CN104834974A - Electrified railway traction power supply scheme optimization design method - Google Patents

Abstract

The invention discloses an electrified railway traction power supply scheme optimization design method. The number and positions of a traction substation, a section post, an AT post, an opening and closing post and other power supply facilities and the capacity of a traction transformer are set as optimization variables, constraint conditions are set according to the design performance and requirements, an optimization objective function (such as the whole-line minimum power supply capacity (Ssub), the minimum average power consumption (Ploss), the minimum project cost (M), and the like) is determined, and an electrified railway traction power supply system design mathematical model conforming to optimal objective or multi-objective satisfactory optimization is established. All possible traction power supply schemes are automatically compared and selected in the limited domain of the constraint conditions by an intelligent optimization algorithm to determine an optimal or satisfactory scheme conforming to the optimization objective. By adopting the method of the invention, the number and positions of traction power supply facilities, the transformer capacity and other design parameters can be determined automatically, efficiently and accurately, the level of refinement of traction power supply design can be improved, and the traction power supply system construction and operation costs can be reduced.

Description

A kind of electric railway traction power supply plan Optimization Design

Technical field

The present invention relates to a kind of electric railway traction power supply plan Optimization Design.

Background technology

As the power supply that electric railway is unique, tractive power supply system, while meeting to electric locomotive reliable power supply, also should pursue high-quality and Effec-tive Function, and this just has higher requirement to traction power supply scheme minute design.Traditional conceptual design too relies on designer's professional experiences, and the determination of main power supply facilities quantity and position is subject to subjective factor impact, and the choosing of the ratio of limited scheme, also lack global optimizing ability, be difficult to the optimum ensureing design capacity.Recent year Railway Design department has successively introduced internationally recognized tractive power supply system design software, the such as OpenPowerNet of ELBAS/WEBANET and the IFB company of German SIGNON company, utilize traction load process simulation means, trend distribution in dynamic calculation system, the probabilistic method extensively adopted before computational accuracy and efficiency are all better than.But above-mentioned business software core algorithm only can carry out computation and analysis to given design proposal, do not possess scheme automatic optimal ability equally.

The key problem of tractive power supply system design determines the number of power supply facilities, position and traction transformer capacity, can by building single goal or multi-objective optimization mathematical model, introduce intelligent optimization algorithm, take full advantage of the computation and analysis ability that computing machine is powerful, result is constantly carried out automatically along improving countermeasure, finally converge on optimal case, the scope of its com-parison and analysis be also manually select excellent incomparable.

Summary of the invention

Object of the present invention is just to provide a kind of electric railway traction power supply plan Optimization Design, the method can determine number, the design parameter such as position and transformer capacity of traction power supply facility automatically, efficiently and accurately, reduce the deficiency of subjective judgement in engineer, overcome the limitation of limited scheme than choosing, improve the level that becomes more meticulous of traction power supply design.

The object of the invention is by following means realize.

A kind of electric railway traction power supply plan Optimization Design, with the number (N of power supply facilities _tS, N _sP, N _aT, N _kB), position (P _tSi, P _sPj, P _aTk, P _kBt, wherein i=1,2 ... N _tS, j=1,2 ... N _sP, k=1,2 ... N _aT, t=1,2 ... N _kB) power supply facilities and traction transformer capacity (S _tSn, n=1,2 ... N _tS) as optimized variable, described power supply facilities comprises traction substation TS, subregion SPAT and switching station KB; Constraint condition is set according to design performance and requirement, includes but not limited to: the minimum operating voltage U of Traction networks T _minwith maximum operating voltage U _max, addressing parameter limit value A, the flat vertical face parameter limit value B of circuit, determine optimization object function; Described optimization aim includes but not limited to: minimum power supply capacity S completely _sub, minimum average B configuration power consumption P _loss, minimum construction costs M; The electric railway traction power supply system design proposal of single goal optimum or muti-criteria satisfactory optimization is met by following step:

1) optimized variable and the initialization assignment of traction power supply conceptual design is read in;

2) tractive power supply system performance requirement, constraint condition and iteration convergence condition is read in;

3) by 1) and 2) the selected optimization object function of data input;

4) intelligent optimization algorithm automatic Iterative optimizing;

5) preferred result is exported after meeting the condition of convergence.

Carry out the design of computing machine automatic optimal to above-mentioned mathematical model, Optimizing Flow as shown in Figure 2.Wherein intelligent optimization algorithm can adopt ant group algorithm, particle cluster algorithm, genetic algorithm etc., to all possible traction power supply scheme calculating target function value in constraint condition restriction of domain, by computing machine automatically than jigging prioritization scheme, completing whole optimizing process when meeting iteration convergence condition, determining optimal power scheme or satisfactory solution.

When adopting the tractive power supply system optimal design of autotransformer AT power supply mode, comprise following concrete steps:

1) first step, input electric railway flat vertical face parameter, runs organization planning, electric locomotive characterisitic parameter, the basic data of the tractive power supply system designs such as externally fed power parameter;

2) second step, according to actual requirement, builds the traction power supply scheme multi-objective optimization design of power model adopting AT power supply mode, comprises optimized variable: the number of main power supply facilities, position and capacity; Optimization object function: minimum power supply capacity, minimal losses and minimum engineering cost: and constraint condition: the minimum operating voltage of electric locomotive, power supply facilities addressing requirement; The optimizing process condition of convergence-iteration precision requirement is set;

3) the 3rd step, for the multi-objective particle based on Pareto entropy, using optimized variable as population, initialized location and speed, as the input parameter of car-net coupled system interactive simulation;

4) the 4th step, utilizes car-net coupled system interactive simulation, calculates the distribution of AT tractive power supply system trend, if constraint condition does not meet, returns the 3rd step, again to population initialization; If meet constraint condition, then go forward one by one to the 5th step;

5) the 5th step, calculation optimization target function value, sets up the external archive of certain capacity, is used for storing the Pareto entropy that obtains of optimizing process;

6) the 6th step, the change of twice iteration entropy before and after utilizing; The situation of reflection Pareto forward position redistribution, infers the Evolving State of population, as convergence state, diversified state and dead state;

7) the 7th step, judges whether optimizing process meets the condition of convergence; If do not meet the condition of convergence arranged in second step, then return the 6th step, proceed iteration optimization; The condition of convergence arranged in second step if meet, then iterative process terminates, and exports Pareto and separates (corresponding single object optimization) or disaggregation (corresponding multiple-objection optimization);

8) the 8th step, if single object optimization design, then Pareto separates corresponding design proposal and is designated as optimal case; If multi-objective optimization design of power, fuzzy membership function can be applied to evaluate the satisfaction that in each Pareto solution, each objective function is corresponding, the design proposal being satisfied with the maximum solution correspondence of angle value is designated as optimal case;

9) the 9th step, export electric railway traction power supply plan, process of optimization terminates.

Compared with prior art, the invention has the beneficial effects as follows:

One, avoid the deficiency that existing traction power supply Design Method too relies on designer's subjective experience, main design parameters is defined as optimized variable, comprise traction substation, subregion institute, AT and the number of the power supply facilities such as switching station and position and traction transformer capacity, according to design performance and requirement, constraint condition is set, comprise the minimum operating voltage of Traction networks and maximum operating voltage limit value, addressing parameter limit value, circuit flat vertical face parameter limit value etc., with clear and definite functional form, optimization aim is described, comprise minimum power supply capacity completely, minimum average B configuration power consumption, minimum construction costs etc., set up the electric railway traction power supply system design mathematic model meeting single goal optimum or muti-criteria satisfactory optimization.

Two, computing machine automatic optimal is carried out to above-mentioned mathematical model, utilize intelligent optimization algorithm, comprise ant group algorithm, particle cluster algorithm, genetic algorithm etc., carry out, automatically than choosing, determining the optimal case or the satisfactory solution that meet optimization aim to all possible traction power supply scheme in constraint condition restriction of domain.

Three, the present invention can combine with optimizing scheduling with control, organization of driving with train traction computing further, strengthens the adaptability of traction power supply design proposal, reduces tractive power supply system construction and operating cost.

Below in conjunction with the drawings and specific embodiments, the invention will be further described.

Accompanying drawing explanation

In Fig. 1 tractive power supply system, distribution schematic diagram (for autotransformer feeding system) is implemented in power supply

Fig. 2 traction power supply scheme optimization design flow diagram

Fig. 3 is that the embodiment of the present invention is based on the power supply plan multi-objective optimization design of power process flow diagram improving population intelligent algorithm

Embodiment

Example is designed to, N to adopt the tractive power supply system of autotransformer (AT) power supply mode _tS, N _aT, N _sP, N _kBbe respectively the traction substation needing to arrange completely, AT institute, subregion institute and switching station number, P _tSi, P _sPj, P _aTk, P _kBtbe respectively traction substation, AT institute, subregion and the location variable of switching station, A, B, C and D are the codomain of corresponding optional institute, T be the system cloud gray model cycle (unit can for minute, hour or day etc.) S _ifor each traction substation calculated capacity, P _ijfor not each electric substation active power output in the same time, K represents online train number, P _lkjfor not each locomotive active power demand in the same time, U _lkfor electric locomotive termination net-fault voltage-to-ground, U _{l min}and U _{l max}be respectively the limit value requirement of locomotive tractive characteristic to supply conductor voltage, in addition, according to actual design needs, corresponding constraint condition can also be supplemented.Therefore, with all fronts traction substation calculated capacity sum S _subminimum and average active power loss P _lossminimum is optimization aim, can set up following Model for Multi-Objective Optimization, shown in (1):

$\left\{\begin{array}{c}\min f_1=S_{\mathrm{sub}}=\underset{i=1}{\overset{N_{\mathrm{TS}}}{\mathrm{\Σ}}}{S}_i\\ \min f_2=P_{\mathrm{loss}}=\frac{{\∫}_0^T(\underset{i=1}{\overset{N_{\mathrm{TS}}}{\mathrm{\Σ}}}{P}_{\mathrm{ij}}-\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{P}_{\mathrm{Lkj}})\mathrm{dt}}{T}\end{array}\right.---\left(1\right)$

Constraint condition:

s.t U _{L min}≤U _Lk≤U _{L max}

N _TS,N _AT,N _SP,N _KB≠0

P _TSi∈A(i＝1,2,...,N _TS)

P _SPj∈B(j＝1,2,...,N _SP)

(2)

P _ATk∈C(k＝1,2,...,N _AT)

P _KBt∈D(t＝1,2,...,N _KB)

For the chaos multi-objective particle based on Pareto entropy, optimizing is carried out to above-mentioned mathematical model.First, within the scope of optimized variable feasible zone, initialization generates a population, and particle initial velocity is one group of random number in velocity range.Then, utilize existing traction power supply simulation calculation platform (as ELBAS/WEBANET, OpenPowerNet etc.), calculate trend distribution and optimization object function value.In iterative process, adopt approximate Pareto Distribution Entropy and difference entropy to assess the Evolving State of population, and follow the tracks of adjustment evolution strategy and mutation operator dynamically as feedback information, and utilize chaotic disturbance to adjust variable.By coordinating the relation between multiple objective function, calculate the Pareto disaggregation meeting constraint condition.Concrete Optimizing Flow is shown in Fig. 3.

For multiple-objection optimization, application fuzzy membership function evaluates the satisfaction that in each Pareto solution, each objective function is corresponding, and ambiguity in definition membership function is such as formula shown in (3):

${\mathrm{\μ}}_m=\left\{\begin{array}{cc}1& f_m\≤f_m^{\min }\\ \frac{f_m^{\max }-f_m}{f_m^{\max }-f_m^{\min }}& f_m^{\min }\≤f_m\≤f_m^{\max }\\ 0& f_m\≥f_m^{\max }\end{array}\right.---\left(3\right)$

F in formula _mrepresent m target function value, represent minimum value and maximal value in m target function value.Separate for Pareto and concentrate each solution, what application of formula (4) asked its correspondence is satisfied with angle value, and the design proposal being satisfied with the maximum solution correspondence of angle value is designated as optimal case or satisfactory solution.

$\mathrm{\μ}=\frac{1}{M}\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{\mathrm{\μ}}_m---\left(4\right).$

Claims (3)

1. an electric railway traction power supply plan Optimization Design, is characterized in that: with the number (N of power supply facilities _tS, N _sP, N _aT, N _kB), position (P _tSi, P _sPj, P _aTk, P _kBt, wherein i=1,2 ... N _tS, j=1,2 ... N _sP, k=1,2 ... N _aT, t=1,2 ... N _kB) power supply facilities and traction transformer capacity (S _tSn, n=1,2 ... N _tS) as optimized variable, described power supply facilities comprises traction substation TS, self coupling institute AT (according to autotransformer feeding system), subregion institute SP and switching station KB; Constraint condition is set according to design performance and requirement, includes but not limited to: the minimum operating voltage U of Traction networks T _minwith maximum operating voltage U _max, addressing parameter limit value A, the flat vertical face parameter limit value B of circuit, determine optimization object function; Described optimization aim includes but not limited to: minimum power supply capacity S completely _sub, minimum average B configuration power consumption P _loss, minimum construction costs M; The electric railway traction power supply system design proposal of single goal optimum or muti-criteria satisfactory optimization is met by following step:

1) optimized variable and the initialization assignment of traction power supply conceptual design is read in;

2) tractive power supply system performance requirement, constraint condition and iteration convergence condition is read in;

3) by 1) and 2) the selected optimization object function of data input;

4) intelligent optimization algorithm automatic Iterative optimizing;

5) preferred result is exported after meeting the condition of convergence.

2. a kind of electric railway traction power supply plan Optimization Design according to claim 1, is characterized in that: described intelligent optimization algorithm can adopt ant group algorithm, particle cluster algorithm, genetic algorithm; To all possible traction power supply scheme automatic optimal in constraint condition restriction of domain.

3. a kind of electric railway traction power supply plan Optimization Design according to claim 1, is characterized in that: when adopting the tractive power supply system optimal design of autotransformer AT power supply mode, comprise following concrete steps:

1) first step, input electric railway flat vertical face parameter, runs organization planning, electric locomotive characterisitic parameter, the basic data of the tractive power supply system designs such as externally fed power parameter;

2) second step, according to actual requirement, builds the traction power supply scheme multi-objective optimization design of power model adopting AT power supply mode, comprises optimized variable: the number of main power supply facilities, position and capacity; Optimization object function: minimum power supply capacity, minimal losses and minimum engineering cost: and constraint condition: the minimum operating voltage of electric locomotive, power supply facilities addressing requirement; The optimizing process condition of convergence-iteration precision requirement is set;

3) the 3rd step, for the multi-objective particle based on Pareto entropy, using optimized variable as population, initialized location and speed, as the input parameter of car-net coupled system interactive simulation;

4) the 4th step, utilizes car-net coupled system interactive simulation, calculates the distribution of AT tractive power supply system trend, if constraint condition does not meet, returns the 3rd step, again to population initialization; If meet constraint condition, then go forward one by one to the 5th step;

5) the 5th step, calculation optimization target function value, sets up the external archive of certain capacity, is used for storing the Pareto entropy that obtains of optimizing process;

6) the 6th step, the change of twice iteration entropy before and after utilizing; The situation of reflection Pareto forward position redistribution, infers the Evolving State of population, as convergence state, diversified state and dead state;

7) the 7th step, judges whether optimizing process meets the condition of convergence; If do not meet the condition of convergence arranged in second step, then return the 6th step, proceed iteration optimization; The condition of convergence arranged in second step if meet, then iterative process terminates, and exports Pareto and separates (corresponding single object optimization) or disaggregation (corresponding multiple-objection optimization);

8) the 8th step, if single object optimization design, then Pareto separates corresponding design proposal and is designated as optimal case; If multi-objective optimization design of power, fuzzy membership function can be applied to evaluate the satisfaction that in each Pareto solution, each objective function is corresponding, the design proposal being satisfied with the maximum solution correspondence of angle value is designated as optimal case;

9) the 9th step, export electric railway traction power supply plan, process of optimization terminates.