CN102122310A - Train diagram-based traction load modeling method - Google Patents

Train diagram-based traction load modeling method Download PDF

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CN102122310A
CN102122310A CN 201110034392 CN201110034392A CN102122310A CN 102122310 A CN102122310 A CN 102122310A CN 201110034392 CN201110034392 CN 201110034392 CN 201110034392 A CN201110034392 A CN 201110034392A CN 102122310 A CN102122310 A CN 102122310A
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train
model
induction motor
traction
traction load
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CN102122310B (en
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江全元
陈宏伟
周盛
余丹萍
耿光超
苗轶群
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Zhejiang University ZJU
China Railway Electrification Engineering Group Co Ltd
China Railway Electrification Survey Design and Research Institute Co Ltd
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Abstract

The invention discloses a train diagram-based traction load modeling method. The modeling method comprises the following steps of: obtaining train number of each power supply arm in each time interval of a running period according to a train diagram, equalizing each train on one power supply arm to an induction motor, equalizing each traction load to a parallel model of multiple induction motor models and static index models, performing parameter identification on a traction load model by using particle swarm optimization (PSO) according to the measured voltage and power waveform, and establishing the traction load model. Compared with the conventional traction load modeling method, the train diagram-based traction load modeling method has the advantages that: the influence of running time variance of a train on the traction load voltage and power is taken into consideration, the generality and the accuracy of the traction load model are greatly improved on the premise of keeping good astringency, the established traction load model is more accordant with the actual conditions, and the requirements of railway/power related departments on traction power supply quality assessment, electrical energy quality assessment and power system stability assessment are met.

Description

A kind of traction load modeling method based on route map of train
Technical field
The invention belongs to operation, emulation and the power system stability analysis field of electric system, relate in particular to a kind of traction load modeling method based on route map of train.
Background technology
In recent years, the China Express Railway cause has obtained swift and violent development, high-speed railway traction power supply load has characteristics such as impact is strong, higher hamonic wave is abundant, its electric integrated part throttle characteristics of accurate description, seek rational load model structure and obtain model parameter accurately, foundation is used for the traction load model of operation, emulation and the stability analysis of electric system, has become the focus of academia, the research of engineering circle.
Widely used method is that a supply arm of traction net is set up the Matlab/simulink realistic model in the traction load modeling field at present, by building detailed electro-magnetic transient models such as contact net circuit, traction substation, electric locomotive, obtain the equivalent model of traction load through emulation [1-2]This method has following shortcoming: (1) time-domain-simulation calculated amount is bigger, needs long computing time; (2) time-domain-simulation is not combined with route map of train, the model that emulation obtained does not have versatility; (3) be not complete electric insulation between the different supply arms of traction net, existence influences each other; (4) with tractive power supply system and locomotive traction kinematic train as the two independent parts analysis, do not consider of the influence of traction net voltage to electric current, do not make the injection current of electric locomotive and actual value deviation bigger; (5) ignored the influence of electric system, made analysis result not exclusively correct tractive transformer secondary side voltage magnitude and phase angle.
Pertinent literature:
[1] Zhang Guangdong. tractive power supply system integrated load model structural research [D]. Hunan: Hunan University, 2009.
[2] Fanglei. high-speed railway tractive power supply system digital modeling and emulation [D]. Sichuan: Southwest Jiaotong University, 2010.
Summary of the invention
The objective of the invention is for abundant train on supply arm diverse location and quantity to the influence of traction load characteristic, be different from existing by building time domain simulation model, set up the method for traction load model under fixedly the train quantity and situation, a kind of traction load modeling method based on route map of train is provided.
The step of the technical solution used in the present invention is as follows:
1) departure interval with route map of train is an one-period, in one-period, with per 5 seconds was an interval, each time interval gets wherein and to add a voltage disturbance in traction substation 25kV side in 1 second, recovery voltage then, obtain each time dependent value of voltage, power constantly of each supply arm by trend calculating of traction net or measured data, step-length is 0.01 second;
2) use according to route map of train table look-up-method of interpolation calculates the position and the speed of train, obtains train operation quantity and speed on each supply arm;
3) be an induction motor model with each train equivalence on the supply arm, describe the traction running part of online each locomotive of traction, the consumption equivalence on the reaction component in the locomotive traction loop and the line impedance of tractive power supply system is a static exponential model;
With the induction motor model that is an equivalence of a plurality of induction motor model parallel connections on the supply arm, because type of locomotive is identical, so the equivalent inertia constant of equivalent model is constant, excitation reactance is equivalent to the parallel connection of n platform induction motor excitation reactance value, machine torque Tm is directly proportional with train quantity, that is:
Figure 201110034392X100002DEST_PATH_IMAGE001
5) according to the data that obtain in the step 1), utilize particle cluster algorithm (PSO) that induction motor model in parallel is added static exponential model and carry out parameter identification, set up the traction load model;
6) parameter that obtains according to identification in the step 5) compares load model emulation power and measured data, the correctness of verification model.
The static exponential model of described step 3) specifically describes as follows:
Figure 37502DEST_PATH_IMAGE002
In the formula:
Figure 201110034392X100002DEST_PATH_IMAGE003
---static active power value
Figure 892326DEST_PATH_IMAGE004
---the static reactive power value
Figure 201110034392X100002DEST_PATH_IMAGE005
---stable state active power value
Figure 859014DEST_PATH_IMAGE006
---the stable state reactive power value
Figure 201110034392X100002DEST_PATH_IMAGE007
---steady state voltage value
---magnitude of voltage
Figure 201110034392X100002DEST_PATH_IMAGE009
---the active power index, to be identified
Figure 202018DEST_PATH_IMAGE010
---the reactive power index, to be identified
Described step 4) induction motor model in parallel specifically describes as follows:
State equation:
Figure 201110034392X100002DEST_PATH_IMAGE011
The induction-motor load power absorbed is:
Figure 962164DEST_PATH_IMAGE012
In the formula: ---the reactance of the equivalent excitation transient state of induction motor in parallel
Figure 416148DEST_PATH_IMAGE014
---synchronous reactance
Figure 201110034392X100002DEST_PATH_IMAGE015
---the transient state time constant
---inertia time constant
---rotor speed;
Figure 408560DEST_PATH_IMAGE018
---synchronous rotational speed;
---the equivalent machine torque of induction motor in parallel
Figure 808449DEST_PATH_IMAGE020
---the induction motor transient internal voltage
Figure 201110034392X100002DEST_PATH_IMAGE021
---the induction electric machine rotor anglec of rotation
Figure 749729DEST_PATH_IMAGE022
---dynamic active power
Figure 201110034392X100002DEST_PATH_IMAGE023
---dynamic reactive power
Figure 424424DEST_PATH_IMAGE024
Figure 201110034392X100002DEST_PATH_IMAGE025
。
Described step 5) induction motor model in parallel adds being described below of static exponential model:
In the formula:
Figure 36375DEST_PATH_IMAGE022
---dynamic active power
---dynamic reactive power
Figure 740075DEST_PATH_IMAGE003
---static active power value
Figure 321229DEST_PATH_IMAGE004
---the static reactive power value
Figure 201110034392X100002DEST_PATH_IMAGE027
---total active power
Figure 843346DEST_PATH_IMAGE028
---total reactive power
Described step 5) particle cluster algorithm (PSO) is described below:
Its evolution equation can be described as:
Figure 201110034392X100002DEST_PATH_IMAGE029
Figure 509951DEST_PATH_IMAGE030
In the formula:
Figure 201110034392X100002DEST_PATH_IMAGE031
---the current location of particulate
Figure 775716DEST_PATH_IMAGE032
---the current flight speed of particulate
Figure DEST_PATH_IMAGE033
---the position that particulate lived through with best adaptive value
Figure 211377DEST_PATH_IMAGE034
,
Figure DEST_PATH_IMAGE035
---aceleration pulse, usually between 0 ~ 2
Figure 167045DEST_PATH_IMAGE036
,
Figure DEST_PATH_IMAGE037
---two separate random functions
The flow process of elementary particle group algorithm is as follows:
1), particulate group's random site and speed are carried out initial setting according to initialization procedure;
2) calculate the adaptive value of each particulate;
3) for each particulate, the adaptive value of its adaptive value with the desired positions that lived through compared, if better, then with it as current desired positions;
4) to each particulate, the adaptive value of its adaptive value with the desired positions that experienced of the overall situation compared, if better, then with it as current overall desired positions;
5) according to equation evolved in the speed and the position of particulate;
6) as not reaching termination condition (be generally enough good adaptive value or reach a default maximum algebraically), then return step 2).
The present invention compared with prior art, the beneficial effect that has:
1) existing traction load modeling method, be based on and build detailed tractive power supply system model, carry out load modeling according to the data that emulation obtains, calculated amount is bigger, and the present invention calculates by traction net trend or measured data obtains raw data, the effective rapidity and accuracy that must improve calculating;
2) traditional traction load model can not reflect the time variation of traction load under the train operation state, and the traction load model that the present invention sets up is based on and sets up route map of train under, is fit to and the situation of different trains distributions, and model has better generality.
Description of drawings
Fig. 1 is based on the traction load modeling method process flow diagram of route map of train;
Fig. 2 is the route map of train of example;
Fig. 3 is the present invention under four train situations (based on the traction load modeling of route map of train) load modeling result and measured data comparison diagram;
Fig. 4 is the present invention under five train situations (based on the traction load modeling of route map of train) load modeling result and measured data comparison diagram.
Embodiment
Traction load modeling method based on route map of train comprises the steps:
1) departure interval with route map of train is an one-period, in one-period, with per 5 seconds was an interval, each time interval gets wherein and to add a voltage disturbance in traction substation 25kV side in 1 second, recovery voltage then, obtain each time dependent value of voltage, power constantly of each supply arm by trend calculating of traction net or measured data, step-length is 0.01 second;
2) use according to route map of train table look-up-method of interpolation calculates the position and the speed of train, obtains train operation quantity and speed on each supply arm;
3) be an induction motor model with each train equivalence on the supply arm, describe the traction running part of online each locomotive of traction, the consumption equivalence on the reaction component in the locomotive traction loop and the line impedance of tractive power supply system is a static exponential model;
4) induction motor model that is an equivalence with a plurality of induction motor model parallel connections on the supply arm, because type of locomotive is identical, so the equivalent inertia constant of equivalent model is constant, excitation reactance is equivalent to the parallel connection of n platform induction motor excitation reactance value, machine torque Tm is directly proportional with train quantity, that is:
Figure 55366DEST_PATH_IMAGE001
In the formula: ---the reactance of induction motor excitation transient state
Figure DEST_PATH_IMAGE039
---the reactance of the equivalent excitation transient state of induction motor in parallel
Figure 946148DEST_PATH_IMAGE040
---the induction motor machine torque
Figure DEST_PATH_IMAGE041
---the equivalent machine torque of induction motor in parallel
Figure 826379DEST_PATH_IMAGE042
---train quantity;
5) according to the data that obtain in the step 1), utilize particle cluster algorithm (PSO) that induction motor model in parallel is added static exponential model and carry out parameter identification, set up the traction load model;
6) parameter that obtains according to identification in the step 5) compares load model emulation power and measured data, the correctness of verification model.
The static exponential model of described step 3) specifically describes as follows:
Figure 451264DEST_PATH_IMAGE002
In the formula:
Figure 340723DEST_PATH_IMAGE003
---static active power value
---the static reactive power value
---stable state active power value
Figure 507765DEST_PATH_IMAGE006
---the stable state reactive power value
---steady state voltage value
Figure 731253DEST_PATH_IMAGE008
---magnitude of voltage
Figure 736642DEST_PATH_IMAGE009
---the active power index, to be identified
Figure 618011DEST_PATH_IMAGE010
---the reactive power index, to be identified
Described step 4) induction motor model in parallel specifically describes as follows:
State equation:
Figure 114851DEST_PATH_IMAGE011
The induction-motor load power absorbed is:
Figure 483385DEST_PATH_IMAGE012
In the formula:
Figure 141899DEST_PATH_IMAGE013
---the reactance of the equivalent excitation transient state of induction motor in parallel
Figure 510563DEST_PATH_IMAGE014
---synchronous reactance
---the transient state time constant
Figure 565293DEST_PATH_IMAGE016
---inertia time constant
Figure 394709DEST_PATH_IMAGE017
---rotor speed;
Figure 250669DEST_PATH_IMAGE018
---synchronous rotational speed;
Figure 89312DEST_PATH_IMAGE019
---the equivalent machine torque of induction motor in parallel
Figure 698017DEST_PATH_IMAGE020
---the induction motor transient internal voltage
Figure 963913DEST_PATH_IMAGE021
---the induction electric machine rotor anglec of rotation
Figure 41591DEST_PATH_IMAGE022
---dynamic active power
Figure 683925DEST_PATH_IMAGE023
---dynamic reactive power
Figure 421504DEST_PATH_IMAGE024
Figure 858302DEST_PATH_IMAGE025
。
Described step 5) induction motor model in parallel adds being described below of static exponential model:
Figure 423275DEST_PATH_IMAGE026
In the formula:
Figure 603721DEST_PATH_IMAGE022
---dynamic active power
Figure 187018DEST_PATH_IMAGE023
---dynamic reactive power
Figure 794717DEST_PATH_IMAGE003
---static active power value
---the static reactive power value
Figure 283653DEST_PATH_IMAGE027
---total active power
Figure 737768DEST_PATH_IMAGE028
---total reactive power
Described step 5) particle cluster algorithm (PSO) is described below:
Its evolution equation can be described as:
Figure 516368DEST_PATH_IMAGE029
Figure 55934DEST_PATH_IMAGE030
In the formula: ---the current location of particulate
Figure 136071DEST_PATH_IMAGE032
---the current flight speed of particulate
Figure 85573DEST_PATH_IMAGE033
---the position that particulate lived through with best adaptive value
Figure 846855DEST_PATH_IMAGE034
, ---aceleration pulse, usually between 0 ~ 2
Figure 56961DEST_PATH_IMAGE036
,
Figure 177364DEST_PATH_IMAGE037
---two separate random functions
The flow process of elementary particle group algorithm is as follows:
1), particulate group's random site and speed are carried out initial setting according to initialization procedure;
2) calculate the adaptive value of each particulate;
3) for each particulate, the adaptive value of its adaptive value with the desired positions that lived through compared, if better, then with it as current desired positions;
4) to each particulate, the adaptive value of its adaptive value with the desired positions that experienced of the overall situation compared, if better, then with it as current overall desired positions;
5) according to equation evolved in the speed and the position of particulate;
6) as not reaching termination condition (be generally enough good adaptive value or reach a default maximum algebraically), then return step 2).
Below in conjunction with accompanying drawing, embodiments of the invention are elaborated, the process flow diagram of this invention is as shown in Figure 1.
Embodiment:
Consider example route map of train as shown in Figure 2, calculate under the different train quantity situations through the traction net that the data by electric voltage dropping generation power swing adopt emulation mode of the present invention that traction load is carried out modeling, each step division is as follows:
1) gets the interior at interval variable power process of a tracking (3 minutes), to be an interval 5 seconds, this, the inside got wherein that a period of time adds the process that voltage fluctuation recovers then to traction substation 25kV side is artificial in 5 second, thereby had obtained many groups noisy data.Concrete voltage situation of change in time is shown below:
Figure DEST_PATH_IMAGE043
Calculated each supply arm power situation of change in time when the voltage fluctuation of 25kV side by the traction network simulation, calculating step-length is 0.01 second.
2) according to the position of the time-position curve of route map of train and top each electric substation, by calculating the locomotive number that can calculate each each period of supply arm in the one-period.
3) will draw each online locomotive equivalence is an induction motor model, many locomotive equivalences on supply arm are the model of a plurality of induction motor parallel connections, and the consumption equivalence on the reaction component in the locomotive traction loop and the line impedance of tractive power supply system is a static exponential model.
4) certain the period train quantity that calculates according to route map of train is set up the traction load model, is shown below:
Figure 409631DEST_PATH_IMAGE001
State equation:
Figure 273682DEST_PATH_IMAGE011
The induction-motor load power absorbed is:
Figure 291316DEST_PATH_IMAGE012
The static load model:
Figure 97467DEST_PATH_IMAGE002
Total active power, reactive power:
Figure 833342DEST_PATH_IMAGE026
In the formula:
Figure 501084DEST_PATH_IMAGE044
---the induction motor machine torque
Figure 373225DEST_PATH_IMAGE042
---train quantity;
Figure 350277DEST_PATH_IMAGE013
---the reactance of the equivalent excitation transient state of induction motor in parallel
Figure 573448DEST_PATH_IMAGE014
---synchronous reactance
---the transient state time constant
Figure 771528DEST_PATH_IMAGE016
---inertia time constant
Figure 385394DEST_PATH_IMAGE017
---rotor speed;
---synchronous rotational speed;
Figure 839826DEST_PATH_IMAGE019
---the equivalent machine torque of induction motor in parallel
Figure 201406DEST_PATH_IMAGE020
---the induction motor transient internal voltage
Figure 5414DEST_PATH_IMAGE021
---the induction electric machine rotor anglec of rotation
Figure 203177DEST_PATH_IMAGE022
---dynamic active power
---dynamic reactive power
Figure 941829DEST_PATH_IMAGE025
。
Figure 626888DEST_PATH_IMAGE003
---static active power value
Figure 978235DEST_PATH_IMAGE004
---the static reactive power value
Figure 48828DEST_PATH_IMAGE005
---stable state active power value
Figure 460218DEST_PATH_IMAGE006
---the stable state reactive power value
Figure 835835DEST_PATH_IMAGE007
---steady state voltage value
Figure 977491DEST_PATH_IMAGE008
---magnitude of voltage
Figure 918902DEST_PATH_IMAGE009
---the active power index
Figure 235614DEST_PATH_IMAGE010
---the reactive power index
Figure 629686DEST_PATH_IMAGE027
---total active power
Figure 572104DEST_PATH_IMAGE028
---total reactive power
Parameter to be identified in the top model is:
Figure DEST_PATH_IMAGE045
。
5) utilize the PSO algorithm that above-mentioned model is carried out parameter identification, obtain final traction load model, under same parameter identification result, the active power of 4 trains and 5 trains, reactive power Model Calculation value and the contrast of measured data value are as shown in Figure 3, Figure 4.
According to Fig. 3, Fig. 4 as can be known a kind of traction load modeling method of proposing of the present invention based on route map of train make the result of traction load modeling have good versatility, result of calculation is more accurate, reliably, the demand of realistic use.

Claims (5)

1. the traction load modeling method based on route map of train is characterized in that comprising the steps:
1) departure interval with route map of train is an one-period, in one-period, with per 5 seconds was an interval, each time interval gets wherein and to add a voltage disturbance in traction substation 25kV side in 1 second, recovery voltage then, obtain each time dependent value of voltage, power constantly of each supply arm by trend calculating of traction net or measured data, step-length is 0.01 second;
2) use according to route map of train table look-up-method of interpolation calculates the position and the speed of train, obtains train operation quantity and speed on each supply arm;
3) be an induction motor model with each train equivalence on the supply arm, describe the traction running part of online each locomotive of traction, the consumption equivalence on the reaction component in the locomotive traction loop and the line impedance of tractive power supply system is a static exponential model;
4) induction motor model that is an equivalence with a plurality of induction motor model parallel connections on the supply arm, because type of locomotive is identical, so the equivalent inertia constant of equivalent model is constant, excitation reactance is equivalent to the parallel connection of n platform induction motor excitation reactance value, machine torque Tm is directly proportional with train quantity, that is:
Figure 418197DEST_PATH_IMAGE001
In the formula:
Figure 999220DEST_PATH_IMAGE002
---the reactance of induction motor excitation transient state
Figure 754686DEST_PATH_IMAGE003
---the reactance of the equivalent excitation transient state of induction motor in parallel
Figure 222708DEST_PATH_IMAGE004
---the induction motor machine torque
Figure 38217DEST_PATH_IMAGE005
---the equivalent machine torque of induction motor in parallel
Figure 106536DEST_PATH_IMAGE006
---train quantity;
5) according to the data that obtain in the step 1), utilize particle cluster algorithm that induction motor model in parallel is added static exponential model and carry out parameter identification, set up the traction load model;
6) parameter that obtains according to identification in the step 5) compares load model emulation power and measured data, the correctness of verification model.
2. a kind of traction load modeling method based on route map of train according to claim 1 is characterized in that the static exponential model of described step 3) specifically describes as follows:
Figure 665694DEST_PATH_IMAGE007
In the formula:
Figure 253801DEST_PATH_IMAGE008
---static active power value;
---the static reactive power value;
Figure 530247DEST_PATH_IMAGE010
---stable state active power value;
Figure 34041DEST_PATH_IMAGE011
---the stable state reactive power value;
Figure 866868DEST_PATH_IMAGE012
---steady state voltage value;
---magnitude of voltage;
---the active power index, to be identified;
---the reactive power index, to be identified.
3. a kind of traction load modeling method based on route map of train according to claim 1 is characterized in that described step 4) induction motor model in parallel specifically describes as follows:
State equation:
Figure 996662DEST_PATH_IMAGE016
The induction-motor load power absorbed is:
Figure 449509DEST_PATH_IMAGE017
In the formula:
Figure 589503DEST_PATH_IMAGE018
---the reactance of the equivalent excitation transient state of induction motor in parallel
Figure 169520DEST_PATH_IMAGE019
---synchronous reactance
Figure 711360DEST_PATH_IMAGE020
---the transient state time constant
Figure 335108DEST_PATH_IMAGE021
---inertia time constant
Figure 962399DEST_PATH_IMAGE022
---rotor speed;
Figure 611686DEST_PATH_IMAGE023
---synchronous rotational speed;
Figure 132666DEST_PATH_IMAGE024
---the equivalent machine torque of induction motor in parallel
Figure 802682DEST_PATH_IMAGE025
---the induction motor transient internal voltage
Figure 792635DEST_PATH_IMAGE026
---the induction electric machine rotor anglec of rotation
Figure 573509DEST_PATH_IMAGE027
---dynamic active power
---dynamic reactive power
Figure 58421DEST_PATH_IMAGE029
Figure 535670DEST_PATH_IMAGE030
。
4. a kind of traction load modeling method based on route map of train according to claim 1 is characterized in that described step 5) induction motor model in parallel adds being described below of static exponential model:
Figure 120235DEST_PATH_IMAGE031
In the formula:
Figure 615807DEST_PATH_IMAGE027
---dynamic active power
Figure 627626DEST_PATH_IMAGE028
---dynamic reactive power
Figure 326592DEST_PATH_IMAGE008
---static active power value
Figure 370640DEST_PATH_IMAGE009
---the static reactive power value
---total active power
Figure 654171DEST_PATH_IMAGE033
---total reactive power.
5. a kind of traction load modeling method based on route map of train according to claim 1 is characterized in that being described below of described step 5) particle cluster algorithm:
Its evolution equation can be described as:
Figure 230645DEST_PATH_IMAGE034
Figure 281647DEST_PATH_IMAGE035
In the formula: ---the current location of particulate
---the current flight speed of particulate
Figure 654357DEST_PATH_IMAGE038
---the position that particulate lived through with best adaptive value
Figure 517838DEST_PATH_IMAGE039
,
Figure 452296DEST_PATH_IMAGE040
---aceleration pulse, usually between 0 ~ 2
Figure 117764DEST_PATH_IMAGE041
, ---two separate random functions
The flow process of elementary particle group algorithm is as follows:
1), particulate group's random site and speed are carried out initial setting according to initialization procedure;
2) calculate the adaptive value of each particulate;
3) for each particulate, the adaptive value of its adaptive value with the desired positions that lived through compared, if better, then with it as current desired positions;
4) to each particulate, the adaptive value of its adaptive value with the desired positions that experienced of the overall situation compared, if better, then with it as current overall desired positions;
5) according to equation evolved in the speed and the position of particulate;
6) as not reaching termination condition (be generally enough good adaptive value or reach a default maximum algebraically), then return step 2).
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CN103631984A (en) * 2013-08-28 2014-03-12 南车青岛四方机车车辆股份有限公司 Aided design system and design method of railway vehicle traction system
CN104158189A (en) * 2014-09-02 2014-11-19 贵州电网公司电网规划研究中心 Electrified railway traction power supply load modeling method based on parameter identification
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CN113911171A (en) * 2021-09-18 2022-01-11 卡斯柯信号有限公司 Rail transit train control method, device and medium based on power supply capacity
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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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CN115222160A (en) * 2022-09-16 2022-10-21 中铁电气化勘测设计研究院有限公司 Rail transit traction load prediction method based on measured big data
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