CN107832855A - Line loss multi-source diagnostic method and system based on correlation analysis - Google Patents
Line loss multi-source diagnostic method and system based on correlation analysis Download PDFInfo
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Abstract
The present invention proposes that a kind of line loss multi-source diagnostic method and system, this method based on correlation analysis comprise the following steps:According to the facility information on the power network of acquisition basis, collection information and insertion information, the key factor for influenceing line loss is analyzed using clustering methodology, obtained more because of cluster analysis result;Multi-data source collection is inputted, carries out source end system long link transmission, and utilizes default diagnostic rule, carries out multi-source insertion diagnosis, and exports multi-source insertion diagnostic result;Line loss per unit Indexes Abnormality threshold value is set, and dynamic diagnosis is carried out using correlation coefficient process extremely to distribution and taiwan area line losses indices;According to more because cluster analysis result and multi-source penetrate diagnostic result, correlation analysis comprehensive diagnos is carried out to grid line loss with reference to the physics solid-state factor and dynamic statistics factor for influenceing line loss.The present invention can effectively lift power network source business department line loss governance efficiency.
Description
Technical field
The present invention relates to grid line loss ID Technology field, more particularly to a kind of line loss based on correlation analysis is more
Source diagnostic method and system.
Background technology
Cluster analysis refers to the analysis that the set of physics or abstract object is grouped into the multiple classes being made up of similar object
Process.Cluster comes from many fields, data is collected on the basis of similar to classify, these technical methods are used as describing number
According to, the similitude between measurement different data sources, and data source is categorized into different clusters.Coefficient correlation is to reflect change
The statistical indicator of dependency relation level of intimate between amount.Coefficient correlation is calculated by product moment method, equally with two variables and each
Based on the deviation of average value, it is multiplied by two deviations to reflect degree of correlation between two variables.
Cluster analysis draws attention in the power domain of big data.However, in power domain, grid line loss is influenceed
Factor is a lot, relatively simple currently for the diagnostic method of grid line loss, it is impossible to reference to physics solid-state factor and dynamic statistics because
Element carries out comprehensive diagnos to grid line loss, and therefore, power network source business department line loss governance efficiency is low, i.e., Controlling line loss is horizontal
It is not high.
The content of the invention
It is contemplated that at least solves one of above-mentioned technical problem.
Therefore, it is an object of the present invention to propose a kind of line loss multi-source diagnostic method based on correlation analysis, should
Method can effectively lift power network source business department line loss governance efficiency.
It is another object of the present invention to propose a kind of line loss multi-source diagnostic system based on correlation analysis.
To achieve these goals, the embodiment of first aspect present invention proposes a kind of line loss based on correlation analysis
Multi-source diagnostic method, comprises the following steps:According to the facility information on the power network of acquisition basis, collection information and insertion information, profit
The key factor for influenceing line loss is analyzed with clustering methodology, obtained more because of cluster analysis result;Multi-data source collection is inputted,
Source end system long link transmission is carried out, and utilizes default diagnostic rule, carries out multi-source insertion diagnosis, and exports multi-source insertion and examines
Disconnected result;Line loss per unit Indexes Abnormality threshold value is set, and action is entered using correlation coefficient process extremely to distribution and taiwan area line losses indices
State diagnoses;According to described more because of cluster analysis result and multi-source insertion diagnostic result, with reference to the physics solid-state for influenceing line loss
Factor and dynamic statistics factor carry out correlation analysis comprehensive diagnos to grid line loss.
In addition, the line loss multi-source diagnostic method according to the above embodiment of the present invention based on correlation analysis can also have
Additional technical characteristic as follows:
In some instances, the key factor for influenceing line loss comprises at least:Count the date, load, line length,
Model, load factor, sale of electricity is formed, public affairs specially become accounting, power grid architecture, distribution transforming tri-phase unbalance factor, distribution transforming low-voltage.
In some instances, the setting line loss per unit Indexes Abnormality threshold value, and using correlation coefficient process to distribution and taiwan area
Line losses indices carry out dynamic diagnosis extremely, further comprise:Diagnose distribution line loss per unit variable quantity and each lower public special power transformation of extension
The coefficient correlation of amount;Diagnose the coefficient correlation of taiwan area line loss per unit variable quantity and user's electricity;Diagnose distribution line loss per unit variable quantity with
The coefficient correlation of tri-phase unbalance factor;Diagnose the coefficient correlation of taiwan area line loss per unit variable quantity and low-voltage.
In some instances, the diagnosis distribution line loss per unit variable quantity and each lower phase relation hung public affairs and specially become electricity
Number, further comprises:The electricity in every distribution transforming preset time under the circuit is obtained, is set to { X1、X2、X3、、Xn};Obtain the line
The variable quantity of line loss per unit, is set to { Y in the preset time of road1、Y2、Y3、、Yn};Calculate X, Y coefficient correlation;If the phase relation
Number is more than preset value ρ0, then judge that the two is related.
In some instances, the multi-data source collection comprises at least:Grid equipment account data source, GIS graphics relationship numbers
Match somebody with somebody interpenetrating relationship data source according to source, battalion.
Line loss multi-source diagnostic method based on correlation analysis according to embodiments of the present invention, consolidate from the physics for influenceing line loss
State factor and dynamic statistics factor carry out correlation analysis comprehensive diagnos to grid line loss, and auxiliary positioning abnormal problem can be more straight
Influence of all kinds of factors to line loss is checked in sight, and line loss per unit abnormal index diagnostic result is that the line loss improvement of power network source business department carries
For a variety of auxiliary reference decision-makings, so as to effectively lift power network source business department line loss governance efficiency, and then line loss is lifted
Managerial skills.
To achieve these goals, the embodiment of second aspect of the present invention proposes a kind of line loss based on correlation analysis
Multi-source diagnostic system, including:Analysis module, for the facility information on the power network basis according to acquisition, collection information and insertion letter
Breath, the key factor for influenceing line loss is analyzed using clustering methodology, obtained more because of cluster analysis result;Insertion diagnosis mould
Block, for inputting multi-data source collection, source end system long link transmission is carried out, and utilize default diagnostic rule, carried out multi-source and pass through
Logical diagnosis, and export multi-source insertion diagnostic result;Dynamic diagnosis module, for setting line loss per unit Indexes Abnormality threshold value, and use
Correlation coefficient process carries out dynamic diagnosis extremely to distribution and taiwan area line losses indices;Comprehensive diagnosis module, for according to it is described it is more because
Cluster analysis result and multi-source insertion diagnostic result, with reference to the physics solid-state factor and dynamic statistics factor pair for influenceing line loss
Grid line loss carries out correlation analysis comprehensive diagnos.
In addition, the line loss multi-source diagnostic system according to the above embodiment of the present invention based on correlation analysis can also have
Additional technical characteristic as follows:
In some instances, the key factor for influenceing line loss comprises at least:Count the date, load, line length,
Model, load factor, sale of electricity is formed, public affairs specially become accounting, power grid architecture, distribution transforming tri-phase unbalance factor, distribution transforming low-voltage.
In some instances, the dynamic diagnosis module is used for:Diagnose distribution line loss per unit variable quantity and each lower extension is public
Specially become the coefficient correlation of electricity;Diagnose the coefficient correlation of taiwan area line loss per unit variable quantity and user's electricity;Distribution line loss per unit is diagnosed to become
The coefficient correlation of change amount and tri-phase unbalance factor;Diagnose the coefficient correlation of taiwan area line loss per unit variable quantity and low-voltage.
In some instances, the dynamic diagnosis modular diagnostic distribution line loss per unit variable quantity and each lower public special power transformation of extension
The coefficient correlation of amount, including:The electricity in every distribution transforming preset time under the circuit is obtained, is set to { X1、X2、X3、、Xn};Obtain
The variable quantity of line loss per unit, is set to { Y in the circuit preset time1、Y2、Y3、、Yn};Calculate X, Y coefficient correlation;If the phase
Relation number is more than preset value ρ0, then judge that the two is related.
In some instances, the multi-data source collection comprises at least:Grid equipment account data source, GIS graphics relationship numbers
Match somebody with somebody interpenetrating relationship data source according to source, battalion.
Line loss multi-source diagnostic system based on correlation analysis according to embodiments of the present invention, consolidate from the physics for influenceing line loss
State factor and dynamic statistics factor carry out correlation analysis comprehensive diagnos to grid line loss, and auxiliary positioning abnormal problem can be more straight
Influence of all kinds of factors to line loss is checked in sight, and line loss per unit abnormal index diagnostic result is that the line loss improvement of power network source business department carries
For a variety of auxiliary reference decision-makings, so as to effectively lift power network source business department line loss governance efficiency, and then line loss is lifted
Managerial skills.
The additional aspect and advantage of the present invention will be set forth in part in the description, and will partly become from the following description
Obtain substantially, or recognized by the practice of the present invention.
Brief description of the drawings
The above-mentioned and/or additional aspect and advantage of the present invention will become in the description from combination accompanying drawings below to embodiment
Substantially and it is readily appreciated that, wherein:
Fig. 1 is the flow chart of the line loss multi-source diagnostic method according to an embodiment of the invention based on correlation analysis;
Fig. 2 is the detailed stream of the line loss multi-source diagnostic method in accordance with another embodiment of the present invention based on correlation analysis
Journey schematic diagram;
Fig. 3 is the structural frames of the line loss multi-source diagnostic system according to an embodiment of the invention based on correlation analysis
Figure.
Embodiment
Embodiments of the invention are described below in detail, the example of the embodiment is shown in the drawings, wherein from beginning to end
Same or similar label represents same or similar element or the element with same or like function.Below with reference to attached
The embodiment of figure description is exemplary, is only used for explaining the present invention, and is not considered as limiting the invention.
In the description of the invention, it is to be understood that term " " center ", " longitudinal direction ", " transverse direction ", " on ", " under ",
The orientation or position relationship of the instruction such as "front", "rear", "left", "right", " vertical ", " level ", " top ", " bottom ", " interior ", " outer " are
Based on orientation shown in the drawings or position relationship, it is for only for ease of the description present invention and simplifies description, rather than instruction or dark
Show that the device of meaning or element there must be specific orientation, with specific azimuth configuration and operation, thus it is it is not intended that right
The limitation of the present invention.In addition, term " first ", " second " are only used for describing purpose, and it is not intended that instruction or hint are relative
Importance.
In the description of the invention, it is necessary to illustrate, unless otherwise clearly defined and limited, term " installation ", " phase
Even ", " connection " should be interpreted broadly, for example, it may be being fixedly connected or being detachably connected, or be integrally connected;Can
To be mechanical connection or electrical connection;Can be joined directly together, can also be indirectly connected by intermediary, Ke Yishi
The connection of two element internals.For the ordinary skill in the art, with concrete condition above-mentioned term can be understood at this
Concrete meaning in invention.
Below in conjunction with accompanying drawing description line loss multi-source diagnostic method based on correlation analysis according to embodiments of the present invention and
System.
Fig. 1 is the flow chart of the line loss multi-source diagnostic method according to an embodiment of the invention based on correlation analysis.
Fig. 2 is the detailed process signal of the line loss multi-source diagnostic method in accordance with another embodiment of the present invention based on correlation analysis
Figure.As shown in figure 1, and combine Fig. 2, this method comprises the following steps:
Step S1:According to the facility information on the power network of acquisition basis, collection information and insertion information, clustering methodology is utilized
The key factor for influenceing line loss is analyzed, obtained more because of cluster analysis result.
Wherein, the facility information on power network basis is used to accessing and proofreading equipment account data, mainly including transformer station, electricity
Factory, circuit, transformer, bus, switch etc..The base profile information of power network collection is used to access the related information of collection, mainly
Including meter archives, stoichiometric point archives, user profile, taiwan area information etc..Collection information is used to accessing and checking collection operation number
According to the main positive active indicating value including the equipment measuring point moment, positive active indicating value.
In one embodiment of the invention, the key factor for influenceing line loss comprises at least:Count date, load, line
Road length, model, load factor, sale of electricity composition, public specially change accounting, power grid architecture, distribution transforming tri-phase unbalance factor, distribution transforming low-voltage
Deng.Based on this, what is obtained is more because cluster analysis result mainly includes statistics date-line loss per unit, load-line loss per unit, circuit length
Degree-line loss per unit, model-line loss per unit, load factor-line loss per unit, sale of electricity composition-line loss per unit, public specially change accounting-line loss per unit, power network frame
Structure-line loss per unit, distribution transforming tri-phase unbalance factor-line loss per unit, distribution transforming low-voltage-line loss per unit etc..
In one embodiment of the invention, above-described clustering methodology, arbitrarily selected from n data object first
K object is as initial cluster center;And for remaining other objects, then according to their similarities with these cluster centres
(distance), (cluster centre representated by) cluster most like with it is assigned these to respectively;Then calculate and each obtained again
The cluster centre (averages of all objects in the cluster) newly clustered;This process is constantly repeated until canonical measure function starts
Untill convergence.Typically all using mean square deviation as canonical measure function.K cluster has the characteristics that:Each cluster to the greatest extent may be used in itself
Can it is compact, it is and separated as far as possible between respectively clustering.
Based on this, the key factor for influenceing line loss is analyzed using clustering methodology, obtained more because of cluster analysis knot
Fruit, specifically include:It it is specially { the statistics date it is determined that influenceing the clustering factor collection of line loss per unit;Load;Line length;Model;
Load factor;Sale of electricity is formed;It is public specially to become accounting;Power grid architecture;Tri-phase unbalance factor;Low-voltage }.Wherein, 1) the statistics date includes
Natural environmental factor, single line at all seasons in line loss per unit change profile situation and all circuits in same season
Line loss per unit change profile in section;2) what is simultaneously, suffered from this also has electricity sales amount composition (industry, business, resident etc.) to exist
Different red-letter days affect electricity sales amount distribution and total amount, influence line loss per unit indirectly;3) power grid architecture mainly considers radial line and ring
Influence of the net handle circuit to line loss per unit;4) load size, load current is influenceed, increases the impedance loss of circuit and distribution transforming,
Influence line loss per unit change;5) line length, the influence change profile of overall length and trunk line length to line loss per unit is considered;6) model,
Consider backbone wire type and the influence change profile with Variant number to line loss per unit;7) load factor, zero load, underloading, again are considered
Carry the influence change profile to line loss per unit;8) it is public specially to become accounting, consider because metering point mode and the public loss on transmission accounting that becomes are to circuit line
The influence change profile of loss rate, three kinds of situations of Main Analysis are (public specially to become accounting equilibrium;Public affairs become accounting and are more than specially change;Public affairs become accounting
Become less than special).
Step S2:Multi-data source collection is inputted, carries out source end system long link transmission, and utilizes default diagnostic rule, is entered
The insertion diagnosis of row multi-source, and export multi-source insertion diagnostic result.Wherein, multi-data source collection comprises at least:Grid equipment machine account number
Match somebody with somebody interpenetrating relationship data source according to source, GIS graphics relationships data source, battalion.
Specifically, equipment is under the jurisdiction of different business department not from capital input, operation, monitoring in the management of power network present situation
With in management system, it is necessary to carry out the insertion of multi-data source operation system to the affiliated locking relation of equipment in Controlling line loss
Diagnosis, using default diagnostic rule, realize multi-source insertion diagnosis and result output.
Step S3:Line loss per unit Indexes Abnormality threshold value is set, and it is different to distribution and taiwan area line losses indices using correlation coefficient process
Often carry out dynamic diagnosis.In other words, i.e., line loss per unit abnormal distribution and platform are obtained by setting line loss per unit Indexes Abnormality threshold value
Area, dynamic diagnosis is carried out using correlation coefficient process extremely to distribution and taiwan area line losses indices.
Specifically, in one embodiment of the invention, line loss per unit Indexes Abnormality threshold value is set, and uses correlation coefficient process
Dynamic diagnosis is carried out extremely to distribution and taiwan area line losses indices, further comprised:Diagnose distribution line loss per unit variable quantity and each
It is lower to hang the public coefficient correlation for specially becoming electricity;Diagnose the coefficient correlation of taiwan area line loss per unit variable quantity and user's electricity;Diagnose distribution line
The coefficient correlation of loss rate variable quantity and tri-phase unbalance factor;Diagnose the coefficient correlation of taiwan area line loss per unit variable quantity and low-voltage.
Wherein, more specifically, calculate diagnosis distribution line loss per unit variable quantity and specially become the related of electricity to each lower public affairs of hanging
Coefficient, further comprise:The electricity in every distribution transforming preset time (being such as set as nearest some months) under the circuit is obtained, if
For { X1、X2、X3、、Xn};The variable quantity of line loss per unit in the circuit preset time is obtained, is set to { Y1、Y2、Y3、、Yn};Calculate X, Y
Coefficient correlation;If coefficient correlation is more than preset value ρ0, then judge that the two is related.In other words, i.e., line is entered to negative high damage circuit
Change relation is investigated, and seeks coefficient correlation.By calculating, circuit line loss per unit variable quantity and each are lower to hang the public phase relation for specially becoming electricity
Number, if related, judging the public affairs, specially modified line change relation is wrong.
Similarly, the diagnosable coefficient correlation for calculating taiwan area line loss per unit variable quantity and user's electricity;Diagnosable calculating distribution line
The coefficient correlation of loss rate variable quantity and tri-phase unbalance factor;The diagnosable phase relation for calculating taiwan area line loss per unit variable quantity and low-voltage
Number.To reduce redundancy, no longer repeat one by one herein.
Step S4:According to more because cluster analysis result and multi-source insertion diagnostic result, with reference to influence line loss physics solid-state
Factor and dynamic statistics factor carry out correlation analysis comprehensive diagnos to grid line loss, auxiliary positioning abnormal problem, lift line loss
Managerial skills.
Specifically, distribution and taiwan area, each distribution and taiwan area contained in Grid possess different features, utilize
Coefficient correlation can represent two similarity degrees between wiring closet or taiwan area.Enter Correlation series to this to calculate and verify, can
Row distance cluster is entered with the close and distant distance to distribution and taiwan area;And then with reference to more because of cluster analysis result, examined with reference to multi-source insertion
Disconnected result and correlation analysis, grid line loss is carried out from the physics solid-state factor and dynamic statistics factor for influenceing line loss related
Property analysis integrated diagnosis, be business for the distribution or taiwan area, quick auxiliary positioning abnormal problem that the line loss per unit of input is abnormal
Personnel provide reference, lift line loss managerial skills.
To sum up, the line loss multi-source diagnostic method based on correlation analysis is somebody's turn to do, utilizes cluster analysis and correlation analysis side
Method carries out comprehensive diagnos to grid line loss, including:Multi-data source collection, correlation analysis, more because of cluster analysis, line losses indices
Abnormal, multi-source insertion diagnosis, result data collection etc..The multi-source diagnostic method be with power network basis facility information, collection information,
Information is penetrated as input, first, is analyzed using clustering methodology influenceing line loss key multiple-factor.Secondly, using correlation
Y-factor method Y carries out dynamic diagnosis extremely to distribution and taiwan area line losses indices, with reference to cluster and the insertion of coefficient correlation overview display multi-source
Variance data, auxiliary positioning problem, lifting power network source business department line loss governance efficiency.
Line loss multi-source diagnostic method based on correlation analysis according to embodiments of the present invention, consolidate from the physics for influenceing line loss
State factor and dynamic statistics factor carry out correlation analysis comprehensive diagnos to grid line loss, and auxiliary positioning abnormal problem can be more straight
Influence of all kinds of factors to line loss is checked in sight, and line loss per unit abnormal index diagnostic result is that the line loss improvement of power network source business department carries
For a variety of auxiliary reference decision-makings, so as to effectively lift power network source business department line loss governance efficiency, and then line loss is lifted
Managerial skills.
Further embodiment of the present invention also proposed a kind of line loss multi-source diagnostic system based on correlation analysis.
Fig. 3 is the structural frames of the line loss multi-source diagnostic system according to an embodiment of the invention based on correlation analysis
Figure.As shown in figure 3, being somebody's turn to do the line loss multi-source diagnostic system 100 based on correlation analysis includes:Analysis module 110, insertion diagnosis mould
Block 120, dynamic diagnosis module 130 and comprehensive diagnosis module 140.
Wherein, analysis module 110 is used for facility information, collection information and the insertion information on the power network basis according to acquisition,
The key factor for influenceing line loss is analyzed using clustering methodology, obtained more because of cluster analysis result.
Wherein, the facility information on power network basis is used to accessing and proofreading equipment account data, mainly including transformer station, electricity
Factory, circuit, transformer, bus, switch etc..The base profile information of power network collection is used to access the related information of collection, mainly
Including meter archives, stoichiometric point archives, user profile, taiwan area information etc..Collection information is used to accessing and checking collection operation number
According to the main positive active indicating value including the equipment measuring point moment, positive active indicating value.
In one embodiment of the invention, the key factor for influenceing line loss comprises at least:Count date, load, line
Road length, model, load factor, sale of electricity composition, public specially change accounting, power grid architecture, distribution transforming tri-phase unbalance factor, distribution transforming low-voltage
Deng.Based on this, what is obtained is more because cluster analysis result mainly includes statistics date-line loss per unit, load-line loss per unit, circuit length
Degree-line loss per unit, model-line loss per unit, load factor-line loss per unit, sale of electricity composition-line loss per unit, public specially change accounting-line loss per unit, power network frame
Structure-line loss per unit, distribution transforming tri-phase unbalance factor-line loss per unit, distribution transforming low-voltage-line loss per unit etc..
In one embodiment of the invention, above-described clustering methodology, arbitrarily selected from n data object first
K object is as initial cluster center;And for remaining other objects, then according to their similarities with these cluster centres
(distance), (cluster centre representated by) cluster most like with it is assigned these to respectively;Then calculate and each obtained again
The cluster centre (averages of all objects in the cluster) newly clustered;This process is constantly repeated until canonical measure function starts
Untill convergence.Typically all using mean square deviation as canonical measure function.K cluster has the characteristics that:Each cluster to the greatest extent may be used in itself
Can it is compact, it is and separated as far as possible between respectively clustering.
Based on this, the key factor for influenceing line loss is analyzed using clustering methodology, obtained more because of cluster analysis knot
Fruit, specifically include:It it is specially { the statistics date it is determined that influenceing the clustering factor collection of line loss per unit;Load;Line length;Model;
Load factor;Sale of electricity is formed;It is public specially to become accounting;Power grid architecture;Tri-phase unbalance factor;Low-voltage }.Wherein, 1) the statistics date includes
Natural environmental factor, single line at all seasons in line loss per unit change profile situation and all circuits in same season
Line loss per unit change profile in section;2) what is simultaneously, suffered from this also has electricity sales amount composition (industry, business, resident etc.) to exist
Different red-letter days affect electricity sales amount distribution and total amount, influence line loss per unit indirectly;3) power grid architecture mainly considers radial line and ring
Influence of the net handle circuit to line loss per unit;4) load size, load current is influenceed, increases the impedance loss of circuit and distribution transforming,
Influence line loss per unit change;5) line length, the influence change profile of overall length and trunk line length to line loss per unit is considered;6) model,
Consider backbone wire type and the influence change profile with Variant number to line loss per unit;7) load factor, zero load, underloading, again are considered
Carry the influence change profile to line loss per unit;8) it is public specially to become accounting, consider because metering point mode and the public loss on transmission accounting that becomes are to circuit line
The influence change profile of loss rate, three kinds of situations of Main Analysis are (public specially to become accounting equilibrium;Public affairs become accounting and are more than specially change;Public affairs become accounting
Become less than special).
Insertion diagnostic module 120 is used to input multi-data source collection, carries out source end system long link transmission, and utilize default
Diagnostic rule, multi-source insertion diagnosis is carried out, and export multi-source insertion diagnostic result.Wherein, multi-data source collection comprises at least:Power network
Interpenetrating relationship data source is matched somebody with somebody by Unit account of plant data source, GIS graphics relationships data source, battalion.
Specifically, equipment is under the jurisdiction of different business department not from capital input, operation, monitoring in the management of power network present situation
With in management system, it is necessary to carry out the insertion of multi-data source operation system to the affiliated locking relation of equipment in Controlling line loss
Diagnosis, using default diagnostic rule, realize multi-source insertion diagnosis and result output.
Dynamic diagnosis module 130 is used to set line loss per unit Indexes Abnormality threshold value, and using correlation coefficient process to distribution and platform
Area's line losses indices carry out dynamic diagnosis extremely.In other words, i.e., it is abnormal to obtain line loss per unit by setting line loss per unit Indexes Abnormality threshold value
Distribution and taiwan area, dynamic diagnosis is carried out using correlation coefficient process extremely to distribution and taiwan area line losses indices.
Specifically, in one embodiment of the invention, in setting line loss per unit Indexes Abnormality threshold value, and coefficient correlation is used
When method carries out dynamic diagnosis extremely to distribution and taiwan area line losses indices, dynamic diagnosis module 130 is used for:Distribution line loss per unit is diagnosed to become
Change amount and each lower coefficient correlation hung public affairs and specially become electricity;Diagnose the phase relation of taiwan area line loss per unit variable quantity and user's electricity
Number;Diagnose the coefficient correlation of distribution line loss per unit variable quantity and tri-phase unbalance factor;Diagnose taiwan area line loss per unit variable quantity and low-voltage
Coefficient correlation.
Wherein, more specifically, dynamic diagnosis module 130 calculates diagnosis distribution line loss per unit variable quantity and each lower extension is public
Specially become the coefficient correlation of electricity, including:Obtain under the circuit in every distribution transforming preset time (being such as set as nearest some months)
Electricity, be set to { X1、X2、X3、、Xn};The variable quantity of line loss per unit in the circuit preset time is obtained, is set to { Y1、Y2、Y3、、Yn};
Calculate X, Y coefficient correlation;If coefficient correlation is more than preset value ρ0, then judge that the two is related.In other words, i.e., to negative high damage line
Road enters line and becomes relation investigation, seeks coefficient correlation.Specially become electricity by calculating the lower public affairs of hanging of circuit line loss per unit variable quantity and each
Coefficient correlation, if related, judge the public affairs specially modified line become relation it is wrong.
Similarly, the diagnosable coefficient correlation for calculating taiwan area line loss per unit variable quantity and user's electricity;Diagnosable calculating distribution line
The coefficient correlation of loss rate variable quantity and tri-phase unbalance factor;The diagnosable phase relation for calculating taiwan area line loss per unit variable quantity and low-voltage
Number.To reduce redundancy, no longer repeat one by one herein.
Comprehensive diagnosis module 140 is used for basis more because of cluster analysis result and multi-source insertion diagnostic result, with reference to influence line
The physics solid-state factor and dynamic statistics factor of damage carry out correlation analysis comprehensive diagnos to grid line loss, and auxiliary positioning is asked extremely
Topic, lift line loss managerial skills.
Specifically, distribution and taiwan area, each distribution and taiwan area contained in Grid possess different features, utilize
Coefficient correlation can represent two similarity degrees between wiring closet or taiwan area.Enter Correlation series to this to calculate and verify, can
Row distance cluster is entered with the close and distant distance to distribution and taiwan area;And then with reference to more because of cluster analysis result, examined with reference to multi-source insertion
Disconnected result and correlation analysis, grid line loss is carried out from the physics solid-state factor and dynamic statistics factor for influenceing line loss related
Property analysis integrated diagnosis, be business for the distribution or taiwan area, quick auxiliary positioning abnormal problem that the line loss per unit of input is abnormal
Personnel provide reference, lift line loss managerial skills.
To sum up, the line loss multi-source diagnostic system based on correlation analysis is somebody's turn to do, utilizes cluster analysis and correlation analysis side
Method carries out comprehensive diagnos to grid line loss, including:Multi-data source collection, correlation analysis, more because of cluster analysis, line losses indices
Abnormal, multi-source insertion diagnosis, result data collection etc..The multi-source diagnostic system be with power network basis facility information, collection information,
Information is penetrated as input, first, is analyzed using clustering methodology influenceing line loss key multiple-factor.Secondly, using correlation
Y-factor method Y carries out dynamic diagnosis extremely to distribution and taiwan area line losses indices, with reference to cluster and the insertion of coefficient correlation overview display multi-source
Variance data, auxiliary positioning problem, lifting power network source business department line loss governance efficiency.
It should be noted that it is somebody's turn to do the specific implementation and the present invention of the line loss multi-source diagnostic system based on correlation analysis
The specific implementation of the line loss multi-source diagnostic method based on correlation analysis of above-described embodiment is similar, specifically refers to method
Partial description, in order to reduce redundancy, here is omitted.
Line loss multi-source diagnostic system based on correlation analysis according to embodiments of the present invention, consolidate from the physics for influenceing line loss
State factor and dynamic statistics factor carry out correlation analysis comprehensive diagnos to grid line loss, and auxiliary positioning abnormal problem can be more straight
Influence of all kinds of factors to line loss is checked in sight, and line loss per unit abnormal index diagnostic result is that the line loss improvement of power network source business department carries
For a variety of auxiliary reference decision-makings, so as to effectively lift power network source business department line loss governance efficiency, and then line loss is lifted
Managerial skills.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or the spy for combining the embodiment or example description
Point is contained at least one embodiment or example of the present invention.In this manual, to the schematic representation of above-mentioned term not
Necessarily refer to identical embodiment or example.Moreover, specific features, structure, material or the feature of description can be any
One or more embodiments or example in combine in an appropriate manner.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that:Not
In the case of departing from the principle and objective of the present invention a variety of change, modification, replacement and modification can be carried out to these embodiments, this
The scope of invention is by claim and its equivalent limits.
Claims (10)
1. a kind of line loss multi-source diagnostic method based on correlation analysis, it is characterised in that comprise the following steps:
According to the facility information on the power network of acquisition basis, collection information and insertion information, using clustering methodology to influenceing line loss
Key factor analyzed, obtain more because of cluster analysis result;
Multi-data source collection is inputted, carries out source end system long link transmission, and utilizes default diagnostic rule, multi-source insertion is carried out and examines
It is disconnected, and export multi-source insertion diagnostic result;
Line loss per unit Indexes Abnormality threshold value is set, and Mobile state is entered to distribution and taiwan area line losses indices using correlation coefficient process extremely and examined
It is disconnected;
According to described more because of cluster analysis result and multi-source insertion diagnostic result, with reference to the physics solid-state factor for influenceing line loss
And dynamic statistics factor carries out correlation analysis comprehensive diagnos to grid line loss.
2. the line loss multi-source diagnostic method according to claim 1 based on correlation analysis, it is characterised in that the influence
The key factor of line loss comprises at least:Statistics date, load, line length, model, load factor, sale of electricity composition, public specially become account for
Than, power grid architecture, distribution transforming tri-phase unbalance factor, distribution transforming low-voltage.
3. the line loss multi-source diagnostic method according to claim 2 based on correlation analysis, it is characterised in that the setting
Line loss per unit Indexes Abnormality threshold value, and dynamic diagnosis is carried out using correlation coefficient process extremely to distribution and taiwan area line losses indices, enter one
Step includes:
Diagnose distribution line loss per unit variable quantity and each lower coefficient correlation hung public affairs and specially become electricity;
Diagnose the coefficient correlation of taiwan area line loss per unit variable quantity and user's electricity;
Diagnose the coefficient correlation of distribution line loss per unit variable quantity and tri-phase unbalance factor;
Diagnose the coefficient correlation of taiwan area line loss per unit variable quantity and low-voltage.
4. the line loss multi-source diagnostic method according to claim 3 based on correlation analysis, it is characterised in that the diagnosis
Distribution line loss per unit variable quantity and each lower coefficient correlation hung public affairs and specially become electricity, further comprise:
The electricity in every distribution transforming preset time under the circuit is obtained, is set to { X1、X2、X3、、Xn};
The variable quantity of line loss per unit in the circuit preset time is obtained, is set to { Y1、Y2、Y3、、Yn};
Calculate X, Y coefficient correlation;
If the coefficient correlation is more than preset value ρ0, then judge that the two is related.
5. the line loss multi-source diagnostic method according to claim 1 based on correlation analysis, it is characterised in that the majority
Comprised at least according to source collection:Interpenetrating relationship data source is matched somebody with somebody by grid equipment account data source, GIS graphics relationships data source, battalion.
A kind of 6. line loss multi-source diagnostic system based on correlation analysis, it is characterised in that including:
Analysis module, for the facility information on the power network basis according to acquisition, collection information and insertion information, utilize cluster analysis
Method is analyzed the key factor for influenceing line loss, is obtained more because of cluster analysis result;
Diagnostic module is penetrated, for inputting multi-data source collection, carries out source end system long link transmission, and advise using default diagnosis
Then, multi-source insertion diagnosis is carried out, and exports multi-source insertion diagnostic result;
Dynamic diagnosis module, for setting line loss per unit Indexes Abnormality threshold value, and using correlation coefficient process to distribution and taiwan area line loss
Indexes Abnormality carries out dynamic diagnosis;
Comprehensive diagnosis module, for according to described more because cluster analysis result and the multi-source penetrate diagnostic result, with reference to influence
The physics solid-state factor and dynamic statistics factor of line loss carry out correlation analysis comprehensive diagnos to grid line loss.
7. the line loss multi-source diagnostic system according to claim 6 based on correlation analysis, it is characterised in that the influence
The key factor of line loss comprises at least:Statistics date, load, line length, model, load factor, sale of electricity composition, public specially become account for
Than, power grid architecture, distribution transforming tri-phase unbalance factor, distribution transforming low-voltage.
8. the line loss multi-source diagnostic system according to claim 7 based on correlation analysis, it is characterised in that the dynamic
Diagnostic module is used for:
Diagnose distribution line loss per unit variable quantity and each lower coefficient correlation hung public affairs and specially become electricity;
Diagnose the coefficient correlation of taiwan area line loss per unit variable quantity and user's electricity;
Diagnose the coefficient correlation of distribution line loss per unit variable quantity and tri-phase unbalance factor;
Diagnose the coefficient correlation of taiwan area line loss per unit variable quantity and low-voltage.
9. the line loss multi-source diagnostic system according to claim 8 based on correlation analysis, it is characterised in that the dynamic
Diagnostic module diagnoses distribution line loss per unit variable quantity and each lower coefficient correlation hung public affairs and specially become electricity, including:
The electricity in every distribution transforming preset time under the circuit is obtained, is set to { X1、X2、X3、、Xn};
The variable quantity of line loss per unit in the circuit preset time is obtained, is set to { Y1、Y2、Y3、、Yn};
Calculate X, Y coefficient correlation;
If the coefficient correlation is more than preset value ρ0, then judge that the two is related.
10. the line loss multi-source diagnostic system according to claim 6 based on correlation analysis, it is characterised in that described more
Data source collection comprises at least:Interpenetrating relationship data source is matched somebody with somebody by grid equipment account data source, GIS graphics relationships data source, battalion.
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Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108520357A (en) * | 2018-04-10 | 2018-09-11 | 北京中电普华信息技术有限公司 | A kind of method of discrimination, device and the server of line loss abnormal cause |
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Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN201629574U (en) * | 2010-01-13 | 2010-11-10 | 山东电力集团公司 | Opening type power network loss real-time analysis system |
CN102254256A (en) * | 2011-07-21 | 2011-11-23 | 福建省电力有限公司福州电业局 | Transmission and distribution network integration comprehensive line loss management analysis system and processing flow thereof |
CN105279353A (en) * | 2014-06-17 | 2016-01-27 | 国网山西省电力公司电力科学研究院 | Low voltage transformer area line loss calculating method based on cluster characteristic and network vector |
CN105303469A (en) * | 2015-11-25 | 2016-02-03 | 广州供电局有限公司 | Method and system for line loss abnormal reason data mining and analysis |
US20170060161A1 (en) * | 2015-08-24 | 2017-03-02 | Dominion Resources, Inc. | Systems and methods for stabilizer control |
CN106570778A (en) * | 2016-10-14 | 2017-04-19 | 国网信通亿力科技有限责任公司 | Big data-based data integration and line loss analysis and calculation method |
CN107039970A (en) * | 2017-03-13 | 2017-08-11 | 广东电网有限责任公司信息中心 | Gong Biantai areas line loss per unit abnormal cause detection method and system |
CN107046286A (en) * | 2017-05-12 | 2017-08-15 | 国网上海市电力公司 | Low-voltage power distribution station area theoretical line loss caluclation method based on forward-backward sweep method |
-
2017
- 2017-09-14 CN CN201710828464.5A patent/CN107832855B/en active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN201629574U (en) * | 2010-01-13 | 2010-11-10 | 山东电力集团公司 | Opening type power network loss real-time analysis system |
CN102254256A (en) * | 2011-07-21 | 2011-11-23 | 福建省电力有限公司福州电业局 | Transmission and distribution network integration comprehensive line loss management analysis system and processing flow thereof |
CN105279353A (en) * | 2014-06-17 | 2016-01-27 | 国网山西省电力公司电力科学研究院 | Low voltage transformer area line loss calculating method based on cluster characteristic and network vector |
US20170060161A1 (en) * | 2015-08-24 | 2017-03-02 | Dominion Resources, Inc. | Systems and methods for stabilizer control |
CN105303469A (en) * | 2015-11-25 | 2016-02-03 | 广州供电局有限公司 | Method and system for line loss abnormal reason data mining and analysis |
CN106570778A (en) * | 2016-10-14 | 2017-04-19 | 国网信通亿力科技有限责任公司 | Big data-based data integration and line loss analysis and calculation method |
CN107039970A (en) * | 2017-03-13 | 2017-08-11 | 广东电网有限责任公司信息中心 | Gong Biantai areas line loss per unit abnormal cause detection method and system |
CN107046286A (en) * | 2017-05-12 | 2017-08-15 | 国网上海市电力公司 | Low-voltage power distribution station area theoretical line loss caluclation method based on forward-backward sweep method |
Non-Patent Citations (2)
Title |
---|
张波 等: "营配贯通台区线损异常数据治理方法探讨", 《浙江电力》 * |
陆巍: "基于大数据的配电网线损分析", 《电子技术与软件工程》 * |
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