Method and device for acquiring power supply capacity of heavy haul railway, computer equipment and medium

Abstract

The invention relates to a power supply capacity obtaining method and device of a heavy haul railway, computer equipment and a medium. The method comprises the following steps: acquiring historical power supply data of a to-be-analyzed system and preset evaluation indexes, acquiring relative importance degrees among the evaluation indexes, and obtaining subjective evaluation index weights of the evaluation indexes according to the relative importance degrees; obtaining an evaluation index weight corresponding to each evaluation index based on the similarity between the subjective evaluation index weights and each subjective evaluation index weight, and constructing an evaluation system of the to-be-analyzed system according to the historical power supply data, an index grade threshold value set for each evaluation index in advance and each evaluation index weight. And finally, obtaining the power supply capability of the to-be-analyzed system in the target tracking interval based on the evaluation system through a pre-constructed load flow calculation model of the heavy haul railway. By adopting the method, the evaluation accuracy of the evaluation system is realized, and the acquisition accuracy of the power supply capability is further improved.

Description

Technical Field

[0001] This application relates to the field of heavy-haul railway electrification, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for obtaining power supply capacity for heavy-haul railways. Background Technology

[0002] The power supply capacity of a traction power supply system refers to the maximum power that the system can transmit while meeting safe operation rules. Existing research has studied the traction power supply capacity of key indicators such as traction transformer capacity, contact wire voltage, and contact wire current carrying capacity. Furthermore, in the actual operation of the system, the operating boundary is crucial and directly relates to the system's reliability and safety.

[0003] The calculation of the through-power supply scheme for AT-powered heavy-haul railway traction cables is carried out. The mathematical models of electrical parameters such as equivalent impedance of traction network, current distribution, and voltage drop are derived, and then the calculation method of power supply distance is obtained and the power supply capacity of the system is judged.

[0004] However, current methods for obtaining the ultimate power supply capacity of heavy-haul railways, or traditional methods, suffer from low accuracy. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for obtaining power supply capacity for heavy-haul railways that can improve the accuracy of power supply capacity acquisition, in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a method for obtaining the power supply capacity of a heavy-haul railway, including:

[0007] The historical power supply data of the traction power supply system of the heavy-haul railway to be analyzed, as well as several pre-set evaluation indicators, are obtained. The evaluation indicators include traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance.

[0008] Obtain the relative importance of each evaluation indicator, and derive the subjective evaluation indicator weight of each evaluation indicator based on the relative importance.

[0009] Based on the similarity between the weights of each subjective evaluation indicator and the weights of each subjective evaluation indicator, the weights of each evaluation indicator are obtained.

[0010] Based on historical power supply data, pre-set threshold levels for each evaluation indicator, and weights for each evaluation indicator, an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is constructed.

[0011] By pre-constructing a power flow calculation model for the heavy-haul railway traction power supply system to be analyzed, and evaluating the power supply capacity of the heavy-haul railway traction power supply system to be analyzed based on the heavy-haul railway power supply capacity assessment system, the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval is obtained.

[0012] In conjunction with the first aspect, in one embodiment, a heavy-haul railway power flow calculation model is pre-constructed for the heavy-haul railway traction power supply system to be analyzed. Based on the heavy-haul railway power supply capacity assessment system, the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is assessed to obtain the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval, including:

[0013] Using a pre-set tracking interval, train traction calculations are performed on the traction power supply system of the heavy-haul railway to be analyzed, and the train traction power corresponding to the tracking interval is obtained; the tracking interval is the average of the first boundary value and the second boundary value, and the first boundary value is less than the second boundary value.

[0014] By using the power flow calculation model of heavy-haul railway, power flow calculation is performed based on the traction power of the train, and the power flow calculation results of the traction power supply system of the heavy-haul railway to be analyzed are obtained within the tracking interval.

[0015] The state assessment of the traction power supply system of the heavy-haul railway to be analyzed is carried out using the power flow calculation results.

[0016] Under the condition assessment of the traction power supply system of the heavy-haul railway to be analyzed, the difference between the first boundary value and the second boundary value is obtained;

[0017] If the difference is less than the preset error difference, the tracking interval is taken as the target tracking interval, and the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval is obtained from the state assessment results.

[0018] In conjunction with the first aspect, in one embodiment, the power flow calculation results include system node voltages, multiple node voltages, and multiple node currents;

[0019] Using the power flow calculation results, a state assessment is performed on the traction power supply system of the heavy-haul railway to be analyzed, including:

[0020] When the voltage variation amplitude of the system node voltage is less than or equal to the preset voltage variation amplitude threshold, the evaluation index data of the heavy-haul railway traction power supply system under analysis within the tracking interval are obtained based on the voltage and current of each node.

[0021] Based on the evaluation index data and the heavy-haul railway power supply capacity evaluation system, determine whether the power supply capacity of the heavy-haul railway traction power supply system under analysis is qualified.

[0022] In conjunction with the first aspect, in an exemplary embodiment, the method further includes:

[0023] If the voltage change of a system node exceeds a voltage change threshold, update the system node voltage until the voltage change of the system node is less than or equal to the voltage change threshold.

[0024] In conjunction with the first aspect, in one embodiment, when the traction power supply system of the heavy-haul railway to be analyzed passes the state assessment, obtaining the difference between the first boundary value and the second boundary value includes:

[0025] If the judgment result indicates that the power supply capacity of the heavy-haul railway traction power supply system under analysis is qualified, the difference between the first boundary value and the second boundary value is obtained.

[0026] If the judgment result indicates that the power supply capacity of the heavy-haul railway traction power supply system under analysis is unqualified, the first boundary value is updated to the tracking interval, and a new tracking interval is obtained. Then, the process returns to execute the train traction calculation of the heavy-haul railway traction power supply system under analysis using the tracking interval to obtain the train traction power corresponding to the tracking interval. Through the heavy-haul railway power flow calculation model, power flow calculation is performed based on the train traction power to obtain the power flow calculation result of the heavy-haul railway traction power supply system under analysis within the tracking interval. The power flow calculation result is used to perform the state assessment of the heavy-haul railway traction power supply system under analysis until the heavy-haul railway traction power supply system under analysis passes the state assessment and the difference between the first boundary value and the second boundary value is obtained.

[0027] In conjunction with the first aspect, in one embodiment, a heavy-haul railway power supply capacity assessment system is constructed based on historical power supply data, pre-set threshold levels for each assessment indicator, and the weights of each assessment indicator. This system includes:

[0028] By using the correlation model, the correlation between historical power supply data and indicator level thresholds is obtained, and the correlation between individual indicators between historical power supply data and indicator level thresholds is obtained.

[0029] Based on the weight of each evaluation indicator and the correlation between each individual indicator, the relative membership between historical power supply data and each indicator level is obtained.

[0030] Based on the relative membership degrees, an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is constructed.

[0031] In conjunction with the first aspect, in an exemplary embodiment, based on each relative membership degree, a heavy-haul railway power supply capacity assessment system for the heavy-haul railway traction power supply system to be analyzed is constructed, including:

[0032] Based on each relative membership degree, obtain the characteristic values ​​of historical power supply data relative to all indicator levels;

[0033] Using a binary semantic method, an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is constructed based on feature values.

[0034] In conjunction with the first aspect, in one embodiment, the heavy-haul railway power flow calculation model is constructed through the following steps:

[0035] Obtain the system structure information of the traction power supply system of the heavy-haul railway to be analyzed, and construct the equivalent chain circuit of the traction network based on the system structure information;

[0036] Based on the equivalent chain circuit of the traction network, a power flow calculation model for the heavy-haul railway traction power supply system to be analyzed is constructed.

[0037] Secondly, this application also provides a power supply capacity acquisition device for heavy-haul railways, comprising:

[0038] The first acquisition module is used to acquire historical power supply data of the traction power supply system of the heavy-haul railway to be analyzed, as well as several pre-set evaluation indicators; the evaluation indicators include traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance.

[0039] The subjective weight acquisition module is used to obtain the relative importance of each evaluation indicator and to obtain the subjective evaluation indicator weight of each evaluation indicator based on the relative importance.

[0040] The indicator weight acquisition module is used to obtain the evaluation indicator weight corresponding to each evaluation indicator based on the similarity between the weights of each subjective evaluation indicator and the weights of each subjective evaluation indicator.

[0041] The evaluation system construction module is used to construct an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed, based on historical power supply data, pre-set threshold values ​​for each evaluation indicator, and weights of each evaluation indicator.

[0042] The power supply capacity acquisition module is used to evaluate the power supply capacity of the heavy-haul railway traction power supply system under analysis by using a pre-constructed heavy-haul railway power flow calculation model for the heavy-haul railway traction power supply system under analysis and a heavy-haul railway power supply capacity evaluation system, thereby obtaining the power supply capacity of the heavy-haul railway traction power supply system under analysis within the target tracking interval.

[0043] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0044] The historical power supply data of the traction power supply system of the heavy-haul railway to be analyzed, as well as several pre-set evaluation indicators, are obtained. The evaluation indicators include traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance.

[0045] Obtain the relative importance of each evaluation indicator, and derive the subjective evaluation indicator weight of each evaluation indicator based on the relative importance.

[0046] Based on the similarity between the weights of each subjective evaluation indicator and the weights of each subjective evaluation indicator, the weights of each evaluation indicator are obtained.

[0047] Based on historical power supply data, pre-set threshold levels for each evaluation indicator, and weights for each evaluation indicator, an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is constructed.

[0048] By pre-constructing a power flow calculation model for the heavy-haul railway traction power supply system to be analyzed, and evaluating the power supply capacity of the heavy-haul railway traction power supply system to be analyzed based on the heavy-haul railway power supply capacity assessment system, the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval is obtained.

[0049] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0050] The historical power supply data of the traction power supply system of the heavy-haul railway to be analyzed, as well as several pre-set evaluation indicators, are obtained. The evaluation indicators include traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance.

[0051] Obtain the relative importance of each evaluation indicator, and derive the subjective evaluation indicator weight of each evaluation indicator based on the relative importance.

[0052] Based on the similarity between the weights of each subjective evaluation indicator and the weights of each subjective evaluation indicator, the weights of each evaluation indicator are obtained.

[0053] Based on historical power supply data, pre-set threshold levels for each evaluation indicator, and weights for each evaluation indicator, an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is constructed.

[0054] By pre-constructing a power flow calculation model for the heavy-haul railway traction power supply system to be analyzed, and evaluating the power supply capacity of the heavy-haul railway traction power supply system to be analyzed based on the heavy-haul railway power supply capacity assessment system, the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval is obtained.

[0055] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0056] The historical power supply data of the traction power supply system of the heavy-haul railway to be analyzed, as well as several pre-set evaluation indicators, are obtained. The evaluation indicators include traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance.

[0057] Obtain the relative importance of each evaluation indicator, and derive the subjective evaluation indicator weight of each evaluation indicator based on the relative importance.

[0058] Based on the similarity between the weights of each subjective evaluation indicator and the weights of each subjective evaluation indicator, the weights of each evaluation indicator are obtained.

[0059] Based on historical power supply data, pre-set threshold levels for each evaluation indicator, and weights for each evaluation indicator, an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is constructed.

[0060] By pre-constructing a power flow calculation model for the heavy-haul railway traction power supply system to be analyzed, and evaluating the power supply capacity of the heavy-haul railway traction power supply system to be analyzed based on the heavy-haul railway power supply capacity assessment system, the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval is obtained.

[0061] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for acquiring the power supply capacity of heavy-haul railways obtain historical power supply data of the traction power supply system of the heavy-haul railway to be analyzed, as well as pre-set evaluation indicators such as traction transformer load rate, voltage deviation rate, and three voltage imbalance degrees. The relative importance of each evaluation indicator is obtained, and subjective evaluation indicator weights are derived based on these relative importances. Based on the similarity between the subjective evaluation indicator weights and the weights of each subjective evaluation indicator, the corresponding evaluation indicator weights are obtained. Based on historical power supply data, pre-set indicator level thresholds for each evaluation indicator, and the weights of each evaluation indicator, a heavy-haul railway power supply capacity assessment system for the traction power supply system of the heavy-haul railway to be analyzed is constructed. Finally, using a pre-constructed heavy-haul railway power flow calculation model for the heavy-haul railway power supply system to be analyzed, the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is assessed based on the heavy-haul railway power supply capacity assessment system, thus obtaining the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval. By determining the weights of each evaluation indicator in stages, the final corresponding evaluation indicator weights are obtained. Based on the evaluation indicator weights, evaluation indicators, and historical power supply data, a set pair analysis is performed to obtain the corresponding heavy-haul railway power supply capacity evaluation system, thereby improving the evaluation accuracy of the evaluation system. Finally, through the heavy-haul railway power flow calculation model, the power supply capacity is obtained based on the heavy-haul railway power supply capacity, thus improving the accuracy of power supply capacity acquisition. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is an application environment diagram of a method for obtaining the power supply capacity of a heavy-haul railway in one embodiment;

[0064] Figure 2 This is a flowchart illustrating a method for obtaining the power supply capacity of a heavy-haul railway in one embodiment;

[0065] Figure 3 This is a power supply capacity level classification diagram in one embodiment;

[0066] Figure 4 This is a structural diagram of a heavy-haul railway traction power supply system in one embodiment;

[0067] Figure 5 This is a chain circuit model of the traction network in one embodiment;

[0068] Figure 6 This is a flowchart illustrating a method for obtaining the power supply capacity of a heavy-haul railway in another embodiment;

[0069] Figure 7 Here is a flowchart of the power supply limit calculation algorithm for another embodiment;

[0070] Figure 8a This is a schematic diagram illustrating the change process of the evaluation index during iteration in one embodiment;

[0071] Figure 8b This is a schematic diagram illustrating the change process of the evaluation index during iteration in another embodiment;

[0072] Figure 8c This is a schematic diagram illustrating the change process of the evaluation index during iteration in another embodiment;

[0073] Figure 9 This is a schematic diagram of the power supply capability evaluation results during the iteration in another embodiment;

[0074] Figure 10 This is a structural block diagram of a power supply capacity acquisition device for a heavy-haul railway in one embodiment;

[0075] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0077] The power supply capacity of a traction power supply system refers to the maximum power that the system can transmit while meeting safe operation rules. Existing research has studied the traction power supply capacity of key indicators such as traction transformer capacity, catenary voltage, and catenary current carrying capacity. Furthermore, in actual system operation, the operating boundary is crucial, directly affecting the system's reliability and safety. Currently, there is no method to accurately assess the power supply capacity of a heavy-haul railway traction power supply system and to analyze the ultimate power supply boundary of heavy-haul railways.

[0078] Heavy-haul railways possess advantages such as high transport capacity, high efficiency, and low cost. However, with the continuous increase in transport volume and operating mileage of heavy-haul railways in recent years, the problem of insufficient power supply capacity in the traction power supply system has become increasingly prominent. Therefore, this paper proposes an efficient method for analyzing the power supply status and ultimate power supply capacity of the traction power supply system of heavy-haul railways. A set-pair analysis method is used to comprehensively evaluate the load rate, voltage deviation rate, and three-phase voltage imbalance of the traction transformer, and a binary power flow algorithm is proposed to analyze the ultimate power supply capacity.

[0079] The method for obtaining the power supply capacity of heavy-haul railways provided in this application embodiment can be applied to, for example... Figure 1 The application environment is shown. The heavy-haul railway traction power supply system to be analyzed includes heavy-haul trains, a traction network, traction transformers, and a power grid. This system communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. This data storage system can be integrated onto server 102 or located in the cloud or on other network servers. Server 102 acquires historical power supply data and multiple pre-set evaluation indicators of the heavy-haul railway traction power supply system to be analyzed. These indicators include traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance. The server obtains the relative importance of each indicator and assigns subjective evaluation indicator weights based on these relative importance. Based on the similarity between the subjective evaluation indicator weights and their respective weights, the server acquires the corresponding evaluation indicator weights for each indicator. Using historical power supply data, pre-set indicator level thresholds for each indicator, and their respective weights, a heavy-haul railway power supply capacity evaluation system is constructed. Finally, using a pre-built heavy-haul railway power flow calculation model, the server evaluates the power supply capacity of the heavy-haul railway traction power supply system within the target tracking interval, based on the heavy-haul railway power supply capacity evaluation system, to obtain the power supply capacity of the heavy-haul railway traction power supply system within the target tracking interval. Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0080] In one exemplary embodiment, such as Figure 2 As shown, a method for obtaining the power supply capacity of a heavy-haul railway is provided, which can be applied to... Figure 1 Taking server 102 as an example, the explanation includes the following steps S201 to S205. Wherein:

[0081] Step S201: Obtain historical power supply data of the heavy-haul railway traction power supply system to be analyzed, as well as several pre-set evaluation indicators; the evaluation indicators include traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance.

[0082] Historical power supply data can be understood as the power data provided by the traction network to the heavy-haul railway, which may include power data such as traction network voltage and traction network current; traction transformer load rate can be understood as data that measures the load level of the traction transformer; voltage deviation rate can be understood as data that measures the power utilization level of the traction locomotive; and three-phase voltage imbalance can be understood as data that measures the magnitude of the negative sequence generated in the power system.

[0083] Optionally, server 102 acquires historical power supply data from the traction network to the heavy-haul railway power supply system to be analyzed, and acquires pre-set evaluation indicators, including traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance.

[0084] (1) Load factor of traction transformer:

[0085] When the load rate of a traction transformer is too high, it can cause increased load losses, higher temperatures, shorter service life, and compromised operational safety. To assess the load level of a traction transformer and ensure its safe and economical operation, the traction transformer load rate η is used as a metric. The calculation formula is as follows:

[0086]

[0087] In the formula, and These represent the actual output power of the transformer and the rated capacity of the traction transformer, respectively.

[0088] (2) Voltage deviation rate :

[0089] Heavy-haul railway traction loads are characterized by high power and strong fluctuations, resulting in fluctuations in the traction grid voltage. The standard voltage of the traction grid is 25kV. When the traction grid voltage deviates significantly from the standard voltage, it leads to a decrease in the power utilization level of the traction locomotive. This paper uses the voltage deviation rate... The voltage level is measured using the following formula:

[0090]

[0091] In the formula, and These represent the actual voltage of the traction network and the standard voltage of the traction network, respectively.

[0092] (3) Three-phase voltage imbalance :

[0093] Heavy-haul trains use single-phase power. When a single-phase load is connected to a three-phase power system through a traction substation, it can lead to a significant negative sequence in the power system. This is addressed by considering the three-phase voltage imbalance. To describe this negative order, the calculation formula is as follows:

[0094]

[0095] In the formula, , , These are the phase voltages of phases A, B, and C, respectively.

[0096] Step S202: Obtain the relative importance of each evaluation indicator, and obtain the subjective evaluation indicator weight of each evaluation indicator based on the relative importance.

[0097] Among them, relative importance can be understood as relative data obtained by pairwise comparison, with each other as the benchmark; the weight of subjective evaluation indicators can be understood as weight information related to relativity.

[0098] For example, by having multiple experts make pairwise comparisons of each evaluation indicator, server 102 obtains the relative importance of each evaluation indicator and constructs an intuitionistic fuzzy judgment matrix as follows:

[0099] K = [ ( u 11 , v 11 ) ( u 12 , v 12 ) ⋯ ( u 1 n , v 1 n ) ( u 21 , v 21 ) ( u 22 , v 22 ) ⋯ ( u 2 n , v 2 n ) ⋮ ⋮ ⋱ ⋮ ( u n 1 , v n 1 ) ( u n 2 , v n 2 ) … ( u nn , v nn ) ]

[0100] In the formula, u ij Membership degree represents the degree to which experts consider indicator i to be more important than indicator j. ij The degree of non-membership indicates the degree to which experts consider indicator j to be more important than indicator i.

[0101] To verify the consistency of the importance of each indicator and obtain more reasonable indicator weights, a consistency test needs to be performed on the intuitionistic fuzzy judgment matrix. First, the intuitionistic fuzzy consistency judgment matrix is ​​constructed as follows:

[0102] K ¯ = [ ( u ¯ 11 , v ¯ 11 ) ( u ¯ 12 , v ¯ 12 ) ⋯ ( u ¯ 1 n , v ¯ 1 n ) ( u ¯ 21 , v ¯ 21 ) ( u ¯ 22 , v ¯ 22 ) ⋯ ( u ¯ 2 n , v ¯ 2 n ) ⋮ ⋮ ⋱ ⋮ ( u ¯ n 1 , v ¯ n 1 ) ( u ¯ n 2 , v ¯ n 2 ) … ( u ¯ nn , v ¯ nn ) ]

[0103] The consistency check is performed as follows:

[0104]

[0105] In the formula, π ij Hesitation level represents the subjective uncertainty that arises when experts compare importance.

[0106] The obtained intuitive fuzzy judgment matrix is ​​corrected to obtain multiple sets of weights for each indicator. In order to avoid errors in weight allocation caused by inconsistent opinions among experts, grey relational theory is used to adjust each weight to obtain the final comprehensive weight.

[0107] Based on the intuitionistic fuzzy judgment matrix, calculate the subjective weights of each indicator, and the intuitionistic fuzzy weights. for:

[0108]

[0109] To determine the specific weights of the evaluation indicators, intuitive fuzzy weights were used. Quantification:

[0110]

[0111]

[0112] In the formula, This is a fuzzy transformation function; , and Intuitive fuzzy weights Membership degree, non-membership degree, and hesitation degree; represents the subjective weight of indicator j.

[0113] Step S203: Based on the similarity between the weights of each subjective evaluation indicator and the weights of each subjective evaluation indicator, obtain the evaluation indicator weights corresponding to each evaluation indicator.

[0114] Optionally, the correlation degree of each group of weights is constructed and the comprehensive weight is calculated using the grey relational analysis method. Assume that N experts evaluate J indicators using intuitionistic fuzzy hierarchical analysis to obtain multiple groups of weights. Then the overall weight It can be calculated using the following formula:

[0115]

[0116] In the formula, This represents the weight of the j-th indicator obtained through the i-th expert, with the maximum value among all expert weights used to form the reference weight. ; The resolution coefficient.

[0117] Step S204: Based on historical power supply data, the threshold values ​​for each evaluation indicator set in advance, and the weights of each evaluation indicator, construct an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed.

[0118] Among them, the indicator level threshold can be understood as the boundary value used to divide the indicator level corresponding to the evaluation indicator data; the heavy-haul railway power supply capacity evaluation system can be understood as the correspondence between the evaluation indicator data and the power supply capacity of the heavy-haul railway traction power supply system.

[0119] For example, server 102 combines historical power supply data to form set A, and combines pre-set indicator level thresholds for each evaluation indicator to form set B. Set pair H(A,B) can be constructed from set A and set B. Based on the correlation degree between the data in set A and the individual indicators in set B, and then based on the correlation degree of the individual indicators and the comprehensive weight, the idea of ​​variable fuzzy set is introduced to quantify the membership relationship between historical power supply data and indicator levels, and obtain the relative membership degree between historical power supply data and indicator levels. Based on the relative membership degree, the power supply capacity status of the heavy-haul railway traction power supply system represented by the historical power supply data is accurately described, thereby obtaining the heavy-haul railway power supply capacity evaluation system of the heavy-haul railway traction power supply system to be analyzed.

[0120] Step S205: Using a heavy-haul railway power flow calculation model pre-constructed for the heavy-haul railway traction power supply system to be analyzed, the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is evaluated based on the heavy-haul railway power supply capacity evaluation system, thereby obtaining the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval.

[0121] Among them, the power flow calculation model for heavy-haul railways can be understood as a mathematical model that calculates the voltage and current of each node of a heavy-haul railway; the target tracking interval can be understood as the shortest time interval in which the traction power supply system of a heavy-haul railway provides the ultimate power supply capacity.

[0122] Optionally, server 102 performs train traction calculations on the heavy-haul railway traction power supply system to be analyzed according to a pre-set tracking interval to obtain the train traction power. The tracking interval is determined based on a first boundary value and a second boundary value. The server then performs power flow calculations based on the train traction power using a power flow calculation model pre-built for the heavy-haul railway traction power supply system to be analyzed, and obtains the corresponding power flow calculation results. The server then performs a state assessment on the heavy-haul railway traction power supply system to be analyzed based on the power flow calculation results. If the state assessment is passed, the server obtains the difference between the first boundary value and the second boundary value. If the difference is less than a preset error value, the server determines the tracking interval as the target tracking interval and obtains the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval from the state assessment results.

[0123] In the aforementioned method for obtaining the power supply capacity of heavy-haul railways, historical power supply data of the traction power supply system of the heavy-haul railway to be analyzed, as well as pre-set evaluation indicators such as traction transformer load rate, voltage deviation rate, and three voltage imbalance degrees, are obtained to determine the relative importance of each evaluation indicator. Subjective evaluation indicator weights for each indicator are then derived based on their relative importance. Based on the similarity between the weights of each subjective evaluation indicator and the weights of each subjective evaluation indicator, the corresponding evaluation indicator weights for each evaluation indicator are obtained. Based on historical power supply data, pre-set indicator level thresholds for each evaluation indicator, and the weights of each evaluation indicator, a heavy-haul railway power supply capacity evaluation system for the traction power supply system of the heavy-haul railway to be analyzed is constructed. Finally, using a pre-constructed heavy-haul railway power flow calculation model for the heavy-haul railway power supply system to be analyzed, the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is evaluated based on the heavy-haul railway power supply capacity evaluation system, thus obtaining the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval. By determining the weights of each evaluation indicator in stages, the final corresponding evaluation indicator weights are obtained. Based on the evaluation indicator weights, evaluation indicators, and historical power supply data, a set pair analysis is performed to obtain the corresponding heavy-haul railway power supply capacity evaluation system, thereby improving the evaluation accuracy of the evaluation system. Finally, through the heavy-haul railway power flow calculation model, the power supply capacity is obtained based on the heavy-haul railway power supply capacity, thus improving the accuracy of power supply capacity acquisition.

[0124] In one embodiment, a heavy-haul railway power flow calculation model is pre-constructed for the heavy-haul railway traction power supply system to be analyzed. Based on the heavy-haul railway power supply capacity assessment system, the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is assessed to obtain the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval, including:

[0125] Train traction calculations are performed on the heavy-haul railway traction power supply system under analysis using a pre-set tracking interval to obtain the train traction power corresponding to the tracking interval. The tracking interval is the average of a first boundary value and a second boundary value, where the first boundary value is less than the second boundary value. Power flow calculations are performed on the heavy-haul railway traction power supply system under analysis based on the train traction power using a heavy-haul railway power flow calculation model to obtain the power flow calculation results of the heavy-haul railway traction power supply system under analysis within the tracking interval. The state of the heavy-haul railway traction power supply system under analysis is then assessed using the power flow calculation results. If the heavy-haul railway traction power supply system under analysis passes the state assessment, the difference between the first boundary value and the second boundary value is obtained. If the difference is less than a preset error difference, the tracking interval is taken as the target tracking interval, and the power supply capacity of the heavy-haul railway traction power supply system under analysis within the target tracking interval is obtained from the state assessment results.

[0126] Among them, the tracking interval can be understood as a time interval, the train traction power can be understood as the online power of the train running on the heavy-haul railway, the power flow calculation result can be understood as the power data such as the node voltage, node current and system node voltage of each node in the heavy-haul railway traction power supply system, and the state assessment can be understood as the process of assessing the system state and corresponding power supply capacity of the heavy-haul railway traction power supply system itself.

[0127] For example, server 102 sets the initial tracking interval for heavy-load trains. and with 0 and As the initial boundary values ​​A and B for the bisection method, the average value of A and B is used. To track train traction calculations, the online train power P+jQ is obtained. This online train power P+jQ is then input into a heavy-haul railway power flow calculation model to perform power flow calculations, yielding the corresponding results. These results are used to evaluate the stability and power supply capacity of the heavy-haul railway traction power supply system under analysis. If the system is stable and its power supply capacity is adequate, the difference between A and B is obtained. If this difference is less than a preset error difference, then... As the target final interval, the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target clock interval is obtained from the aforementioned state assessment process.

[0128] According to the aforementioned implementation method, the limit power supply capacity is searched by the bisection power flow algorithm, which combines the idea of ​​the bisection method, greatly speeds up the determination of the target tracking interval, and thus speeds up the acquisition of the limit power supply capacity of the heavy-haul railway traction power supply system to be analyzed.

[0129] In one embodiment, the power flow calculation results include system node voltages, multiple node voltages, and multiple node currents. Using the power flow calculation results, a state assessment is performed on the heavy-haul railway traction power supply system to be analyzed, including: when the voltage variation amplitude of the system node voltage is less than or equal to a preset voltage variation amplitude threshold, obtaining the assessment index data of the heavy-haul railway traction power supply system to be analyzed within the tracking interval based on the voltage of each node and the current of each node; and determining whether the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is qualified based on the assessment index data and the heavy-haul railway power supply capacity assessment system.

[0130] Optionally, server 102 reads the system voltage, multiple node voltages, and multiple node currents of the heavy-haul railway traction power supply system to be analyzed from the power flow calculation results. If the voltage change amplitude of the system node voltage is less than or equal to the preset voltage change amplitude threshold within the tracking interval, it is determined that the current heavy-haul railway traction power supply system to be analyzed is in a steady state. Based on the node voltages and currents, the server calculates the evaluation index data of the heavy-haul railway traction power supply system to be analyzed within the tracking interval. The server then queries the heavy-haul railway power supply capacity evaluation system according to the evaluation index data to determine whether the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is qualified.

[0131] Based on the above implementation method, the operational stability of the heavy-haul railway traction power supply system is ensured by judging the steady state of the system. Furthermore, the subsequent judgment on whether the power supply capacity is qualified is made after judging the steady state, which also reduces the computing cost.

[0132] In an exemplary embodiment, the method further includes: updating the system node voltage when the voltage change amplitude of the system node voltage is greater than a voltage change amplitude threshold, until the voltage change amplitude of the system node voltage is less than or equal to the voltage change amplitude threshold.

[0133] For example, if the voltage change amplitude of the system node voltage is greater than the voltage change amplitude threshold during the tracking interval, it indicates that the heavy-haul railway traction power supply system under analysis is in an unstable state and the electrical parameters need to be adjusted. The server 102 can adjust the system node voltage and recalculate the power flow according to the adjusted system node voltage until the voltage change amplitude of the system node voltage is less than or equal to the voltage change amplitude threshold, so that the heavy-haul railway traction power supply system under analysis tends to stabilize.

[0134] According to the aforementioned implementation method, real-time dynamic adjustment of the traction power supply system of heavy-haul railways can be realized, effectively responding to sudden electrical fluctuations, reducing the risk of system instability, and mitigating voltage fluctuations through proactive adjustment, avoiding voltage collapse or equipment damage, thereby improving the stability of the power supply system.

[0135] In one embodiment, when the traction power supply system of the heavy-haul railway to be analyzed passes the state assessment, obtaining the difference between the first boundary value and the second boundary value includes:

[0136] If the judgment result indicates that the power supply capacity of the heavy-haul railway traction power supply system under analysis is qualified, the difference between the first boundary value and the second boundary value is obtained.

[0137] If the judgment result indicates that the power supply capacity of the heavy-haul railway traction power supply system under analysis is unqualified, the first boundary value is updated to the tracking interval, and a new tracking interval is obtained. Then, the process returns to execute the train traction calculation of the heavy-haul railway traction power supply system under analysis using the tracking interval to obtain the train traction power corresponding to the tracking interval. Through the heavy-haul railway power flow calculation model, power flow calculation is performed based on the train traction power to obtain the power flow calculation result of the heavy-haul railway traction power supply system under analysis within the tracking interval. The power flow calculation result is used to perform the state assessment of the heavy-haul railway traction power supply system under analysis until the heavy-haul railway traction power supply system under analysis passes the state assessment and the difference between the first boundary value and the second boundary value is obtained.

[0138] Optionally, if the judgment result indicates that the power supply capacity of the heavy-haul railway traction power supply system under analysis is qualified, the server 102 obtains the difference between the first boundary value A and the second boundary value B; if the judgment result indicates that the power supply capacity of the heavy-haul railway traction power supply system under analysis is unqualified, the server 102 updates the first boundary value A to the tracking interval. According to the new first boundary value Second boundary value Calculate the new tracking interval and perform train traction calculations on the heavy-haul railway traction power supply system under analysis using the new tracking interval to obtain the new train traction power corresponding to the new tracking interval. Using the heavy-haul railway power flow calculation model, perform power flow calculations based on the new train traction power to obtain the new power flow calculation results of the heavy-haul railway traction power supply system under analysis within the new tracking interval. Using the new power flow calculation results, perform a state assessment of the heavy-haul railway traction power supply system under analysis. Repeat the above steps until the heavy-haul railway traction power supply system under analysis passes the state assessment and obtains the difference between the new first boundary value and the second boundary value.

[0139] Based on the above implementation method, when the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is unqualified, the boundary value is updated iteratively using the previous tracking interval to realize the iterative update of the tracking interval by the bisection method, thereby accelerating the determination speed of the target tracking interval and thus improving the speed of obtaining the limit power supply capacity of the heavy-haul railway traction power supply system to be analyzed.

[0140] In one embodiment, a heavy-haul railway power supply capacity assessment system for the heavy-haul railway traction power supply system to be analyzed is constructed based on historical power supply data, pre-set threshold values ​​for each assessment indicator, and weights of each assessment indicator. This includes: obtaining the correlation between historical power supply data and indicator level thresholds using a correlation model to obtain the correlation between individual indicators between the historical power supply data and the indicator level represented by the indicator level thresholds; obtaining the relative membership between historical power supply data and each indicator level based on the weights of each assessment indicator and the correlation between each individual indicator; and constructing the heavy-haul railway power supply capacity assessment system for the heavy-haul railway traction power supply system to be analyzed based on each relative membership.

[0141] Among them, the correlation model can be understood as a mathematical or theoretical model that measures the degree of correlation between two or more objects, events, states or variables. The correlation degree can be understood as the degree of correlation between two or more objects, events, states or variables. The correlation degree of a single indicator can be understood as the degree of correlation between data and indicator levels. The relative membership degree can be understood as an indicator used to measure the strength of the membership relationship between an element and multiple categories or sets in fuzzy set or multi-class classification problems. It can include the strength of the membership relationship of historical power supply data to each indicator level.

[0142] For example, server 102 uses set pair analysis to comprehensively evaluate power supply capacity. Through a correlation model, the relationship between two sets is quantified, and their similarities, differences, and inverse relationships are analyzed. Let set A consist of historical power supply data, and set B consist of the level thresholds of evaluation indicators. Then, a set pair can be constructed from set A and set B. The correlation between the sample data of the j-th indicator in set A and the single indicator of level h in set B. It can be calculated using the following formula:

[0143] μ hj = { 1 − 2 M h − 1 , j − x j M h − 1 , j − M h − 2 , j , x j ∈ ( M h − 2 , j , M h − 1 , j ] 1 ，x j ∈ ( M h − 1 , j , M h , j ] 1 − 2 x j − M h , j M h , j − M h + 1 , j , x j ∈ ( M h , j , M h + 1 , j ] − 1 , other

[0144] In the formula, and They represent the first The left and right endpoints of the standard grade range h for each indicator.

[0145] After obtaining the correlation degree of individual indicators, and combining the weights of each evaluation indicator, the concept of variable fuzzy sets is introduced to quantify the membership relationship between historical power supply data and indicator level h, resulting in the relative membership degree between historical power supply data and indicator level h, as follows:

[0146] D = 1 + CW 2 = [ d 1 , d 2 , ⋯ , d H ] T

[0147] Finally, based on the relative membership between historical power supply data and the levels of various evaluation indicators, an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is constructed.

[0148] According to the aforementioned implementation method, by utilizing the degree of connection and fuzzy membership, a multi-level, multi-angle, and multi-grade quantitative evaluation of power supply capacity can be achieved, avoiding superficial comparisons of single indicators. This allows for the construction of a more realistic assessment system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed, thereby improving the accuracy of power supply capacity acquisition.

[0149] In an exemplary embodiment, a heavy-haul railway power supply capacity assessment system for the heavy-haul railway traction power supply system to be analyzed is constructed based on each relative membership degree, including: obtaining the feature values ​​of historical power supply data relative to all index levels according to each relative membership degree; and constructing the heavy-haul railway power supply capacity assessment system for the heavy-haul railway traction power supply system to be analyzed based on the feature values ​​using a binary semantic method.

[0150] Among them, the eigenvalue can be understood as the data used to quantify the power supply capacity assessment results, and the binary semantic method can be understood as the decision of whether or not to start for the corresponding power supply capacity level.

[0151] Optionally, to avoid judgment bias caused by using the maximum membership principle to determine power supply capacity level, and to more accurately describe the power supply capacity status of historical power supply data, server 102 introduces a feature value p to quantify the evaluation results. The feature value p is as follows:

[0152]

[0153] Finally, a binary semantic method is introduced to determine the power supply capacity of historical power supply data. This allows for further subdivision of the data within the established power supply capacity level, providing a more intuitive and detailed description of the evaluation samples. Figure 3 As shown, this yields the evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed.

[0154] Based on the above implementation method, by introducing feature values ​​to quantify the power supply capacity assessment results, the accuracy of describing the power supply capacity status of historical power supply data is improved. Furthermore, the binary semantic method is used to further subdivide the power supply capacity level corresponding to each historical power supply data, thereby improving the accuracy of power supply capacity classification of the constructed heavy load power supply capacity assessment system.

[0155] In one embodiment, the power flow calculation model for heavy-haul railways is constructed through the following steps: obtaining the system structure information of the traction power supply system of the heavy-haul railway to be analyzed, and constructing the equivalent chain circuit of the traction network based on the system structure information; and constructing the power flow calculation model of the heavy-haul railway corresponding to the traction power supply system of the heavy-haul railway to be analyzed based on the equivalent chain circuit of the traction network.

[0156] For example, such as Figure 4 As shown, the heavy-haul railway traction power supply system mainly consists of the power grid, traction transformers, traction network, and heavy-haul trains. The traction transformers convert the three-phase power supplied by the power grid into single-phase power, which is then transmitted to the heavy-haul trains through the traction network. Based on these characteristics, server 102 can establish the system's equivalent impedance model and power flow calculation model.

[0157] The traction network consists of an overhead contact line, positive feeders, and rails. Its structure is generally a parallel multi-conductor transmission line. Using a chain circuit model, the traction transformer and the traction network can be equivalently represented as... Figure 5 The traction net chain circuit shown.

[0158] Figure 5 In The current drawn by the traction locomotive; the traction transformer is equivalently represented using a model of parallel admittance of current sources. It is an equivalent current source. It is parallel admittance; and These are the impedance and admittance matrices of the traction network, each with a length of L. The calculation formulas are as follows:

[0159]

[0160] In the formula, and These are the unit impedance matrix and unit admittance matrix of the traction network, respectively. The nodal admittance matrix of the entire traction network can be obtained by superimposing the nodal admittance matrices of each cross section in a staggered manner, and the calculation formula is as follows:

[0161] Y ( n ) = [ Z 1 − 1 + Y 1 − Z 1 − 1 ⋯ ⋯ 0 − Z 1 − 1 Z 1 − 1 + Y 2 + Z 2 − 1 ⋮ ⋮ ⋱ ⋱ ⋮ ⋮ Z n-2 − 1 + Y n − 1 + Z n − 1 − 1 − Z n- 1 − 1 0 ⋯ ⋯ − Z n- 1 − 1 Z n- 1 − 1 + Y n ]

[0162] The traction locomotive is modeled as a power source connected between the overhead contact line and the rails. When the voltage between the overhead contact line and the rails at the location of the traction locomotive is... At that time, the power of the traction locomotive With current The relationship between them is as follows:

[0163]

[0164] The power flow calculation model for heavy-haul railways is established as follows:

[0165] [ U 1 k + 1 U 2 k + 1 ⋮ U n − 1 k + 1 U n k + 1 ] = [ Z 1 − 1 + Y 1 − Z 1 − 1 ⋯ ⋯ 0 − Z 1 − 1 Z 1 − 1 + Y 2 + Z 2 − 1 ⋮ ⋮ ⋱ ⋱ ⋮ ⋮ Z n-2 − 1 + Y n − 1 + Z n − 1 − 1 − Z n- 1 − 1 0 ⋯ ⋯ − Z n- 1 − 1 Z n- 1 − 1 + Y n ] − 1 [ I 1 k I 2 k ⋮ I n − 1 k I n k ]

[0166] in, It is the node voltage. It is the node current.

[0167] According to the aforementioned implementation method, modeling the traction network as a chain circuit helps to analyze faults, voltage drops and power flow segment by segment, enhances the controllability and adjustability of the model, and through staggered superposition, the node admittance matrix of the system can be accurately calculated, thereby establishing a power flow calculation model that supports efficient power flow analysis of large systems.

[0168] In one exemplary embodiment, such as Figure 6 As shown, a specific implementation of a method for obtaining power supply capacity for heavy-haul railways is provided (the following data are all specific examples and are not limited to this specific case to implement this application). Wherein:

[0169] Step 1: Analyze the structure of the traction power supply system of heavy-haul railways, construct the equivalent chain circuit of the traction network, and establish a power flow calculation model for heavy-haul railways:

[0170] like Figure 4 As shown, the heavy-haul railway traction power supply system mainly consists of the power grid, traction transformers, traction network, and heavy-haul trains. The traction transformers convert the three-phase power supplied by the power grid into single-phase power, which is then transmitted to the heavy-haul trains through the traction network. Based on these characteristics, an equivalent impedance model and a power flow calculation model for the system are established.

[0171] The traction network consists of an overhead contact line, positive feeders, and rails. Its structure is generally a parallel multi-conductor transmission line. Using a chain circuit model, the traction transformer and the traction network can be equivalently represented as follows: Figure 5 The model shown.

[0172] Figure 5 In The current drawn by the traction locomotive; the traction transformer is equivalently represented using a model of current source parallel admittance. It is an equivalent current source. It is parallel admittance; and These are the impedance and admittance matrices of the traction network, each with a length of L. The calculation formulas are as follows:

[0173]

[0174] In the formula, and These are the unit impedance matrix and unit admittance matrix of the traction network, respectively. The nodal admittance matrix of the entire traction network can be obtained by superimposing the nodal admittance matrices of each cross section in a staggered manner, as shown in the following formula:

[0175] Y ( n ) = [ Z 1 − 1 + Y 1 − Z 1 − 1 ⋯ ⋯ 0 − Z 1 − 1 Z 1 − 1 + Y 2 + Z 2 − 1 ⋮ ⋮ ⋱ ⋱ ⋮ ⋮ Z n-2 − 1 + Y n − 1 + Z n − 1 − 1 − Z n- 1 − 1 0 ⋯ ⋯ − Z n- 1 − 1 Z n- 1 − 1 + Y n ]

[0176] The traction locomotive is modeled as a power source connected between the overhead contact line and the rails. When the voltage between the overhead contact line and the rails at the location of the traction locomotive is... At that time, the power of the traction locomotive With current The relationship between them is as follows:

[0177]

[0178] The continuous power flow model of the system is established as follows:

[0179] [ U 1 k + 1 U 2 k + 1 ⋮ U n − 1 k + 1 U n k + 1 ] = [ Z 1 − 1 + Y 1 − Z 1 − 1 ⋯ ⋯ 0 − Z 1 − 1 Z 1 − 1 + Y 2 + Z 2 − 1 ⋮ ⋮ ⋱ ⋱ ⋮ ⋮ Z n-2 − 1 + Y n − 1 + Z n − 1 − 1 − Z n- 1 − 1 0 ⋯ ⋯ − Z n- 1 − 1 Z n- 1 − 1 + Y n ] − 1 [ I 1 k I 2 k ⋮ I n − 1 k I n k ]

[0180] in, It is the node voltage. It is the node current.

[0181] Step 2: Establish evaluation indicators for the power supply capacity assessment system, including traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance.

[0182] (1) Load factor of traction transformer:

[0183] When the load rate of a traction transformer is too high, it can cause problems such as increased load loss, higher temperature, shortened service life, and compromised operational safety. To assess the load level of a traction transformer and ensure its safe and economical operation, the traction transformer load rate η is used as a metric. The calculation formula is as follows:

[0184]

[0185] In the formula, and These represent the actual output power of the transformer and the rated capacity of the traction transformer, respectively.

[0186] (2) Voltage deviation rate :

[0187] Heavy-haul railway traction loads are characterized by high power and strong fluctuations, resulting in fluctuations in the traction grid voltage. The standard voltage of the traction grid is 25kV. When the traction grid voltage deviates significantly from the standard voltage, it leads to a decrease in the power utilization level of the traction locomotive. This paper uses the voltage deviation rate... The voltage level is measured using the following formula:

[0188]

[0189] In the formula, and These represent the actual voltage of the traction network and the standard voltage of the traction network, respectively.

[0190] (3) Three-phase voltage imbalance :

[0191] Heavy-haul trains use single-phase power. When a single-phase load is connected to a three-phase power system through a traction substation, it can lead to a significant negative sequence in the power system. This is addressed by considering the three-phase voltage imbalance. To describe this negative order, the calculation formula is as follows:

[0192]

[0193] In the formula, , , These are the phase voltages of phases A, B, and C, respectively.

[0194] Step 3: Based on the indicators obtained in Step 2, multiple experts conduct pairwise comparisons of the evaluation indicators to obtain the relative importance of each indicator. An intuitionistic fuzzy judgment matrix is ​​then constructed and a consistency test is performed.

[0195] Based on the indicators obtained in step 2, multiple experts conducted pairwise comparisons of the evaluation indicators to obtain the relative importance of each indicator and constructed the following intuitionistic fuzzy judgment matrix:

[0196] K = [ ( u 11 , v 11 ) ( u 12 , v 12 ) ⋯ ( u 1 n , v 1 n ) ( u 21 , v 21 ) ( u 22 , v 22 ) ⋯ ( u 2 n , v 2 n ) ⋮ ⋮ ⋱ ⋮ ( u n 1 , v n 1 ) ( u n 2 , v n 2 ) … ( u nn , v nn ) ]

[0197] In the formula, u ij Membership degree represents the degree to which experts consider indicator i to be more important than indicator j. ij The degree of non-membership indicates the degree to which experts consider indicator j to be more important than indicator i.

[0198] To verify the consistency of the importance of each indicator and obtain more reasonable indicator weights, a consistency test needs to be performed on the intuitionistic fuzzy judgment matrix. First, the intuitionistic fuzzy consistency judgment matrix is ​​constructed as follows:

[0199] K ¯ = [ ( u ¯ 11 , v ¯ 11 ) ( u ¯ 12 , v ¯ 12 ) ⋯ ( u ¯ 1 n , v ¯ 1 n ) ( u ¯ 21 , v ¯ 21 ) ( u ¯ 22 , v ¯ 22 ) ⋯ ( u ¯ 2 n , v ¯ 2 n ) ⋮ ⋮ ⋱ ⋮ ( u ¯ n 1 , v ¯ n 1 ) ( u ¯ n 2 , v ¯ n 2 ) … ( u ¯ nn , v ¯ nn ) ]

[0200] The consistency check is performed as follows:

[0201]

[0202] In the formula, π ij Hesitation level represents the subjective uncertainty that arises when experts compare importance.

[0203] Step 4: Correct the intuitionistic fuzzy judgment matrix obtained in Step 3 to obtain multiple sets of weights for each indicator. Construct the correlation degree of each set of weights using the grey relational analysis method and calculate the comprehensive weight:

[0204] The intuitive fuzzy judgment matrix obtained in step 3 is modified to obtain multiple sets of weights for each indicator. In order to avoid errors in weight allocation caused by inconsistent opinions among experts, grey relational theory is used to adjust each weight to obtain the final comprehensive weight.

[0205] Based on the intuitionistic fuzzy judgment matrix, calculate the subjective weights of each indicator, and the intuitionistic fuzzy weights. for:

[0206]

[0207] To determine the specific weights of the evaluation indicators, intuitive fuzzy weights were used. Quantification:

[0208]

[0209]

[0210] In the formula, This is a fuzzy transformation function; , and Intuitive fuzzy weights Membership degree, non-membership degree, and hesitation degree; represents the subjective weight of indicator j.

[0211] The correlation degree of each group of weights is constructed by using the grey relational analysis method, and the comprehensive weight is calculated. Assuming N experts evaluate J indicators using intuitionistic fuzzy hierarchical analysis, multiple groups of weights are obtained. Then the overall weight It can be calculated using the following formula:

[0212]

[0213] In the formula, This represents the weight of the j-th indicator obtained through the i-th expert, with the maximum value among all expert weights used to form the reference weight. ; The resolution coefficient can be set to 0.5.

[0214] Step 5: Based on the comprehensive weights obtained in Step 4, the power supply capacity is comprehensively evaluated using set pair analysis. The power supply status is determined through binary semantics, and the evaluation results are quantified by introducing eigenvalues ​​to establish a power supply capacity evaluation system for heavy-haul railways.

[0215] Based on the comprehensive weights obtained in step 4, a set pair analysis method is used to comprehensively evaluate the power supply capacity. Through a correlation degree model, the relationship between two sets is quantified, and their similarities, differences, and inverse relationships are analyzed. Assuming a set of sample data to be evaluated is collected, let set A be formed by this data, and set B is formed by the level thresholds of the evaluation indicators, then a set pair can be constructed from set A and set B. The correlation between the sample data of the j-th indicator in set A and the single indicator of level h in set B. It can be calculated using the following formula:

[0216] μ hj = { 1 − 2 M h − 1 , j − x j M h − 1 , j − M h − 2 , j , x j ∈ ( M h − 2 , j , M h − 1 , j ] 1 ，x j ∈ ( M h − 1 , j , M h , j ] 1 − 2 x j − M h , j M h , j − M h + 1 , j , x j ∈ ( M h , j , M h + 1 , j ] − 1 , other

[0217] In the formula, and They represent the first The left and right endpoints of the standard grade range h for each indicator.

[0218] After obtaining the correlation degree of individual indicators, and combining it with the comprehensive weight, the concept of variable fuzzy sets is introduced to quantify the membership relationship between the sample and the indicator level h, resulting in the relative membership degree between the sample data and the indicator level h, as follows:

[0219] D = 1 + CW 2 = [ d 1 , d 2 , ⋯ , d H ] T

[0220] To avoid judgment bias caused by using the maximum membership principle to determine power supply capability levels, and to more accurately describe the power supply capability status of the evaluation samples, an eigenvalue p is introduced to quantify the evaluation results. The eigenvalue p is as follows:

[0221]

[0222] Finally, a binary semantic method is introduced to determine the power supply capability of the samples. This allows for further subdivision within each power supply capability level, based on the established level, providing a more intuitive and detailed description of the samples. Figure 3 As shown.

[0223] Step 6: Based on the power supply capacity assessment system obtained in Step 5, and combined with the heavy-haul railway power flow calculation model obtained in Step 1, a bipartite power flow algorithm is proposed to quickly search the limit power supply capacity of the traction power supply system.

[0224] Based on the heavy-haul railway power supply capacity assessment system obtained in step 5, this application proposes a bisection power flow algorithm to search for the ultimate power supply capacity. This algorithm combines the idea of ​​bisection and, compared with the traditional variable step size power flow algorithm, can significantly reduce the solution time. The flowchart of the specific search process is as follows: Figure 7 As shown, the specific steps are as follows:

[0225] Step 6.1: Set the initial tracking interval for heavy-haul trains and with 0 and Use A and B as the initial boundary values ​​for the bisection method.

[0226] Step 6.2: Use the average of A and B To track the interval, train traction calculations are performed to obtain the online train power P+jQ, and trains are arranged according to the timetable for that interval. The system node admittance matrix Ys is obtained based on the line information.

[0227] Step 6.3: Calculate the train cross-sectional current Update system node voltage .

[0228] Step 6.4: Determine whether the system node voltages have converged. If yes, read the voltage and current of each node and proceed to step 6.5. If no, perform power flow calculation with the updated system voltage and return to step 6.3.

[0229] Step 6.5: Calculate each evaluation index based on the voltage and current of each node in the system to determine whether the power supply capacity is qualified. If yes, proceed to step 6.6; otherwise, update the boundary value A. Then return to step 6.2.

[0230] Step 6.6: Determine if the difference between boundary values ​​A and B is less than the minimum error. If so, output the result. To determine the minimum tracking interval; otherwise, update the boundary value B. Then return to step 6.2.

[0231] Example of implementation method:

[0232] Multiple experts in traction power supply system power supply capacity assessment conducted pairwise comparisons of the assessment indicators, and after consistency testing, obtained multiple sets of weights as shown in Table 1.

[0233] Table 1. Weight matrix obtained from scores by multiple experts

[0234]

[0235] Using the maximum value as the reference weight, the correlation coefficient matrix is ​​obtained as follows.

[0236] ζ = [ 0 . 9 2 7 8 0 . 4 3 2 1 0 . 3 3 3 3 0 . 7 6 7 4 0 . 4 4 4 6 0 . 3 5 2 1 1 . 0 0 0 0 0 . 4 1 1 8 0 . 3 3 7 4 0 . 8 9 8 9 0 . 4 3 3 2 0 . 3 3 6 5 ]

[0237] The correlation degree of the indicators is calculated and normalized to obtain the final comprehensive weight. The comprehensive weight and the boundary of the indicator level are shown in Table 2.

[0238] Table 2 Indicator Level Boundaries and Overall Weights

[0239]

[0240] Using the "unqualified" threshold and the traction network voltage threshold in the proposed evaluation system as judgment criteria, a bipartite power flow algorithm is employed to search for the system's ultimate power supply capacity. During the search process using the "unqualified" threshold in the proposed evaluation system as the judgment criterion, the changes in each indicator and the power supply capacity evaluation results are as follows: Figure 8a , Figure 8b , Figure 8c as well as Figure 9 As shown.

[0241] Under both judgment criteria, the following are the results of each indicator when the system reaches the limit power supply boundary.

[0242] Table 3 Search results for extreme power supply capacity under different judgment criteria.

[0243]

[0244] As shown in Table 3, if only the critical value of the traction grid voltage is used as the criterion, when the system reaches the limit power supply boundary, the load rate of the traction transformer and the three-phase voltage imbalance have far exceeded the critical value, which is unacceptable. If the "unqualified" critical value in the proposed evaluation system is used as the criterion, when the system reaches the limit power supply boundary, the load rate of the traction transformer and the three-phase voltage imbalance slightly exceed the critical value, while the traction grid voltage is also close to but does not exceed the critical value. Since the traction transformer is allowed to operate under a small overload state for a long time in actual system operation, using the "unqualified" critical value in the evaluation system proposed in this paper as the criterion is more in line with the logic of practical application. Therefore, using a traditional single indicator to evaluate power supply capacity is prone to one-sidedness and inaccuracy in the evaluation results, while the evaluation system proposed in this paper can more comprehensively and accurately evaluate the power supply capacity of the system.

[0245] Compared with the prior art, this application has the following technical advantages:

[0246] 1. The proposed power supply capacity assessment system for heavy-haul railway traction power supply system comprehensively considers the impact of traction transformer load rate, traction grid voltage and three-phase voltage imbalance on power supply capacity, and can accurately and comprehensively assess the power supply capacity of heavy-haul railway traction power supply system.

[0247] 2. The proposed bisection power flow algorithm combines the idea of ​​the bisection method with power flow calculation, realizing a fast search for the ultimate power supply capacity of the traction power supply system of heavy-haul railways. This avoids the problem of slow speed in solving the ultimate power supply capacity of heavy-haul railway traction power supply systems caused by variable step size power flow algorithms with large initial tracking intervals and small initial tracking step sizes.

[0248] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0249] Based on the same inventive concept, this application also provides a power supply capacity acquisition device for heavy-haul railways to implement the aforementioned method for acquiring power supply capacity of heavy-haul railways. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the power supply capacity acquisition device for heavy-haul railways provided below can be found in the limitations of the power supply capacity acquisition method for heavy-haul railways described above, and will not be repeated here.

[0250] In one exemplary embodiment, such as Figure 10 As shown, a power supply capacity acquisition device for heavy-haul railways is provided, comprising: a first acquisition module 1001, a subjective weight acquisition module 1002, an index weight acquisition module 1003, an evaluation system construction module 1004, and a power supply capacity acquisition module 1005, wherein:

[0251] The first acquisition module 1001 is used to acquire historical power supply data of the heavy-haul railway traction power supply system to be analyzed, as well as multiple pre-set evaluation indicators. The evaluation indicators include traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance.

[0252] The subjective weight acquisition module 1002 is used to obtain the relative importance of each evaluation indicator and to obtain the subjective evaluation indicator weight of each evaluation indicator based on the relative importance.

[0253] The indicator weight acquisition module 1003 is used to obtain the evaluation indicator weight corresponding to each evaluation indicator based on the similarity between the weights of each subjective evaluation indicator and the weights of each subjective evaluation indicator.

[0254] The evaluation system construction module 1004 is used to construct an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed, based on historical power supply data, pre-set threshold values ​​for each evaluation indicator, and weights of each evaluation indicator.

[0255] The power supply capacity acquisition module 1005 is used to evaluate the power supply capacity of the heavy-haul railway traction power supply system under analysis based on the heavy-haul railway power supply capacity assessment system, using a pre-constructed heavy-haul railway power flow calculation model for the heavy-haul railway traction power supply system to be analyzed, and to obtain the power supply capacity of the heavy-haul railway traction power supply system under analysis within the target tracking interval.

[0256] In one embodiment, the power supply capability acquisition module 1005 further includes a traction calculation submodule, a power flow calculation submodule, a state assessment submodule, a first acquisition submodule, and a power supply capability acquisition submodule, wherein:

[0257] The traction calculation submodule is used to perform train traction calculations on the traction power supply system of the heavy-haul railway to be analyzed using a pre-set tracking interval, and to obtain the train traction power corresponding to the tracking interval; the tracking interval is the average of the first boundary value and the second boundary value, and the first boundary value is less than the second boundary value.

[0258] The power flow calculation submodule is used to perform power flow calculations based on the train traction power using the heavy-haul railway power flow calculation model, and obtain the power flow calculation results of the heavy-haul railway traction power supply system under analysis within the tracking interval.

[0259] The condition assessment submodule is used to perform condition assessment on the traction power supply system of the heavy-haul railway to be analyzed, using the power flow calculation results.

[0260] The first acquisition submodule is used to acquire the difference between the first boundary value and the second boundary value when the traction power supply system of the heavy-haul railway to be analyzed has passed the state assessment.

[0261] The power supply capacity acquisition submodule is used to take the tracking interval as the target tracking interval when the difference is less than the preset error difference, and obtain the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval from the state assessment results.

[0262] In one embodiment, the power flow calculation results include system node voltages, multiple node voltages, and multiple node currents; the state assessment submodule is further used to obtain the evaluation index data of the heavy-haul railway traction power supply system to be analyzed within the tracking interval based on the voltage change amplitude of each node and the current of each node when the voltage change amplitude of the system node voltage is less than or equal to a preset voltage change amplitude threshold; and to determine whether the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is qualified based on the evaluation index data and the heavy-haul railway power supply capacity assessment system.

[0263] In an exemplary embodiment, the state assessment submodule is further configured to update the system node voltage when the voltage change amplitude of the system node voltage is greater than the voltage change amplitude threshold, until the voltage change amplitude of the system node voltage is less than or equal to the voltage change amplitude threshold.

[0264] In one embodiment, the first acquisition submodule is further configured to: acquire the difference between the first boundary value and the second boundary value if the judgment result indicates that the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is qualified; update the first boundary value to the tracking interval and obtain a new tracking interval if the judgment result indicates that the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is unqualified; return to execute the steps of performing train traction calculation on the heavy-haul railway traction power supply system to be analyzed using the tracking interval to obtain the train traction power corresponding to the tracking interval; perform power flow calculation based on the train traction power using the heavy-haul railway power flow calculation model to obtain the power flow calculation result of the heavy-haul railway traction power supply system to be analyzed within the tracking interval; and perform state assessment on the heavy-haul railway traction power supply system to be analyzed using the power flow calculation result, until the heavy-haul railway traction power supply system to be analyzed passes the state assessment and the difference between the first boundary value and the second boundary value is obtained.

[0265] In one embodiment, the evaluation system construction module 1004 further includes a single-item connection degree acquisition submodule, a relative membership degree acquisition submodule, and an evaluation system construction submodule, wherein:

[0266] The single-item correlation degree acquisition submodule is used to acquire the correlation degree between historical power supply data and indicator level thresholds through the correlation degree model, and obtain the single-item correlation degree between the historical power supply data and the indicator level represented by the indicator level threshold.

[0267] The relative membership acquisition submodule is used to obtain the relative membership between historical power supply data and each indicator level based on the weight of each evaluation indicator and the correlation between each individual indicator.

[0268] The evaluation system construction submodule is used to construct an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed based on each relative membership degree.

[0269] In an exemplary embodiment, the evaluation system construction submodule is further used to obtain the feature values ​​of historical power supply data relative to all indicator levels based on each relative membership degree; and to construct a heavy-haul railway power supply capacity evaluation system for the heavy-haul railway traction power supply system to be analyzed based on the feature values ​​using a binary semantic method.

[0270] In one embodiment, the power supply acquisition device for heavy-haul railways further includes a model pre-construction module, which is used to acquire the system structure information of the traction power supply system of the heavy-haul railway to be analyzed, and construct an equivalent chain circuit of the traction network based on the system structure information; and construct a power flow calculation model of the heavy-haul railway corresponding to the traction power supply system of the heavy-haul railway to be analyzed based on the equivalent chain circuit of the traction network.

[0271] Each module in the aforementioned power supply capacity acquisition device for heavy-haul railways can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0272] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores historical power supply data, evaluation indicators, relative importance, subjective evaluation indicator weights, evaluation indicator weights, indicator level thresholds, a heavy-haul railway power supply capacity evaluation system, and power supply capacity. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for obtaining the power supply capacity of a heavy-haul railway.

[0273] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0274] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for obtaining the power supply capacity of a heavy-haul railway as described in the above embodiment.

[0275] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method for obtaining the power supply capacity of a heavy-haul railway as described in the above embodiment.

[0276] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the method for obtaining the power supply capacity of a heavy-haul railway as described in the above embodiments.

[0277] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0278] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0279] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0280] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for obtaining the power supply capacity of a heavy-haul railway, characterized in that, The method includes: The historical power supply data of the traction power supply system of the heavy-haul railway to be analyzed, as well as several pre-set evaluation indicators, are obtained. The evaluation indicators include traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance. Obtain the relative importance of each of the evaluation indicators, and obtain the subjective evaluation indicator weight of each of the evaluation indicators based on the relative importance of each of the evaluation indicators; Based on the similarity between the weights of each subjective evaluation indicator and the weights of each subjective evaluation indicator, the evaluation indicator weights corresponding to each evaluation indicator are obtained. Based on the historical power supply data, the index level thresholds pre-set for each of the evaluation indicators, and the weights of each of the evaluation indicators, an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is constructed. By pre-constructing a heavy-haul railway power flow calculation model for the heavy-haul railway traction power supply system to be analyzed, and evaluating the power supply capacity of the heavy-haul railway traction power supply system based on the heavy-haul railway power supply capacity assessment system, the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval is obtained.

2. The method according to claim 1, characterized in that, The process involves using a pre-constructed heavy-haul railway power flow calculation model for the heavy-haul railway traction power supply system to assess its power supply capacity based on the heavy-haul railway power supply capacity assessment system, thereby obtaining the power supply capacity of the heavy-haul railway traction power supply system within the target tracking interval. This includes: The train traction power of the heavy-haul railway traction power supply system to be analyzed is calculated using a pre-set tracking interval to obtain the train traction power corresponding to the tracking interval; the tracking interval is the average of a first boundary value and a second boundary value, wherein the first boundary value is less than the second boundary value. Using the heavy-haul railway power flow calculation model, power flow calculation is performed based on the train traction power to obtain the power flow calculation results of the heavy-haul railway traction power supply system to be analyzed within the tracking interval; Using the power flow calculation results, a state assessment is performed on the traction power supply system of the heavy-haul railway to be analyzed; When the traction power supply system of the heavy-haul railway to be analyzed passes the state assessment, the difference between the first boundary value and the second boundary value is obtained. If the difference is less than a preset error difference, the tracking interval is taken as the target tracking interval, and the power supply capacity of the heavy-haul railway traction power supply system to be analyzed within the target tracking interval is obtained from the state assessment results.

3. The method according to claim 2, characterized in that, The power flow calculation results include system node voltages, multiple node voltages, and multiple node currents; The process of using the power flow calculation results to perform a state assessment of the traction power supply system of the heavy-haul railway to be analyzed includes: When the voltage variation amplitude of the system node voltage is less than or equal to a preset voltage variation amplitude threshold, the evaluation index data of the heavy-haul railway traction power supply system to be analyzed within the tracking interval are obtained based on the voltage of each node and the current of each node. Based on the evaluation index data and the heavy-haul railway power supply capacity evaluation system, determine whether the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is qualified.

4. The method according to claim 3, characterized in that, The method further includes: If the voltage change of the system node voltage is greater than the voltage change threshold, the system node voltage is updated until the voltage change of the system node voltage is less than or equal to the voltage change threshold.

5. The method according to claim 3, characterized in that, The step of obtaining the difference between the first boundary value and the second boundary value when the traction power supply system of the heavy-haul railway to be analyzed has passed the state assessment includes: If the judgment result indicates that the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is qualified, the difference between the first boundary value and the second boundary value is obtained. If the judgment result indicates that the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is unqualified, the first boundary value is updated to the tracking interval, and a new tracking interval is obtained. Then, the process returns to perform train traction calculation on the heavy-haul railway traction power supply system to be analyzed using the tracking interval to obtain the train traction power corresponding to the tracking interval. Through the heavy-haul railway power flow calculation model, power flow calculation is performed based on the train traction power to obtain the power flow calculation result of the heavy-haul railway traction power supply system to be analyzed within the tracking interval. The process of using the power flow calculation result to perform state assessment on the heavy-haul railway traction power supply system to be analyzed continues until the heavy-haul railway traction power supply system to be analyzed passes the state assessment and the difference between the first boundary value and the second boundary value is obtained.

6. The method according to claim 1, characterized in that, The process of constructing a heavy-haul railway power supply capacity assessment system for the heavy-haul railway traction power supply system to be analyzed, based on the historical power supply data, pre-set indicator level thresholds for each of the assessment indicators, and the weights of each of the assessment indicators, includes: By using the correlation model, the correlation between the historical power supply data and the indicator level threshold is obtained, and the correlation between the historical power supply data and the indicator level represented by the indicator level threshold is obtained. Based on the weights of each evaluation indicator and the correlation degree of each individual indicator, the relative membership degree between the historical power supply data and each indicator level is obtained. Based on the relative membership degrees, an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is constructed.

7. The method according to claim 6, characterized in that, The assessment system for the heavy-haul railway power supply capacity of the traction power supply system to be analyzed is constructed based on the relative membership degrees of each of the aforementioned systems, including: Based on the relative membership degrees, the characteristic values ​​of the historical power supply data relative to all indicator levels are obtained; Using a binary semantic method, an evaluation system for the power supply capacity of the heavy-haul railway traction power supply system to be analyzed is constructed based on the feature values.

8. The method according to claim 1, characterized in that, The heavy-haul railway power flow calculation model is constructed through the following steps: Obtain the system structure information of the heavy-haul railway traction power supply system to be analyzed, and construct the equivalent chain circuit of the traction network based on the system structure information; Based on the equivalent chain circuit of the traction network, a power flow calculation model for the heavy-haul railway traction power supply system to be analyzed is constructed.

9. A device for acquiring power supply capacity for heavy-haul railways, characterized in that, The device includes: The first acquisition module is used to acquire historical power supply data of the traction power supply system of the heavy-haul railway to be analyzed, as well as several pre-set evaluation indicators; the evaluation indicators include traction transformer load rate, voltage deviation rate, and three-phase voltage imbalance. The subjective weight acquisition module is used to obtain the relative importance between each of the evaluation indicators and to obtain the subjective evaluation indicator weight of each of the evaluation indicators based on the relative importance. The indicator weight acquisition module is used to obtain the evaluation indicator weight corresponding to each evaluation indicator based on the similarity between the weights of each subjective evaluation indicator and the weights of each subjective evaluation indicator. The evaluation system construction module is used to construct the heavy-haul railway power supply capacity evaluation system of the heavy-haul railway traction power supply system to be analyzed based on the historical power supply data, the index level thresholds set in advance for each of the evaluation indicators, and the weights of each of the evaluation indicators. The power supply capacity acquisition module is used to evaluate the power supply capacity of the heavy-haul railway traction power supply system under analysis based on the heavy-haul railway power supply capacity evaluation system, using a heavy-haul railway power flow calculation model pre-constructed for the heavy-haul railway traction power supply system to be analyzed, and to obtain the power supply capacity of the heavy-haul railway traction power supply system under analysis within the target tracking interval.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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