CN110020756A - A kind of Transmission Expansion Planning in Electric method based on big data cluster and Interest frequency - Google Patents
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Abstract
The present invention relates to a kind of Transmission Expansion Planning in Electric methods based on big data cluster and Interest frequency, from all kinds of indexs of transmission line of electricity, the respective characteristic distributions of each index are determined using the method for cluster, sorted out according to the necessity of index and line construction, determine the construction priority of each transmission line of electricity, consider that economic electric transmission ability and safe stability of power system plan the route of different construction grades step by step simultaneously, and establishes corresponding Transmission Expansion Planning in Electric model.The present invention can overcome the blindness of Transmission Expansion Planning in Electric in the prior art.
Description
Technical field
The present invention relates to power grid construction technical field, especially a kind of power transmission network based on big data cluster and Interest frequency
Planing method.
Background technique
Under big data era, data use the most important thing for increasingly becoming enterprise development.With national power demand
It is growing, transmission line of electricity daily caused by operation data it is thousands of.How efficiently and accurately is electricity using these data
Net operation provides foundation with planning, becomes a research emphasis of each grid company.In order to play the potential valence of these data
Value, has important practical significance to making rational planning for for power transmission network.
All transmission lines of electricity are carried out unified optimization mostly by existing Electric power network planning method, or to a certain part route into
Professional etiquette is drawn, and optimization object is excessive, is difficult to obtain legitimate result or part route underload fortune applied to then existing in bulk power grid
Capable problem does not carry out one for the different of all kinds of indexs such as region, payload size and ability to transmit electricity locating for different routes
The Transmission Expansion Planning in Electric of a synthesis, it is inefficient and have certain blindness.
Summary of the invention
In view of this, the purpose of the present invention is to propose to a kind of Transmission Expansion Planning in Electric sides based on big data cluster and Interest frequency
Method can scientifically improve the efficiency of power network line planning, overcome blindness in the prior art.
The present invention is realized using following scheme: a kind of Transmission Expansion Planning in Electric method based on big data cluster and Interest frequency,
Specifically includes the following steps:
Step S1: obtaining the operation data of power grid, obtains a series of overall targets for being able to reflect route operating status；
Step S2: passing through K-medoids clustering algorithm for the data of the different indexs of each route of power grid respectively and cluster,
Obtain the distribution situation of classification belonging to each index of each route and each routing indicator data；
Step S3: distribution results and power grid structures according to each routing indicator data determine route in the important of the whole network
Property, and line construction priority is divided with this；
Step S4: transmission tine planning model is built by class according to different priorities；
Step S5: it solves the layout of roads model of each priority respectively with particle swarm optimization algorithm, obtains the overall situation of model
Optimal solution；
Step S6: the final network optimization scheme of the whole network is obtained according to the result of step S5.
Further, step S2 specifically includes the following steps:
Step S21: in D overall target of each route, there are N number of data sample, N under each overall targetd(d=
It 1.2.3...D is) the corresponding data sample of d-th of overall target, from NdIn randomly select kd(d=1.2.3...D) a data
As particle Oj(d)(j=1.2.3...kd), particle namely initial cluster center point；
Step S22: with kdFor parameter, the N by each achievement data in addition to cluster centre point respectivelyd-kdA data according to
K is assigned to the nearest principle of cluster centre pointdIn a class, wherein the calculating of the distance the index for being used to sort out each data
Using following formula:
distanceij(d)=| | Xi-Xj||1
In formula, distanceij(d) indicate that i-th of non-central particle is between j-th of center in d-th of overall target
Distance, XiWith XjRespectively correspond the value of i-th non-central particle and j-th of center in d-th of overall target；
Step S23: for the k under d-th of overall target of each routedIn a class, the non-cluster center points of sequential selection
According to Orandom, calculate OrandomWith Oj(d)(j=1.2.3...kd) cost function E (O after exchanger), cost function E (Or) namely
For OrandomWith the distance between other data points in class and same Oj(d)(j=1.2.3...kd) and class between other data points
The difference of distance sum, calculation formula are as follows:
D (r, O in formularandom), d (r, Oj) it is respectively Orandom、OjWith the distance between other data points in class, viIt is i-th
The track data sample set of a classification, r are track data sample；
Step S24: cost function E (O is calculatedr), if E (Or) < 0, then enable OrandomAs new cluster centre point, generate
One group of new cluster class；Otherwise it does not exchange, retains former central point and cluster result；
Step S25: step S22- step S24 is repeated, until reaching the condition of convergence or maximum number of iterations.
Further, step S3 specifically: by the classification of each index clustered according to necessity with transmission line construction
Property size assign corresponding weight, in conjunction with corresponding cluster result, calculating enlarging of all categories urgently spends, finally according to enlarging
The size result urgently spent divides the priority of line construction.
Further, step S4 specifically: construct corresponding transmission line of electricity network differentiation mathematical model；In view of difference
Under environment, the influence of different loads and different ability to transmit electricity to the necessity of transmission line construction it is different, by ability to transmit electricity pole
Limit and economic electric transmission ability and traditional expansion investments expense, power grids circuits operating cost and rejection penalty problem are examined jointly
Consider, obtains power transmission network expansion investments expense respectively and power grids circuits operating cost is the mathematical model of objective function, following institute
Show:
In formula, Y is annual fee, including construction cost and operating cost, and A is the investment cost of every km new route；xlFor l
The length of new route；N is to allow newly-built power transmission line number；B is year cost of losses coefficient, γlFor the resistance of branch l；Pl
The active power conveyed for branch l under normal operation；M is the transmission of electricity corridor number of all routes；C is penalty coefficient；W is total
Overload amount, PlmaxFor the transimission power upper limit of route l；Ω is overload sets of lines.
Further, step S5 specifically: the model in step S4 is resolved using particle swarm algorithm, obtains and enables Y
When minimum, the optimal item number of each node line enlarging, to realize classification differentiation optimization.
The present invention determines that the respective distribution of each index is special from all kinds of indexs of transmission line of electricity, using the method for cluster
Point is sorted out according to the necessity of index and line construction, determines the construction priority of each transmission line of electricity, while considering to pass through
Ji ability to transmit electricity and safe stability of power system plan the route of different construction grades step by step, and establish corresponding
Transmission Expansion Planning in Electric model.
Compared with prior art, the invention has the following beneficial effects: using method of the invention, it is possible to overcome the prior art
Middle layout of roads is inefficient and has the problem of certain blindness, from all kinds of indexs, while using Clustering with
And particle swarm algorithm, the Transmission Expansion Planning in Electric model of corresponding science can be established.
Detailed description of the invention
Fig. 1 is the flow diagram of the embodiment of the present invention.
Fig. 2 is the method frame schematic diagram of the embodiment of the present invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
It is noted that described further below be all exemplary, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms used herein has usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singular
Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
As shown in Figure 1 and Figure 2, a kind of transmission of electricity network planning based on big data cluster and Interest frequency is present embodiments provided
The method of drawing, specifically includes the following steps:
Step S1: obtaining the operation data of power grid, obtains a series of overall targets for being able to reflect route operating status；
Step S2: passing through K-medoids clustering algorithm for the data of the different indexs of each route of power grid respectively and cluster,
Obtain the distribution situation of classification belonging to each index of each route and each routing indicator data；
Step S3: distribution results and power grid structures according to each routing indicator data determine route in the important of the whole network
Property, and line construction priority is divided with this；
Step S4: transmission tine planning model is built by class according to different priorities；
Step S5: it solves the layout of roads model of each priority respectively with particle swarm optimization algorithm, obtains the overall situation of model
Optimal solution；
Step S6: the final network optimization scheme of the whole network is obtained according to the result of step S5.
Preferably, in the present embodiment, in step S1, existing power network line operating status overall target data acquisition: from
The data relevant to operation of power networks level of efficiency that power grid PMS2.0 system, PIS2.0 system obtain, including historical data and
Statistical data, the relationship of comprehensive various factors and the transmission line of electricity ability to transmit electricity limit, in conjunction with the action character of different data, simultaneously
Consider that the requirement of economy and reliability, combing and recombination obtain as follows convenient for the overall target of analysis circuit operation:
1, line load rate=[route peak load/route maximum carries capacity] * 100%；
2, peak load growth rate=[(peak load average value summation/the first two years peak load average value is total at the end of third
With) -1] * 100%；
3, line load rate=[average burden with power/highest burden with power] * 100% (in certain time)；
4, benchmark is discounted (old) rate=[accumulated depreciation value/initial asset value] * 100%；
5, the out-of-limit probability of route and confidence level (counted and obtained by out-of-limit record)；
6, system overload probability；
But the present embodiment is not limited to above-mentioned six kinds of indexs.
In the present embodiment, step S2 specifically includes the following steps:
Step S21: in D overall target of each route, there are N number of data sample, N under each overall targetd(d=
It 1.2.3...D is) the corresponding data sample of d-th of overall target, from NdIn randomly select kd(d=1.2.3...D) a data
As particle Oj(d)(j=1.2.3...kd), particle namely initial cluster center point；
Step S22: with kdFor parameter, the N by each achievement data in addition to cluster centre point respectivelyd-kdA data according to
K is assigned to the nearest principle of cluster centre pointdIn a class, wherein the calculating of the distance the index for being used to sort out each data
Using following formula:
distanceij(d)=| | Xi-Xj||1
In formula, distanceij(d) indicate that i-th of non-central particle is between j-th of center in d-th of overall target
Distance, XiWith XjRespectively correspond the value of i-th non-central particle and j-th of center in d-th of overall target；
Step S23: for the k under d-th of overall target of each routedIn a class, the non-cluster center points of sequential selection
According to Orandom, calculate OrandomWith Oj(d)(j=1.2.3...kd) cost function E (O after exchanger), cost function E (Or) namely
For OrandomWith the distance between other data points in class and same Oj(d)(j=1.2.3...kd) and class between other data points
The difference of distance sum, calculation formula are as follows:
D (r, O in formularandom), d (r, Oj) it is respectively Orandom、OjWith the distance between other data points in class, viIt is i-th
The track data sample set of a classification, r are track data sample；
Step S24: cost function E (O is calculatedr), if E (Or) < 0, then enable OrandomAs new cluster centre point, generate
One group of new cluster class；Otherwise it does not exchange, retains former central point and cluster result；
Step S25: step S22- step S24 is repeated, until reaching the condition of convergence or maximum number of iterations.
In the present embodiment, step S3 specifically: by the classification of each index clustered according to transmission line construction
Necessity size assigns corresponding weight and calculates enlarging of all categories in conjunction with corresponding cluster result and urgently spend, last basis
The size result urgently spent is extended to divide the priority of line construction.That is: the classification of the route of more worried construction is corresponding
Higher construction priority is just drafted and divides three construction priority, respectively level-one (route for being badly in need of enlarging), second level (one
As the route that needs to extend) and three-level (route for not needing enlarging).
In the present embodiment, step S4 specifically: construct corresponding transmission line of electricity network differentiation mathematical model；It considers
Under varying environment, the influence of different loads and different ability to transmit electricity to the necessity of transmission line construction it is different, by energy of transmitting electricity
The power limit and economic electric transmission ability and traditional expansion investments expense, power grids circuits operating cost and rejection penalty problem are total
With consideration, power transmission network expansion investments expense is obtained respectively and power grids circuits operating cost is the mathematical model of objective function, such as
Shown in lower:
In formula, Y is annual fee, including construction cost and operating cost, and A is the investment cost of every km new route；xlFor l
The length of new route；N is to allow newly-built power transmission line number；B is year cost of losses coefficient, γlFor the resistance of branch l；Pl
The active power conveyed for branch l under normal operation；M is the transmission of electricity corridor number of all routes；C is penalty coefficient；W is total
Overload amount, PlmaxFor the transimission power upper limit of route l；Ω is overload sets of lines.
In the present embodiment, step S5 specifically: the model in step S4 is resolved using particle swarm algorithm, is obtained
When enabling Y minimum, the optimal item number of each node line enlarging, to realize classification differentiation optimization.
Preferably, in the present embodiment, specific step is as follows for particle swarm algorithm:
(a) population, including population size N, the position x of each particle (route extends item number) are initializediWith speed vi；
(b) the fitness value F of each particle is calculatedi[i]；
(c) to each particle, with its fitness value Fi[i] and individual extreme value Pbest(i) compare, if Fi[i] > pbest
(i), then F is usedi[i] substitution point Pbest(i)；
(d) to each particle, with its fitness value Fi[i] and global extremum gbestCompare, if Fi[i] > gbest, then
Use Fi[i] replaces gbest；
(e) the position x of more new particle according to the following formulaiWith speed vi；
In formula: c1、c2For Studying factors or accelerator coefficient, generally normal number, generally equal to 2；γ1、γ2It is the section
Interior equally distributed pseudo random number, value range are [0,1]；
If (f) meeting termination condition (error is good enough or reaches maximum cycle) to exit, otherwise (b) is returned to.
By above-mentioned algorithm steps, model optimal solution can be obtained.
The Electric Power Network Planning model of above-mentioned different priorities is solved respectively, the program results of the whole network can be obtained.
The present embodiment determines that the respective distribution of each index is special from all kinds of indexs of transmission line of electricity, using the method for cluster
Point is sorted out according to the necessity of index and line construction, determines the construction priority of each transmission line of electricity, while considering to pass through
Ji ability to transmit electricity and safe stability of power system plan the route of different construction grades step by step, and establish corresponding
Transmission Expansion Planning in Electric model.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The above described is only a preferred embodiment of the present invention, being not that the invention has other forms of limitations, appoint
What those skilled in the art changed or be modified as possibly also with the technology contents of the disclosure above equivalent variations etc.
Imitate embodiment.But without departing from the technical solutions of the present invention, according to the technical essence of the invention to above embodiments institute
Any simple modification, equivalent variations and the remodeling made, still fall within the protection scope of technical solution of the present invention.
Claims (5)
1. a kind of Transmission Expansion Planning in Electric method based on big data cluster and Interest frequency, it is characterised in that: specifically include following step
It is rapid:
Step S1: obtaining the operation data of power grid, obtains a series of overall targets for being able to reflect route operating status；
Step S2: the data of the different indexs of each route of power grid are passed through into K-medoids clustering algorithm respectively and are clustered, are obtained
The distribution situation of classification belonging to each each index of route and each routing indicator data；
Step S3: distribution results and power grid structures according to each routing indicator data determine route in the importance of the whole network, and
Line construction priority is divided with this；
Step S4: transmission tine planning model is built by class according to different priorities；
Step S5: it solves the layout of roads model of each priority respectively with particle swarm optimization algorithm, obtains the global optimum of model
Solution；
Step S6: the final network optimization scheme of the whole network is obtained according to the result of step S5.
2. a kind of Transmission Expansion Planning in Electric method based on big data cluster and Interest frequency according to claim 1, feature
Be: step S2 specifically includes the following steps:
Step S21: in D overall target of each route, there are N number of data sample, N under each overall targetd(d=
It 1.2.3...D is) the corresponding data sample of d-th of overall target, from NdIn randomly select kd(d=1.2.3...D) a data
As particle Oj(d)(j=1.2.3...kd), particle namely initial cluster center point；
Step S22: with kdFor parameter, the N by each achievement data in addition to cluster centre point respectivelyd-kdA data according to it is poly-
The nearest principle of class central point assigns to kdIn a class, wherein the calculating of the distance the index for being used to sort out each data uses
Following formula:
distanceij(d)=| | Xi-Xj||1
In formula, distanceij(d) indicate that i-th of non-central particle is the distance between to j-th of center in d-th of overall target,
XiWith XjRespectively correspond the value of i-th non-central particle and j-th of center in d-th of overall target；
Step S23: for the k under d-th of overall target of each routedIn a class, the non-cluster center point data of sequential selection
Orandom, calculate OrandomWith Oj(d)(j=1.2.3...kd) cost function E (O after exchanger), cost function E (Or) be also
OrandomWith the distance between other data points in class and same Oj(d)(j=1.2.3...kd) and class between other data points away from
Difference from sum, calculation formula are as follows:
D (r, O in formularandom), d (r, Oj) it is respectively Orandom、OjWith the distance between other data points in class, viFor i-th of class
Other track data sample set, r are track data sample；
Step S24: cost function E (O is calculatedr), if E (Or) < 0, then enable OrandomAs new cluster centre point, generate new
One group of cluster class；Otherwise it does not exchange, retains former central point and cluster result；
Step S25: step S22- step S24 is repeated, until reaching the condition of convergence or maximum number of iterations.
3. a kind of Transmission Expansion Planning in Electric method based on big data cluster and Interest frequency according to claim 1, feature
It is: step S3 specifically: assign the classification of each index clustered to phase according to the necessity size of transmission line construction
The weight answered calculates enlarging of all categories and urgently spends in conjunction with corresponding cluster result, the size finally urgently spent according to enlarging
As a result the priority of line construction is divided.
4. a kind of Transmission Expansion Planning in Electric method based on big data cluster and Interest frequency according to claim 1, feature
It is: step S4 specifically: construct corresponding transmission line of electricity network differentiation mathematical model；In view of under varying environment, it is different
The influence of load and different ability to transmit electricities to the necessity of transmission line construction is different, and the ability to transmit electricity limit and economy is defeated
Electric energy power and traditional expansion investments expense, power grids circuits operating cost and rejection penalty problem consider jointly, obtain respectively
Power transmission network expansion investments expense and power grids circuits operating cost are the mathematical model of objective function, as follows:
In formula, Y is annual fee, including construction cost and operating cost, and A is the investment cost of every km new route；xlIt is new for the l articles
Build the length of route；N is to allow newly-built power transmission line number；B is year cost of losses coefficient, γlFor the resistance of branch l；PlIt is positive
The active power that branch l is conveyed under normal operating condition；M is the transmission of electricity corridor number of all routes；C is penalty coefficient；W is total excessively negative
Lotus amount, PlmaxFor the transimission power upper limit of route l；Ω is overload sets of lines.
5. a kind of Transmission Expansion Planning in Electric method based on big data cluster and Interest frequency according to claim 4, feature
It is: step S5 specifically: the model in step S4 is resolved using particle swarm algorithm, is obtained when enabling Y minimum, each node
The optimal item number of route enlarging, to realize classification differentiation optimization.
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| CN111144541A (en) * | 2019-12-12 | 2020-05-12 | 中国地质大学（武汉） | Microwave filter debugging method based on multi-population particle swarm optimization method |
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