CN104834974B - A kind of electric railway traction power supply plan optimum design method - Google Patents
A kind of electric railway traction power supply plan optimum design method Download PDFInfo
- Publication number
- CN104834974B CN104834974B CN201510241043.3A CN201510241043A CN104834974B CN 104834974 B CN104834974 B CN 104834974B CN 201510241043 A CN201510241043 A CN 201510241043A CN 104834974 B CN104834974 B CN 104834974B
- Authority
- CN
- China
- Prior art keywords
- power supply
- optimization
- traction
- pareto
- design
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a kind of electric railway traction power supply plan optimum design methods, by the way that number, position and the traction transformer capacity of the power supply facilities such as traction substation, subregion institute, AT institutes (according to AT power supply modes) and switching station are set as optimized variable, according to design performance and requirement setting constraints, optimization object function (such as completely minimum power supply capacity (S is determinedsub), minimum average B configuration power consumption (Ploss), minimum project cost (M) etc.), establish and meet that single goal is optimal or the electric railway traction power supply system design mathematic model of muti-criteria satisfactory optimization.All possible traction power supply scheme is carried out automatically than choosing in constraints restriction of domain using intelligent optimization algorithm, the optimal case or satisfactory solution the method for the present invention for being determined for compliance with optimization aim can automatically, efficiently and accurately determine the design parameters such as number, position and the transformer capacity of traction power supply facility, the level that becomes more meticulous of traction power supply design is improved, reduces tractive power supply system construction and operating cost.
Description
Technical field
The present invention relates to a kind of electric railway traction power supply plan optimum design methods.
Background technology
As the unique power supply of electric railway, tractive power supply system is meeting to the same of electric locomotive reliable power supply
When, high-quality and Effec-tive Function should be also pursued, this just proposes higher requirement to traction power supply scheme minute design.Traditional
Conceptual design excessively relies on designer's professional experiences, main power supply facilities quantity and position it is determining easily by subjective factor shadow
It rings, and the ratio choosing of limited scheme, also lacks global optimizing ability, it is difficult to ensure the optimal of design capacity.Recent year iron
Road design department has successively introduced internationally recognized tractive power supply system design software, such as the ELBAS/ of SIGNON companies of Germany
The OpenPowerNet of WEBANET and IFB companies, using traction load process simulation means, trend is divided in dynamic computing system
Cloth, computational accuracy and efficiency probabilistic method widely used before being superior to.But above-mentioned business software core algorithm is only capable of pair
Given designing scheme is calculated and is analyzed, and does not have scheme automatic optimal ability equally.
The key problem of tractive power supply system design is to determine number, position and the traction transformer capacity of power supply facilities,
It can introduce intelligent optimization algorithm by building single goal or multi-objective optimization mathematical model, it is strong to take full advantage of computer
Big calculating and analysis ability so that result constantly carries out automatically along improvement plan, finally converges on optimal case, point
Analyse the range that compares be also manually select it is excellent incomparable.
Invention content
It is an object of the invention to provide a kind of electric railway traction power supply plan optimum design method, this method can be certainly
The design parameters such as number, position and transformer capacity dynamic, that efficiently and accurately determine traction power supply facility, reduce engineer
The deficiency of middle subjective judgement overcomes limitation of the limited scheme than choosing, improves the level that becomes more meticulous of traction power supply design.
The purpose of the present invention is what is realized by following means.
A kind of electric railway traction power supply plan optimum design method, with the number (N of power supply facilitiesTS、NSP、NAT、
NKB), position (PTSi,PSPj,PATr,PKBs, wherein i=1,2 ... NTS, j=1,2 ... NSP, r=1,2 ... NAT, s=1,
2,...NKB) power supply facilities and traction transformer capacity (STSn, n=1,2 ... NTS) variable as an optimization, the power supply facilities
Including traction substation TS, subregion SPAT and switching station KB；According to design performance and requirement setting constraints, including but not
It is limited to：The minimum operating voltage U of Traction networks TLminWith maximum operating voltage ULmax, addressing parameter limit value, circuit equal vertical face parameter limit
Value, determines optimization object function；The optimization aim includes but not limited to：Completely minimum power supply capacity, minimum average B configuration power consumption, most
Few project cost；Obtain meeting that single goal is optimal or the electric railway traction of muti-criteria satisfactory optimization is supplied by the steps
Electric system designing scheme：
1) optimized variable of traction power supply conceptual design and initialization assignment are read in；
2) tractive power supply system performance requirement, constraints and iteration convergence condition are read in；
3) by the selected optimization object function of data input 1) and 2)；
4) intelligent optimization algorithm automatic Iterative optimizing；
5) preferred result is exported after meeting the condition of convergence.
Computer automatic optimal design is carried out to above-mentioned mathematical model, Optimizing Flow is as shown in Figure 2.Wherein intelligent optimization is calculated
Ant group algorithm, particle cluster algorithm, genetic algorithm etc. may be used in method, and all possible traction is supplied in constraints restriction of domain
Electric scheme calculating target function value by computer automatically than selecting screening and optimizing scheme, is completed when meeting iteration convergence condition
Entire optimization process determines optimal power scheme or satisfactory solution.
Using auto-transformer AT power supply modes tractive power supply system optimization design when, including following specific steps：
1) vertical face parameter is equalled in the first step, input electric railway, runs organization planning, and electric locomotive characterisitic parameter is external
The basic data of the tractive power supply systems design such as power supply parameter；
2) second step, according to actual requirement, structure uses the traction power supply scheme multi-objective optimization design of power of AT power supply modes
Model, including optimized variable:Number, position and the capacity of main power supply facilities；Optimization object function:Minimum power supply capacity, most
Small loss and minimum engineering cost:And constraints:The minimum operating voltage of electric locomotive, power supply facilities addressing requirement；It sets excellent
The change process condition of convergence-iteration precision requirement；
3) third walks, by taking the multi-objective particle based on Pareto entropys as an example, using optimized variable as particle
Group, initialized location and speed, the input parameter as vehicle-net coupled system interactive simulation；
4) the 4th step using vehicle-net coupled system interactive simulation, calculates the distribution of AT tractive power supply systems trend, if constraint
Condition is unsatisfactory for, and returns to third step, population is initialized again；It is progressive to the 5th step if meeting constraints；
5) the 5th step, calculation optimization target function value are established the external archive of certain capacity, are obtained for storing optimization process
The Pareto entropys arrived；
6) the 6th step utilizes the variation of the front and rear entropy of iteration twice；Reflect the situation of Pareto forward positions redistribution, infer kind
The Evolving State of group, such as convergence state, diversified state and dead state；
7) the 7th step, judges whether optimization process meets the condition of convergence；If being unsatisfactory for the condition of convergence set in second step,
The 6th step is then returned, continues iteration optimization；If meeting the condition of convergence set in second step, iterative process terminates, defeated
Go out Pareto solutions (corresponding single object optimization) or disaggregation (corresponding multiple-objection optimization)；
8) the 8th step is designed if single object optimization, then Pareto solves corresponding designing scheme and is denoted as optimal case；If
It is corresponding full can to evaluate each object function in each Pareto solutions using fuzzy membership function for multi-objective optimization design of power
The corresponding designing scheme of solution for being satisfied with angle value maximum is denoted as optimal case by meaning degree；
9) the 9th step, exports electric railway traction power supply plan, and process of optimization terminates.
Compared with prior art, the beneficial effects of the invention are as follows：
First, the deficiency that existing traction power supply Design Method excessively relies on designer's subjective experience is avoided, by master
Design parameter is wanted to be defined as optimized variable, including traction substation, subregion institute, AT and the power supply facilities such as switching station number
It is electric including the minimum work of Traction networks according to design performance and requirement setting constraints with position and traction transformer capacity
Pressure and maximum operating voltage limit value, addressing parameter limit value, circuit put down vertical face parameter limit value etc., are described with specific functional form excellent
Change target, including completely minimum power supply capacity, minimum average B configuration power consumption, minimum project cost etc., foundation meet single goal it is optimal or
The electric railway traction power supply system design mathematic model of muti-criteria satisfactory optimization.
2nd, computer automatic optimal is carried out to above-mentioned mathematical model, using intelligent optimization algorithm, including ant group algorithm, grain
Swarm optimization, genetic algorithm etc. carry out automatically than selecting, really all possible traction power supply scheme in constraints restriction of domain
Surely meet the optimal case or satisfactory solution of optimization aim.
3rd, the present invention can be further combined with train traction computing with control, organization of driving with optimizing scheduling, and enhancing is led
Draw the adaptability of power-supply service scheme, reduce tractive power supply system construction and operating cost.
The invention will be further described with reference to the accompanying drawings and detailed description.
Description of the drawings
Distribution schematic diagram is implemented in power supply in Fig. 1 tractive power supply systems (by taking autotransformer feeding system as an example).
Fig. 2 traction power supply scheme optimization design flow diagrams.
Fig. 3 is the embodiment of the present invention based on the power supply plan multi-objective optimization design of power flow for improving particle swarm intelligence algorithm
Figure.
Specific embodiment
For using the design of the tractive power supply system of auto-transformer (AT) power supply mode, NTS, NAT, NSP, NKBRespectively
Completely need the traction substation set, AT institutes, subregion institute and switching station's number, PTSi,PATr,PSPj,PKBsRespectively traction becomes
Electric institute, AT institutes, subregion and switching station location variable, A, C, B and D be the codomain of corresponding optional institute, and T is all for system operation
Phase (unit can be minute, hour or day etc.) SiCapacity, P are calculated for each traction substationijFor different moments each electric substation
Active power exports, and K represents online train number, PLkjFor different moments each locomotive active power demand, ULkFor electric locomotive end
Contact net voltage-to-ground, ULminAnd ULmaxRespectively limit value requirement of the locomotive tractive characteristic to supply conductor voltage, in addition, according to reality
Border design needs, supplemented with corresponding constraints.Therefore, the sum of capacity S is calculated with all fronts traction substationsubIt is minimum
With average active power loss PlossMinimum optimization aim can establish following Model for Multi-Objective Optimization, as shown in formula (1):
F in formula1For first object function, f2For the second object function；
Constraints：
By taking the chaos multi-objective particle based on Pareto entropys as an example, above-mentioned Model for Multi-Objective Optimization is carried out
Optimizing.First, one population of initialization generation in the range of optimized variable feasible zone, particle initial velocity is in velocity interval
One group of interior random number.Then, using existing traction power supply simulation calculation platform (such as ELBAS/WEBANET,
OpenPowerNet etc.), calculate trend distribution and optimization object function value.In iterative process, using approximate Pareto
Distribution Entropy and poor entropy assess the Evolving State of population, and dynamically tracked as feedback information and adjust evolution strategy and variation
Operator, and variable is adjusted using chaotic disturbance.By coordinating the relationship between multiple object functions, calculate and meet constraint
The Pareto disaggregation of condition.Specific Optimizing Flow is shown in Fig. 3.
For multiple-objection optimization, each object function in each Pareto solutions is evaluated using fuzzy membership function and is corresponded to
Satisfaction, shown in ambiguity in definition membership function such as formula (3)：
μ in formulamFor object function fmBe subordinate to angle value, fmRepresent m-th of target function value,Represent m-th of mesh
Minimum value and maximum value in offer of tender numerical value.M=1,2；Each solution is concentrated for Pareto solutions, asks its corresponding using formula (4)
Angle value is satisfied with, the corresponding designing scheme of solution for being satisfied with angle value maximum is denoted as optimal case or satisfactory solution.
μ corresponding is satisfied with angle value for Pareto solutions in formula.
Claims (1)
1. a kind of electric railway traction power supply plan optimum design method, it is characterised in that：It is powered using auto-transformer AT
During the tractive power supply system optimization design of mode, including following specific steps：
1) the following base of the setting optimization process condition of convergence-iteration precision requirement and input tractive power supply system design
Plinth data：Vertical face parameter is equalled in electric railway, runs organization planning, electric locomotive characterisitic parameter, externally fed power parameter；
2)NTS, NAT, NSP, NKBRespectively completely need the traction substation set, AT institutes, subregion institute and switching station's number, PTSi,
PATr,PSPj,PKBsRespectively traction substation, AT institutes, subregion and switching station location variable, A, C, B and D are optional for correspondence
The codomain of institute, T are the system operation period, and unit is minute, hour or day, and Si calculates capacity, P for each traction substationij
For different moments each electric substation's active power output, K represents online train number, PLkjFor different moments each locomotive active power
Demand, ULkNet-fault voltage-to-ground, U are terminated for electric locomotiveLminAnd ULmaxRespectively locomotive tractive characteristic is to supply conductor voltage
Limit value requirement calculates the sum of capacity S with all fronts traction substationsubMinimum and average active power loss PlossMinimum optimization
Target establishes following Model for Multi-Objective Optimization, as shown in formula (1):
F in formula1For first object function, f2For the second object function；
Constraints：
Chaos multi-objective particle based on Pareto entropys carries out optimizing to above-mentioned Model for Multi-Objective Optimization；First,
One population of initialization generation in the range of optimized variable feasible zone, particle initial velocity be one group in velocity interval with
Machine number；Then, using existing traction power supply simulation calculation platform ELBAS/WEBANET or OpenPowerNet, tide is calculated
Flow distribution and optimization object function value；In iterative process, population is assessed using approximate Pareto Distribution Entropies and poor entropy
Evolving State, and dynamically tracked as feedback information and adjust evolution strategy and mutation operator, and utilize chaotic disturbance pair
Variable is adjusted；By coordinating the relationship between multiple object functions, the Pareto disaggregation for meeting constraints is calculated；
For multiple-objection optimization, it is corresponding full that each object function in each Pareto solutions is evaluated using fuzzy membership function
Meaning degree, shown in ambiguity in definition membership function such as formula (3)：
μ in formulamFor object function fmBe subordinate to angle value, fmRepresent m-th of target function value,Represent m-th of target letter
Minimum value and maximum value in numerical value；M=1,2；Each solution is concentrated for Pareto solutions, its corresponding satisfaction is asked using formula (4)
The corresponding designing scheme of solution for being satisfied with angle value maximum is denoted as optimal case or satisfactory solution by angle value,
μ corresponding is satisfied with angle value for Pareto solutions in formula；
The Pareto disaggregation that above-mentioned calculating meets constraints is as follows：
The first step, using optimized variable as population, initialized location and speed, as vehicle-net coupled system interactive simulation
Input parameter；
Second step using vehicle-net coupled system interactive simulation, calculates the distribution of AT tractive power supply systems trend, if constraints is not
Satisfaction then returns to the first step, and population is initialized again；It is progressive to third step if meeting constraints；
Third walks, calculation optimization target function value, establishes the external archive of certain capacity, obtains for storing optimization process
Pareto entropys；
4th step utilizes the variation of the front and rear entropy of iteration twice；Reflect the situation of Pareto forward positions redistribution, infer population into
Change state：Convergence state, diversified state and dead state；
5th step, judges whether optimization process meets the condition of convergence；If the condition of convergence set in being unsatisfactory for step 1), returns
4th step, continues iteration optimization；If the condition of convergence set in meeting step 1), iterative process terminate, output corresponds to
The disaggregation of multiple-objection optimization.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510241043.3A CN104834974B (en) | 2015-05-13 | 2015-05-13 | A kind of electric railway traction power supply plan optimum design method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510241043.3A CN104834974B (en) | 2015-05-13 | 2015-05-13 | A kind of electric railway traction power supply plan optimum design method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104834974A CN104834974A (en) | 2015-08-12 |
| CN104834974B true CN104834974B (en) | 2018-06-19 |
Family
ID=53812848
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510241043.3A Active CN104834974B (en) | 2015-05-13 | 2015-05-13 | A kind of electric railway traction power supply plan optimum design method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104834974B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105653818B (en) * | 2016-01-21 | 2019-01-04 | 中铁二院工程集团有限责任公司 | A kind of electric railway traction net impedance computation method |
| CN108629446A (en) * | 2018-04-13 | 2018-10-09 | 昆明理工大学 | Consider the charging station addressing constant volume method of the reliability containing distributed power distribution network |
| CN109687484B (en) * | 2019-01-22 | 2022-03-15 | 西南交通大学 | Optimization design method for external power grid access scheme of electrified railway |
| CN109659980B (en) * | 2019-01-22 | 2022-07-08 | 西南交通大学 | Energy management optimization method for traction power supply system integrating hybrid energy storage and photovoltaic device |
| CN112446086B (en) * | 2019-08-27 | 2024-04-26 | 中车株洲电力机车研究所有限公司 | Simulation method and device for magnetic suspension train |
| CN112101653A (en) * | 2020-09-10 | 2020-12-18 | 湘潭大学 | Novel electrified railway traction load prediction method |
| CN113517699B (en) * | 2021-07-28 | 2022-05-10 | 盾石磁能科技有限责任公司 | Voltage supporting method and device for long and large interval of traction network and terminal equipment |
| CN115117892A (en) * | 2022-02-22 | 2022-09-27 | 中铁第一勘察设计院集团有限公司 | Optimization design method for reactive power compensation scheme of electrified railway power through line |
| CN114597951B (en) * | 2022-04-06 | 2023-05-05 | 西南交通大学 | Optimization method for energy-saving operation of electric automobile participating in alternating-current urban rail power supply system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102267466A (en) * | 2011-05-09 | 2011-12-07 | 同济大学 | Self-traction running control method for urban rail vehicle based on energy optimization |
| CN102495934A (en) * | 2011-12-15 | 2012-06-13 | 南京理工大学 | Design method for railway transport drawing power balanced run chart based on particle swarm algorithm |
| CN104260759A (en) * | 2014-10-08 | 2015-01-07 | 北京交通大学 | Method and system for optimizing energy conservation of urban rail transit |
| CN104494467A (en) * | 2014-12-29 | 2015-04-08 | 湖南华大紫光科技股份有限公司 | Tidal current control device for V/v traction substation of electrified railway |
-
2015
- 2015-05-13 CN CN201510241043.3A patent/CN104834974B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102267466A (en) * | 2011-05-09 | 2011-12-07 | 同济大学 | Self-traction running control method for urban rail vehicle based on energy optimization |
| CN102495934A (en) * | 2011-12-15 | 2012-06-13 | 南京理工大学 | Design method for railway transport drawing power balanced run chart based on particle swarm algorithm |
| CN104260759A (en) * | 2014-10-08 | 2015-01-07 | 北京交通大学 | Method and system for optimizing energy conservation of urban rail transit |
| CN104494467A (en) * | 2014-12-29 | 2015-04-08 | 湖南华大紫光科技股份有限公司 | Tidal current control device for V/v traction substation of electrified railway |
Non-Patent Citations (3)
| Title |
|---|
| "牵引供电系统优化设计研究";李群湛 等;《西南交通大学学报》;19920301(第1期);第83-90页 * |
| "基于Pareto熵的多目标粒子群优化算法";胡旺 等;《软件学报》;20140515;第25卷(第5期);第1-6节 * |
| "牵引供电系统优化设计技术的应用研究";宫衍圣;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20040315(第1期);第二-四章 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104834974A (en) | 2015-08-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104834974B (en) | A kind of electric railway traction power supply plan optimum design method | |
| Puttamadappa et al. | Demand side management of small scale loads in a smart grid using glow-worm swarm optimization technique | |
| Sharma et al. | Reserve constrained multi-area economic dispatch employing differential evolution with time-varying mutation | |
| CN111062632B (en) | 5G energy Internet virtual power plant economic dispatching method based on edge intelligence | |
| CN106026084B (en) | A kind of AGC power dynamic allocation methods based on virtual power generation clan | |
| CN104536304B (en) | A kind of power system load MAS control method based on Matlab and Netlogo | |
| CN107612045A (en) | A kind of Power Plant generated energy intelligent dispensing system and method | |
| CN113904380B (en) | Virtual power plant adjustable resource accurate control method considering demand response | |
| CN105391090A (en) | Multi-intelligent-agent multi-target consistency optimization method of intelligent power grid | |
| CN103559656A (en) | Demand response control system and method | |
| CN107092991A (en) | A kind of adaptive economic load dispatching distribution method of intelligent grid | |
| CN113285457B (en) | Distributed economic dispatching method and system for regional power system under non-ideal communication | |
| CN108830479A (en) | It is a kind of meter and the full cost chain of power grid master match collaborative planning method | |
| CN104537178A (en) | Electric power system joint simulation modeling method based on Matlab and Netlogo | |
| CN116914751B (en) | Intelligent power distribution control system | |
| CN110474353A (en) | Layer-stepping energy-storage system and its power grid frequency modulation control method for coordinating of participation | |
| CN110858718A (en) | Alternating current micro-grid distributed event-driven frequency control method considering economy | |
| CN109784604A (en) | A kind of flexible job shop manufacturing recourses distribution method based on whale algorithm | |
| CN104680427A (en) | Planning system for comprehensive optimization of regional power distribution network | |
| CN107808200B (en) | Short-time maintenance plan optimization method and device for power distribution network | |
| CN107688294A (en) | A kind of manufacture system fuzzy control power-economizing method based on real-time production data | |
| CN107392350B (en) | Comprehensive optimization method for power distribution network extension planning containing distributed energy and charging stations | |
| CN111343095B (en) | Method for realizing controller load balance in software defined network | |
| Badawy et al. | Agent-based coordination techniques for matching supply and demand in energy networks | |
| CN102622000B (en) | Fuzzy neural network-based slurry supply system flow control method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| EXSB | Decision made by sipo to initiate substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |