Methods, apparatus and computer equipment for analyzing current imbalance limitation information

Abstract

This application relates to a method, apparatus, and computer device for analyzing current-carrying imbalance limitation information. The method includes: obtaining the actual impedance parameters of parallel cables in the same direction; constructing and adjusting an optimized cable imbalance model based on the actual impedance parameters using a dynamic programming algorithm; identifying the target cable series impedance value; calculating the current-carrying imbalance based on the target cable series impedance value; adjusting the target cable series impedance value using a preset adjustment strategy; returning to the above steps to obtain the current-carrying imbalance corresponding to each target cable series impedance value, thereby identifying the current-carrying imbalance limitation information and the actual impedance parameters; and then iteratively returning to the above steps to obtain the current-carrying imbalance limitation information of the parallel cables in the same direction. This method can improve the accuracy of current-carrying imbalance limitation analysis of laid cables.

Description

Technical Field

[0001] This application relates to the field of power cable technology, and in particular to a method, apparatus and computer equipment for analyzing current imbalance limitation information. Background Technology

[0002] High-voltage cables play a vital role in urban power grid construction, offering advantages such as small footprint, high power supply reliability, and minimal environmental impact, significantly enhancing grid transmission capacity. In recent years, with the development of power cable technology and urbanization, power cables have been widely used in power grid construction due to their unique characteristics. However, the problem of cable parameter asymmetry has become increasingly prominent. Furthermore, the power supply load in many large and medium-sized cities is rapidly increasing. To meet the demands of larger power loads, parallel multi-circuit cable lines can effectively reduce the current carrying capacity of each cable, which is of great significance for guiding cable type selection and ensuring the safe operation of the power grid. However, uneven cable laying methods and different cable arrangement methods can cause imbalances in the three-phase voltage and current of the cables, affecting the stable operation of the cable lines and even causing safety accidents. Therefore, while meeting the requirements for reducing three-phase unbalanced voltage and current, it is necessary to consider the power loss caused by the connection impedance to prevent excessive impedance loss from affecting the normal operation of the cable lines.

[0003] Currently, numerous studies have been conducted both domestically and internationally on issues related to cable imbalance. However, the focus on mitigating cable imbalance has been relatively concentrated on pre-laying preparations. Issues such as three-phase imbalance in already laid cables, or imbalances caused by inadequate training or errors by installation personnel, have often not been adequately addressed. Therefore, a strategy for limiting the current-carrying imbalance of already laid cables is needed. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer device, computer-readable storage medium, and computer program product for analyzing current imbalance limitation information to address the aforementioned technical problems.

[0005] Firstly, this application provides a method for analyzing current-carrying imbalance limitation information. The method includes:

[0006] Obtain the actual impedance parameters of the parallel cables in the same direction, and based on the actual impedance parameters, construct an optimization model for the cable imbalance of the parallel cables in the same direction using a dynamic programming algorithm;

[0007] By using a model optimization strategy, the cable imbalance optimization model is adjusted to obtain the cable imbalance model, and based on the cable imbalance model, the target cable series impedance value of the parallel cables in the same direction is identified.

[0008] Based on the target cable series impedance value, the current-carrying imbalance of the parallel cables in the same direction is calculated, and the target cable series impedance value is adjusted through a preset adjustment strategy. Then, the step of calculating the current-carrying imbalance of the parallel cables in the same direction based on the target cable series impedance value is returned to be executed, so as to obtain the current-carrying imbalance corresponding to each target cable series impedance value.

[0009] Based on the current imbalance corresponding to the series impedance value of each target cable, the limiting information of the current imbalance of the parallel cables in the same direction is identified, as well as the true impedance parameters of the parallel cables in the same direction.

[0010] Replace the actual impedance parameter with the true impedance parameter, and return to the step of constructing the cable imbalance optimization model of the parallel cable in the same direction based on the actual impedance parameter using a dynamic programming algorithm, until the preset number of iterations is met, and use the constraint information obtained in the last iteration as the current-carrying imbalance constraint information of the parallel cable in the same direction.

[0011] Optionally, the step of constructing an optimization model for the cable imbalance of the parallel cables in the same direction based on the actual impedance parameters using a dynamic programming algorithm includes:

[0012] Obtain the circuit model of the parallel cable in the same direction and the series impedance circuit model of the parallel cable in the same direction, and identify the first current calculation equation corresponding to the circuit model and the second current calculation model corresponding to the series impedance circuit model.

[0013] Based on the first current calculation equation and the second current calculation model, the current-carrying supplementary equation for parallel cables in the same direction under series impedance conditions is identified. Based on the actual impedance parameters and the current-carrying supplementary equation, the cable imbalance optimization model for the parallel cables in the same direction is constructed using the dynamic programming algorithm.

[0014] Optionally, adjusting the cable imbalance optimization model through a model optimization strategy to obtain the cable imbalance model includes:

[0015] Based on the model optimization strategy, identify the constraint information of the cable imbalance model, and identify the model index information corresponding to each constraint information;

[0016] Based on the cable imbalance optimization model, identify the index value corresponding to each model index information, and identify the index value range corresponding to each constraint limit information;

[0017] Identify the constraint information corresponding to the index value of each model index information, and when there are abnormal index values ​​that do not belong to the range of index values ​​corresponding to the constraint information, adjust the cable imbalance optimization model based on the abnormal index values ​​to obtain a new cable imbalance optimization model;

[0018] Replace the existing cable imbalance optimization model with the new cable imbalance optimization model, and return to the previous iteration to execute the cable imbalance optimization model. Identify the index value corresponding to each model index information until there are no abnormal index values ​​that do not belong to the index value range corresponding to the constraint information. Then, use the cable imbalance optimization model obtained in the last iteration as the cable imbalance model.

[0019] Optionally, identifying the target cable series impedance value of the parallel cables in the same direction based on the cable unbalance model includes:

[0020] Based on the cable imbalance model, identify the initial cable series impedance values ​​of the cable imbalance model;

[0021] Based on the initial cable series impedance values ​​of the cable unbalance model, the target cable series impedance value of the parallel cables in the same direction is iteratively identified by an improved particle swarm optimization algorithm.

[0022] Optionally, calculating the current-carrying imbalance of the parallel cables in the same direction based on the series impedance value of the target cable includes:

[0023] Based on the target cable series impedance value, the cable unbalance model is adjusted to obtain the target cable unbalance model;

[0024] Using the target cable imbalance model, the predicted current values ​​of each phase of the parallel cable in the same direction are predicted, and the current-carrying imbalance of the parallel cable in the same direction is calculated based on the predicted current values ​​of each phase.

[0025] Optionally, the step of identifying the current-carrying imbalance limitation information of the parallel cables in the same direction based on the current-carrying imbalance corresponding to the series impedance values ​​of each of the target cables, and the true impedance parameters of the parallel cables in the same direction, includes:

[0026] Based on the current imbalance degree corresponding to the series impedance value of each target cable, the current imbalance degree distribution information is identified, and the current imbalance degree range corresponding to the current imbalance degree distribution information is identified.

[0027] The current-carrying imbalance range is used as the limiting information for the current-carrying imbalance of the parallel cable in the same direction, and the impedance parameters in the target cable imbalance model are identified to obtain the true impedance parameters of the parallel cable in the same direction.

[0028] Secondly, this application also provides an apparatus for analyzing current-carrying imbalance limitation information. The apparatus includes:

[0029] The acquisition module is used to acquire the actual impedance parameters of the parallel cables in the same direction, and based on the actual impedance parameters, construct an optimization model of the cable imbalance of the parallel cables in the same direction through a dynamic programming algorithm.

[0030] The adjustment module is used to adjust the cable imbalance optimization model through a model optimization strategy to obtain the cable imbalance model, and based on the cable imbalance model, identify the target cable series impedance value of the parallel cables in the same direction.

[0031] The calculation module is used to calculate the current imbalance of the parallel cables in the same direction based on the series impedance value of the target cables, and adjust the series impedance value of the target cables through a preset adjustment strategy. Then, it returns to the step of calculating the current imbalance of the parallel cables in the same direction based on the series impedance value of the target cables, and obtains the current imbalance corresponding to each series impedance value of the target cables.

[0032] The identification module is used to identify the current imbalance information of the parallel cables in the same direction, as well as the actual impedance parameters of the parallel cables in the same direction, based on the current imbalance corresponding to the series impedance value of each target cable.

[0033] The iteration module is used to replace the actual impedance parameter with the true impedance parameter and return to the execution step of constructing the cable imbalance optimization model of the parallel cable in the same direction based on the actual impedance parameter through dynamic programming algorithm, until the preset number of iterations is met, and the limitation information obtained in the last iteration is used as the current-carrying imbalance limitation information of the parallel cable in the same direction.

[0034] Optionally, the acquisition module is specifically used for:

[0035] Obtain the circuit model of the parallel cable in the same direction and the series impedance circuit model of the parallel cable in the same direction, and identify the first current calculation equation corresponding to the circuit model and the second current calculation model corresponding to the series impedance circuit model.

[0036] Based on the first current calculation equation and the second current calculation model, the current-carrying supplementary equation for parallel cables in the same direction under series impedance conditions is identified. Based on the actual impedance parameters and the current-carrying supplementary equation, the cable imbalance optimization model for the parallel cables in the same direction is constructed using the dynamic programming algorithm.

[0037] Optionally, the adjustment module is specifically used for:

[0038] Based on the model optimization strategy, identify the constraint information of the cable imbalance model, and identify the model index information corresponding to each constraint information;

[0039] Based on the cable imbalance optimization model, the index value corresponding to each model index information is identified, and the range of index values ​​corresponding to each constraint limit information is identified.

[0040] Identify the constraint information corresponding to the index value of each model index information, and when there are abnormal index values ​​that do not belong to the range of index values ​​corresponding to the constraint information, adjust the cable imbalance optimization model based on the abnormal index values ​​to obtain a new cable imbalance optimization model;

[0041] Replace the existing cable imbalance optimization model with the new cable imbalance optimization model, and return to the previous iteration to execute the cable imbalance optimization model. Identify the index value corresponding to each model index information until there are no abnormal index values ​​that do not belong to the index value range corresponding to the constraint information. Then, use the cable imbalance optimization model obtained in the last iteration as the cable imbalance model.

[0042] Optionally, the adjustment module is specifically used for:

[0043] Based on the cable imbalance model, identify the initial cable series impedance values ​​of the cable imbalance model;

[0044] Based on the initial cable series impedance values ​​of the cable unbalance model, the target cable series impedance value of the parallel cables in the same direction is iteratively identified by an improved particle swarm optimization algorithm.

[0045] Optionally, the computing module is specifically used for:

[0046] Based on the target cable series impedance value, the cable unbalance model is adjusted to obtain the target cable unbalance model;

[0047] Using the target cable imbalance model, the predicted current values ​​of each phase of the parallel cable in the same direction are predicted, and the current-carrying imbalance of the parallel cable in the same direction is calculated based on the predicted current values ​​of each phase.

[0048] Optionally, the identification module is specifically used for:

[0049] Based on the current imbalance degree corresponding to the series impedance value of each target cable, the current imbalance degree distribution information is identified, and the current imbalance degree range corresponding to the current imbalance degree distribution information is identified.

[0050] The current-carrying imbalance range is used as the limiting information for the current-carrying imbalance of the parallel cable in the same direction, and the impedance parameters in the target cable imbalance model are identified to obtain the true impedance parameters of the parallel cable in the same direction.

[0051] Thirdly, this application provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the first aspects.

[0052] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0053] Fifthly, this application provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0054] The aforementioned method, apparatus, and computer equipment for analyzing current-carrying imbalance limitation information acquire the actual impedance parameters of the parallel cables in the same direction. Based on these parameters, a dynamic programming algorithm is used to construct an optimization model for the cable imbalance of the parallel cables. The model is then adjusted using an optimization strategy to obtain the cable imbalance model. Based on this model, a target series impedance value for the parallel cables in the same direction is identified. The current-carrying imbalance of the parallel cables is calculated based on this target series impedance value. A preset adjustment strategy is used to adjust the target series impedance value, and the process is then repeated. The process involves calculating the current-carrying imbalance of the parallel cables in the same direction, obtaining the current-carrying imbalance corresponding to the series impedance values ​​of each target cable. Based on the current-carrying imbalance corresponding to the series impedance values ​​of each target cable, the limiting information of the current-carrying imbalance of the parallel cables in the same direction, as well as the actual impedance parameters of the parallel cables in the same direction, are identified. The actual impedance parameters are then replaced with the actual impedance parameters, and the process returns to the step of constructing an optimization model for the cable imbalance of the parallel cables in the same direction using a dynamic programming algorithm based on the actual impedance parameters. This process continues until a preset number of iterations is met, at which point the limiting information obtained from the last iteration is used as the limiting information for the current-carrying imbalance of the parallel cables in the same direction. This solution improves the realism and accuracy of the model by constructing a cable imbalance optimization model using parallel cables in the same direction with series impedance. Then, by improving the constraints of this model, the solution obtains a cable imbalance model, ensuring that it conforms to the actual conditions of the line and the principle of minimizing line loss, thereby improving the realism and accuracy of the simulation. Then, by adjusting the target cable series impedance value, the relationship between different target series impedance values ​​and the current imbalance distribution is analyzed to analyze the limitation range of the current imbalance and determine the limitation information of the current imbalance. Finally, this scheme identifies the actual impedance parameters corresponding to the updated and adjusted model and iteratively executes the above scheme to further improve the accuracy of the limitation range analysis. This improves the accuracy of the limitation analysis of the current imbalance of the laid cable without damaging the cable structure of the radiant cable. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating a method for analyzing current imbalance limitation information in one embodiment;

[0056] Figure 2 This is a schematic diagram of a parallel cable circuit model without series impedance in one embodiment;

[0057] Figure 3 This is a schematic diagram of a parallel cable circuit model with series impedance in one embodiment;

[0058] Figure 4 This is a flowchart illustrating an example of analyzing current imbalance limitation information in one embodiment;

[0059] Figure 5 This is a structural block diagram of a device for analyzing current imbalance limitation information in one embodiment;

[0060] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0061] 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.

[0062] The method for analyzing current imbalance limitation information provided in this application embodiment can be applied to the application environment of current imbalance limitation of laid cables. This method can be applied to terminals, servers, or systems including terminals and servers, and is implemented through interaction between the terminals and servers. Specifically, the terminal constructs a cable imbalance optimization model using parallel cables with series impedance, thereby improving the model's realism and accuracy. Then, this solution improves the constraints of this model to obtain a cable imbalance model, ensuring compliance with the actual conditions of the line and the principle of minimum line loss, thus improving the simulation's realism and accuracy. Next, by adjusting the target cable series impedance value, the relationship between different target series impedance values ​​and the current imbalance distribution is analyzed to analyze the current imbalance limitation range and determine the current imbalance limitation information. Finally, this solution identifies and updates the actual impedance parameters corresponding to the adjusted model, iteratively executing the above scheme to further improve the accuracy of the limitation range analysis, thereby improving the accuracy of current imbalance limitation analysis of laid cables without damaging the cable structure of the radiating cable.

[0063] In one embodiment, such as Figure 1 As shown, a method for analyzing current imbalance limitation information is provided. Taking the application of this method to a terminal as an example, the method includes the following steps:

[0064] Step S101: Obtain the actual impedance parameters of the parallel cables in the same direction, and based on the actual impedance parameters, construct an optimization model for the cable imbalance of the parallel cables in the same direction using a dynamic programming algorithm.

[0065] In this embodiment, the terminal acquires the current data of the parallel cable in the same direction, calculates the first impedance data of the parallel cable using a current impedance algorithm, and then actually measures the second impedance data of the parallel cable in the same direction. Finally, the terminal calculates the average of the first and second impedance data to obtain the actual impedance data of the parallel cable in the same direction, and obtains the actual impedance parameters of the parallel cable in the same direction by querying the parameters corresponding to the actual impedance data through the impedance data parameter conversion table. Then, based on the actual impedance parameters, the terminal constructs an optimization model for the cable imbalance of the parallel cable in the same direction using a dynamic programming algorithm. This cable imbalance optimization model is a function model based on the mean value of the mutual unbalanced degree (MMUD). The specific construction process will be explained in detail later.

[0066] Step S102: Adjust the cable unbalance optimization model through model optimization strategy to obtain the cable unbalance model, and identify the target cable series impedance value of parallel cables in the same direction based on the cable unbalance model.

[0067] In this embodiment, the terminal adjusts the cable imbalance optimization model through a model optimization strategy to obtain a cable imbalance model, and identifies the target series impedance value of the parallel cables in the same direction based on the cable imbalance model. The model optimization strategy involves adjusting the model parameters of the cable imbalance optimization model based on various constraints of the model. These constraints include, but are not limited to, conditions such as the line series impedance, the line series impedance loss, the line meeting voltage drop limits after the line series impedance is introduced, and the line meeting reactive power loss limits after the line series impedance is introduced. The specific adjustment process will be explained in detail later. The target cable series impedance value is the sum of the series impedance and the reasonable value of the parallel cables in the same direction. This reasonable series impedance value is used to adjust the current-carrying imbalance between the cables in the parallel cables in the same direction, thereby limiting the current-carrying imbalance between the cables to remain within a reasonable range. The specific identification process will be explained in detail later.

[0068] Step S103: Based on the series impedance value of the target cable, calculate the current-carrying imbalance of the parallel cables in the same direction, and adjust the series impedance value of the target cable through a preset adjustment strategy. Then, return to execute the step of calculating the current-carrying imbalance of the parallel cables in the same direction based on the series impedance value of the target cable to obtain the current-carrying imbalance corresponding to each series impedance value of the target cable.

[0069] In this embodiment, the terminal calculates the current-carrying imbalance of the parallel cables in the same direction based on the series impedance value of the target cables, and adjusts the series impedance value of the target cables using a preset adjustment strategy. It then returns to execute the step of calculating the current-carrying imbalance of the parallel cables in the same direction based on the series impedance value of the target cables, thus obtaining the current-carrying imbalance corresponding to each series impedance value of the target cables. The preset adjustment strategy consists of the unit change value of the series impedance value of the target cables and the number of iterations for that unit change value, thereby obtaining the current-carrying imbalance corresponding to different series impedance values ​​of the target cables.

[0070] Step S104: Based on the current imbalance corresponding to the series impedance value of each target cable, identify the limiting information of the current imbalance of the parallel cables in the same direction, as well as the true impedance parameters of the parallel cables in the same direction.

[0071] In this embodiment, the terminal identifies the current imbalance limit information of parallel cables in the same direction, as well as the true impedance parameters of the parallel cables, based on the current imbalance degree corresponding to the series impedance values ​​of each target cable. The current imbalance limit information refers to the limited range of the current imbalance degree. The true impedance parameters of the parallel cables in the same direction are the impedance parameters of the target cable imbalance model obtained after adjusting the series impedance values ​​of the target cables. The specific identification process will be explained in detail later.

[0072] Step S105: Replace the actual impedance parameter with the real impedance parameter, and return to the step of constructing the cable imbalance optimization model of the parallel cable in the same direction based on the actual impedance parameter through dynamic programming algorithm, until the preset number of iterations is met, and use the constraint information obtained in the last iteration as the current-carrying imbalance constraint information of the parallel cable in the same direction.

[0073] In this embodiment, the terminal replaces the actual impedance parameter with the real impedance parameter and returns to execute the steps of constructing the cable imbalance optimization model of the parallel cable in the same direction based on the actual impedance parameter and through dynamic programming algorithm, until the preset number of iterations is met, and the limitation information obtained in the last iteration is used as the current-carrying imbalance limitation information of the parallel cable in the same direction.

[0074] Based on the above scheme, by improving the constraints of the model, a cable imbalance model is obtained, thereby ensuring compliance with the actual conditions of the line and the principle of minimizing line loss, thus improving the realism and accuracy of the simulation. Then, by adjusting the target cable series impedance value, the relationship between different target series impedance values ​​and the current imbalance distribution is analyzed to analyze the limitation range of the current imbalance and determine the limitation information of the current imbalance. Finally, this scheme further improves the accuracy of the analysis of the limitation range by identifying and updating the actual impedance parameters corresponding to the adjusted model and iteratively executing the above scheme, thereby improving the accuracy of the limitation analysis of the current imbalance of the laid cable without damaging the cable structure of the radial cable.

[0075] Optionally, based on actual impedance parameters, a dynamic programming algorithm is used to construct an optimization model for the cable imbalance of parallel cables in the same direction. This includes: obtaining the circuit model of the parallel cables in the same direction and the series impedance circuit model of the parallel cables in the same direction, and identifying the first current calculation equation corresponding to the circuit model and the second current calculation model corresponding to the series impedance circuit model; based on the first current calculation equation and the second current calculation model, identifying the current-carrying supplementary equation of the parallel cables in the same direction under the condition of series impedance, and based on the actual impedance parameters and the current-carrying supplementary equation, using a dynamic programming algorithm, constructing an optimization model for the cable imbalance of parallel cables in the same direction.

[0076] In this embodiment, the terminal acquires the circuit model of the parallel cable in the same direction and the series impedance circuit model of the parallel cable in the same direction, and identifies the first current calculation equation corresponding to the circuit model and the second current calculation model corresponding to the series impedance circuit model. Then, based on the first current calculation equation and the second current calculation model, the terminal identifies the current-carrying supplementary equation of the parallel cable in the same direction under the condition of series impedance, and constructs the cable imbalance optimization model of the parallel cable in the same direction using a dynamic programming algorithm based on the actual impedance parameters and the current-carrying supplementary equation.

[0077] Specifically, the function model corresponding to the cable imbalance optimization model is as follows:

[0078] minMMUD

[0079] In the above formula, the mean value of mutual unbalanced degree (MMUD) is a measure of the parallel unbalanced degree, characterized by the ratio of the difference in current carrying capacity of each sub-cable in the same phase to its mean current carrying capacity. The formula for calculating the mean value of the parallel unbalanced degree is as follows:

[0080]

[0081] The current values ​​in the above formulas are calculated using current calculation equations, represented by a matrix. The current calculation equations are as follows:

[0082]

[0083] In the formula, By analogy, we can obtain ΔU S Z CS Z SC Z SS , and I S The expression.

[0084] like Figure 2 The figure shows a circuit model of parallel cables in phase without series impedance. This model contains a total of 6 cables, I C1 I C2 , ..., I C6 This indicates the core current of each sub-cable.

[0085] After inserting a resistor, the parallel cable model with the same phase is as follows: Figure 3 As shown, the equation for calculating the current is as follows:

[0086]

[0087] In the formula, By analogy, we can obtain ΔU S Z CS Z SC Z SS , and I S The expression for Z is needed. Since the series resistance is equivalent to the increase in the self-impedance of the cable core, it is necessary to adjust Z. cc Corrections and recalculations are made. Cables 1 and 2 belong to phase a, cables 3 and 4 belong to phase b, and cables 5 and 6 belong to phase c. Therefore, the following supplementary equations for current and voltage are obtained:

[0088]

[0089] Based on the current and voltage supplementary equations, the terminal calculates the actual current value of each cable, substitutes back the formula for calculating the average value of parallel unbalance, and obtains the cable unbalance optimization model.

[0090] Based on the above scheme, by comprehensively analyzing parallel cables with added series resistance and parallel cables without added series resistance, the supplementary equations for current and voltage are identified, thereby determining the cable imbalance optimization model and improving the practicality and accuracy of the determined cable imbalance optimization model.

[0091] Optionally, the cable imbalance optimization model is adjusted using a model optimization strategy to obtain a cable imbalance model. This includes: identifying the constraints and limitations of the cable imbalance model based on the model optimization strategy, and identifying the model index information corresponding to each constraint and limitation; identifying the index value corresponding to each model index information and identifying the range of index values ​​corresponding to each constraint and limitation; identifying the constraint and limitation information corresponding to the index value of each model index information, and when there are abnormal index values ​​that do not belong to the range of index values ​​corresponding to the constraint and limitation information, adjusting the cable imbalance optimization model based on the abnormal index values ​​to obtain a new cable imbalance optimization model; replacing the original cable imbalance optimization model with the new cable imbalance optimization model, and returning to execute the process of identifying the index value corresponding to each model index information based on the cable imbalance optimization model until there are no abnormal index values ​​that do not belong to the range of index values ​​corresponding to the constraint and limitation information, and then using the cable imbalance optimization model obtained in the last iteration as the cable imbalance model.

[0092] In this embodiment, the terminal identifies the constraints and limitations of the cable imbalance model based on a model optimization strategy, and identifies the model index information corresponding to each constraint and limitation. Specifically, the model index information corresponding to each constraint and limitation is as follows:

[0093] 1) The series impedance loss of the line is less than the maximum allowable value of the line.

[0094]

[0095] 2) The series impedance loss of the line meets the experimental feasibility, i.e., the actual manufacturing limitations of the impedance.

[0096]

[0097] 3) After an impedance is introduced into the line, the line meets the voltage drop limit.

[0098]

[0099] 4) The line impedance is sufficient to meet the reactive power loss limit.

[0100]

[0101] Then, based on the cable imbalance optimization model, the terminal identifies the index value corresponding to each model index and the range of index values ​​corresponding to each constraint. The index range corresponding to each constraint is a range preset by the operator in the terminal, and this range can be changed as the operator adjusts it.

[0102] The terminal identifies the constraint information corresponding to the index value of each model indicator. When there are abnormal index values ​​that do not fall within the range of the constraint information, the cable imbalance optimization model is adjusted based on the abnormal index values ​​to obtain a new cable imbalance optimization model. Specifically, the method for adjusting the cable imbalance optimization model is to identify the model parameters corresponding to the abnormal index values, and then adjust the model parameters based on the abnormal index values ​​to obtain a new cable imbalance optimization model.

[0103] The terminal replaces the cable imbalance optimization model with the new cable imbalance optimization model and returns to execute the cable imbalance optimization model. It identifies the index value corresponding to each model index information until there are no abnormal index values ​​that do not belong to the index value range corresponding to the constraint information. Then, the cable imbalance optimization model obtained in the last iteration is used as the cable imbalance model.

[0104] Based on the above scheme, the cable imbalance optimization model is adjusted by adjusting various constraints to ensure that it meets the actual conditions of the line and the principle of minimizing line loss, thereby improving the simulation realism and accuracy of the model.

[0105] Optionally, based on the cable unbalance model, the target cable series impedance value of the parallel cables in the same direction is identified, including: based on the cable unbalance model, identifying each initial cable series impedance value of the cable unbalance model; based on each initial cable series impedance value of the cable unbalance model, the target cable series impedance value of the parallel cables in the same direction is iteratively identified by an improved particle swarm optimization algorithm.

[0106] In this embodiment, the terminal identifies the initial cable series impedance values ​​of the cable imbalance model based on the cable imbalance model. These initial cable series impedance values ​​are obtained by initializing the particle population using an improved particle swarm optimization algorithm. Then, based on these initial cable series impedance values ​​from the cable imbalance model, the terminal iteratively identifies the target cable series impedance values ​​for parallel cables in the same direction using the improved particle swarm optimization algorithm.

[0107] Specifically, this scheme employs an improved particle swarm optimization (IPSO) algorithm to solve the dynamic programming model. Traditional IPSO algorithms, due to their fixed inertia coefficients and flight times, exhibit poor dynamic performance during particle flight and are unsuitable for solving two-stage dynamic programming models. In the initial stage of a two-stage dynamic programming model, particles are randomly distributed across a larger solution space and are far from the optimal solution location. Therefore, particles need strong flight capabilities to approach the optimal solution location more quickly. In the later stages, as particles get closer to the optimal solution location, they should have more precise flight steps to avoid crossing the optimal solution location and oscillating back and forth around it. To address these two shortcomings, an improved IPSO algorithm is adopted.

[0108] The difference between the improved particle swarm optimization algorithm and the traditional particle swarm optimization algorithm is:

[0109] 1) Adaptive inertia coefficient

[0110] Employs an inertia coefficient that adapts to the number of flight iterations.

[0111]

[0112] In the formula, g is the current iteration number of the particle population; g max is the maximum number of iterations to terminate the IPSO algorithm; w0 is the initial inertia coefficient.

[0113] 2) Adaptive flight time

[0114] Introducing an adaptive time-of-flight mechanism, the improved method for calculating particle position is as follows:

[0115]

[0116] In the above formula, g is the current iteration number of the particle population; g max Let T be the maximum number of iterations required to terminate the IPSO algorithm, T be the particle position iteration parameters, T0 be the initial particle position iteration parameters, and Vn be the initial particle velocity. The particle position is obtained from the previous iteration. This represents the particle position in this iteration.

[0117] Based on the above scheme, the target cable series impedance value of the parallel cable in the same direction is optimized by improving the particle swarm optimization algorithm, thereby improving the accuracy and practicality of the obtained target cable series impedance value of the parallel cable in the same direction.

[0118] Optionally, based on the series impedance value of the target cable, the current-carrying imbalance of the parallel cable in the same direction is calculated, including: adjusting the cable imbalance model based on the series impedance value of the target cable to obtain the target cable imbalance model; predicting the predicted current value of each phase cable of the parallel cable in the same direction through the target cable imbalance model, and calculating the current-carrying imbalance of the parallel cable in the same direction based on the predicted current value of each phase cable.

[0119] In this embodiment, the terminal adjusts the cable unbalance model based on the target cable series impedance value to obtain the target cable unbalance model. Then, the terminal uses the target cable unbalance model to predict the predicted current values ​​of each phase of the parallel cable in the same direction, and calculates the current-carrying unbalance of the parallel cable in the same direction based on the predicted current values ​​of each phase. The predicted current values ​​of each phase cable can be calculated using the current calculation equation and the current supplementary equation described above. The algorithm for calculating the current-carrying unbalance is a conventional current-carrying unbalance algorithm, which will not be elaborated upon in this solution.

[0120] Based on the above scheme, by updating the target cable series impedance value target cable unbalance model, the predicted current value of each phase cable is calculated, thereby obtaining the current-carrying unbalance of the parallel cables in the same direction, improving the accuracy and practicality of the current-carrying unbalance of the parallel cables in the same direction.

[0121] Optionally, based on the current imbalance corresponding to the series impedance value of each target cable, the limiting information of the current imbalance of the parallel cables in the same direction and the true impedance parameters of the parallel cables in the same direction are identified, including: based on the current imbalance corresponding to the series impedance value of each target cable, identifying the distribution information of the current imbalance, and identifying the range of the current imbalance corresponding to the distribution information of the current imbalance; using the range of the current imbalance as the limiting information of the current imbalance of the parallel cables in the same direction, and identifying the impedance parameters in the target cable imbalance model to obtain the true impedance parameters of the parallel cables in the same direction.

[0122] In this embodiment, the terminal identifies the current imbalance distribution information based on the current imbalance corresponding to the series impedance values ​​of each target cable, and identifies the current imbalance range corresponding to the current imbalance distribution information. The current imbalance distribution information is the gradient change distribution corresponding to the change in current imbalance as the series impedance value of the target cable changes. The current imbalance range is the range between the maximum and minimum current imbalance in the aforementioned current imbalance distribution information.

[0123] The terminal uses the current imbalance range as the limiting information for the current imbalance of parallel cables in the same direction, and identifies the impedance parameters in the target cable imbalance model to obtain the true impedance parameters of the parallel cables in the same direction.

[0124] Based on the above scheme, by identifying the current imbalance corresponding to different target cable series impedance values, the limiting information of the current imbalance of parallel cables in the same direction is determined, thereby improving the accuracy of the determined limiting information of the current imbalance of parallel cables in the same direction.

[0125] This application also provides an example of analyzing current imbalance limitation information, such as... Figure 4 As shown, the specific processing procedure includes the following steps:

[0126] Step S401: Obtain the actual impedance parameters of the parallel cables in the same direction.

[0127] Step S402: Obtain the circuit model of the parallel cable in the same direction and the series impedance circuit model of the parallel cable in the same direction, and identify the first current calculation equation corresponding to the circuit model and the second current calculation model corresponding to the series impedance circuit model.

[0128] Step S403: Based on the first current calculation equation and the second current calculation model, identify the current-carrying supplementary equation for parallel cables in the same direction under the condition of series impedance, and based on the actual impedance parameters and the current-carrying supplementary equation, construct the cable imbalance optimization model for parallel cables in the same direction through a dynamic programming algorithm.

[0129] Step S404: Based on the model optimization strategy, identify the constraint information of the cable imbalance model and identify the model index information corresponding to each constraint information.

[0130] Step S405: Based on the cable imbalance optimization model, identify the index value corresponding to each model index information, and identify the range of index values ​​corresponding to each constraint limit information.

[0131] Step S406: Identify the constraint information corresponding to the index value of each model index information, and when there are abnormal index values ​​that do not belong to the range of index values ​​corresponding to the constraint information, adjust the cable imbalance optimization model based on the abnormal index values ​​to obtain a new cable imbalance optimization model.

[0132] Step S407: Replace the cable imbalance optimization model with the new cable imbalance optimization model, and return to execute the cable imbalance optimization model. Identify the index value corresponding to each model index information until there are no abnormal index values ​​that do not belong to the index value range corresponding to the constraint information. Then, use the cable imbalance optimization model obtained in the last iteration as the cable imbalance model.

[0133] Step S408: Based on the cable imbalance model, identify the initial cable series impedance values ​​of the cable imbalance model.

[0134] Step S409: Based on the initial cable series impedance values ​​of the cable unbalance model, the target cable series impedance value of the parallel cables in the same direction is iteratively identified by an improved particle swarm optimization algorithm.

[0135] Step S410: Based on the target cable series impedance value, adjust the cable unbalance model to obtain the target cable unbalance model.

[0136] Step S411: Using the target cable unbalance model, predict the predicted current value of each phase cable of the parallel cable in the same direction, and calculate the current-carrying unbalance of the parallel cable in the same direction based on the predicted current value of each phase cable.

[0137] Step S412: Adjust the target cable series impedance value using a preset adjustment strategy, return to the step of calculating the current imbalance of parallel cables in the same direction based on the target cable series impedance value, and obtain the current imbalance corresponding to each target cable series impedance value.

[0138] Step S413: Based on the current imbalance degree corresponding to the series impedance value of each target cable, identify the current imbalance degree distribution information and identify the current imbalance degree range corresponding to the current imbalance degree distribution information.

[0139] Step S414: Use the current unbalance range as the limiting information of the current unbalance of the parallel cable in the same direction, and identify the impedance parameters in the target cable unbalance model to obtain the true impedance parameters of the parallel cable in the same direction.

[0140] Step S415: Replace the actual impedance parameter with the real impedance parameter, and return to the step of constructing the cable imbalance optimization model of the parallel cable in the same direction based on the actual impedance parameter through dynamic programming algorithm, until the preset number of iterations is met, and use the constraint information obtained in the last iteration as the current-carrying imbalance constraint information of the parallel cable in the same direction.

[0141] 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.

[0142] Based on the same inventive concept, this application also provides an apparatus for analyzing current imbalance limitation information to implement the analysis method for current imbalance limitation information mentioned above. The solution provided by this apparatus is similar to the implementation described in the above method. Therefore, the specific limitations in one or more embodiments of the apparatus for analyzing current imbalance limitation information provided below can be found in the limitations of the analysis method for current imbalance limitation information above, and will not be repeated here.

[0143] In one embodiment, such as Figure 5 As shown, an analysis device for current imbalance limitation information is provided, comprising: an acquisition module 510, an adjustment module 520, a calculation module 530, an identification module 540, and an iteration module 550, wherein:

[0144] The acquisition module 510 is used to acquire the actual impedance parameters of the parallel cables in the same direction, and based on the actual impedance parameters, construct an optimization model of the cable imbalance of the parallel cables in the same direction through a dynamic programming algorithm.

[0145] The adjustment module 520 is used to adjust the cable imbalance optimization model through a model optimization strategy to obtain the cable imbalance model, and based on the cable imbalance model, identify the target cable series impedance value of the parallel cables in the same direction.

[0146] The calculation module 530 is used to calculate the current imbalance of the parallel cable in the same direction based on the series impedance value of the target cable, and adjust the series impedance value of the target cable through a preset adjustment strategy, and return to execute the step of calculating the current imbalance of the parallel cable in the same direction based on the series impedance value of the target cable, so as to obtain the current imbalance corresponding to each series impedance value of the target cable.

[0147] The identification module 540 is used to identify the current imbalance information of the parallel cables in the same direction, as well as the actual impedance parameters of the parallel cables in the same direction, based on the current imbalance corresponding to the series impedance value of each target cable.

[0148] The iteration module 550 is used to replace the actual impedance parameter with the true impedance parameter and return to the execution step of constructing the cable imbalance optimization model of the parallel cable in the same direction based on the actual impedance parameter through a dynamic programming algorithm until the preset number of iterations is met. Then, the limitation information obtained in the last iteration is used as the current-carrying imbalance limitation information of the parallel cable in the same direction.

[0149] Optionally, the acquisition module 510 is specifically used for:

[0150] Obtain the circuit model of the parallel cable in the same direction and the series impedance circuit model of the parallel cable in the same direction, and identify the first current calculation equation corresponding to the circuit model and the second current calculation model corresponding to the series impedance circuit model.

[0151] Based on the first current calculation equation and the second current calculation model, the current-carrying supplementary equation for parallel cables in the same direction under series impedance conditions is identified. Based on the actual impedance parameters and the current-carrying supplementary equation, the cable imbalance optimization model for the parallel cables in the same direction is constructed using the dynamic programming algorithm.

[0152] Optionally, the adjustment module 520 is specifically used for:

[0153] Based on the model optimization strategy, identify the constraint information of the cable imbalance model, and identify the model index information corresponding to each constraint information;

[0154] Based on the cable imbalance optimization model, identify the index value corresponding to each model index information, and identify the index value range corresponding to each constraint limit information;

[0155] Identify the constraint information corresponding to the index value of each model index information, and when there are abnormal index values ​​that do not belong to the range of index values ​​corresponding to the constraint information, adjust the cable imbalance optimization model based on the abnormal index values ​​to obtain a new cable imbalance optimization model;

[0156] Replace the existing cable imbalance optimization model with the new cable imbalance optimization model, and return to the previous iteration to execute the cable imbalance optimization model. Identify the index value corresponding to each model index information until there are no abnormal index values ​​that do not belong to the index value range corresponding to the constraint information. Then, use the cable imbalance optimization model obtained in the last iteration as the cable imbalance model.

[0157] Optionally, the adjustment module 520 is specifically used for:

[0158] Based on the cable imbalance model, identify the initial cable series impedance values ​​of the cable imbalance model;

[0159] Based on the initial cable series impedance values ​​of the cable unbalance model, the target cable series impedance value of the parallel cables in the same direction is iteratively identified by an improved particle swarm optimization algorithm.

[0160] Optionally, the computing module 530 is specifically used for:

[0161] Based on the target cable series impedance value, the cable unbalance model is adjusted to obtain the target cable unbalance model;

[0162] Using the target cable imbalance model, the predicted current values ​​of each phase of the parallel cable in the same direction are predicted, and the current-carrying imbalance of the parallel cable in the same direction is calculated based on the predicted current values ​​of each phase.

[0163] Optionally, the identification module 540 is specifically used for:

[0164] Based on the current imbalance degree corresponding to the series impedance value of each target cable, the current imbalance degree distribution information is identified, and the current imbalance degree range corresponding to the current imbalance degree distribution information is identified.

[0165] The current-carrying imbalance range is used as the limiting information for the current-carrying imbalance of the parallel cable in the same direction, and the impedance parameters in the target cable imbalance model are identified to obtain the true impedance parameters of the parallel cable in the same direction.

[0166] Each module in the aforementioned device for analyzing current imbalance limitation information 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 operations corresponding to each module.

[0167] In one embodiment, a computer device, which may be a server, is provided, and its internal structure may be as shown in Figure Y. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores XX data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for analyzing current imbalance limitation information.

[0168] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for analyzing current imbalance limitation information. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0169] Those skilled in the art will understand that Figure 6 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.

[0170] In one 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 steps of the method described in any one of the first aspects.

[0171] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0172] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0173] 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.

[0174] 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 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, etc., and are not limited to these.

[0175] 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 specification.

[0176] 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 analyzing current-carrying imbalance limitation information, characterized in that, The method includes: Obtain the actual impedance parameters of the parallel cables in the same direction, and based on the actual impedance parameters, construct an optimization model for the cable imbalance of the parallel cables in the same direction using a dynamic programming algorithm; By using a model optimization strategy, the cable imbalance optimization model is adjusted to obtain the cable imbalance model, and based on the cable imbalance model, the target cable series impedance value of the parallel cables in the same direction is identified. Based on the target cable series impedance value, the current-carrying imbalance of the parallel cables in the same direction is calculated, and the target cable series impedance value is adjusted through a preset adjustment strategy. Then, the step of calculating the current-carrying imbalance of the parallel cables in the same direction based on the target cable series impedance value is returned to be executed, so as to obtain the current-carrying imbalance corresponding to each target cable series impedance value. Based on the current imbalance corresponding to the series impedance value of each target cable, the limiting information of the current imbalance of the parallel cables in the same direction is identified, as well as the true impedance parameters of the parallel cables in the same direction. Replace the actual impedance parameter with the true impedance parameter, and return to the step of constructing the cable imbalance optimization model of the parallel cable in the same direction based on the actual impedance parameter using a dynamic programming algorithm, until the preset number of iterations is met, and use the constraint information obtained in the last iteration as the current-carrying imbalance constraint information of the parallel cable in the same direction.

2. The method according to claim 1, characterized in that, The process of constructing an optimization model for the cable imbalance of the parallel cables in the same direction based on the actual impedance parameters using a dynamic programming algorithm includes: Obtain the circuit model of the parallel cable in the same direction and the series impedance circuit model of the parallel cable in the same direction, and identify the first current calculation equation corresponding to the circuit model and the second current calculation model corresponding to the series impedance circuit model. Based on the first current calculation equation and the second current calculation model, the current-carrying supplementary equation for parallel cables in the same direction under series impedance conditions is identified. Based on the actual impedance parameters and the current-carrying supplementary equation, the cable imbalance optimization model for the parallel cables in the same direction is constructed using the dynamic programming algorithm.

3. The method according to claim 1, characterized in that, The process of adjusting the cable imbalance optimization model through a model optimization strategy to obtain the cable imbalance model includes: Based on the model optimization strategy, identify the constraint information of the cable imbalance model, and identify the model index information corresponding to each constraint information; Based on the cable imbalance optimization model, the index value corresponding to each model index information is identified, and the range of index values ​​corresponding to each constraint limit information is identified. Identify the constraint information corresponding to the index value of each model index information, and when there are abnormal index values ​​that do not belong to the range of index values ​​corresponding to the constraint information, adjust the cable imbalance optimization model based on the abnormal index values ​​to obtain a new cable imbalance optimization model; Replace the existing cable imbalance optimization model with the new cable imbalance optimization model, and return to the previous iteration to execute the cable imbalance optimization model. Identify the index value corresponding to each model index information until there are no abnormal index values ​​that do not belong to the index value range corresponding to the constraint information. Then, use the cable imbalance optimization model obtained in the last iteration as the cable imbalance model.

4. The method according to claim 1, characterized in that, The step of identifying the target series impedance value of the parallel cables in the same direction based on the cable unbalance model includes: Based on the cable imbalance model, identify the initial cable series impedance values ​​of the cable imbalance model; Based on the initial cable series impedance values ​​of the cable unbalance model, the target cable series impedance value of the parallel cables in the same direction is iteratively identified by an improved particle swarm optimization algorithm.

5. The method according to claim 1, characterized in that, The calculation of the current-carrying imbalance of the parallel cables in the same direction based on the series impedance value of the target cable includes: Based on the target cable series impedance value, the cable unbalance model is adjusted to obtain the target cable unbalance model; Using the target cable imbalance model, the predicted current values ​​of each phase of the parallel cable in the same direction are predicted, and the current-carrying imbalance of the parallel cable in the same direction is calculated based on the predicted current values ​​of each phase.

6. The method according to claim 5, characterized in that, The method of identifying the current-carrying imbalance limit information of the parallel cables in the same direction based on the current-carrying imbalance corresponding to the series impedance values ​​of each of the target cables, and the true impedance parameters of the parallel cables in the same direction, includes: Based on the current imbalance degree corresponding to the series impedance value of each target cable, the current imbalance degree distribution information is identified, and the current imbalance degree range corresponding to the current imbalance degree distribution information is identified. The current-carrying imbalance range is used as the limiting information for the current-carrying imbalance of the parallel cable in the same direction, and the impedance parameters in the target cable imbalance model are identified to obtain the true impedance parameters of the parallel cable in the same direction.

7. An analysis device for current-carrying imbalance limitation information, characterized in that, The device includes: The acquisition module is used to acquire the actual impedance parameters of the parallel cables in the same direction, and based on the actual impedance parameters, construct an optimization model of the cable imbalance of the parallel cables in the same direction through a dynamic programming algorithm. The adjustment module is used to adjust the cable imbalance optimization model through a model optimization strategy to obtain the cable imbalance model, and based on the cable imbalance model, identify the target cable series impedance value of the parallel cables in the same direction. The calculation module is used to calculate the current imbalance of the parallel cables in the same direction based on the series impedance value of the target cables, and adjust the series impedance value of the target cables through a preset adjustment strategy. Then, it returns to the step of calculating the current imbalance of the parallel cables in the same direction based on the series impedance value of the target cables, and obtains the current imbalance corresponding to each series impedance value of the target cables. The identification module is used to identify the current imbalance information of the parallel cables in the same direction, as well as the actual impedance parameters of the parallel cables in the same direction, based on the current imbalance corresponding to the series impedance value of each target cable. The iteration module is used to replace the actual impedance parameter with the true impedance parameter and return to the execution step of constructing the cable imbalance optimization model of the parallel cable in the same direction based on the actual impedance parameter through dynamic programming algorithm, until the preset number of iterations is met, and the limitation information obtained in the last iteration is used as the current-carrying imbalance limitation information of the parallel cable in the same direction.

8. 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 6.

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

10. 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 6.

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