in the corresponding associated equipment set, generates an intrinsic factor normal signal or an intrinsic factor abnormal signal through analysis, sends the intrinsic factor abnormal signal to the operation early warning supervision module through the server, and sends the intrinsic factor normal signal to the auxiliary data diagnosis and analysis module through the server; the auxiliary data diagnosis and analysis module processes and analyzes auxiliary data of the power grid equipment i when receiving the intrinsic factor normal signal, generates an intrinsic factor normal signal or an intrinsic factor abnormal signal through analysis, sends the intrinsic factor abnormal signal to the operation early warning supervision module through the server, and sends the intrinsic factor normal signal to the extrinsic factor data diagnosis and analysis module through the server;
when the exogenous data diagnosis and analysis module receives the exogenous normal signal, the power grid equipment i is marked with a control area, the environmental condition in the control area is analyzed, the exogenous normal signal or the exogenous abnormal signal is generated through analysis, and the exogenous normal signal or the exogenous abnormal signal is sent to the operation early warning supervision module through the server; when the operation early warning supervision module receives the external cause normal signal, the text information of 'corresponding power grid equipment operation safety and stability' is edited and displayed, and when the operation early warning supervision module receives the internal cause abnormal signal, the additional cause abnormal signal or the external cause abnormal signal, the corresponding abnormal text information is edited and displayed, and corresponding abnormal early warning voice is sent.
Further, the specific analysis process of the endogenous data diagnosis and analysis comprises the following steps:
acquiring a voltage average deviation value and a current average deviation value of the power grid equipment i in the detection period, acquiring a voltage maximum floating value and a current maximum floating value of the power grid equipment i in the detection period, and carrying out normalization calculation on the voltage average deviation value, the current average deviation value, the voltage maximum floating value and the current maximum floating value to obtain an internal factor initial evaluation coefficient; randomly acquiring twenty groups of factor initial evaluation coefficients of power grid equipment i in unit time, drawing the twenty groups of factor initial evaluation coefficients into a rectangular coordinate system, and establishing a horizontal straight line in the rectangular coordinate system by taking a corresponding preset factor initial evaluation coefficient threshold value as an ordinate;
marking the sum of the numbers of the intrinsic factor primary evaluation coefficients above the horizontal line as KT1, marking the sum of the numbers of the intrinsic factor primary evaluation coefficients below the horizontal line as KT2, and generating an intrinsic factor abnormal signal if KT1-KT2 is more than or equal to KT3, wherein KT3 is a preset judgment value and the value of KT3 is larger than zero; if KT1-KT2 < KT3, marking an intrinsic factor primary evaluation coefficient positioned above a horizontal straight line as an intrinsic factor defective coefficient, connecting a coordinate point corresponding to the intrinsic factor defective coefficient with the horizontal straight line through a vertical line section, marking the length of a corresponding vertical line section as an intrinsic factor defective bias value, summing all the intrinsic factor defective bias values, marking the sum value as ZT, carrying out numerical calculation on KT1 and ZT to obtain an intrinsic factor diagnosis coefficient, carrying out numerical comparison on the intrinsic factor diagnosis coefficient and a corresponding preset intrinsic factor diagnosis coefficient threshold, generating an intrinsic factor abnormal signal if the intrinsic factor diagnosis coefficient exceeds the preset intrinsic factor diagnosis coefficient threshold, carrying out internal zone temperature and humidity monitoring analysis, and generating an intrinsic factor normal signal or an intrinsic factor abnormal signal according to the intrinsic factor abnormal signal.
Further, the specific analysis process of the inner zone temperature and humidity monitoring analysis is as follows:
marking a plurality of monitoring ranges in the power grid equipment i, marking an area covered by the corresponding monitoring range as a detection area u, wherein u is a natural number larger than 1, collecting the times of faults of the corresponding detection area u in the history operation process, marking the times as YGu, comparing YGu with a corresponding preset threshold value YGmax, marking the corresponding detection area u as a vulnerable area if YG is larger than or equal to YGmax, and marking the corresponding detection area u as a difficult-to-damage area if YG is smaller than YGmax;
acquiring real-time temperature and real-time humidity of a detection area i, performing difference calculation on the real-time temperature and a corresponding preset proper temperature value, obtaining a temperature difference value by taking an absolute value, performing difference calculation on the real-time humidity and a corresponding preset proper humidity value, obtaining a humidity difference value by taking an absolute value, and performing numerical calculation on the temperature difference value and the humidity difference value to obtain a zone detection value; corresponding preset area detection thresholds QJ1 and QJ2 are obtained, the QJ1 and the QJ2 correspond to the difficult-to-damage area and the easy-to-damage area, and the QJ1 is more than QJ2 is more than 0;
if the region detection value of the corresponding vulnerable region exceeds QJ2 or the corresponding difficult-to-damage region exceeds QJ1, marking the corresponding vulnerable region or the difficult-to-damage region as a risk region, dividing the difference value of the region detection value of the corresponding risk region and the corresponding preset region detection threshold value by the corresponding preset region detection threshold value to obtain a region detection overexpansion value, comparing the region detection overexpansion value with the corresponding preset region detection overexpansion threshold value in a numerical mode, generating an intrinsic factor abnormal signal if the region detection overexpansion value exceeds the preset region detection overexpansion threshold value, otherwise, summing all the region detection overexpansion values and marking the sum value as CH1, marking the sum of the number of the risk regions as CH2, carrying out weighting sum calculation on CH1 and CH2 to obtain an intrinsic factor abnormal coefficient, carrying out numerical comparison on the intrinsic factor abnormal coefficient and the preset intrinsic factor abnormal coefficient threshold value, if the intrinsic factor abnormal coefficient exceeds the preset intrinsic factor abnormal coefficient threshold value, generating the intrinsic factor normal signal if the intrinsic factor abnormal coefficient threshold value does not exceed the preset intrinsic factor threshold value.
Further, the specific operation process of the auxiliary data diagnosis and analysis module comprises the following steps:
acquiring an access and connection line of power grid equipment i, marking a corresponding line as e, setting a plurality of monitoring points on the corresponding line, acquiring a temperature expression magnitude value and a heating speed magnitude value of the corresponding monitoring points, respectively comparing the temperature expression magnitude value and the heating speed magnitude value with a corresponding preset temperature expression threshold value and a corresponding preset heating speed threshold value in numerical value, marking the corresponding monitoring points as normal points if the temperature expression magnitude value and the heating speed magnitude value do not exceed the corresponding preset threshold value, and marking the corresponding monitoring points as consideration points if the other conditions are met; calculating the difference value between the number of normal points in the corresponding line e and the number of considered points to obtain a point difference coefficient, and marking the corresponding line e as a diagnosis line if the point difference coefficient does not exceed a corresponding preset point difference coefficient threshold value;
if a diagnosis line exists in the power grid equipment i, generating an cause abnormality signal; if no diagnosis line exists in the power grid equipment i, a point difference coefficient set is built up, the point difference coefficient set is subjected to mean calculation to obtain a point difference mean value, the point difference coefficient set is subjected to variance calculation to obtain a point difference deviation coefficient, the point difference mean value and the point difference deviation coefficient are respectively subjected to numerical comparison with a corresponding preset point difference mean value threshold value and a preset point difference deviation coefficient threshold value, if the point difference mean value exceeds the preset point difference mean value threshold value and the point difference deviation coefficient does not exceed the preset point difference deviation coefficient threshold value, an cause abnormal signal is generated, and otherwise, an cause normal signal is generated.
Further, the specific operation process of the exogenous data diagnosis and analysis module comprises the following steps:
marking a circle with a radius of R1 by taking a corresponding power grid device i as a circle center, marking the corresponding circular area as a management and control area, acquiring environment data of the management and control area which is required to be monitored by the corresponding power grid device i, respectively comparing each type of management and control area data with a corresponding data range in a numerical value manner, marking the management and control area data which is not in the corresponding data range as risk data, and generating an exogenous abnormal signal if the risk data exists in the corresponding management and control area; if no risk data exists in the corresponding control area, collecting the mutation speed of the data of each type of control area in the corresponding control area, generating an exogenous abnormal signal if the mutation speed exceeds a corresponding preset mutation speed threshold value, and generating an exogenous normal signal if the mutation speed does not exceed the corresponding preset mutation speed threshold value.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the power grid equipment i in the corresponding associated equipment set is subjected to internal factor monitoring analysis to generate an internal factor normal signal or an internal factor abnormal signal, the power grid equipment i is subjected to auxiliary data processing analysis to generate an auxiliary factor normal signal or an auxiliary factor abnormal signal in the generated internal factor normal signal, the environment condition in a management and control area to which the power grid equipment i belongs is analyzed to generate an external factor normal signal or an external factor abnormal signal in the generated auxiliary factor normal signal, and the external factor normal signal or the external factor abnormal signal is sent to an operation early warning supervision module through a server, so that step-by-step multi-factor analysis is realized, the layer-by-layer progression is realized, and the accuracy of a fault diagnosis analysis result is remarkably improved;
2. according to the invention, when the operation early warning supervision module receives the internal cause abnormal signal, the additional cause abnormal signal or the external cause abnormal signal, the corresponding abnormal text information is edited and displayed, and the corresponding abnormal early warning voice is sent, so that the effective monitoring and the automatic fault diagnosis analysis of all the power grid equipment in the supervision area are realized, the effective supervision of the power grid equipment in the supervision area is facilitated, the equipment checking and the maintenance are conveniently and timely carried out, the safe, stable and continuous operation of the corresponding power grid equipment is ensured, and the intelligent degree is high.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is an overall system block diagram of the present invention.
Description of the embodiments
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.
Embodiment one: as shown in fig. 1, the method for diagnosing the power grid equipment fault based on big data provided by the invention comprises the following steps:
step one, acquiring all power grid equipment in a monitoring area, dividing the same type of power grid equipment into corresponding associated equipment sets, and marking the corresponding power grid equipment in the corresponding associated equipment sets;
step two, carrying out intrinsic factor monitoring analysis on corresponding power grid equipment to generate an intrinsic factor normal signal or an intrinsic factor abnormal signal, carrying out step five when generating the intrinsic factor abnormal signal, and carrying out step three when generating the intrinsic factor normal signal;
step three, carrying out accessory data processing analysis on the corresponding power grid equipment to generate an accessory normal signal or an accessory abnormal signal, carrying out step five when the accessory abnormal signal is generated, and carrying out step four when the accessory normal signal is generated;
dividing the power grid equipment into a control area, analyzing the environmental conditions in the control area to generate an exogenous normal signal or an exogenous abnormal signal, and performing the next step;
and fifthly, when an external cause normal signal is generated, editing text information corresponding to safe and stable operation of power grid equipment, displaying the text information, and when an internal cause abnormal signal, an additional cause abnormal signal or an external cause abnormal signal is generated, editing corresponding abnormal text information, displaying the text information and sending corresponding abnormal early warning voice.
Embodiment two: as shown in fig. 2, the difference between the embodiment and the embodiment 1 is that the power grid equipment fault diagnosis system based on big data provided by the invention comprises a server, a power grid equipment acquisition and marking module, an intrinsic factor data diagnosis and analysis module, an auxiliary data diagnosis and analysis module, an extrinsic factor data diagnosis and analysis module and an operation early warning supervision module, wherein the server is in communication connection with the power grid equipment acquisition and marking module, the intrinsic factor data diagnosis and analysis module, the auxiliary data diagnosis and analysis module, the extrinsic factor data diagnosis and analysis module and the operation early warning supervision module; the power grid equipment acquisition and marking module is used for acquiring a supervision area, acquiring all power grid equipment in the supervision area, identifying and classifying all power grid equipment, dividing all power grid equipment of the same type into corresponding associated equipment sets, marking the corresponding power grid equipment in the corresponding associated equipment sets as i, wherein i is a natural number greater than or equal to 1;
the intrinsic factor data diagnosis and analysis module performs intrinsic factor monitoring analysis on the power grid equipment i in the corresponding associated equipment set, generates an intrinsic factor normal signal or an intrinsic factor abnormal signal through analysis, sends the intrinsic factor abnormal signal to the operation early warning supervision module through the server, and sends the intrinsic factor normal signal to the auxiliary data diagnosis and analysis module through the server; the specific analysis process of the intrinsic factor data diagnosis analysis is as follows:
collecting a voltage average deviation value and a current average deviation value of the power grid equipment i in a detection period, and collecting a voltage maximum floating value and a current maximum floating value of the power grid equipment i in the detection period, wherein the voltage average deviation value and the current average deviation value are data values representing the deviation degree of voltage and current compared with a preset voltage standard value and a preset current standard value, and the voltage maximum floating value and the current maximum floating value are data values representing the voltage fluctuation amplitude and the current fluctuation amplitude; the voltage average deviation value FRi, the current average deviation value FGi, the voltage maximum floating value FYi and the current maximum floating value FEi are normalized through the formula NPi =a1, fri+a2, FGi +a3, fyi+a4, so as to obtain an internal factor initial evaluation coefficient NPi; wherein a1, a2, a3 and a4 are preset weight coefficients, the values of a1, a2, a3 and a4 are all larger than zero, and the larger the numerical value of the internal factor initial evaluation coefficient NPi is, the worse the operation condition of the power grid equipment i corresponding to the detection period is indicated;
randomly acquiring twenty groups of factor initial evaluation coefficients of power grid equipment i in unit time, drawing the twenty groups of factor initial evaluation coefficients into a rectangular coordinate system, and establishing a horizontal straight line in the rectangular coordinate system by taking a corresponding preset factor initial evaluation coefficient threshold value as an ordinate; marking the sum of the numbers of the intrinsic factor primary evaluation coefficients above the horizontal line as KT1, marking the sum of the numbers of the intrinsic factor primary evaluation coefficients below the horizontal line as KT2, and generating an intrinsic factor abnormal signal if KT1-KT2 is more than or equal to KT3, wherein KT3 is a preset judgment value and the value of KT3 is larger than zero; if KT1-KT2 is less than KT3, marking an intrinsic factor primary evaluation coefficient positioned above a horizontal straight line as an intrinsic factor defective coefficient, connecting a coordinate point corresponding to the intrinsic factor defective coefficient with the horizontal straight line through a vertical line segment, marking the length of a corresponding vertical line segment as an intrinsic factor defective bias value, summing all the intrinsic factor defective bias values, and marking the sum value as ZT;
calculating the numerical values of KT1 and ZT through a formula NZi =b1 xKT1+b2 xZT to obtain an internal factor diagnosis coefficient NZi, wherein b1 and b2 are preset weight coefficients, and b1 is larger than b2 and larger than 0; and, the larger the value of the internal factor diagnosis coefficient NZi is, the larger the operation risk of the corresponding power grid equipment i is; comparing the internal factor diagnosis coefficient with a corresponding preset internal factor diagnosis coefficient threshold value in a numerical mode, and generating an internal factor abnormal signal if the internal factor diagnosis coefficient exceeds the preset internal factor diagnosis coefficient threshold value; if the internal factor diagnosis coefficient does not exceed the preset internal factor diagnosis coefficient threshold value, the internal area temperature and humidity monitoring analysis is carried out, specifically: marking a plurality of monitoring ranges in the power grid equipment i, marking an area covered by the corresponding monitoring range as a detection area u, wherein u is a natural number larger than 1, collecting the times of faults of the corresponding detection area u in the history operation process, marking the times as YGu, comparing YGu with a corresponding preset threshold value YGmax, marking the corresponding detection area u as a vulnerable area if YG is larger than or equal to YGmax, and marking the corresponding detection area u as a difficult-to-damage area if YG is smaller than YGmax;
acquiring real-time temperature and real-time humidity of a detection area i, performing difference calculation on the real-time temperature and a corresponding preset proper temperature value, obtaining a temperature difference value by taking an absolute value, performing difference calculation on the real-time humidity and a corresponding preset proper humidity value, obtaining a humidity difference value by taking an absolute value, and performing numerical calculation on the temperature difference value and the humidity difference value to obtain a zone detection value; corresponding preset area detection thresholds QJ1 and QJ2 are obtained, the QJ1 and the QJ2 correspond to the difficult-to-damage area and the easy-to-damage area, and the QJ1 is more than QJ2 and more than 0, namely the supervision intensity of the difficult-to-damage area is lower than that of the easy-to-damage area;
if the area detection value of the corresponding vulnerable area exceeds QJ2 or the corresponding difficult-to-damage area exceeds QJ1, marking the corresponding vulnerable area or difficult-to-damage area as a risk area, dividing the difference value between the area detection value of the corresponding risk area and the corresponding preset area detection threshold value by the corresponding preset area detection threshold value to obtain an area detection overestimate value, comparing the area detection overestimate value with the corresponding preset area detection overestimate threshold value in a numerical mode, and if the area detection overestimate value exceeds the preset area detection overestimate threshold value, generating an intrinsic factor abnormal signal; if the existing region super-occupation value exceeds the preset region super-occupation threshold value, summing all the region super-occupation values, marking the sum value as CH1, and marking the sum of the number of the risk regions as CH2;
the method comprises the steps of carrying out weighting summation on CH1 and CH2 through a formula NYi =rp1+rp2+CH2 to obtain an inner region abnormal coefficient NYi, wherein rp1 and rp2 are preset weight coefficients, and rp2 is larger than rp1 and larger than 0; and, the larger the numerical value of the inner region anomaly coefficient NYi is, the larger the operation risk of the corresponding power grid equipment i is; the inner region anomaly coefficient NYi is numerically compared with a preset inner region anomaly coefficient threshold, if the inner region anomaly coefficient NYi exceeds the preset inner region anomaly coefficient threshold, an inner cause anomaly signal is generated, and if the inner region anomaly coefficient NYi does not exceed the preset inner region anomaly coefficient threshold, an inner cause normal signal is generated.
The auxiliary data diagnosis and analysis module processes and analyzes auxiliary data of the power grid equipment i when receiving the intrinsic factor normal signal, generates an intrinsic factor normal signal or an intrinsic factor abnormal signal through analysis, sends the intrinsic factor abnormal signal to the operation early warning supervision module through the server, and sends the intrinsic factor normal signal to the extrinsic factor data diagnosis and analysis module through the server; the specific operation process of the auxiliary data diagnosis and analysis module is as follows:
the method comprises the steps of collecting an access and connection line of power grid equipment i, marking a corresponding line as e, setting a plurality of monitoring points on the corresponding line, and collecting a temperature expression magnitude and a heating speed magnitude of the corresponding monitoring points, wherein the temperature expression magnitude is a data magnitude representing the temperature magnitude, and the heating speed magnitude is a data magnitude representing the rising speed of the temperature; respectively carrying out numerical comparison on the temperature expression magnitude and the temperature rise speed magnitude and the corresponding preset temperature expression threshold and preset temperature rise speed threshold, if the temperature expression magnitude and the temperature rise speed magnitude do not exceed the corresponding preset threshold, marking the corresponding monitoring point as a normal point, and otherwise marking the corresponding monitoring point as an examination point; calculating the difference between the number of normal points in the corresponding line e and the number of considered points to obtain a point difference coefficient, performing numerical comparison between the point difference coefficient and a corresponding preset point difference coefficient threshold value, and marking the corresponding line e as a diagnosis line if the point difference coefficient does not exceed the corresponding preset point difference coefficient threshold value;
if a diagnosis line exists in the power grid equipment i, generating an cause abnormality signal; if no diagnosis line exists in the power grid equipment i, establishing a point difference coefficient set of all the point difference coefficients, carrying out mean calculation on the point difference coefficient set to obtain a point difference mean value, carrying out variance calculation on the point difference coefficient set to obtain a point difference deviation coefficient, wherein the point difference deviation coefficient is a data magnitude representing the line condition deviation between each line in the power grid equipment i; and respectively carrying out numerical comparison on the point difference mean value and the point difference deviation coefficient as well as a corresponding preset point difference mean value threshold value and a preset point difference deviation coefficient threshold value, if the point difference mean value exceeds the preset point difference mean value threshold value and the point difference deviation coefficient does not exceed the preset point difference deviation coefficient threshold value, indicating that the line performance condition in the corresponding power grid equipment i is poor as a whole, generating an cause abnormal signal, and otherwise generating an cause normal signal.
When the exogenous data diagnosis and analysis module receives the exogenous normal signal, the power grid equipment i is marked with a control area, the environmental condition in the control area is analyzed, the exogenous normal signal or the exogenous abnormal signal is generated through analysis, and the exogenous normal signal or the exogenous abnormal signal is sent to the operation early warning supervision module through the server; the specific operation process of the exogenous data diagnosis and analysis module is as follows:
marking a circle with a radius of R1 by taking a corresponding power grid device i as a circle center, marking the corresponding circular area as a management and control area, acquiring environment data (including environment temperature data, environment humidity data, environment smoke data and the like) of the management and control area which is required to be monitored by the corresponding power grid device i, respectively comparing each type of management and control area data with a corresponding data range in a numerical value mode, marking the management and control area data which is not in the corresponding data range as risk data, for example, marking the environment temperature data as risk data if the environment temperature data of the corresponding management and control area is not in a corresponding preset temperature data range;
if risk data exists in the corresponding management and control area, generating an exogenous abnormal signal; if no risk data exists in the corresponding control area, acquiring mutation speed of data of each type of control area in the corresponding control area, wherein the mutation speed is a data value representing the size of the corresponding data change rate, comparing the mutation speed with a corresponding preset mutation speed threshold value, generating an exogenous abnormal signal if the mutation speed exceeds the corresponding preset mutation speed threshold value, and generating an exogenous normal signal if the mutation speed does not exceed the corresponding preset mutation speed threshold value. When the operation early warning supervision module receives the external cause normal signal, the text information of 'corresponding power grid equipment operation safety and stability' is edited and displayed, and when the operation early warning supervision module receives the internal cause abnormal signal, the additional cause abnormal signal or the external cause abnormal signal, the corresponding abnormal text information is edited and displayed, and corresponding abnormal early warning voice is sent.
The working principle of the invention is as follows: when the system is used, all power grid equipment in a monitoring area is identified and classified through the power grid equipment acquisition and marking module, all power grid equipment of the same type is marked into a corresponding associated equipment set, and corresponding power grid equipment in the corresponding associated equipment set is marked; the method comprises the steps that an internal factor data diagnosis analysis module conducts internal factor monitoring analysis on power grid equipment i in a corresponding associated equipment set to generate an internal factor normal signal or an internal factor abnormal signal, the internal factor abnormal signal is sent to an operation early warning supervision module through a server, the internal factor normal signal is sent to an auxiliary data diagnosis analysis module through the server, the auxiliary data diagnosis analysis module conducts auxiliary data processing analysis on the power grid equipment i to generate an auxiliary factor normal signal or an auxiliary factor abnormal signal, the auxiliary factor abnormal signal is sent to the operation early warning supervision module through the server, the auxiliary factor normal signal is sent to an external factor data diagnosis analysis module through the server, the external factor data diagnosis analysis module analyzes the environment conditions in a management area to which the power grid equipment i belongs to generate an external factor normal signal or an external factor abnormal signal, the external factor normal signal or the external factor abnormal signal is sent to the operation early warning supervision module through the server, when the operation early warning supervision module receives the internal factor abnormal signal, the auxiliary factor abnormal signal or the external factor abnormal signal, corresponding abnormal text information is edited and displayed, corresponding abnormal early warning voice is sent, effective monitoring and automatic fault diagnosis are conducted on all power grids in the area, the power grid in the area is conducted, the power grid is conducted, the fault diagnosis is conducted, the environment conditions in the area are effectively monitored, the power grid is controlled, the environment conditions in the area are monitored, the power grid are controlled by the power grid equipment are effectively, the environment conditions are analyzed in a proper condition, the power grid is controlled by the area, the equipment is continuously, the equipment is monitored and the device is stable in the condition, and the device is stable in the operation, and the device is guaranteed and the device is in the operation and the device is safe and can be monitored and can and safe and can be used.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. The power grid equipment fault diagnosis method based on big data is characterized by comprising the following steps of:
step one, acquiring all power grid equipment in a monitoring area, dividing the same type of power grid equipment into corresponding associated equipment sets, and marking the corresponding power grid equipment in the corresponding associated equipment sets;
step two, carrying out intrinsic factor monitoring analysis on corresponding power grid equipment to generate an intrinsic factor normal signal or an intrinsic factor abnormal signal, carrying out step five when generating the intrinsic factor abnormal signal, and carrying out step three when generating the intrinsic factor normal signal;
step three, carrying out accessory data processing analysis on the corresponding power grid equipment to generate an accessory normal signal or an accessory abnormal signal, carrying out step five when the accessory abnormal signal is generated, and carrying out step four when the accessory normal signal is generated;
dividing the power grid equipment into a control area, analyzing the environmental conditions in the control area to generate an exogenous normal signal or an exogenous abnormal signal, and performing the next step;
and fifthly, when an external cause normal signal is generated, editing text information corresponding to safe and stable operation of power grid equipment, displaying the text information, and when an internal cause abnormal signal, an additional cause abnormal signal or an external cause abnormal signal is generated, editing corresponding abnormal text information, displaying the text information and sending corresponding abnormal early warning voice.
2. The power grid equipment fault diagnosis system based on the big data is characterized by comprising a server, a power grid equipment acquisition and marking module, an internal cause data diagnosis and analysis module, an auxiliary data diagnosis and analysis module, an external cause data diagnosis and analysis module and an operation early warning supervision module; the power grid equipment acquisition and marking module is used for acquiring a supervision area, acquiring all power grid equipment in the supervision area, identifying and classifying all power grid equipment, dividing all power grid equipment of the same type into corresponding associated equipment sets, marking the corresponding power grid equipment in the corresponding associated equipment sets as i, wherein i is a natural number greater than or equal to 1;
the intrinsic factor data diagnosis and analysis module performs intrinsic factor monitoring analysis on the power grid equipment i in the corresponding associated equipment set, generates an intrinsic factor normal signal or an intrinsic factor abnormal signal through analysis, sends the intrinsic factor abnormal signal to the operation early warning supervision module through the server, and sends the intrinsic factor normal signal to the auxiliary data diagnosis and analysis module through the server; the auxiliary data diagnosis and analysis module processes and analyzes auxiliary data of the power grid equipment i when receiving the intrinsic factor normal signal, generates an intrinsic factor normal signal or an intrinsic factor abnormal signal through analysis, sends the intrinsic factor abnormal signal to the operation early warning supervision module through the server, and sends the intrinsic factor normal signal to the extrinsic factor data diagnosis and analysis module through the server;
when the exogenous data diagnosis and analysis module receives the exogenous normal signal, the power grid equipment i is marked with a control area, the environmental condition in the control area is analyzed, the exogenous normal signal or the exogenous abnormal signal is generated through analysis, and the exogenous normal signal or the exogenous abnormal signal is sent to the operation early warning supervision module through the server; when the operation early warning supervision module receives the external cause normal signal, the text information of 'corresponding power grid equipment operation safety and stability' is edited and displayed, and when the operation early warning supervision module receives the internal cause abnormal signal, the additional cause abnormal signal or the external cause abnormal signal, the corresponding abnormal text information is edited and displayed, and corresponding abnormal early warning voice is sent.
3. The big data based power grid equipment fault diagnosis system according to claim 2, wherein the specific analysis process of the intrinsic factor data diagnosis analysis comprises:
acquiring a voltage average deviation value and a current average deviation value of the power grid equipment i in the detection period, acquiring a voltage maximum floating value and a current maximum floating value of the power grid equipment i in the detection period, and carrying out normalization calculation on the voltage average deviation value, the current average deviation value, the voltage maximum floating value and the current maximum floating value to obtain an internal factor initial evaluation coefficient; randomly acquiring twenty groups of factor initial evaluation coefficients of power grid equipment i in unit time, drawing the twenty groups of factor initial evaluation coefficients into a rectangular coordinate system, and establishing a horizontal straight line in the rectangular coordinate system by taking a corresponding preset factor initial evaluation coefficient threshold value as an ordinate;
marking the sum of the numbers of the intrinsic factor primary evaluation coefficients above the horizontal line as KT1, marking the sum of the numbers of the intrinsic factor primary evaluation coefficients below the horizontal line as KT2, and generating an intrinsic factor abnormal signal if KT1-KT2 is more than or equal to KT3, wherein KT3 is a preset judgment value and the value of KT3 is larger than zero; if KT1-KT2 < KT3, marking an intrinsic factor primary evaluation coefficient positioned above a horizontal straight line as an intrinsic factor defective coefficient, connecting a coordinate point corresponding to the intrinsic factor defective coefficient with the horizontal straight line through a vertical line section, marking the length of a corresponding vertical line section as an intrinsic factor defective bias value, summing all the intrinsic factor defective bias values, marking the sum value as ZT, carrying out numerical calculation on KT1 and ZT to obtain an intrinsic factor diagnosis coefficient, carrying out numerical comparison on the intrinsic factor diagnosis coefficient and a corresponding preset intrinsic factor diagnosis coefficient threshold, generating an intrinsic factor abnormal signal if the intrinsic factor diagnosis coefficient exceeds the preset intrinsic factor diagnosis coefficient threshold, carrying out internal zone temperature and humidity monitoring analysis, and generating an intrinsic factor normal signal or an intrinsic factor abnormal signal according to the intrinsic factor abnormal signal.
4. The grid equipment fault diagnosis system based on big data according to claim 3, wherein the specific analysis process of the inner zone temperature and humidity monitoring analysis is as follows:
marking a plurality of monitoring ranges in the power grid equipment i, marking an area covered by the corresponding monitoring range as a detection area u, wherein u is a natural number larger than 1, collecting the times of faults of the corresponding detection area u in the history operation process, marking the times as YGu, comparing YGu with a corresponding preset threshold value YGmax, marking the corresponding detection area u as a vulnerable area if YG is larger than or equal to YGmax, and marking the corresponding detection area u as a difficult-to-damage area if YG is smaller than YGmax;
acquiring real-time temperature and real-time humidity of a detection area i, performing difference calculation on the real-time temperature and a corresponding preset proper temperature value, obtaining a temperature difference value by taking an absolute value, performing difference calculation on the real-time humidity and a corresponding preset proper humidity value, obtaining a humidity difference value by taking an absolute value, and performing numerical calculation on the temperature difference value and the humidity difference value to obtain a zone detection value; corresponding preset area detection thresholds QJ1 and QJ2 are obtained, the QJ1 and the QJ2 correspond to the difficult-to-damage area and the easy-to-damage area, and the QJ1 is more than QJ2 is more than 0;
if the region detection value of the corresponding vulnerable region exceeds QJ2 or the corresponding difficult-to-damage region exceeds QJ1, marking the corresponding vulnerable region or the difficult-to-damage region as a risk region, dividing the difference value of the region detection value of the corresponding risk region and the corresponding preset region detection threshold value by the corresponding preset region detection threshold value to obtain a region detection overexpansion value, comparing the region detection overexpansion value with the corresponding preset region detection overexpansion threshold value in a numerical mode, generating an intrinsic factor abnormal signal if the region detection overexpansion value exceeds the preset region detection overexpansion threshold value, otherwise, summing all the region detection overexpansion values and marking the sum value as CH1, marking the sum of the number of the risk regions as CH2, carrying out weighting sum calculation on CH1 and CH2 to obtain an intrinsic factor abnormal coefficient, carrying out numerical comparison on the intrinsic factor abnormal coefficient and the preset intrinsic factor abnormal coefficient threshold value, if the intrinsic factor abnormal coefficient exceeds the preset intrinsic factor abnormal coefficient threshold value, generating the intrinsic factor normal signal if the intrinsic factor abnormal coefficient threshold value does not exceed the preset intrinsic factor threshold value.
5. The big data based power grid equipment fault diagnosis system according to claim 2, wherein the specific operation process of the auxiliary data diagnosis analysis module comprises:
acquiring an access and connection line of power grid equipment i, marking a corresponding line as e, setting a plurality of monitoring points on the corresponding line, acquiring a temperature expression magnitude value and a heating speed magnitude value of the corresponding monitoring points, respectively comparing the temperature expression magnitude value and the heating speed magnitude value with a corresponding preset temperature expression threshold value and a corresponding preset heating speed threshold value in numerical value, marking the corresponding monitoring points as normal points if the temperature expression magnitude value and the heating speed magnitude value do not exceed the corresponding preset threshold value, and marking the corresponding monitoring points as consideration points if the other conditions are met; calculating the difference value between the number of normal points in the corresponding line e and the number of considered points to obtain a point difference coefficient, and marking the corresponding line e as a diagnosis line if the point difference coefficient does not exceed a corresponding preset point difference coefficient threshold value;
if a diagnosis line exists in the power grid equipment i, generating an cause abnormality signal; if no diagnosis line exists in the power grid equipment i, a point difference coefficient set is built up, the point difference coefficient set is subjected to mean calculation to obtain a point difference mean value, the point difference coefficient set is subjected to variance calculation to obtain a point difference deviation coefficient, the point difference mean value and the point difference deviation coefficient are respectively subjected to numerical comparison with a corresponding preset point difference mean value threshold value and a preset point difference deviation coefficient threshold value, if the point difference mean value exceeds the preset point difference mean value threshold value and the point difference deviation coefficient does not exceed the preset point difference deviation coefficient threshold value, an cause abnormal signal is generated, and otherwise, an cause normal signal is generated.
6. The big data based power grid equipment fault diagnosis system according to claim 2, wherein the specific operation process of the external factor data diagnosis analysis module comprises:
marking a circle with a radius of R1 by taking a corresponding power grid device i as a circle center, marking the corresponding circular area as a management and control area, acquiring environment data of the management and control area which is required to be monitored by the corresponding power grid device i, respectively comparing each type of management and control area data with a corresponding data range in a numerical value manner, marking the management and control area data which is not in the corresponding data range as risk data, and generating an exogenous abnormal signal if the risk data exists in the corresponding management and control area; if no risk data exists in the corresponding control area, collecting the mutation speed of the data of each type of control area in the corresponding control area, generating an exogenous abnormal signal if the mutation speed exceeds a corresponding preset mutation speed threshold value, and generating an exogenous normal signal if the mutation speed does not exceed the corresponding preset mutation speed threshold value.
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