Method, device and equipment for analyzing traction network, storage medium and computer program

Abstract

The invention relates to the technical field of traction power supply, in particular to a method and device for analyzing a traction network, equipment, a storage medium and a computer program, and the method comprises the steps: determining an index fitting model of a predetermined order; determining a first resonant frequency function corresponding to the distribution parameters of the predetermined type according to the index fitting model and at least two groups of sample data obtained at a measurement position which is a predetermined distance away from the traction substation on the traction network of the same structure; differentiating the first resonant frequency function through the distribution parameter, and determining the contribution degree of the distribution parameter of the wire at the measurement position to the resonant frequency. According to the method, the device, the equipment, the storage medium and the computer program disclosed by the invention, the resonant frequency function corresponding to the distribution parameter of the specific position on the traction network is more accurately obtained through an exponential fitting mode, and then the contribution degree of the distribution parameter to the resonant frequency of the traction network is obtained.

Description

Technical Field

[0001] The present disclosure relates to the field of traction power supply technology, and in particular to a method, device, equipment, storage medium and computer program for analyzing a traction network. Background Art

[0002] As the energy source for the operation of electrified railway vehicles, the safety and stable operation of the traction power supply system has always been the focus of research and attention of relevant technicians. Due to the electrical matching problem between the running electrified locomotive or EMU and the traction power supply system, high-order harmonic overvoltage events in the traction power supply system occur from time to time. This will cause adverse effects such as power supply voltage distortion and interference with railway communications, and may even cause resonance to cause a sharp increase in voltage, which may lead to overvoltage problems, causing safety accidents such as burning of on-board equipment, burning of traction substation equipment, and protective tripping of substations, resulting in vehicle shutdown and other consequences with accompanying economic losses.

[0003] The traction network is an important part of the traction power supply system. It is the circuit that supplies power to the locomotive and has a great influence on the resonant frequency of the traction power supply system. Analyzing the impact of the traction network on the resonant frequency of the traction power supply system is one of the problems that need to be solved urgently in the field. Summary of the invention

[0004] The present disclosure provides a method, device, equipment, storage medium and computer program for analyzing a traction network to solve the problem of obtaining the contribution of distributed parameters at a specific location on the traction network to a power supply system.

[0005] In a first aspect, the present disclosure provides a method for analyzing a traction network, comprising: determining an exponential fitting model of a predetermined order; determining a first resonant frequency function corresponding to a predetermined type of distributed parameter based on the exponential fitting model and at least two sets of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of similar structure; wherein each set of sample data includes a value of a distributed parameter of the conductor at the measurement position and a value of a measured resonant frequency; and determining a contribution of the distributed parameter of the conductor at the measurement position to the resonant frequency by differentiating the first resonant frequency function through the distributed parameter.

[0006] In some embodiments, in each group of sample data, the material reference value of the conductor is the same, and a first resonant frequency function corresponding to a predetermined type of distribution parameter is determined based on an exponential fitting model and at least two groups of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of similar structure, including: for each group of sample data, determining a value of a proportional factor corresponding to the value of a predetermined type of distribution parameter in the group of sample data based on the material reference value; determining a second resonant frequency function corresponding to the proportional factor based on the exponential fitting model, the values ​​of all proportional factors in each group of sample data and the value of the resonant frequency; and determining a contribution of the predetermined type of distribution parameter of the conductor at the measurement position to the resonant frequency by differentiating the first resonant frequency function through the distribution parameter, including: differentiating the second resonant frequency function through the proportional factor to obtain a contribution of the proportional factor of the conductor at the measurement position to the resonant frequency.

[0007] In some embodiments, a first resonant frequency function corresponding to a predetermined type of distribution parameter is determined based on an exponential fitting model and at least two sets of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of similar structure, including: using a neural network convolution algorithm, a statistical indicator of the R-square degree of fitting, and at least two sets of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of similar structure, to determine a first resonant frequency function corresponding to a predetermined type of distribution parameter.

[0008] In some embodiments, the exponential fit model is the following formula:

[0009]

[0010] Among them, f r (x) is the resonant frequency, x is the distribution parameter of the predetermined type, a k and b k is a constant, and N is a predetermined order.

[0011] In some embodiments, the predetermined order is 2.

[0012] In some embodiments, before determining the first resonant frequency function corresponding to the predetermined type of distribution parameter based on the exponential fitting model and at least two sets of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of the same structure, the method further includes:

[0013] Two sets of sample data containing numerical values ​​of distribution parameters of different predetermined types are obtained as follows:

[0014] Acquire a set of sample data at a first location at a predetermined distance from a traction substation on a traction network of a similar structure;

[0015] Acquire another set of sample data at a second location at a predetermined distance from the traction substation on a traction network of the same structure; or

[0016] The connection mode of the wires corresponding to the distribution parameters of the predetermined type at the first position is changed to obtain another set of sample data at the first position.

[0017] In a second aspect, the present disclosure provides a device for analyzing a traction network, comprising: a first determination module, for determining an exponential fitting model of a predetermined order; a second determination module, for determining a first resonant frequency function corresponding to a predetermined type of distributed parameter based on the exponential fitting model and at least two sets of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of similar structure; wherein each set of sample data includes a numerical value of a predetermined type of distributed parameter of the conductor at the measurement position and a numerical value of the resonant frequency obtained by measurement, and the numerical values ​​of the predetermined type of distributed parameter of at least two sets of sample data are different; a third determination module, for determining a contribution of the predetermined type of distributed parameter of the conductor at the measurement position to the resonant frequency by differentiating the first resonant frequency function through the distributed parameter.

[0018] In a third aspect, the present disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned method.

[0019] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned method when executed by a processor.

[0020] In a fifth aspect, the present disclosure provides a computer program product, including a computer program / instructions, which implements the steps of the above-mentioned method when executed by a processor.

[0021] The present disclosure provides a method, device, equipment, storage medium and computer program for analyzing a traction network, which more accurately obtains the resonant frequency function corresponding to the distribution parameters at a specific position on the traction network by exponential fitting, and then obtains the contribution of the distribution parameters to the resonant frequency of the traction network. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0023] Figure 1 It is a schematic diagram of the structure of a multi-conductor transmission line chain network model;

[0024] Figure 2 is a flow chart of a method for analyzing a traction network provided by an embodiment of the present disclosure;

[0025] Figure 3It is the impedance distribution parameter matrix of the traction network of AT double-line 14 conductors;

[0026] Figure 4 It is a setting interface for obtaining the function of fitting distribution parameters and traction network resonant frequency using Matlab software;

[0027] Figure 5 is a schematic diagram of a device for analyzing a traction network provided by an embodiment of the present disclosure;

[0028] Figure 6 This is an example of a descending bar chart of the contribution of the proportional factors corresponding to various types of distributed parameters obtained by applying the method disclosed in the present invention to the resonant frequency.

[0029] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, and to fully understand and implement how the present disclosure applies technical means to solve technical problems and achieve the corresponding technical effects, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. The embodiments of the present disclosure and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the scope of protection of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0032] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] The traction network has complex and diverse connection forms. In order to simplify and systematize the calculation of the impedance parameters of the traction network, a complete traction network can be equivalent to a multi-conductor transmission line chain network model. The biggest feature of this model is that it is composed of multiple slice units (segmented models). The slice unit represents a model of a traction network of a certain fixed length (every hundred kilometers). The number of impedance parameters it contains depends on the power supply method used (the number of conductors, materials and connection methods in different power supply methods are different, and the impedance parameters are naturally different). The chain network model is composed of slice units that are spliced ​​together like building blocks according to the actual needs of the system under study. For example, a 400km traction network can be connected by 4 sections of slice units. Figure 1 It is a schematic diagram of the structure of a multi-conductor transmission line chain network model.

[0034] The impedance and admittance Z in the slice unit model i , Y i is the admittance Y per unit length of the conductor P and the impedance per unit length Z P The parameters modeled according to certain electromagnetic properties (Ohm's law, law of electromagnetic induction) are expressed as follows.

[0035]

[0036] Among them, l i Represents the length of the traction network between the i-th node and the i+1-th node.

[0037] Since the impedance and admittance Z in the slice unit model i , Y i Directly affects the resonant frequency of the traction network and determines Z P and Y P , which determines Z i , Y i . Admittance matrix Y P and the impedance matrix Z per unit length P (i.e., the distributed parameter matrix composed of distributed parameters) jointly affects the impedance-frequency characteristics of the entire traction power supply system, thereby determining the resonant frequency and the location where the resonance occurs. Obtaining the contribution of the distributed parameters at a specific location to the resonant frequency of the traction network is conducive to preventing and predicting the occurrence of resonance, thereby guiding the design of the traction power supply system and the prevention of faults, and is therefore an urgent problem to be solved in this field.

[0038] Figure 2is a flow chart of a method for analyzing a traction network provided by an embodiment of the present disclosure. Figure 2 As shown, in the technical solution of this embodiment, a method for analyzing a traction network is provided, and the method includes the following steps S201 to S203:

[0039] Step S201, determining an exponential fitting model of a predetermined order.

[0040] This step is used to generate a model suitable for fitting the curves of the distribution parameters and the resonant frequency of the traction network.

[0041] Specifically, the distribution parameter is an element in the distribution parameter matrix mentioned above. The distribution parameter matrix is ​​directly determined by the structure and line type of the traction network and can be uniquely obtained based on experience. Figure 3 is the impedance distribution parameter matrix of the traction network of the AT double-line 14 conductors, Z P It is symmetrically transposed, satisfying A=B and C=D T In the figure, MW+CW represents the contact wire, PW represents the feeder wire, PF represents the protection wire, 2Rails represents the two rails on a track, and GW represents the ground wire. Up track represents the up track, and Down track represents the down track (up and down industry terms). The elements on the diagonal position of the matrix represent the self-impedance of the conductor itself, and the elements on the non-diagonal position represent the mutual impedance generated by electromagnetic induction between different conductors (for example, Z 06 represents the mutual impedance between the contact line and the feeder). The predetermined type corresponding to the distributed parameter is related to the position of the distributed parameter in the distribution matrix. 00 is the self-impedance of the contact line of MW+CW.

[0042] The distribution parameters and the resonance frequency of the traction network correspond to the measurement position on the traction network. The distribution parameters correspond to the slice unit where the measurement position is located, and the resonance frequency corresponds to the measurement position.

[0043] It should be particularly emphasized that the exponential fitting model determined in the present disclosure is more suitable for fitting the model curve of the distribution parameters and the resonant frequency of the traction network, which is the result obtained based on the test data and calculation of a large amount of distribution parameters and the resonant frequency of the traction network.

[0044] In some embodiments, the exponential fit model is the following formula:

[0045]

[0046] Among them, f r (x) is the resonant frequency, x is the distribution parameter of the predetermined type, a k and b k is a constant, and N is a predetermined order.

[0047] The embodiment of the present disclosure provides a function model for fitting the distribution parameters and the resonant frequency, which can be used to more accurately fit the functional relationship between the two, and further more accurately obtain the contribution of the distribution parameters to the resonant frequency of the traction network.

[0048] In some embodiments, the predetermined order is 2.

[0049] It should be emphasized that the predetermined order of 2 is a better result obtained based on a large number of calculations. If the order is greater than 2, the amount of calculation required to obtain the fitted resonant frequency function using the model will be greatly increased, and overfitting may also occur.

[0050] The embodiment of the present disclosure provides an order of an exponential fitting model suitable for fitting distribution parameters and resonant frequency, which can be used to more accurately fit the functional relationship between the distribution parameters and the resonant frequency.

[0051] Step S202, determining a first resonant frequency function corresponding to a predetermined type of distributed parameter based on an exponential fitting model and at least two sets of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of similar structure; wherein each set of sample data includes a value of a distributed parameter of the conductor at the measurement position and a value of the resonant frequency obtained by measurement.

[0052] The exponential fitting model disclosed in the present invention contains multiple constants whose values ​​need to be determined. It is necessary to train according to the measured data sample data of multiple distribution parameters and resonant frequencies to obtain the actual values ​​of the constants, and finally obtain the exponential fitting function (i.e., the first resonant frequency function) between the distribution participating in the resonant frequencies.

[0053] The sample data used to obtain the first resonant frequency in the present disclosure needs to meet certain conditions: 1) The measurement position of the sample data used for training is the same as the distance from the traction substation, which is a predetermined distance; 2) and the structure of the measured traction network is of the same type; 3) The distribution parameters are all of a predetermined type. For example, there is a traction network from Beijing to Tianjin, and a traction network from Beijing to Shijiazhuang. For these two lines, the traction networks are composed of feeders, contact lines, etc., and the structure of the traction networks is of the same type. The sample data can be obtained at the measurement positions on the two traction networks at the same distance from the traction substation. The obtained distribution parameters can all be the self-impedance of the feeder.

[0054] In some embodiments, before determining the first resonant frequency function corresponding to the predetermined type of distributed parameter based on the exponential fitting model and at least two sets of sample data obtained at a measurement position at a predetermined distance from the traction substation on a traction network of the same structure, it also includes: obtaining two sets of sample data containing numerical values ​​of distributed parameters of different predetermined types according to the following method: obtaining a set of sample data at a first position at a predetermined distance from the traction substation on a traction network of the same structure; obtaining another set of sample data at a second position at a predetermined distance from the traction substation on a traction network of the same structure; or changing the connection method of the wire corresponding to the predetermined type of distributed parameter at the first position, and obtaining another set of sample data at the first position.

[0055] For example, two sets of sample data, one set is obtained at a measurement location (i.e., the first location) 20 km away from the Beijing traction substation in the traction network from Beijing to Tianjin, and the other set is obtained at a measurement location (i.e., the second location) 20 km away from the Beijing traction substation in the traction network from Beijing to Shijiazhuang. The measurement locations can be different or the same. If they are the same, the connection method of the wires at the measurement locations can be changed to obtain distribution parameters with different values, and to measure the corresponding resonant frequency of the traction network when the distribution parameters are used. Because the distribution parameters at the same measurement location must be the same, the measured resonant frequencies are also basically the same, and the effect of fitting the resonant frequency curve with sample data with very close values ​​is poor.

[0056] The embodiment of the present disclosure provides a method for obtaining sample data of a fitting function between a distribution parameter and a resonant frequency of a traction network. Application of this method can obtain more applicable sample data, a more accurate fitting function, and further more accurately obtain the contribution of the distribution parameter to the resonant frequency.

[0057] In some embodiments, in each group of sample data, the material reference value of the conductor is the same, and a first resonant frequency function corresponding to a predetermined type of distribution parameter is determined based on an exponential fitting model and at least two groups of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of similar structure, including: for each group of sample data, determining a value of a proportional factor corresponding to the value of a predetermined type of distribution parameter in the group of sample data based on the material reference value; determining a second resonant frequency function corresponding to the proportional factor based on the exponential fitting model, the values ​​of all proportional factors in each group of sample data and the value of the resonant frequency; and determining a contribution of the predetermined type of distribution parameter of the conductor at the measurement position to the resonant frequency by differentiating the first resonant frequency function through the distribution parameter, including: differentiating the second resonant frequency function through the proportional factor to obtain a contribution of the proportional factor of the conductor at the measurement position to the resonant frequency.

[0058] The distributed parameters can be decomposed into the product of the material reference value and the proportional factor. For traction networks at different locations, the material reference value is a fixed value required by the standard, and this part is uncontrollable. The controllable part is the proportional factor, which is affected by the wire connection method, the reference height of the wire, the reference conductor radius, and the reference distance. Compared with the distributed parameters, the proportional factor removes the uncontrollable part. Analyzing the contribution of the proportional factor to the resonant frequency is more valuable for traction network design.

[0059] The disclosed embodiment provides a method for splitting a distributed parameter into a material reference value of an uncontrollable part and a proportional factor of a controllable part, and generating a fitting function for the relationship between the proportional factor of the controllable part and the resonant frequency of the traction network. This function is used to evaluate the contribution of the controllable part to the resonant frequency of the traction network, which can more effectively guide the design of the conductors of the railway traction network to prevent resonance of the railway.

[0060] In some embodiments, a first resonant frequency function corresponding to a predetermined type of distribution parameter is determined based on an exponential fitting model and at least two sets of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of similar structure, including: using a neural network convolution algorithm, a statistical indicator of the R-square degree of fitting, and at least two sets of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of similar structure, to determine a first resonant frequency function corresponding to a predetermined type of distribution parameter.

[0061] In practical applications, the mature neural network convolution fitting training tool in Matlab software can be used to obtain the first resonant frequency function. Figure 4 It is a setting interface for obtaining the fitting distribution parameters and the resonant frequency function of the traction network using Matlab software.

[0062] The embodiment of the present disclosure provides a method for obtaining a fitting function between a distribution parameter and a resonant frequency of a traction network with good effect. The method can be used to more accurately obtain the contribution of the distribution parameter of the traction network to the resonant frequency of the traction network.

[0063] Step S203: Differentiate the first resonance frequency function by the distributed parameters to determine the contribution of the distributed parameters of the conductor at the measurement position to the resonance frequency.

[0064] The physical meaning of contribution is the magnitude of the change in impedance-frequency characteristics when a certain distributed parameter of the line changes. A positive contribution means that there is a positive correlation between the distributed parameter and the resonant frequency, and the larger the value, the greater the influence of the impedance-frequency characteristics on the parameter; a negative contribution means that there is a negative correlation between the distributed parameter and the resonant frequency, and the larger the absolute value, the greater the influence. The contribution can be used to quantitatively evaluate the influence of each impedance parameter on the impedance-frequency characteristics.

[0065] The present disclosure provides a method, device, equipment, storage medium and computer program for analyzing a traction network, which more accurately obtains the resonant frequency function corresponding to the distribution parameters at a specific position on the traction network by exponential fitting, and then obtains the contribution of the distribution parameters to the resonant frequency of the traction network.

[0066] Table 1 is a summary of some calculation results of the resonance frequency function obtained by fitting the proportional factors and frequencies of various distribution parameters of the traction network of the AT double-line 14 conductors using the method disclosed in the present invention. The exponential fitting model used is the second order, and the contribution refers to the contribution of the proportional factors corresponding to the various distribution parameter types to the resonance frequency. R-square represents the degree of fit, and the closer it is to 1, the higher the degree of fit. Finally, the contribution is generated from low to high. Figure 6 Column chart of . Figure 6 This is an example of a descending bar chart of the contribution of the proportional factors corresponding to various types of distributed parameters obtained by applying the method disclosed in the present invention to the resonant frequency.

[0067] Table 1 Summary of calculation results

[0068]

[0069]

[0070] Figure 5 Schematic diagram of a device for analyzing a traction network provided by an embodiment of the present disclosure. Figure 5 As shown, in the technical solution of this embodiment, a traction network analysis device 500 is provided, including the following modules:

[0071] A first determination module 510 is used to determine an exponential fitting model of a predetermined order;

[0072] A second determination module 520 is used to determine a first resonant frequency function corresponding to a predetermined type of distribution parameter based on an exponential fitting model and at least two groups of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of a similar structure; wherein each group of sample data includes a value of a predetermined type of distribution parameter of the conductor at the measurement position and a value of the resonant frequency obtained by measurement, and the values ​​of the predetermined type of distribution parameter of at least two groups of sample data are different;

[0073] The third determination module 530 is used to determine the contribution of the predetermined type of distributed parameters of the conductor at the measurement position to the resonance frequency by differentiating the first resonance frequency function through the distributed parameters.

[0074] In some embodiments, in each group of sample data, the material reference value of the conductor is the same, and the second determination module 520 is specifically used to: for each group of sample data, determine the value of the proportional factor corresponding to the value of the distribution parameter of a predetermined type in the group of sample data according to the material reference value; determine the second resonant frequency function corresponding to the proportional factor according to the exponential fitting model, the values ​​of all proportional factors in each group of sample data and the value of the resonant frequency; and determine the contribution of the distribution parameter of the predetermined type of the conductor at the measurement position to the resonant frequency by differentiating the first resonant frequency function by the distribution parameter, including: differentiating the second resonant frequency function by the proportional factor to obtain the contribution of the proportional factor of the conductor at the measurement position to the resonant frequency.

[0075] In some embodiments, the second determination module 520 is specifically used to: use a neural network convolution algorithm, an R-square fitting degree statistical indicator, and at least two sets of sample data obtained from a measurement position at a predetermined distance from a traction substation on a traction network of the same structure to determine a first resonant frequency function corresponding to a predetermined type of distribution parameter.

[0076] In some embodiments, the exponential fit model is the following formula:

[0077]

[0078] Among them, f r (x) is the resonant frequency, x is the distribution parameter of the predetermined type, a k and b k is a constant, and N is a predetermined order.

[0079] In some embodiments, the predetermined order is 2.

[0080] In some embodiments, it also includes an acquisition module for obtaining two sets of sample data containing numerical values ​​of distribution parameters of different predetermined types according to the following method: obtaining a set of sample data at a first position at a predetermined distance from a traction substation on a traction network of the same structure; obtaining another set of sample data at a second position at a predetermined distance from the traction substation on a traction network of the same structure; or changing the connection method of the wire corresponding to the distribution parameter of the predetermined type at the first position to obtain another set of sample data at the first position.

[0081] In some implementations of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method of the above embodiment are implemented.

[0082] In some implementations of this embodiment, a computer program product is provided, including a computer program / instructions, and when the computer program is executed by a processor, the steps of the method of the above embodiment are implemented.

[0083] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components to execute the method in the above embodiments.

[0084] The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, and the computer-readable storage medium may include but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0085] The computer-readable storage medium may also store at least one computer executable program / instruction, which may be, for example, a computer-readable instruction. The computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The computer-readable storage medium may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions stored on the computer-readable storage medium, the various methods described above may be performed.

[0086] In addition, the computer device may also include (but not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.), etc.

[0087] The processor may communicate with external devices via an I / O bus via a wired or wireless network.

[0088] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product / computer program product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0089] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0090] It should be noted that in the present disclosure, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element limited by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0091] Although the embodiments disclosed in the present disclosure are as above, the above contents are only embodiments adopted for facilitating the understanding of the present disclosure and are not intended to limit the present disclosure. Any technician in the technical field to which the present disclosure belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present disclosure, but the scope of patent protection of the present disclosure shall still be subject to the scope defined in the attached claims.

Claims

1. A method for analyzing a traction network, characterized in that: include: determining an exponential fitting model of a predetermined order; Determine a first resonant frequency function corresponding to a predetermined type of distribution parameter based on the exponential fitting model and at least two groups of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of the same structure; wherein each group of sample data includes a value of the distribution parameter of the conductor at the measurement position and a value of the resonant frequency obtained by measurement; The first resonant frequency function is differentiated by the distributed parameter to determine the contribution of the distributed parameter of the conductor at the measurement position to the resonant frequency.

2. The method according to claim 1, characterized in that In each group of the sample data, the material reference value of the conductor is the same, and according to the exponential fitting model and at least two groups of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of a similar structure, a first resonant frequency function corresponding to a predetermined type of distribution parameter is determined, including: For each set of sample data, determine the value of the proportional factor corresponding to the value of the distribution parameter of a predetermined type in the set of sample data according to the material reference value; Determine a second resonant frequency function corresponding to the proportional factor according to the exponential fitting model, the values ​​of all proportional factors in each group of sample data and the value of the resonant frequency; The differentiating the first resonant frequency function by the distributed parameter to determine the contribution of the predetermined type of distributed parameter of the conductor at the measuring position to the resonant frequency includes: The second resonant frequency function is differentiated by the proportional factor to obtain the contribution of the proportional factor of the conductor at the measurement position to the resonant frequency.

3. The method according to claim 1, characterized in that Determining a first resonant frequency function corresponding to a predetermined type of distributed parameter according to the exponential fitting model and at least two groups of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of a similar structure, comprising: By using the neural network convolution algorithm and the statistical index of the R-square fitting degree, at least two groups of sample data are obtained from the measurement position at a predetermined distance from the traction substation on the traction network of the same structure to determine the first resonant frequency function corresponding to the distribution parameter of the predetermined type.

4. The method according to claim 1, characterized in that The exponential fitting model is the following formula: Among them, f r (x) is the resonant frequency, x is the distribution parameter of the predetermined type, a k and b k is a constant, and N is a predetermined order.

5. The method according to claim 1, characterized in that The predetermined order is 2.

6. The method according to claim 1, characterized in that Before determining the first resonant frequency function corresponding to the predetermined type of distribution parameter based on the exponential fitting model and at least two groups of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of a similar structure, the method further includes: The two sets of sample data containing numerical values ​​of distribution parameters of different predetermined types are obtained by the following method: Acquire a set of the sample data at a first position on the traction network at a predetermined distance from the traction substation; Acquire another set of the sample data at a second location on the traction network at a predetermined distance from the traction substation; or The connection mode of the wires corresponding to the predetermined type of distribution parameter at the first position is changed, and another set of the sample data is acquired at the first position.

7. A device for analyzing a traction network, characterized in that: include: A first determination module, used to determine an exponential fitting model of a predetermined order; A second determination module is used to determine a first resonant frequency function corresponding to a predetermined type of distribution parameter based on the exponential fitting model and at least two groups of sample data obtained at a measurement position at a predetermined distance from a traction substation on a traction network of the same structure; wherein each group of sample data includes a value of a predetermined type of distribution parameter of the conductor at the measurement position and a value of the resonant frequency obtained by measurement, and the values ​​of the predetermined type of distribution parameter of at least two groups of sample data are different; The third determination module is used to determine the contribution of the predetermined type of distributed parameters of the conductor at the measurement position to the resonance frequency by differentiating the first resonance frequency function through the distributed parameters.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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