CN107274051A - A kind of transformer based on genetic algorithm increases decision-making technique newly - Google Patents
A kind of transformer based on genetic algorithm increases decision-making technique newly Download PDFInfo
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- CN107274051A CN107274051A CN201710296000.4A CN201710296000A CN107274051A CN 107274051 A CN107274051 A CN 107274051A CN 201710296000 A CN201710296000 A CN 201710296000A CN 107274051 A CN107274051 A CN 107274051A
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Abstract
The present invention provides a kind of transformer based on genetic algorithm and increases decision-making technique newly, and the decision-making technique includes：Obtain electricity capacity parameter；The theoretical total capacity of newly-increased transformer is determined according to electricity capacity parameter；An object function is built, the theoretical total capacity using newly-increased transformer is constraints, and target is that minimum cost is paid；The capacity of the number of units for obtaining newly-increased transformer and every transformer is solved using genetic algorithm optimization.By the above method, the number of units and its capacity of newly-increased transformer can not only be rapidly and effectively drawn, had both ensured the electricity needs of power consumer, and ensure the cost minimization of newly-increased transformer；Meanwhile, ensure working stability, non-overloading and the safe operation of newly-increased transformer again, also reduce the reactive loss of power network, lift the operating efficiency of transformer.
Description
Technical field
Field is increased newly the present invention relates to transformer, more particularly to a kind of transformer based on genetic algorithm increases decision-making party newly
Method.
Background technology
In terms of power grid enterprises' angle, as electricity changes deeply, and power sales opening, the pressure of Stiffness agent progressively increases
Greatly, how from the angle of user, the taking the initiative in offering a hand consciousness of employee in Business Process System work is improved, lift user does electric body
Test, be the main consciousness means for lifting competitiveness.
In terms of electricity consumption enterprise angle, in each link of Business Process System, the determination of power supply plan is most important step,
Lectotype selection therein needs to refer to the demand and opinion of client, and many medium and small enterprises shortcoming correlation when applying for electricity consumption is known
Know, the experience with lacking abundance in terms of selection is designed in power program.It is therefore desirable to have a set of Quantitatively Selecting method, is user
Reference proposition in terms of place capacity type selecting is provided.
The content of the invention
It is an object of the invention to provide it is a kind of rapidly and effectively help power consumer determine newly-increased transformer number of units and
The method of its capacity.
In order to achieve the above object, the present invention is adopted the following technical scheme that：A kind of transformation based on genetic algorithm
Device increases decision-making technique newly, it is characterised in that the decision-making technique, including：
Obtain electricity capacity parameter；
The theoretical total capacity of newly-increased transformer is determined according to electricity capacity parameter；
An object function is built, the theoretical total capacity using newly-increased transformer is constraints, and target is that minimum cost is paid；
The capacity of the number of units for obtaining newly-increased transformer and every transformer is solved using genetic algorithm optimization.
Further, the genetic algorithm uses binary coding, and code length is 20, and initial population size is set as
200, maximum iteration is 200.
Further, the electricity capacity parameter includes following parameter：
N is equipment simultaneity factor (simultaneity factor), PmaxFor comprehensive maximum load, P 'maxFor the absolute highest of each composition unit
Load total amount, CAtllFor theoretical total capacity, CAtFor various kinds of equipment total capacity in equipment list, b is calibration power factor, and r is appearance
Measure accounting coefficient.
Further, the theoretical total capacity of newly-increased transformer is determined according to electricity capacity parameter, including：
According to comprehensive maximum load PmaxWith the absolute maximum load total amount P ' of each composition unitmaxDetermine equipment simultaneity factor
(simultaneity factor) n, wherein, n=Pmax/P′max；
According to various kinds of equipment total capacity CA in equipment listtTheoretical total capacity is determined with equipment simultaneity factor (simultaneity factor) n
CAtll, wherein, CAtll=CAt×n/(b×r)。
Further, an object function is built, the theoretical total capacity using newly-increased transformer is constraints, and target is minimum
Cost payout, the object function comprises the following steps：
It is C according to capacityiTransformer unit price f (Ci) determine transformer purchase commodity Fb,
It is C according to capacityiTransformer floor space g (Ci), soil unit price (ten thousand yuan/square metre) d and capacity be CiChange
The interval floor space conversion factor k of depressoriDetermine land purchase fee Ftd,
It is C according to capacityiTransformer maintenance cost h (Ci) determine transformer maintenance cost Fwh,
According to transformer purchase commodity Fb, client's advanced charge Fy, land purchase fee FtdWith transformer maintenance cost FwhIt is determined that
Client's totle drilling cost pays Ft；
Wherein, CiFor i-th transformer capacity, m is transformer number of units.
Further, according to transformer purchase commodity Fb, client's advanced charge Fy, land purchase fee FtdWith transformer maintenance expense
Use FwhDetermine that client's totle drilling cost pays Ft, including formula one：
minFt=Fb+Fy+Ftd+FwhFormula one.
Further, the bound for objective function expression formula is formula two：
After adopting the above technical scheme, the invention has the advantages that：
(included the technical scheme is that having considered the capacity requirement of power consumer, the cost of newly-increased transformer
Transformer purchase commodity, advanced charge, land purchase fee and transformer maintenance cost) and newly-increased transformer job stability, peace
The number of units and its capacity of newly-increased transformer are determined on the basis of Quan Xing, efficiency factor, can not only rapidly and effectively be drawn newly-increased
The number of units and its capacity of transformer, had both ensured the electricity needs of power consumer, and ensure the cost minimization of newly-increased transformer；Together
When, ensure working stability, non-overloading and the safe operation of newly-increased transformer again, also reduce the reactive loss of power network, lifting becomes
The operating efficiency of depressor.
Brief description of the drawings
The invention will be further described below in conjunction with the accompanying drawings：
Fig. 1 is flow chart of the invention；
Fig. 2 is the Optimization Solution procedure chart of genetic algorithm in the present invention.
Embodiment
The technical scheme of the embodiment of the present invention is explained and illustrated with reference to the accompanying drawing of the embodiment of the present invention, but under
State embodiment only the preferred embodiments of the present invention, and not all.Based on the embodiment in embodiment, people in the art
Member obtains other embodiments on the premise of creative work is not made, and belongs to protection scope of the present invention.
As shown in figure 1, the present invention, which discloses a kind of transformer based on genetic algorithm, increases decision-making technique, decision-making technique, bag newly
Include：
11st, electricity capacity parameter is obtained；
12nd, the theoretical total capacity of newly-increased transformer is determined according to electricity capacity parameter；
13rd, an object function is built, the theoretical total capacity using newly-increased transformer is constraints, and target is minimum cost branch
Go out；
14th, solved using genetic algorithm optimization and obtain the number of units of newly-increased transformer and the capacity of every transformer.
Wherein, electricity capacity parameter is obtained in step 11 includes：
N is equipment simultaneity factor (simultaneity factor), PmaxFor comprehensive maximum load, P 'maxFor the absolute highest of each composition unit
Load total amount, CAtllFor theoretical total capacity, CAtFor various kinds of equipment total capacity in equipment list, b is calibration power factor, and r is appearance
Measure accounting coefficient；
In step 12, the theoretical total capacity of newly-increased transformer is determined according to electricity capacity parameter, wherein：
According to comprehensive maximum load PmaxWith the absolute maximum load total amount P ' of each composition unitmaxDetermine equipment simultaneity factor
(simultaneity factor) n, wherein, n=Pmax/P′max；
According to various kinds of equipment total capacity CA in equipment listtTheoretical total capacity is determined with equipment simultaneity factor (simultaneity factor) n
CAtll, wherein, CAtll=CAt×n/(b×r)。
In step 13, an object function is built, the theoretical total capacity using newly-increased transformer is constraints, and target is minimum
Cost payout, the object function comprises the following steps：
It is C according to capacityiTransformer unit price f (Ci) determine transformer purchase commodity Fb,
It is C according to capacityiTransformer floor space g (Ci), soil unit price (ten thousand yuan/square metre) d and capacity be CiChange
The interval floor space conversion factor k of depressoriDetermine land purchase fee Ftd,
It is C according to capacityiTransformer maintenance cost h (Ci) determine transformer maintenance cost Fwh,
According to transformer purchase commodity Fb, client's advanced charge Fy, land purchase fee FtdWith transformer maintenance cost FwhIt is determined that
Client's totle drilling cost pays Ft；
Wherein, CiFor i-th transformer capacity, m is transformer number of units.
Specifically, according to transformer purchase commodity Fb, client's advanced charge Fy, land purchase fee FtdWith transformer maintenance cost
FwhDetermine that client's totle drilling cost pays Ft, including formula one：
minFt=Fb+Fy+Ftd+FwhFormula one.
Secondly, bound for objective function expression formula is formula two：
Finally, in step 14, the number of units for obtaining newly-increased transformer and every transformer are solved using genetic algorithm optimization
Capacity；Specifically, as shown in Fig. 2 genetic algorithm uses binary coding, code length is 20, and initial population size is set as
200, maximum iteration is 200.
In order to verify the validity of the inventive method, following calculated example is designed：
Example one
Certain enterprise need to increase distribution capacity because increasing a collection of production line newly.Soil unit price is 0.25 ten thousand yuan/square metre, every kilovolt
200 yuan of peace investment unit price, every kilovolt-ampere of 3 square metres of floor space, capacity CiThe interval floor space conversion factor system of transformer
One is set to 1.3.Other each parameters are as shown in table 1, table 2, table 3 and table 4.
The relevant parameter table of table 1
| Parameter | Describe (unit) | Numerical value |
| CAt | Equipment total capacity (kVA) | 12000 |
| n | Equipment simultaneity factor (%) | 80 |
| b | Calibration power factor (%) | 90 |
| r | Capacity accounting coefficient (%) | 75 |
By formula CAtll=CAt× n/ (b × r) and table 1 can calculate transformer premier by capacity C AtllFor
14222.22kVA。
The transformer capacity of table 2 correspondence expense
The transformer capacity of table 3 correspondence floor space
The transformer capacity of table 4 correspondence maintenance cost
Finally, optimum results are obtained according to genetic algorithm：Newly-increased transformer number of units is 8, and transformer individual capacity is
2000kVA。
The above method is advantageous in that, can not only rapidly and effectively draw the number of units and its capacity of newly-increased transformer, both
Ensure the electricity needs of power consumer, and ensure the cost minimization of newly-increased transformer；Meanwhile, ensure the work of newly-increased transformer again
Make stable, non-overloading and safe operation, also reduce the reactive loss of power network, lift the operating efficiency of transformer.
In addition to above preferred embodiment, the present invention also has other embodiments, and those skilled in the art can be according to this
Invention is variously modified and deformed, and without departing from the spirit of the present invention, all should belong to appended claims of the present invention and determine
The scope of justice.
Claims (7)
1. a kind of transformer based on genetic algorithm increases decision-making technique newly, it is characterised in that the decision-making technique, including：
Obtain electricity capacity parameter；
The theoretical total capacity of newly-increased transformer is determined according to electricity capacity parameter；
An object function is built, the theoretical total capacity using newly-increased transformer is constraints, and target is that minimum cost is paid；
The capacity of the number of units for obtaining newly-increased transformer and every transformer is solved using genetic algorithm optimization.
2. the transformer according to claim 1 based on genetic algorithm increases decision-making technique newly, it is characterised in that the heredity
Algorithm uses binary coding, and code length is 20, and initial population size is set as 200, and maximum iteration is 200.
3. the transformer according to claim 1 or 2 based on genetic algorithm increases decision-making technique newly, it is characterised in that described
Electricity capacity parameter includes following parameter：
N is equipment simultaneity factor (simultaneity factor), PmaxFor comprehensive maximum load, P 'maxFor the absolute maximum load of each composition unit
Total amount, CAtllFor theoretical total capacity, CAtFor various kinds of equipment total capacity in equipment list, b is calibration power factor, and r accounts for for capacity
Compare coefficient.
4. the transformer according to claim 3 based on genetic algorithm increases decision-making technique newly, it is characterised in that according to electricity consumption
Capacity parameter determines the theoretical total capacity of newly-increased transformer, including：
According to comprehensive maximum load PmaxWith the absolute maximum load total amount P ' of each composition unitmaxDetermine equipment simultaneity factor (simultaneously
Coefficient) n, wherein, n=Pmax/P′max；
According to various kinds of equipment total capacity CA in equipment listtTheoretical total capacity CA is determined with equipment simultaneity factor (simultaneity factor) ntll,
Wherein, CAtll=CAt×n/(b×r)。
5. the transformer according to claim 4 based on genetic algorithm increases decision-making technique newly, it is characterised in that build a mesh
Scalar functions, the theoretical total capacity using newly-increased transformer is constraints, and target is that minimum cost is paid, and the object function includes
Following steps：
It is C according to capacityiTransformer unit price f (Ci) determine transformer purchase commodity Fb,
It is C according to capacityiTransformer floor space g (Ci), soil unit price (ten thousand yuan/square metre) d and capacity be CiTransformer
Interval floor space conversion factor kiDetermine land purchase fee Ftd,
It is C according to capacityiTransformer maintenance cost h (Ci) determine transformer maintenance cost Fwh,
According to transformer purchase commodity Fb, client's advanced charge Fy, land purchase fee FtdWith transformer maintenance cost FwhDetermine client
Totle drilling cost pays Ft；
Wherein, CiFor i-th transformer capacity, m is transformer number of units.
6. the transformer according to claim 5 based on genetic algorithm increases decision-making technique newly, it is characterised in that according to transformation
Device purchase commodity Fb, client's advanced charge Fy, land purchase fee FtdWith transformer maintenance cost FwhDetermine that client's totle drilling cost pays Ft,
Including formula one：
minFt=Fb+Fy+Ftd+FwhFormula one.
7. the transformer according to claim 6 based on genetic algorithm increases decision-making technique newly, it is characterised in that the target
The constraints expression formula of function is formula two：
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| CN201710296000.4A CN107274051A (en) | 2017-04-28 | 2017-04-28 | A kind of transformer based on genetic algorithm increases decision-making technique newly |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108053250A (en) * | 2017-12-15 | 2018-05-18 | 北京理工大学 | A kind of transformer Price optimization method and device based on genetic algorithm |
| CN111222670A (en) * | 2018-11-27 | 2020-06-02 | 青岛海尔智能技术研发有限公司 | Intelligent household electricity optimization method and device and storage medium |
-
2017
- 2017-04-28 CN CN201710296000.4A patent/CN107274051A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108053250A (en) * | 2017-12-15 | 2018-05-18 | 北京理工大学 | A kind of transformer Price optimization method and device based on genetic algorithm |
| CN108053250B (en) * | 2017-12-15 | 2020-12-18 | 北京理工大学 | Genetic algorithm-based transformer price optimization method and device |
| CN111222670A (en) * | 2018-11-27 | 2020-06-02 | 青岛海尔智能技术研发有限公司 | Intelligent household electricity optimization method and device and storage medium |
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| CB02 | Change of applicant information |
Address after: 310007 Huanglong Road, Zhejiang, Hangzhou, No. 8 Applicant after: Zhejiang Electric Power Co., Ltd. Applicant after: North China Electric Power University Applicant after: Zhejiang Huayun Information Technology Co., Ltd. Address before: 310000 Huanglong Road, Zhejiang, Hangzhou, No. 8 Applicant before: State Grid Zhejiang Electric Power Company Applicant before: North China Electric Power University Applicant before: Zhejiang Huayun Information Technology Co., Ltd. |
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Application publication date: 20171020 |