CN106199276B - The intelligent diagnosis system and method for exception information in a kind of power information acquisition system - Google Patents
The intelligent diagnosis system and method for exception information in a kind of power information acquisition system Download PDFInfo
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 157
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Classifications
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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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- 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
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
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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
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/66—Testing of connections, e.g. of plugs or non-disconnectable joints
- G01R31/67—Testing the correctness of wire connections in electric apparatus or circuits
Abstract
The invention discloses the intelligent diagnosis systems and method of exception information in a kind of power information acquisition system, system of the invention is made of anomaly analysis experts database, self-learning module and GIS fault location module three parts, each section cooperates, and enhances the monitoring capacity to power information acquisition system operation.The present invention is to rely on power information acquisition system data collected, all kinds of electricity consumption datas of acquisition are counted by anomaly analysis experts database and self-learning module, are analyzed and diagnosed, determine Exception Type and severity, and the specific location of failure spot is rapidly and accurately locked by GIS fault location module, largely improve the efficiency of accuracy and the troubleshooting of malfunction monitoring.
Description
Technical field
The present invention relates to a kind of exception information diagnostic techniques fields, and in particular to abnormal in a kind of power information acquisition system
The intelligent diagnosing method of information.
Background technique
The construction of power information acquisition system facilitates implementing for national energy conservation and emission reduction policy, has complied with State Grid's body
Make the developing direction of reform.National Development and Reform Committee《If the opinion about further in-depth power system reform》In point out " actively
Carry out demand Side Management and energy efficiency management, by using modern information technologies, cultivates electric energy service, implements demand response
Deng the promotion equilibrium of supply and demand and energy-saving and emission-reduction." printed and distributed in National Energy Board《Distribution network construction modernization system plan (2015-
The year two thousand twenty)》In, it clearly proposes and " promotes power distribution automation and intelligent electricity consumption information acquisition system Construction, realize that power distribution network is objective
Controllably.Meet the diversification load growth requirement such as new energy, distributed energy and electric car, push smart grid construction with mutually
The developing goal of networking depth integration ".
State Grid Corporation of China and Southern Power Grid Company pay much attention to the construction and functional application work of power information acquisition system
Make, State Grid Corporation of China started starting company's power information acquisition system construction comprehensively in 2009, it is contemplated that will realize within 2015
More than 300,000,000 family power information automatic collections.
But current power information acquisition system O&M technology is also more extensive, and there are some urgent problems to be solved.First is that
System O&M efficiency is to be improved, and field adjustable, system monitoring and failure defect elimination need to expend a large amount of manpower and material resources；Second is that being
Data of uniting dispersion, integrated level and accuracy rate are lower, and abnormal directive property is poor, and acquisition data abundant do not play one's part to the full also；Three
That system intelligent degree is lower, it is not comprehensive enough to the support of electric power enterprise service application, to it is various acquisition equipment monitoring and
Abnormal alarm ability also has to be strengthened.The above problem constrains the further development and application of power information acquisition system, is badly in need of
It solves.
Summary of the invention
In view of the above-mentioned problems, the present invention proposes a kind of intelligent diagnosing method of exception information in power information acquisition system,
Power information acquisition system operation monitoring capacity is improved, it is excellent to play technology of the system in terms of big data perception, analysis, monitoring
Gesture supports for power marketing service provision technology, achieves good result in practical applications.
It realizes above-mentioned technical purpose, reaches above-mentioned technical effect, the invention is realized by the following technical scheme：
The intelligent diagnosis system of exception information in a kind of power information acquisition system, including anomaly analysis experts database, learn by oneself
Module and GIS fault location module are practised, is led between the anomaly analysis experts database, self-learning module and GIS fault location module
Wireless network connection is crossed, realizes the shared of fault message；
If the anomaly analysis experts database includes Ganlei's abnormity diagnosis analysis model；
The self-learning module is used for by carrying out with NB Algorithm to the data in anomaly analysis experts database
Training forms the criterion of judgement exception information type corresponding with each abnormity diagnosis analysis model, and for receiving and analyzing use
The exception information that power utilization information collection system provides, using the type for judging that the criterion of exception information type judges exception information, and
It is compared with anomaly analysis experts database, when the abnormity diagnosis analysis model for having the type in anomaly analysis experts database, then will
The exception information is referred in anomaly analysis experts database in corresponding abnormity diagnosis analysis model, and the Exception Type is passed to
GIS fault location module；If there is no the abnormity diagnosis analysis model of the type in anomaly analysis experts database, by the type exception
Staff is passed to, after being analyzed by staff, corresponding new abnormity diagnosis analysis model is set, and by the exception
Information categorization is to new abnormity diagnosis analysis model, by the exception information, new Exception Type, new by way of being manually entered
Abnormity diagnosis analysis model update pass to GIS fault location module into anomaly analysis experts database, and by the Exception Type,
Simultaneously in the abnormal menace level judgment module of GIS fault location module, the new Exception Type and serious etc. is manually set
The association of grade, and self-learning module re-starts training to the data of new anomaly analysis experts database, is formed and judges newly different
The criterion of normal information type, and the new exception information is transmitted to self-learning module again and carries out classification judgement, verify new exception
Whether the judgment criterion of information has been successfully formed；
The GIS fault location module includes that the GPS positioning device being mounted in each equipment, abnormal menace level judge mould
Block and GIS numerical map, GPS positioning device are used to carry out faulty equipment the location information that automatic positioning obtains faulty equipment,
Abnormal menace level judgment module is used to determine the menace level of the exception；GIS numerical map is used for the position of faulty equipment
Information, Exception Type, exception menace level be shown on GIS numerical map, realize the quick search of abort situation.
The abnormity diagnosis analysis model is divided into single diagnostic analysis model and relevant diagnosis analysis model, described single to examine
Disconnected analysis model includes electricity abnormity diagnosis model, voltage and current abnormity diagnosis model, abnormal electricity consumption diagnostic model, load exception
Diagnostic model, wiring abnormity diagnosis model, takes control abnormity diagnosis model at clock abnormity diagnosis model；The relevant diagnosis analysis
Model includes that doubtful stealing model, equipment fault model, misconnection line model, distribution transforming need Extension Model, on-site maintenance model, battery
Failure model, circuit Exception Model, multiplexing electric abnormality model.
The exception menace level judgment module is used to determine the menace level of the exception, specially：According to presetting
Exception Type and menace level between correlativity, menace level judgement is carried out to the Exception Type that receives；The GIS
Numerical map is specifically used for showing the position of faulty equipment, and root on a corresponding position according to the location information of faulty equipment
All kinds of faulty equipments are alerted respectively with different color and mark according to Exception Type and abnormal menace level, and are being carried out
After abnormal flow processing, the result of real-time exhibition troubleshooting.
The abnormal menace level includes：
1 grade：Emergency, the event for having doubtful electricity stealing to occur including user become customer charge switch state to special
Monitoring event class need the event of first time active reporting；Corresponding abnormity diagnosis analysis model includes：Electricity
Abnormity diagnosis model, abnormal electricity consumption diagnostic model, load abnormity diagnosis model, clock abnormity diagnosis model, wiring abnormity diagnosis
Model, doubtful stealing model；
2 grades：Critical event, including power down, parameter modification class are on the influential event of equipment normal operation；It corresponds
Abnormity diagnosis analysis model include：Equipment fault model, battery failure model；
3 grades：More important event, including decompression, time overproof class are on the influential event of the reliable electricity consumption of user；In contrast
The abnormity diagnosis analysis model answered includes：Voltage and current abnormity diagnosis model, circuit Exception Model, distribution transforming need Extension Model, use
Electrical anomaly model；
4 grades：The common event, including command operation remotely-or locally was carried out to equipment, it can be needed to carry out core according to management
The event looked into and handled；Corresponding abnormity diagnosis analysis model includes：On-site maintenance model, takes control at misconnection line model
Abnormity diagnosis model.
The intelligent diagnosing method of exception information, includes the following steps in a kind of power information acquisition system：
Step 1: data prediction:Repeated data in the collected exception information of power information acquisition system is carried out
Pick weight；
Step 2: the exception information collection point owning user information is compared with marketing process, whether archives are checked
Mistake；
Step 3: self-learning module passes through special to anomaly analysis with NB Algorithm when roll checking is without mistaking
Data in family library are trained, and form the criterion of judgement exception information type corresponding with each abnormity diagnosis analysis model,
And the exception information of power information acquisition system offer is provided, exception is judged using the criterion for judging exception information type
The type of information, and be compared with anomaly analysis experts database, thereby executing following operation：
A：If having the abnormity diagnosis analysis model of the type in anomaly analysis experts database, which is referred to
In anomaly analysis experts database in corresponding abnormity diagnosis analysis model, and Exception Type is passed into GIS fault location module；
B：If not having the abnormity diagnosis analysis model of the type in anomaly analysis experts database, the type is passed to extremely
After being analyzed by staff, corresponding new abnormity diagnosis analysis model is arranged, and the exception information is returned in staff
Class is to new abnormity diagnosis analysis model, by the exception information, new Exception Type, new exception by way of being manually entered
Diagnostic analysis model modification passes to GIS fault location module into anomaly analysis experts database, and by the Exception Type, while
In the abnormal menace level judgment module of GIS fault location module, the pass of the new Exception Type and menace level is manually set
Connection, and self-learning module re-starts training to the data of new anomaly analysis experts database, and formation judges new exception information
The criterion of type, and the new exception information is transmitted to self-learning module again and carries out classification judgement, verify new exception information
Whether judgment criterion has been successfully formed；
Step 4: the location information for obtaining faulty equipment is positioned to faulty equipment using GPS positioning device, exception is serious etc.
Grade judgment module determines the menace level of the exception according to the exception information received；Finally utilize GIS numerical map by failure
The menace level of the location information of equipment, Exception Type and exception is shown on GIS numerical map, realizes the quick of abort situation
Inquiry；
Step 5: staff arranges processing sequence according to the menace level of the warning message on GIS data map, go forward side by side
Row processing；
Step 6: after abnormality processing, the result of GIS fault location module real-time exhibition troubleshooting.
In the step 3, the abnormity diagnosis analysis model is divided into single diagnostic analysis model and relevant diagnosis analysis mould
Type, the single diagnostic analysis model include electricity abnormity diagnosis model, voltage and current abnormity diagnosis model, exception electrodiagnosis
Model, clock abnormity diagnosis model, wiring abnormity diagnosis model, takes control abnormity diagnosis model at load abnormity diagnosis model；It is described
Relevant diagnosis analysis model includes that doubtful stealing model, equipment fault model, misconnection line model, distribution transforming need Extension Model, scene
Safeguard model, battery failure model, circuit Exception Model, multiplexing electric abnormality model.
In the step 4, the exception menace level judgment module is used to determine the menace level of the exception, specially：
According to the correlativity between preset Exception Type and menace level, menace level is carried out to the Exception Type received
Judgement；The GIS numerical map is specifically used for showing the position of faulty equipment according to the location information of faulty equipment, and in phase
All kinds of faulty equipments are carried out respectively with different color and mark according to Exception Type and abnormal menace level on the position answered
Alarm.
In the step 4, menace level is specifically divided into：
1 grade：Emergency, the event for having doubtful electricity stealing to occur including user become customer charge switch state to special
Monitoring event class need the event of first time active reporting；Corresponding abnormity diagnosis analysis model includes：Electricity is different
Normal diagnostic model, abnormal electricity consumption diagnostic model, load abnormity diagnosis model, clock abnormity diagnosis model, wiring abnormity diagnosis mould
Type, doubtful stealing model；
2 grades：Critical event, including power down, parameter modification class are corresponding on the influential event of equipment normal operation
Abnormity diagnosis analysis model includes：Equipment fault model, battery failure model；
3 grades：More important event, including decompression, time overproof class are corresponding to it on the influential event of the reliable electricity consumption of user
Abnormity diagnosis analysis model include：Voltage and current abnormity diagnosis model, circuit Exception Model, distribution transforming need Extension Model, electricity consumption
Exception Model；
4 grades：The common event, including command operation remotely-or locally was carried out to equipment, it can be needed to carry out core according to management
The corresponding abnormity diagnosis analysis model of the event looked into and handled includes：On-site maintenance model, misconnection line model take control exception
Diagnostic model.
Beneficial effects of the present invention：
The present invention is to rely on power information acquisition system data collected, passes through anomaly analysis experts database and self study
Module counts all kinds of electricity consumption datas of acquisition, is analyzed and diagnosed, and determines Exception Type and severity, and pass through GIS
Fault location module rapidly and accurately locks the specific location of failure spot, largely improves the accurate of malfunction monitoring
The efficiency of degree and troubleshooting.
Detailed description of the invention
Fig. 1 is a kind of topology diagram of the intelligent diagnosis system of exception information in power information acquisition system.
Fig. 2 is a kind of work flow diagram of the intelligent diagnosing method of exception information in power information acquisition system.
Fig. 3 is a kind of functional frame composition of the Intelligent Diagnosis Technology of exception information in power information acquisition system.
In figure：1- anomaly analysis experts database, 2- self-learning module, 3-GIS fault location module, 4- abnormity diagnosis analyze mould
Type, 5- power information acquisition system, 6-GPS positioning device, 7-GIS numerical map.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
Application principle of the invention is explained in detail with reference to the accompanying drawing.
As shown in Figure 1, in a kind of power information acquisition system exception information intelligent diagnosis system, including anomaly analysis is special
Family library 1, self-learning module 2 and GIS fault location module 3, the anomaly analysis experts database 1, self-learning module 2 and GIS failure
By wireless network connection between locating module 3, the shared of fault message is realized；
If the anomaly analysis experts database 1 includes Ganlei's abnormity diagnosis analysis model 4；In invention, each single exception
In diagnostic analysis model, the various abnormal datas comprising same type, such as：Voltage out-of-limit model includes the various use being collected into
The electric energy meter over-voltage and under-voltage exceptional value at family, we are often referred to as training data.
The self-learning module 2 is used for by carrying out with NB Algorithm to the data in anomaly analysis experts database
Training forms the criterion of judgement exception information type corresponding with each abnormity diagnosis analysis model, and for receiving and analyzing use
The exception information that power utilization information collection system provides, using the type for judging that the criterion of exception information type judges exception information, and
It is compared with anomaly analysis experts database, when the abnormity diagnosis analysis model for having the type in anomaly analysis experts database, then will
The exception information is referred in anomaly analysis experts database in corresponding abnormity diagnosis analysis model, and the Exception Type is passed to
GIS fault location module；If there is no the abnormity diagnosis analysis model of the type in anomaly analysis experts database, by the type exception
Staff is passed to, after being analyzed by staff, corresponding new abnormity diagnosis analysis model is set, and by the exception
Information categorization is to new abnormity diagnosis analysis model, by the exception information, new Exception Type, new by way of being manually entered
Abnormity diagnosis analysis model update pass to GIS fault location module into anomaly analysis experts database, and by the Exception Type,
Simultaneously in the abnormal menace level judgment module of GIS fault location module, the new Exception Type and serious etc. is manually set
(in the present invention, the input of the new Exception Type is exactly artificial judgment and creates new abnormal diagnostic analysis for the association of grade
After model, by the requirement of the title of the exception and some parameters, condition is deposited into experts database in other words), and self study mould
Block re-starts training to the data of new anomaly analysis experts database, forms the criterion for judging new exception information type, and will
The new exception information is transmitted to self-learning module again and carries out classification judgement, and whether the judgment criterion for verifying new exception information has become
Function is formed；
The self-learning module 2 forms the criterion of judgement exception information type corresponding with each abnormity diagnosis analysis model
Seek the prior probability of each abnormity diagnosis analysis model.Specific steps are exemplified below：
Give a training set { (x1,y1),(x2,y2),…,(xn,yn) comprising n training data (such as：x1Represent electricity
Press out-of-limit, y1Represent electric voltage exception), wherein x1=(x1 (1),x1 (2),…,x1 (M))TIt is that (there are many abnormal for voltage out-of-limit for M dimensional vector
Feature, such as：Lower than voltage rating 5%~10%, 10%~20% etc., it is higher than voltage rating 5%~10%, 10%~20%
Deng), y1∈{c1,c2,...ckBelong to one kind in Exception Type.
First by y1It substitutes into formula (1), calculates p (y=ck)=p (y=y1)；
If the jth dimension of M dimensional feature is there are L value, then some value a of certain dimensional featurejl, giving certain classification ckUnder
Conditional probability is：
Such as：x1Voltage out-of-limit is represented, a kind of off-note lower than voltage rating 5%~10% is takenThis is different
The abnormal voltage value a of Chang TezhengjlIt substitutes into formula (2), the base of electric voltage exception type can be obtained in other off-notes and so on
This probability also just completes the forming process for judging the criterion of exception information type.
By the probability acquired, unfiled new abnormal instance X is given, so that it may by the criterion for judging exception information type
(prior probability) is judged (calculating), obtains the posterior probability p (y=c that the exception example belongs to each Exception Typek| X),
The Exception Type of middle maximum probability is the affiliated type of the exception example.
The GIS fault location module includes the GPS positioning device 6 being mounted in each equipment, abnormal menace level judgement
Module and GIS numerical map 7, GPS positioning device are used to carry out faulty equipment the position letter that automatic positioning obtains faulty equipment
Breath, abnormal menace level judgment module are used to determine the menace level of the exception；GIS numerical map is used for the position of faulty equipment
Confidence breath, Exception Type, exception menace level be shown on GIS numerical map, realize the quick search of abort situation.At this
In invention, warping apparatus and exception information be it is associated, by identifying the device number in exception information source, then utilize GPS
Positioning device 6 positions abnormal position.
The abnormity diagnosis analysis model derive to the analysis of electric energy meter and the acquired data of acquisition terminal as a result, it is possible to
The operating status of electric energy metering device is judged.
The abnormity diagnosis analysis model is divided into single diagnostic analysis model and relevant diagnosis analysis model, described single to examine
Disconnected analysis model includes electricity abnormity diagnosis model, voltage and current abnormity diagnosis model, abnormal electricity consumption diagnostic model, load exception
Diagnostic model, wiring abnormity diagnosis model, takes control abnormity diagnosis model at clock abnormity diagnosis model；The relevant diagnosis analysis
Model includes that doubtful stealing model, equipment fault model, misconnection line model, distribution transforming need Extension Model, on-site maintenance model, battery
Failure model, circuit Exception Model, multiplexing electric abnormality model.
The exception menace level judgment module is used to determine the menace level of the exception, specially：According to presetting
Exception Type and menace level between correlativity, menace level judgement is carried out to the Exception Type that receives；The GIS
Numerical map is specifically used for showing the position of faulty equipment, and root on a corresponding position according to the location information of faulty equipment
All kinds of faulty equipments are alerted respectively with different color and mark according to Exception Type and abnormal menace level, and are being carried out
After abnormal flow processing, the result of real-time exhibition troubleshooting.
The abnormal menace level includes：
1 grade：Emergency, the event for having doubtful electricity stealing to occur including user become customer charge switch state to special
Monitoring event class need the event of first time active reporting；Corresponding abnormity diagnosis analysis model includes：Electricity
Abnormity diagnosis model, abnormal electricity consumption diagnostic model, load abnormity diagnosis model, clock abnormity diagnosis model, wiring abnormity diagnosis
Model, doubtful stealing model；
2 grades：Critical event, including power down, parameter modification class are on the influential event of equipment normal operation；It corresponds
Abnormity diagnosis analysis model include：Equipment fault model, battery failure model；
3 grades：More important event, including decompression, time overproof class are on the influential event of the reliable electricity consumption of user；In contrast
The abnormity diagnosis analysis model answered includes：Voltage and current abnormity diagnosis model, circuit Exception Model, distribution transforming need Extension Model, use
Electrical anomaly model；
4 grades：The common event, including command operation remotely-or locally was carried out to equipment, it can be needed to carry out core according to management
The event looked into and handled；Corresponding abnormity diagnosis analysis model includes：On-site maintenance model, takes control at misconnection line model
Abnormity diagnosis model.
As shown in Fig. 2, in a kind of power information acquisition system exception information intelligent diagnosing method, include the following steps：
Step 1: data prediction:Repeated data in the collected exception information of power information acquisition system is carried out
Pick weight；Data are picked indicates to reject identical exception information again, such as：The electric voltage exception monitoring cycle of system is 15 minutes, small
Repeatedly occurred in 15 minutes time voltage surmount standard limits and record electric voltage exception information belong to repetition exception information,
It should carry out data and pick weight.
Step 2: the exception information collection point owning user information is compared with marketing process, whether archives are checked
Mistake；In invention, the archive information of user represents the belonging relation of user and equipment, will have been checked and approved by marketing process realization
User Profile information from sales service system synchronized update to power information acquisition system and GIS fault location module,
Process is likely to occur failure.Therefore, the correctness that need to check user Yu equipment belonging relation avoids exception from reporting phenomenon by mistake.
Step 3: self-learning module passes through special to anomaly analysis with NB Algorithm when roll checking is without mistaking
Data in family library are trained, and form the criterion of judgement exception information type corresponding with each abnormity diagnosis analysis model,
And the exception information of power information acquisition system offer is provided, exception is judged using the criterion for judging exception information type
The type of information, and be compared with anomaly analysis experts database, thereby executing following operation：
A：If having the abnormity diagnosis analysis model of the type in anomaly analysis experts database, which is referred to
In anomaly analysis experts database in corresponding abnormity diagnosis analysis model, and Exception Type is passed into GIS fault location module；
B：If not having the abnormity diagnosis analysis model of the type in anomaly analysis experts database, the type is passed to extremely
After being analyzed by staff, corresponding new abnormity diagnosis analysis model is arranged, and the exception information is returned in staff
Class is to new abnormity diagnosis analysis model, by the exception information, new Exception Type, new exception by way of being manually entered
Diagnostic analysis model modification passes to GIS fault location module into anomaly analysis experts database, and by the Exception Type, while
In the abnormal menace level judgment module of GIS fault location module, the pass of the new Exception Type and menace level is manually set
Connection, and self-learning module re-starts training to the data of new anomaly analysis experts database, and formation judges new exception information
The criterion of type, and the new exception information is transmitted to self-learning module again and carries out classification judgement, verify new exception information
Whether judgment criterion has been successfully formed；
Step 4: the location information for obtaining faulty equipment is positioned to faulty equipment using GPS positioning device, exception is serious etc.
Grade judgment module determines the menace level of the exception according to the exception information received；Finally utilize GIS numerical map by failure
The menace level of the location information of equipment, Exception Type and exception is shown on GIS numerical map, realizes the quick of abort situation
Inquiry；
Step 5: staff arranges processing sequence according to the menace level of the warning message on GIS data map, go forward side by side
Row processing；
Step 6: the GIS numerical map real-time exhibition troubleshooting after abnormality processing, in GIS fault location module
Result；Specially：It is solved when extremely, then the mark of corresponding exception information should just delete on GIS numerical map.
In the step 3, the abnormity diagnosis analysis model is divided into single diagnostic analysis model and relevant diagnosis analysis mould
Type, the single diagnostic analysis model include electricity abnormity diagnosis model, voltage and current abnormity diagnosis model, exception electrodiagnosis
Model, clock abnormity diagnosis model, wiring abnormity diagnosis model, takes control abnormity diagnosis model at load abnormity diagnosis model；It is described
Relevant diagnosis analysis model includes that doubtful stealing model, equipment fault model, misconnection line model, distribution transforming need Extension Model, scene
Safeguard model, battery failure model, circuit Exception Model, multiplexing electric abnormality model.
In the step 4, the exception menace level judgment module is used to determine the menace level of the exception, specially：
According to the correlativity between preset Exception Type and menace level, menace level is carried out to the Exception Type received
Judgement；The GIS numerical map is specifically used for showing the position of faulty equipment according to the location information of faulty equipment, and in phase
All kinds of faulty equipments are carried out respectively with different color and mark according to Exception Type and abnormal menace level on the position answered
Alarm.In an embodiment of the present invention, described to be alerted respectively with different color and mark, specially：Respectively
Emergency, critical event, more important event, the common event are alerted with red, orange, yellow, blue and corresponding mark.
In the step 4, menace level is specifically divided into：
1 grade：Emergency, the event for having doubtful electricity stealing to occur including user become customer charge switch state to special
Monitoring event class need the corresponding abnormity diagnosis analysis model of the event of first time active reporting to include：Electricity is abnormal
Diagnostic model, abnormal electricity consumption diagnostic model, load abnormity diagnosis model, clock abnormity diagnosis model, wiring abnormity diagnosis model,
Doubtful stealing model；
2 grades：Critical event, including power down, parameter modification class are corresponding on the influential event of equipment normal operation
Abnormity diagnosis analysis model includes：Equipment fault model, battery failure model；
3 grades：More important event, including decompression, time overproof class are corresponding to it on the influential event of the reliable electricity consumption of user
Abnormity diagnosis analysis model include：Voltage and current abnormity diagnosis model, circuit Exception Model, distribution transforming need Extension Model, electricity consumption
Exception Model；
4 grades：The common event, including command operation remotely-or locally was carried out to equipment, it can be needed to carry out core according to management
The corresponding abnormity diagnosis analysis model of the event looked into and handled includes：On-site maintenance model, misconnection line model take control exception
Diagnostic model.
As shown in figure 3, for a kind of reality of the intelligent diagnosis system of exception information in power information acquisition system of the invention
Apply example, including acquisition platform layer, support platform layer and service application layer；The acquisition platform layer includes power information acquisition system
System, specifically includes：Special transformer terminals, concentrator, net list and collector, in the collection process of specific power information acquisition,
Acquisition power information is gone by special transformer terminals, concentrator, net list and collector；The support platform layer includes：Anomaly analysis is special
Family library, self-learning module and GIS fault location module；The service application layer includes：Warping apparatus locating module, exception are online
Alarm module, abnormal graph display module, metering anomaly analysis module, unit exception analysis module and multiplexing electric abnormality analyze mould
Block；Anomaly analysis module, unit exception analysis module and the multiplexing electric abnormality analysis module of measuring is by point of self-learning module
Class algorithm realizes the support to its function, and the warping apparatus locating module goes to realize using GPS positioning device；The exception exists
Line alarm module is used to carry out all kinds of faulty equipments with different color and mark according to Exception Type and abnormal menace level
Alarm；The abnormal graph display module is used for after carrying out abnormal flow processing, the result of real-time exhibition troubleshooting.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (7)
1. the intelligent diagnosis system of exception information in a kind of power information acquisition system, it is characterised in that：It is special including anomaly analysis
Family library, self-learning module and GIS fault location module, the anomaly analysis experts database, self-learning module and GIS fault location mould
By wireless network connection between block, the shared of fault message is realized；
If the anomaly analysis experts database includes Ganlei's abnormity diagnosis analysis model, it is divided into single diagnostic analysis model and association is examined
Disconnected analysis model, the single diagnostic analysis model includes electricity abnormity diagnosis model, voltage and current abnormity diagnosis model, exception
Electricity consumption diagnostic model, clock abnormity diagnosis model, wiring abnormity diagnosis model, takes control abnormity diagnosis at load abnormity diagnosis model
Model；The relevant diagnosis analysis model includes that doubtful stealing model, equipment fault model, misconnection line model, distribution transforming need dilatation
Model, on-site maintenance model, battery failure model, circuit Exception Model, multiplexing electric abnormality model；
The self-learning module is used for by being trained with NB Algorithm to the data in anomaly analysis experts database,
The criterion of judgement exception information type corresponding with each abnormity diagnosis analysis model is formed, and uses telecommunications for receiving and analyzing
Cease acquisition system provide exception information, using the type for judging that the criterion of exception information type judges exception information, and with it is different
Normal assayer library is compared, when the abnormity diagnosis analysis model for having the type in anomaly analysis experts database, then this is different
The Exception Type is passed to GIS into anomaly analysis experts database in corresponding abnormity diagnosis analysis model by normal information categorization
Fault location module；If not having the abnormity diagnosis analysis model of the type in anomaly analysis experts database, the type is passed extremely
Staff is passed, after being analyzed by staff, corresponding new abnormity diagnosis analysis model is set, and this is believed extremely
Breath is referred to new abnormity diagnosis analysis model, by the exception information, new Exception Type, new by way of being manually entered
The update of abnormity diagnosis analysis model passes to GIS fault location module into anomaly analysis experts database, and by the Exception Type, together
When in the abnormal menace level judgment module of GIS fault location module, manually set the new Exception Type and menace level
Association, and self-learning module re-starts training to the data of new anomaly analysis experts database, and formation judges new exception
The criterion of information type, and the new exception information is transmitted to self-learning module again and carries out classification judgement, verify new abnormal letter
Whether the judgment criterion of breath has been successfully formed；
The GIS fault location module include the GPS positioning device being mounted in each equipment, abnormal menace level judgment module and
GIS numerical map, GPS positioning device is used to carry out faulty equipment the location information that automatic positioning obtains faulty equipment, abnormal
Menace level judgment module is used to determine the menace level of the exception；GIS numerical map be used for by the location information of faulty equipment,
Exception Type, abnormal menace level are shown on GIS numerical map, realize the quick search of abort situation.
2. the intelligent diagnosis system of exception information, feature in a kind of power information acquisition system according to claim 1
It is：The exception menace level judgment module is used to determine the menace level of the exception, specially：According to preset different
Correlativity between normal type and menace level carries out menace level judgement to the Exception Type received；The GIS number
Map is specifically used for showing the position of faulty equipment according to the location information of faulty equipment, and on a corresponding position according to different
Normal type and abnormal menace level alert all kinds of faulty equipments respectively with different color and mark, and are carrying out exception
After flow processing, the result of real-time exhibition troubleshooting.
3. the intelligent diagnosis system of exception information, feature in a kind of power information acquisition system according to claim 2
It is, the abnormal menace level includes：
1 grade：Emergency has the event of doubtful electricity stealing generation, to the special prison for becoming customer charge switch state including user
Survey event class needs the event of first time active reporting；Corresponding abnormity diagnosis analysis model includes：Electricity is abnormal
Diagnostic model, abnormal electricity consumption diagnostic model, load abnormity diagnosis model, clock abnormity diagnosis model, wiring abnormity diagnosis model,
Doubtful stealing model；
2 grades：Critical event, including power down, parameter modification class are on the influential event of equipment normal operation；It is corresponding different
Often diagnostic analysis model includes：Equipment fault model, battery failure model；
3 grades：More important event, including decompression, time overproof class are on the influential event of the reliable electricity consumption of user；It is corresponding
Abnormity diagnosis analysis model includes：Voltage and current abnormity diagnosis model, circuit Exception Model, distribution transforming need Extension Model, electricity consumption different
Norm type；
4 grades：The common event, including command operation remotely-or locally was carried out to equipment, can be carried out verifying according to management with
The event of processing；Corresponding abnormity diagnosis analysis model includes：On-site maintenance model, misconnection line model take control exception
Diagnostic model.
4. the intelligent diagnosing method of exception information in a kind of power information acquisition system, which is characterized in that include the following steps：
Step 1: data prediction:Repeated data in the collected exception information of power information acquisition system is carried out to pick weight；
Step 2: by the exception information collection point owning user information with marketing process be compared, check archives whether mistake；
Step 3: self-learning module is by using NB Algorithm to anomaly analysis experts database when roll checking is without mistaking
In data be trained, form the criterion for judging exception information type corresponding with each abnormity diagnosis analysis model, and connect
By, analysis power information acquisition system provide exception information, judge exception information using the criterion for judging exception information type
Type, and be compared with anomaly analysis experts database, thereby executing following operation：
A：If having the abnormity diagnosis analysis model of the type in anomaly analysis experts database, which is referred to exception
In assayer library in corresponding abnormity diagnosis analysis model, and Exception Type is passed into GIS fault location module；
B：If not having the abnormity diagnosis analysis model of the type in anomaly analysis experts database, the type is passed into work extremely
After being analyzed by staff, corresponding new abnormity diagnosis analysis model is arranged, and the exception information is referred in personnel
New abnormity diagnosis analysis model, by the exception information, new Exception Type, new abnormity diagnosis by way of being manually entered
Analysis model update passes to GIS fault location module into anomaly analysis experts database, and by the Exception Type, while in GIS
In the abnormal menace level judgment module of fault location module, being associated with for the new Exception Type and menace level is manually set,
And self-learning module re-starts training to the data of new anomaly analysis experts database, and formation judges new exception information type
Criterion, and by the new exception information be transmitted to again self-learning module carry out classification judgement, verify the judgement of new exception information
Whether criterion has been successfully formed；
Step 4: positioning the location information for obtaining faulty equipment to faulty equipment using GPS positioning device, abnormal menace level is sentenced
Disconnected module determines the menace level of the exception according to the exception information received；Finally utilize GIS numerical map by faulty equipment
Location information, Exception Type and exception menace level be shown on GIS numerical map, realize the fast quick checking of abort situation
It askes；
Step 5: staff arranges processing sequence according to the menace level of the warning message on GIS data map, and located
Reason；
Step 6: after abnormality processing, the knot of the GIS numerical map real-time exhibition troubleshooting in GIS fault location module
Fruit.
5. the intelligent diagnosing method of exception information, feature in a kind of power information acquisition system according to claim 4
It is, in the step 3, the abnormity diagnosis analysis model is divided into single diagnostic analysis model and relevant diagnosis analysis model,
The single diagnostic analysis model includes electricity abnormity diagnosis model, voltage and current abnormity diagnosis model, abnormal electrodiagnosis mould
Type, clock abnormity diagnosis model, wiring abnormity diagnosis model, takes control abnormity diagnosis model at load abnormity diagnosis model；The pass
Connection diagnostic analysis model includes that doubtful stealing model, equipment fault model, misconnection line model, distribution transforming need Extension Model, scene dimension
Protect model, battery failure model, circuit Exception Model, multiplexing electric abnormality model.
6. the intelligent diagnosing method of exception information, feature in a kind of power information acquisition system according to claim 4
It is, in the step 4, the exception menace level judgment module is used to determine the menace level of the exception, specially：Root
According to the correlativity between preset Exception Type and menace level, menace level is carried out to the Exception Type received and is sentenced
It is disconnected；The GIS numerical map is specifically used for showing the position of faulty equipment according to the location information of faulty equipment, and corresponding
Position on all kinds of faulty equipments are accused respectively with different color and mark according to Exception Type and abnormal menace level
It is alert.
7. the intelligent diagnosing method of exception information, feature in a kind of power information acquisition system according to claim 6
It is, in the step 4, menace level is specifically divided into：
1 grade：Emergency has the event of doubtful electricity stealing generation, to the special prison for becoming customer charge switch state including user
Survey event class needs the event of first time active reporting, and corresponding abnormity diagnosis analysis model includes：Electricity is abnormal
Diagnostic model, abnormal electricity consumption diagnostic model, load abnormity diagnosis model, clock abnormity diagnosis model, wiring abnormity diagnosis model,
Doubtful stealing model；
2 grades：Critical event, including power down, parameter modification class are on the influential event of equipment normal operation, it is corresponding different
Often diagnostic analysis model includes：Equipment fault model, battery failure model；
3 grades：More important event, including decompression, time overproof class are on the influential event of the reliable electricity consumption of user, it is corresponding
Abnormity diagnosis analysis model includes：Voltage and current abnormity diagnosis model, circuit Exception Model, distribution transforming need Extension Model, electricity consumption different
Norm type；
4 grades：The common event, including command operation remotely-or locally was carried out to equipment, can be carried out verifying according to management with
The event of processing, corresponding abnormity diagnosis analysis model include：On-site maintenance model, misconnection line model take control exception
Diagnostic model.
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