CN110245461A - A kind of urban track traffic traction load modeling method, system and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a kind of urban track traffic traction load modeling methods, system and storage medium, random traction load emulation is realized based on Train Dynamic tracking interval, the tracking interval feature of urban railway transit train is analyzed by measured data, the train operation organization available cosine function that changes with time is obtained by approximating method to be described, Cauchy's distribution is obeyed in the fluctuation that the train operation organization and true train tracking interval indicated by comparison cosine function obtains train operation organization, the randomness in train travelling process can be indicated with the random number that Cauchy's distribution density function generates, train operation can be by indexing constantly, establish sliding time window, retrieve the train run in power supply zone, the traction power of train in window is overlapped, the modeling method can accurate description city rail traction load Distribution characteristics, model structure is succinct, meaning of parameters is clear, so that model has preferable practicability.
Description
Technical field
The present embodiments relate to urban track traffic traction power supply technical fields, and in particular to a kind of urban track traffic
Traction load modeling method, system and storage medium.
Background technique
Urban track traffic had been developed rapidly in China in recent years, since transport power is strong, energy conservation and environmental protection, efficiency are higher
Many advantages, such as domestic rail traffic is had broad application prospects, especially suburbs and Intercity Transportation.With train
Freight volume increases and speed is promoted, and influence of the traction load to urban distribution network can not be ignored, and in numerous relevant researchs, traction is negative
Lotus models a basis for being electric railway operating analysis and necessary technological means.
Traction load modeling method mainly has three categories: constant current anodizing process, average freight volume method and operation figure method.Constant current
Method is most simple, but cannot embody the dynamic of traction load, so being generally used for the load evaluation of more coarseness；Average fortune
Amount method is the calculation method based on statistics and probability, and step is simple, using convenient, but it is larger to calculate error, more than 20%, not enough
Accurately；Figure method is run compared to two methods of front, then is most accurate calculation method, it is described by dynamics and timetable
Train load and its motion process, but there is also disadvantages, are mainly manifested in: first is that calculating process is complicated, at high cost, second is that cannot
Reflect many random behaviors in train operation.
How to construct an accurate and reliable, practical stochastic model is a difficult point, and some documents are bent using segmentation
Line fitting obtains the mathematical formulae of daily load curve, can relatively accurately describe load variations, but due to the multiplicity of load curve
Property, it requires to modify formula according to actual measurement in application, the practicability is poor；Document also is built according to train operating condition and the equation of motion
The simulation algorithm of vertical train flow can accurately generate train operation as a result, however excessively complicated for supply load analysis.
Summary of the invention
For this purpose, the embodiment of the present invention provides a kind of urban track traffic traction load modeling method, system and storage medium,
To solve the problems, such as that existing traction load modeling method accuracy and reliability is poor, not very practical.
To achieve the goals above, the embodiment of the present invention provides the following technical solutions:
According to a first aspect of the embodiments of the present invention, a kind of urban track traffic traction load modeling method, institute are proposed
The method of stating includes:
First bus is described in the tracking interval δ (t) of moment t using cosine function model；
The tracking interval δ (t) of train is modified using the random number Δ h for meeting Cauchy's distribution and obtains revised chase after
Track interval δ (t)+Δ h；
Enabling t=t+, (δ (t)+Δ h) substitutes into the tracking interval that the cosine function model obtains next train；
Successively calculate the revised tracking interval for obtaining all trains that the same day passes through；
Power supply zone is defined using the time window T of sliding, according between the revised tracking of all trains of same day process
Every the train that retrieval travels in the power supply zone；
It adds up to the operation power of the train travelled in the power supply zone, establishes traction load model.
Further, described to describe first bus before the tracking interval δ (t) of moment t using cosine function model, also wrap
It includes:
It is changed with time and is fitted according to the actual measurement tracking interval data of train, establish cosine function model.
It is further, described to describe first bus in the tracking interval δ (t) of moment t using cosine function model, comprising:
The cosine function model is described in detail below:
In formula: δ0For departure interval daily mean, t1And t2Respectively early, evening peak corresponds to the moment, and A characterizes peak period
With idle period departure interval difference.
Further, described that the tracking interval δ (t) of train is modified using the random number Δ h for meeting Cauchy's distribution
Before obtaining revised tracking interval δ (t)+Δ h, further includes:
The actual measurement tracking interval data of train are compared with the tracking interval being calculated, determines and meets Cauchy's distribution
Random number correction term.
Further, described that the tracking interval δ (t) of first bus is modified using the random number Δ h for meeting Cauchy's distribution
Obtain revised tracking interval δ (t)+Δ h, comprising:
Generate N number of random number U for being uniformly distributed (0,1) sectionk, wherein [1, N] k ∈, N are that the same day train of process is total
Number；
By random number UkInput Cauchy's inverse function generates the random number Δ h for meeting Cauchy's distribution, and Cauchy's inverse function is specifically retouched
It states as follows:
Wherein, μ and γ is respectively location parameter and scale parameter.
Further, the time window T using sliding defines power supply zone, and all trains passed through according to the same day are repaired
Tracking interval after just retrieves the train travelled in the power supply zone, including；
Definition T is traveling total duration of the train in the power supply zone, τkFor the train u being calculatedkIt is revised
Tracking interval, d are that last column train travelled in the power supply zone enters the cumulative time of the power supply zone；
The traction calculated result of single-row train is denoted as structural array [x, l, I], wherein x, l and I respectively indicate train into
Traveling duration, place kilometer post and load current after entering the power supply zone；
If the emulation moment is z, unit is the second, and first bus is denoted as z=0, k=1 and d=0 at the time of entering the power supply zone,
Starting emulation；
It is as follows to establish constraint formulations:
It is recycled, train number k is recorded if meeting above formula to set Φ and is continued based on above formula, otherwise terminate to follow
Ring；
It enablesWhereinIt takes traction and calculates data；
Z=z+1 and d=d+1 are enabled, judges whether to meet equation d=τk, train u is represented if meetingkEnter the confession
Electric subregion enables d=0 and k=k+1；
Judge whether to meet simulation cycles constraint formulations, satisfaction then continues cycling through, otherwise terminates to emulate.
According to a second aspect of the embodiments of the present invention, a kind of urban track traffic traction load modeling, institute are proposed
The system of stating includes:
Tracking interval fitting unit, for describing first bus in the tracking interval δ (t) of moment t using cosine function model；
The tracking interval δ (t) of train is modified using the random number Δ h for meeting Cauchy's distribution and obtains revised chase after
Track interval δ (t)+Δ h；
Enabling t=t+, (δ (t)+Δ h) substitutes into the tracking interval that the cosine function model obtains next train；
Successively calculate the revised tracking interval for obtaining all trains that the same day passes through；
Traction load superpositing unit passed through all for defining power supply zone using the time window T of sliding according to the same day
The revised tracking interval of train retrieves the train travelled in the power supply zone；
It adds up to the operation power of the train travelled in the power supply zone, establishes traction load model.
According to a third aspect of the embodiments of the present invention, a kind of computer storage medium is proposed, the computer storage is situated between
Comprising one or more program instructions in matter, one or more of program instructions are for executing described in any item sides as above
Method.
The embodiment of the present invention has the advantages that
A kind of urban track traffic traction load modeling method, system and the storage medium that the embodiment of the present invention proposes, base
Random traction load emulation is realized in Train Dynamic tracking interval, and the tracking of urban railway transit train is analyzed by measured data
Spaced features obtain the train operation organization available cosine function that changes with time by approximating method and are described, by right
Cauchy is obeyed in the fluctuation that the train operation organization and true train tracking interval indicated than cosine function obtains train operation organization
Distribution can indicate the randomness in train travelling process with the random number that Cauchy's distribution density function generates, and train operation can
It is indexed by constantly, establishes sliding time window, the train run in power supply zone is retrieved, to leading for the train in window
Draw power to be overlapped, the modeling method can accurate description city rail traction load distribution characteristics, model structure is succinct, ginseng
Number clear, so that model has preferable practicability.
Detailed description of the invention
It, below will be to embodiment party in order to illustrate more clearly of embodiments of the present invention or technical solution in the prior art
Formula or attached drawing needed to be used in the description of the prior art are briefly described.It should be evident that the accompanying drawings in the following description is only
It is merely exemplary, it for those of ordinary skill in the art, without creative efforts, can also basis
The attached drawing of offer, which is extended, obtains other implementation attached drawings.
Fig. 1 is a kind of flow diagram for urban track traffic traction load modeling method that the embodiment of the present invention 1 provides；
In a kind of urban track traffic traction load modeling method that Fig. 2 provides for the embodiment of the present invention 1 between train tracking
Figure is changed over time every measured value and match value；
Cauchy point is obeyed in a kind of urban track traffic traction load modeling method that Fig. 3 provides for the embodiment of the present invention 1
The train operation organization measured value and match value difference schematic diagram of cloth；
Time window signal in a kind of urban track traffic traction load modeling method that Fig. 4 provides for the embodiment of the present invention 1
Figure.
Specific embodiment
Embodiments of the present invention are illustrated by particular specific embodiment below, those skilled in the art can be by this explanation
Content disclosed by book is understood other advantages and efficacy of the present invention easily, it is clear that described embodiment is the present invention one
Section Example, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
Embodiment 1
As shown in Figure 1, the present embodiment proposes a kind of urban track traffic traction load modeling method, this method comprises:
S100, first bus is described in the tracking interval δ (t) of moment t using cosine function model.
Further, first bus is described before the tracking interval δ (t) of moment t using cosine function model, further includes: root
It changes with time and is fitted according to the actual measurement tracking interval data of train, establish cosine function model.As shown in Fig. 2, actual measurement
Value refers to the actual measurement tracking interval data of train, and match value is referred between the train tracking obtained using cosine function models fitting
Every, by actual measurement Value Data analyze urban railway transit train tracking interval feature, by approximating method obtain train tracking
The available cosine function that changes with time is spaced to be described.
Further, cosine function model is described in detail below:
In formula: δ0For departure interval daily mean, t1And t2Respectively early, evening peak corresponds to the moment, and A characterizes peak period
With idle period departure interval difference, t ∈ [5,23] refers to the time of train operation generally between early 5 points to 23 points of evening.
S200, the tracking interval δ (t) of train is modified using the random number Δ h for meeting Cauchy's distribution and is corrected
Tracking interval δ (t) afterwards+Δ h.
Further, acquisition is modified to the tracking interval δ (t) of train using the random number Δ h for meeting Cauchy's distribution
Before revised tracking interval δ (t)+Δ h, further includes: by the actual measurement tracking interval data of train and the tracking being calculated
Interval compares, and determines the random number correction term for meeting Cauchy's distribution.The probabilistic feature of Train Dynamic tracking interval is used
Density function indicates, is compared by the match value of measured data and cosine function, and Cauchy point can be used by obtaining this density function
Cloth indicates, derives as follows:
Δ h=z (t)-δ (t)；
In formula, z (t) is that the corresponding train of t moment surveys tracking interval, obtains probability density such as Fig. 3 institute of fluctuating range
Show, it is basic that Symmetrical fluctuation is presented, show that Cauchy (Cauchy) distribution can be described preferably through curve fitting analysis for nonlinearity
The wave characteristic, function are denoted as:
In formula, μ and γ are respectively position and scale parameter.Therefore Train Dynamic tracking is being described using cosine function model
A correction term is added on the basis of interval, this correction term is indicated with random number Δ h, and Cauchy point is obeyed in the generation of this random number
Cloth.
Further, acquisition is modified to the tracking interval δ (t) of first bus using the random number Δ h for meeting Cauchy's distribution
Revised tracking interval δ (t)+Δ h, comprising:
S210, N number of random number U for being uniformly distributed (0,1) section is generatedk, wherein [1, N] k ∈, N are the column passed through on the same day
Vehicle sum；
S220, by random number UkInput Cauchy's inverse function generates the random number Δ h for meeting Cauchy's distribution, Cauchy's inverse function tool
Body is described as follows:
Wherein, μ and γ is respectively location parameter and scale parameter.In step S210, train ukRespectively correspond generation one
A random number Uk, U is utilized according to Cauchy's inverse functionkRandom number Δ h, random number Δ the h conduct that one meets Cauchy's distribution is generated to repair
Positve term is to train ukTracking interval δ (t) be modified and obtain revised tracking interval δ (t)+Δ h.
S300, enabling t=t+, (δ (t)+Δ h) substitutes into the tracking interval that cosine function model obtains next train.Likewise,
The tracking interval of next train is modified using the random number for meeting Cauchy's distribution.
S400, the revised tracking interval for obtaining all trains that the same day passes through successively is calculated.It repeats the above steps, directly
To the revised tracking interval for obtaining N number of train.
S500, power supply zone is defined using the time window T of sliding, is chased after according to the revised of all trains that the same day passes through
The train travelled in power supply zone is retrieved at track interval.
Its time window T indicates to can be considered static, corresponding, the time as shown in figure 4, train successively travels on the line
Window T is slided according to the direction opposite with This train is bound for XXX, and with the sliding of time window T, train sequentially enters time window T institute's generation
The power supply zone of table, until accumulative tracking interval and last column train entry time window in time window T between adjacent train
The sum of cumulative time be more than time window T length, count train quantity all in time window T.
Step S500 is specifically included:
Defining T first is traveling total duration of the train in power supply zone, and N is the train sum passed through on the same day, τkFor meter
Obtained train ukRevised tracking interval, i.e. δ (t)+Δ h, d be travelled in power supply zone it is last rows of
Vehicle enters the cumulative time of power supply zone；
The traction calculated result of single-row train is denoted as structural array [x, l, I], wherein x, l and I respectively indicate train into
Operation duration, place kilometer post and load current after entering power supply zone；
If the emulation moment is z, unit is the second, and first bus is denoted as z=0, k=1 and d=0 at the time of entering power supply zone, starting
Emulation；
It is as follows to establish constraint formulations:
It is recycled, train number k is recorded if meeting above formula to set Φ and is continued based on above formula, otherwise terminate to follow
Ring；
It enablesWhereinIt takes traction and calculates data；
Z=z+1 and d=d+1 are enabled, judges whether to meet equation d=τk, train u is represented if meetingkEnter power supply point
Area enables d=0 and k=k+1；
Judge whether to meet simulation cycles constraint formulations, satisfaction then continues cycling through, otherwise terminates to emulate.
S600, it adds up to the operation power of the train travelled in power supply zone, establishes traction load model.Pass through
The train run in power supply zone is retrieved, the traction power of the train travelled in time window is overlapped, obtains power transformation
Total traction load in institute exit.
In city rail traffic route, the operating voltage of train is generally 1500V or 750V, according to monitoring obtain each
The load current and rating formula of train, the traction power for calculating train all in time window T, which is overlapped, to be obtained
Obtain the traction load in power supply zone representated by time window T.
A kind of urban track traffic traction load modeling method that the present embodiment proposes, it is real based on Train Dynamic tracking interval
Now random traction load emulation, the tracking interval feature of urban railway transit train is analyzed by measured data, by fitting side
Method obtains the train operation organization available cosine function that changes with time and is described, and passes through the train that comparison cosine function indicates
Cauchy's distribution is obeyed in the fluctuation that tracking interval and true train tracking interval obtain train operation organization, with Cauchy's distribution density letter
The random number that number generates can indicate the randomness in train travelling process, and train operation can be established and be slided by indexing constantly
Dynamic time window, retrieves the train run in power supply zone, is overlapped to the traction power of the train in window, the modeling
Method can accurate description city rail traction load distribution characteristics, model structure is succinct, meaning of parameters is clear so that model have
Standby preferable practicability.
Embodiment 2
Corresponding with above-described embodiment 1, the present embodiment proposes a kind of urban track traffic traction load modeling,
System includes:
Tracking interval fitting unit, for describing first bus in the tracking interval δ (t) of moment t using cosine function model；
The tracking interval δ (t) of train is modified using the random number Δ h for meeting Cauchy's distribution and obtains revised chase after
Track interval δ (t)+Δ h；
Enabling t=t+, (δ (t)+Δ h) substitutes into the tracking interval that cosine function model obtains next train；
Successively calculate the revised tracking interval for obtaining all trains that the same day passes through；
Traction load superpositing unit passed through all for defining power supply zone using the time window T of sliding according to the same day
The revised tracking interval of train retrieves the train travelled in power supply zone；
It adds up to the operation power of the train travelled in power supply zone, establishes traction load model.
Function performed by each component is equal in a kind of urban track traffic traction load modeling provided in this embodiment
It is discussed in detail in above-described embodiment 1, therefore does not do excessively repeat here.
Embodiment 3
Corresponding with above-described embodiment 1, the present embodiment proposes a kind of computer storage medium, computer storage medium
In comprising one or more program instructions, one or more program instructions are used to execute a kind of city rail friendship such as embodiment 1
Logical traction load modeling.
Although above having used general explanation and specific embodiment, the present invention is described in detail, at this
On the basis of invention, it can be made some modifications or improvements, this will be apparent to those skilled in the art.Therefore,
These modifications or improvements without departing from theon the basis of the spirit of the present invention are fallen within the scope of the claimed invention.
Claims (8)
1. a kind of urban track traffic traction load modeling method, which is characterized in that the described method includes:
First bus is described in the tracking interval δ (t) of moment t using cosine function model；
The tracking interval δ (t) of train is modified using the random number Δ h for meeting Cauchy's distribution obtaining revised tracking
Every δ (t)+Δ h；
Enabling t=t+, (δ (t)+Δ h) substitutes into the tracking interval that the cosine function model obtains next train；
Successively calculate the revised tracking interval for obtaining all trains that the same day passes through；
Power supply zone is defined using the time window T of sliding, according to the revised tracking interval for all trains that the same day passes through, inspection
The train that rope travels in the power supply zone；
It adds up to the operation power of the train travelled in the power supply zone, establishes traction load model.
2. a kind of urban track traffic traction load modeling method according to claim 1, described to use cosine function mould
Type describes first bus before the tracking interval δ (t) of moment t, further includes:
It is changed with time and is fitted according to the actual measurement tracking interval data of train, establish cosine function model.
3. a kind of urban track traffic traction load modeling method according to claim 1, which is characterized in that the use
Cosine function model describes first bus in the tracking interval δ (t) of moment t, comprising:
The cosine function model is described in detail below:
In formula: δ0For departure interval daily mean, t1And t2Respectively early, evening peak corresponds to the moment, and A characterizes peak period and sky
Idle section departure interval difference.
4. a kind of urban track traffic traction load modeling method according to claim 1, which is characterized in that the use
The random number Δ h for meeting Cauchy's distribution the tracking interval δ (t) of train is modified obtain revised tracking interval δ (t)+
Before Δ h, further includes:
The actual measurement tracking interval data of train are compared with the tracking interval being calculated, determine meet Cauchy distribution with
Machine number correction term.
5. a kind of urban track traffic traction load modeling method according to claim 1, which is characterized in that the use
The random number Δ h for meeting Cauchy's distribution the tracking interval δ (t) of first bus is modified obtain revised tracking interval δ (t)+
Δ h, comprising:
Generate N number of random number U for being uniformly distributed (0,1) sectionk, wherein [1, N] k ∈, N are the train sum passed through on the same day；
By random number UkInput Cauchy's inverse function generates the random number Δ h for meeting Cauchy's distribution, and Cauchy's inverse function specifically describes such as
Under:
Wherein, μ and γ is respectively location parameter and scale parameter.
6. a kind of urban track traffic traction load modeling method according to claim 1, which is characterized in that the use
The time window T of sliding defines power supply zone, according to the revised tracking interval for all trains that the same day passes through, retrieves described
The train travelled in power supply zone, comprising:
Definition T is traveling total duration of the train in the power supply zone, τkFor the train u being calculatedkRevised tracking
Interval, d are that last column train travelled in the power supply zone enters the cumulative time of the power supply zone；
The traction calculated result of single-row train is denoted as structural array [x, l, I], wherein x, 1 and I respectively indicate train and enter institute
Traveling duration, place kilometer post and load current after stating power supply zone；
If the emulation moment is z, unit is the second, and first bus is denoted as z=0, k=1 and d=0 at the time of entering the power supply zone, is started
Emulation；
It is as follows to establish constraint formulations:
It is recycled, train number k is recorded if meeting above formula to set Φ and is continued based on above formula, otherwise end loop；
It enablesWhereinIt takes traction and calculates data；
Z=z+1 and d=d+1 are enabled, judges whether to meet equation d=τk, train u is represented if meetingkEnter the power supply point
Area enables d=0 and k=k+1；
Judge whether to meet simulation cycles constraint formulations, satisfaction then continues cycling through, otherwise terminates to emulate.
7. a kind of urban track traffic traction load modeling, which is characterized in that the system comprises:
Tracking interval fitting unit, for describing first bus in the tracking interval δ (t) of moment t using cosine function model；
The tracking interval δ (t) of train is modified using the random number Δ h for meeting Cauchy's distribution obtaining revised tracking
Every δ (t)+Δ h；
Enabling t=t+, (δ (t)+Δ h) substitutes into the tracking interval that the cosine function model obtains next train；
Successively calculate the revised tracking interval for obtaining all trains that the same day passes through；
Traction load superpositing unit, for defining power supply zone using the time window T of sliding, all trains passed through according to the same day
Revised tracking interval, retrieve the train that travels in the power supply zone；
It adds up to the operation power of the train travelled in the power supply zone, establishes traction load model.
8. a kind of computer storage medium, which is characterized in that refer in the computer storage medium comprising one or more programs
It enables, one or more of program instructions are for executing as the method according to claim 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112297883A (en) * | 2020-10-26 | 2021-02-02 | 北京市地铁运营有限公司 | Control method and control device for urban rail transit vehicle-mounted energy storage system |
| CN115230779A (en) * | 2022-06-09 | 2022-10-25 | 上海电力大学 | Optimal control method, medium and equipment for peak-load-shifting starting of subway train |
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