CN110135495A - What a kind of grid equipment was efficiently ablated ice necessity sentences knowledge method and system - Google Patents
What a kind of grid equipment was efficiently ablated ice necessity sentences knowledge method and system Download PDFInfo
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- CN110135495A CN110135495A CN201910401329.1A CN201910401329A CN110135495A CN 110135495 A CN110135495 A CN 110135495A CN 201910401329 A CN201910401329 A CN 201910401329A CN 110135495 A CN110135495 A CN 110135495A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The present invention relates to electrical engineering technical fields, disclose a kind of grid equipment ice-melt necessity sentences knowledge method and system, effectively to excavate ice trouble related data, targetedly calculate ice trouble threat and melted ice necessity, operational efficiency of power grid under the conditions of icing is promoted；The method comprise the steps that choosing the history icing disaster related data in region to be analyzed, is pre-processed history icing disaster related data to obtain initial data set, initial data set is divided into training dataset and validation data set；Binary tree computation model is established, and whether verify binary tree computation model effective；Obtain grid equipment to be analyzed in the following setting time icing disaster related data input binary tree computation model, if the output valve of binary tree computation model belongs to first threshold range, sentence know the grid equipment there is no deicing necessity；If the output valve of binary tree computation model belongs to second threshold range, sentence know the grid equipment have deicing necessity.
Description
Technical field
The present invention relates to electrical engineering technical field more particularly to a kind of grid equipment ice-melt necessity sentence knowledge method and
System.
Background technique
In recent years, China's power grid melts clearing ice technology and equipment is fast-developing, has become winter power network safety operation
Important support.But ice, which is efficiently ablated, in power grid equips and can not use at any time, it is necessary in O&M and dispatcher to hidden danger point icing
Growth pattern judges, and relevant unit carries out melted ice planning deployment, and in place to hilllock, dispatching of power netwoks switches related technical personnel
After Load adjustment, melting de-icing work could normally carry out.Therefore, the de-icing work that melts of power grid sentences accurate melted ice necessity
Disconnected and sufficient melted ice time proposes requirement.For the actual conditions of electric power enterprise, on the one hand, traditional is melted
The judgement of ice necessity relies primarily on historical experience, and decision objectivity is insufficient, analyzes relevant environmental data limited, it tends to be difficult to suitable
Answer the quick variation of mima type microrelief hidden danger point grid equipment icing feature；On the other hand, ice is then efficiently ablated closer to ice trouble time of origin
Necessity is more obvious, and specific melted ice judgement and sufficient melted ice preparation are difficult to take into account.To avoid ice trouble, power grid fortune
Dimension department carries out that ice preparation is efficiently ablated on a large scale in advance, seriously consumes manpower and material resources, increases O&M cost.
Therefore, how effectively to excavate ice trouble related data, targetedly calculate ice trouble threat and melted ice necessity, promoted
Operational efficiency of power grid under the conditions of icing becomes a urgent problem.
Summary of the invention
Knowledge method and system are sentenced it is an object of that present invention to provide a kind of grid equipment ice-melt necessity, effectively to excavate ice
Evil related data targetedly calculates ice trouble threat and melted ice necessity, promotes operational efficiency of power grid under the conditions of icing.
To achieve the above object, the present invention provides what a kind of grid equipment was efficiently ablated ice necessity to sentence knowledge method, including with
Lower step:
S1: the history icing disaster related data in region to be analyzed is chosen by sets requirement, by the history icing disaster
Related data is pre-processed to obtain initial data set, and the initial data set is divided into training dataset and verify data
Collection；
S2: the output threshold range of binary decision tree is set as two, the training dataset is inputted into the y-bend and is determined
Whether plan tree establishes binary tree computation model, and effective using the validation data set verifying binary tree computation model, if
It is invalid then adjust the historical heat monitoring data and re-establish binary tree computation model, until the binary tree computation model has
Effect；
S3: icing disaster related data input described two of the grid equipment to be analyzed in the following setting time is obtained
Fork tree computation model, if the output valve of the binary tree computation model belongs to first threshold range, sentence know the grid equipment do not have
There is deicing necessity；If the output valve of binary tree computation model belongs to second threshold range, sentence know the grid equipment have deicing
Necessity.
Preferably, it in the S1, is pre-processed the history icing disaster related data to obtain initial data set, be had
Body the following steps are included:
By the history icing disaster related data according to whether occur icing disaster divided to obtain it is disaster-stricken and not by
Two class data of calamity；
The two classes data are arranged according to chronological order respectively, by all nonnumeric data in data
Quantization obtains initial data set.
Preferably, the S2 specifically includes the following steps:
S21: assuming that x is input variable, y is output variable, and an input variable is considered as a region, establishes formula:
In formula, j is each feature in region, and s is the value of each feature, R1For the first sub-regions, R2It is second
Subregion, c1For section R1Interior output average value, c2For section R2Interior output average value；
Wherein:
In formula, x ∈ Rm, m=1,2, RmFor m-th of region of division, cmFor the output average value in m-th of region；
The each value s for successively traversing each feature j calculates the error of each current possible cut-off, selection
Make the smallest cut-off of error as optimal cut-off s, the corresponding variable of the optimal cut-off be considered as optimal cutting variable j,
Selection is to (j, s)；
S22: with it is selected be two sub-regions by region division to (j, s), and determine corresponding output valve, establish y-bend
Decision tree formula are as follows:
R1(j, s)=and x | x(j)≤s},R2(j, s)=and x | x(j)> s } (3)
S23: above-mentioned S21-S22 is repeated, continues to divide two sub-regions, the input space is divided into m region
R1, R2, R3 ..., Rm generate binary tree computation model, calculation formula are as follows:
In formula, I is weight coefficient, and M can value range for m's.
Preferably, in the S2, the output threshold range of binary decision tree is set as two, respectively includes first threshold model
0~0.3 is enclosed, second threshold range 0.7~1.
Preferably, the S3 specifically includes the following steps:
Validation data set is inputted into binary tree computation model, by the identifying result of binary tree computation model and practical icing calamity
Evil result be compared, if be more than 85% judging result it is consistent with actual result, then it is assumed that binary tree computation model is effective.
Preferably, it is further comprised the steps of: after the S3
S4: the related data of icing disaster described in S3 and practical deicing situation are included into historical data, updated current
Binary tree computation model, the binary tree computation model after being optimized is for calculating next time；
S5: repeating S3-S4, realizes and updates to the iteration of binary tree computation model.
Preferably, in the S1, the icing disaster related data includes icing Tripping data, broken string data and falls
One of tower data or several any combination.
Preferably, the sets requirement is the essential characteristic number that the information that icing disaster related data includes is disaster-stricken equipment
According to environmental characteristic data when occurring with disaster.
As a general technical idea, knowledge system is sentenced the present invention also provides a kind of melted ice necessity of grid equipment,
Including memory, processor and it is stored in the computer program that can be run on the memory and on the processor, it is special
Sign is, when the processor executes described program the step of the realization above method.
The invention has the following advantages:
What the present invention provided a kind of grid equipment ice-melt necessity sentences knowledge method and system, and the history for treating analyzed area is covered
Ice damage evil related data carry out calculating analysis, establish binary tree computation model, then will need to sentence knowledge grid equipment it is real-time
Icing disaster related data inputs the binary tree computation model, can fast and accurately obtain identifying result, can provide icing
Ice necessity judging result is efficiently ablated in crucial hidden danger point, and de-icing work development is melted in guidance, and service power grid O&M, scheduling etc. are related specially
Industry.
Below with reference to accompanying drawings, the present invention is described in further detail.
Detailed description of the invention
The attached drawing constituted part of this application is used to provide further understanding of the present invention, schematic reality of the invention
It applies example and its explanation is used to explain the present invention, do not constitute improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is that the grid equipment of the preferred embodiment of the present invention is efficiently ablated ice necessity and sentences knowledge method flow diagram.
Specific embodiment
The embodiment of the present invention is described in detail below in conjunction with attached drawing, but the present invention can be defined by the claims
Implement with the multitude of different ways of covering.
Unless otherwise defined, all technical terms used hereinafter and the normally understood meaning of those skilled in the art
It is identical." first ", " second " used in present patent application specification and claims and similar word are simultaneously
Any sequence, quantity or importance are not indicated, and are intended merely to facilitate and corresponding components are distinguished.Equally, " one
It is a " or the similar word such as " one " do not indicate that quantity limits, but indicate that there are at least one.
Embodiment 1
Referring to Fig. 1, knowledge method is sentenced the present embodiment provides a kind of melted ice necessity of grid equipment, comprising the following steps:
S1: being chosen the history icing disaster related data in region to be analyzed by sets requirement, and history icing disaster is related
Data are pre-processed to obtain initial data set, and initial data set is divided into training dataset and validation data set；
S2: the output threshold range of binary decision tree is set as two, training dataset input binary decision tree is established
Binary tree computation model, and it is whether effective using validation data set verifying binary tree computation model, history heat is adjusted if invalid
Point monitoring data re-establish binary tree computation model, until binary tree computation model is effective；
S3: it obtains icing disaster related data input binary tree of the grid equipment to be analyzed in the following setting time and calculates
Model, if the output valve of binary tree computation model belongs to first threshold range, sentence know the grid equipment there is no deicing necessity；
If the output valve of binary tree computation model belongs to second threshold range, sentence know the grid equipment have deicing necessity.
The melted ice necessity of above-mentioned grid equipment sentences knowledge method, treats the history icing disaster dependency number of analyzed area
According to calculating analysis is carried out, binary tree computation model is established, then will need to sentence the real-time icing disaster correlation of the grid equipment of knowledge
Data input the binary tree computation model, can fast and accurately obtain identifying result, can provide icing key hidden danger point and melt
De-icing work development, the relevant specialities such as service power grid O&M, scheduling are melted in deicing necessity judging result, guidance.
Specifically, region to be analyzed is chosen in the late three decades because of the related data of transmission line of electricity disaster caused by icing, the phase
Pass data are icing Tripping data, broken string data and the data of falling tower etc..It is required that the data include the essential characteristic of disaster-stricken equipment
Environmental data etc. when data, disaster occur, specifically include: shaft tower anti-ice grade, route anti-ice grade, route are away from ground height
Degree, the disaster-stricken time, gas epidemic disaster, wind speed, wind direction, since when time Precipitation Process adds up precipitation to disaster day at line alignment
Amount, disaster occur preceding 24 hours accumulative precipitation, cause calamity ice covering thickness, microfeature (being mima type microrelief or non-mima type microrelief), institute
In site elevation etc..Meanwhile it collecting according to identical data demand using disaster-stricken equipment as other institutes within the scope of the center of circle, radius 2km
There are route and shaft tower in the relevant information of disaster-stricken same time.
Further, by history icing disaster related data according to whether occur icing disaster divided to obtain it is disaster-stricken and
Not disaster-stricken two classes data, including " disaster-stricken " and " not disaster-stricken " two class.Two class data are arranged according to chronological order respectively
All nonnumeric data quantizations in data are obtained initial data set by column.In the present embodiment, when carrying out data quantization,
For example, line alignment Dong-west is to being set as 1, north-south is to being set as 2, and northeast-southwest is to being set as 3, and northwest-southeast is to being set as 4；It is disaster-stricken
30 divide when time is the morning 8, are denoted as 8.5, wind direction is that east wind is set as 1, and west wind is set as 2, and south wind is set as 3, and north wind is set as 4, the southeast
Wind is set as 5, and northeaster is set as 6, and southwester is set as 7, and northwester is set as 8, and microfeature is that mima type microrelief is set as 1, non-mima type microrelief
It is set as 0.And column " whether disaster-stricken " item is finally added in all data, the data that icing disaster has occurred are assigned a value of 1, are not sent out
The data of raw icing disaster are assigned a value of 0.
70% that above-mentioned primary data is concentrated is used as training set, and in addition 30% as verifying collection.It is built according to the training set
Vertical binary decision tree, the specific steps are as follows:
Assuming that x is input variable, y is output variable, and an input variable is considered as a region, establishes formula:
In formula, j is each feature in region, and s is the value of each feature, R1For the first sub-regions, R2It is second
Subregion, c1For section R1Interior output average value, c2For section R2Interior output average value；
Wherein:
In formula, x ∈ Rm, m=1,2, RmFor m-th of region of division, cmFor the output average value in m-th of region；
The each value s for successively traversing each feature j calculates the error of each current possible cut-off, selection
Make the smallest cut-off of error as optimal cut-off s, the corresponding variable of the optimal cut-off be considered as optimal cutting variable j,
Selection is to (j, s)；
With it is selected be two sub-regions by region division to (j, s), and determine corresponding output valve, in the present embodiment,
The output threshold range of binary decision tree is set as two, respectively includes first threshold range 0~0.3, second threshold range 0.7
If~1 output threshold range shows that no ice trouble threatens in the first threshold range, necessary without ice is efficiently ablated, if output threshold value model
It is trapped among within the scope of the second threshold, shows there is ice trouble threat, have melted ice necessary.
Establish binary decision tree formula are as follows:
R1(j, s)=and x | x(j)≤s},R2(j, s)=and x | x(j)> s } (3)
Continue to divide two sub-regions according to above-mentioned partiting step, the input space be divided into m region R1,
R2, R3 ..., Rm generate binary tree computation model, calculation formula are as follows:
In formula, I is weight coefficient, and M can value range for m's.
Further, by validation data set input binary tree computation model, by the identifying result of binary tree computation model with
Practical icing disaster result is compared, if be more than 85% judging result it is consistent with actual result, then it is assumed that binary tree calculate
Model is effective.
It in the present embodiment, is verified using 65 groups of data, wherein 9 groups the device data of ice trouble does not occur in binary tree
Output result in computation model is greater than 0.7, i.e., algorithm, which is judged as, has occurred ice trouble, sentences knowledge mistake, other with assessment situation
Unanimously, it is correct to sentence knowledge.To the disaster-stricken identifying result in verification data are as follows: it is correct to sentence knowledge all greater than 0.7 for output result.Therefore
Sentence and know success rate 86%, it is believed that the binary tree computation model is effective.
Specifically, by taking certain month certain a year icing process as an example, the prediction meteorological data of 35kV route, prediction 1-7
Its ice covering thickness data, terrain environment feature, equipment characteristic etc. input above-mentioned binary tree computation model, and carrying out melted ice must
The property wanted judges that obtained identifying result is as follows:
1 powerline ice-covering disaster related data of table and identifying result
According to above-mentioned table 1 it is found that the transmission line of electricity can be disaster-stricken, the danger of falling tower easily occurs, needs that ice is efficiently ablated in time.
Verified, when secondary icing process is in tower, 4 adjacent base shaft towers occur different degrees of the base shaft tower of the route
Deformation.In the present embodiment, the data in above-mentioned table 1 are included into historical data and update current binary tree computation model, are obtained
Binary tree computation model after optimization calculates for next, and when being calculated every time using the binary tree computation model, weight
Multiple S3-S4 is realized and is updated to the iteration of binary tree computation model.It, can be with by being automatically updated to binary tree computation model
Guarantee that binary tree computation model can maintain validity and accuracy, inaccuracy caused by avoiding the historical data chosen remote
Sentence the automation and validity of knowledge as a result, improving grid equipment and ice being efficiently ablated.
Embodiment 2
With above method embodiment correspondingly, the present embodiment provides a kind of grid equipment be efficiently ablated ice necessity sentence knowledge system
System, including memory, processor and is stored in the computer program that can be run on the memory and on the processor, institute
State the step of realizing the above method when processor executes described program.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (9)
1. a kind of melted ice necessity of grid equipment sentences knowledge method, which comprises the following steps:
S1: being chosen the history icing disaster related data in region to be analyzed by sets requirement, and the history icing disaster is related
Data are pre-processed to obtain initial data set, and the initial data set is divided into training dataset and validation data set；
S2: the output threshold range of binary decision tree is set as two, the training dataset is inputted into the binary decision tree
Binary tree computation model is established, and whether effective using the validation data set verifying binary tree computation model, if in vain
It then adjusts the historical heat monitoring data and re-establishes binary tree computation model, until the binary tree computation model is effective；
S3: it obtains icing disaster related data of the grid equipment to be analyzed in the following setting time and inputs the binary tree
Computation model, if the output valve of the binary tree computation model belongs to first threshold range, sentence know the grid equipment do not remove
Ice necessity；If the output valve of binary tree computation model belongs to second threshold range, sentences and know the grid equipment and have deicing necessary
Property.
2. the melted ice necessity of grid equipment according to claim 1 sentences knowledge method, which is characterized in that in the S1,
It is pre-processed the history icing disaster related data to obtain initial data set, specifically includes the following steps:
By the history icing disaster related data according to whether occur icing disaster divided to obtain it is disaster-stricken and not disaster-stricken two
Class data；
The two classes data are arranged according to chronological order respectively, by all nonnumeric data quantizations in data
Obtain initial data set.
3. the melted ice necessity of grid equipment according to claim 1 sentences knowledge method, which is characterized in that the S2 is specific
The following steps are included:
S21: assuming that x is input variable, y is output variable, and an input variable is considered as a region, establishes formula:
In formula, j is each feature in region, and s is the value of each feature, R1For the first sub-regions, R2For second sub-district
Domain, c1For section R1Interior output average value, c2For section R2Interior output average value；
Wherein:
In formula, x ∈ Rm, m=1,2, RmFor m-th of region of division, cmFor the output average value in m-th of region；
The each value s for successively traversing each feature j, calculates the error of each current possible cut-off, and selection makes to miss
The smallest cut-off of difference is considered as optimal cutting variable j as optimal cut-off s, by the corresponding variable of the optimal cut-off, selects
To (j, s)；
S22: with it is selected be two sub-regions by region division to (j, s), and determine corresponding output valve, establish Binary decision
Set formula are as follows:
R1(j, s)=and x | x(j)≤s},R2(j, s)=and x | x(j)> s } (3)
S23: repeating above-mentioned S21-S22, continue to divide two sub-regions, the input space is divided into m region R1,
R2, R3 ..., Rm generate binary tree computation model, calculation formula are as follows:
In formula, I is weight coefficient, and M can value range for m's.
4. the melted ice necessity of grid equipment according to claim 1 sentences knowledge method, which is characterized in that in the S2,
The output threshold range of binary decision tree is set as two, respectively includes first threshold range 0~0.3, second threshold range 0.7
~1.
5. the melted ice necessity of grid equipment according to claim 1 sentences knowledge method, which is characterized in that the S3 is specific
The following steps are included:
Validation data set is inputted into binary tree computation model, by the identifying result of binary tree computation model and practical icing disaster knot
Fruit is compared, if be more than 85% judging result it is consistent with actual result, then it is assumed that binary tree computation model is effective.
6. the melted ice necessity of grid equipment according to claim 1 sentences knowledge method, which is characterized in that after the S3
It further comprises the steps of:
S4: the related data of icing disaster described in S3 and practical deicing situation are included into historical data, update current y-bend
Computation model is set, the binary tree computation model after being optimized calculates for next time；
S5: repeating S3-S4, realizes and updates to the iteration of binary tree computation model.
7. the melted ice necessity of grid equipment according to claim 1 sentences knowledge method, which is characterized in that in the S1,
The icing disaster related data includes icing Tripping data, broken string one of data and the data of falling tower or several
Any combination.
8. the melted ice necessity of grid equipment according to claim 1 sentences knowledge method, which is characterized in that the setting is wanted
Seek the environmental characteristic when essential characteristic data and disaster generation that the information for including for icing disaster related data is disaster-stricken equipment
Data.
9. what a kind of grid equipment was efficiently ablated ice necessity sentences knowledge system, including memory, processor and it is stored in the memory
Computer program that is upper and can running on the processor, which is characterized in that the processor is realized when executing described program
The step of the claims 1-8 any described method.
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Application publication date: 20190816 |