A method, system and computer-readable storage medium for evaluating the maximum transmission capacity of multi-circuit DC power based on extreme gradient boosting

Abstract

The present invention discloses a method, system and computer-readable storage medium for evaluating the maximum transmission capacity of multiple DC power grids based on extreme gradient improvement. The method evaluates the maximum transmission capacity of multiple DC power grids by constructing a maximum transmission capacity evaluation model. The method comprises: building a time-domain simulation model according to the multi-circuit DC grid structure of a target power grid, batch-adjusting regional generator output and load level parameters, and generating a simulation file for evaluating the maximum transmission capacity of multiple DC power grids; determining static safety and stability constraints affecting the maximum transmission capacity of multiple DC power grids; calculating the optimal maximum transmission capacity of multiple DC power grids for each simulation file under the static safety and stability constraints using a particle swarm algorithm, and forming a sample set S based on the adjusted generator output and load level parameters. p ; Determine the hyperparameters of the XGboost algorithm; Use the XGboost algorithm to learn and use the obtained convergence model as a multi-circuit DC limit transmission capacity evaluation model.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system and computer-readable storage medium for evaluating the maximum transmission capacity of multiple direct current circuits based on extreme gradient boosting. Background Art

[0002] The application of clean energy sources such as photovoltaics and hydropower faces the challenge of difficult transmission and consumption due to their high altitudes and distance from major load centers. Improving the grid's ability to absorb clean energy such as photovoltaics and hydropower is key to achieving rational resource allocation and coordinated operation among regional power grids, and ensuring overall grid security and stability. In the context of cross-regional and long-distance power transmission, transmitting new energy via multiple DC lines offers greater economic advantages over multiple AC lines, and currently demonstrates broad application prospects in actual power grids.

[0003] Currently, most multi-circuit HVDC transmission projects utilize hybrid HVDC transmission, combining conventional HVDC with flexible HVDC. Conventional HVDC converters consume significant amounts of reactive power during stable operation. Increasing the AC system current I flowing into the commutation bus also increases the active power P transmitted by the HVDC and the reactive power Q consumed. Excessive reactive power consumption causes the commutation bus voltage U to drop, making it impossible to maintain it within the normal voltage range. Furthermore, when increasing I is insufficient to offset the voltage drop caused by increased reactive power consumption, the HVDC transmission power will decline, leading to a maximum power operating point within the HVDC converter station. Although conventional HVDC converters are typically equipped with reactive power compensation devices on the AC side, their maximum transmission capacity is still constrained by static voltage stability and maximum power curves. Flexible HVDC converter stations can achieve active and reactive power decoupling control, but their transmission capacity is still constrained by the converter station capacity. Failure to accurately and effectively assess the maximum transmission capacity of multi-circuit HVDC systems will hinder the scheduling and consumption of renewable energy generation, and the static safety of the power grid cannot be guaranteed. Therefore, objectively assessing the maximum transmission capacity of multi-circuit HVDC systems is crucial. Existing research primarily uses model-driven methods, such as the optimal power flow method and the continuation power flow method, to calculate and assess the maximum transmission capacity of multi-circuit DC power transmission systems. These methods often suffer from computational complexity and time-consuming nature. Furthermore, when applied to real power grids, the model dimensions are high, making them incapable of adapting to diverse scenarios such as generator output fluctuations and load level variations. Furthermore, some research has employed data-driven methods supported by machine learning to assess the maximum transmission capacity of regional power grids interconnected via AC. These studies have demonstrated that these methods can achieve relatively reliable maximum transmission capacity assessments and have promising potential for application in real power grids. However, for scenarios where a sending power grid containing large-scale renewable energy sources is asynchronously interconnected with a receiving power grid via multiple conventional / flexible DC lines, there is currently a technical gap in how to apply data-driven methods to efficiently and accurately assess the maximum transmission capacity of multi-circuit DC power transmission systems. Summary of the Invention

[0004] In view of this, the present invention provides a multi-circuit DC limit transmission capacity assessment method, system, and computer-readable storage medium based on extreme gradient boosting, which are used to at least address the problems of existing multi-circuit DC limit transmission capacity assessment technologies, such as being time-consuming, only applicable to specific scenarios, and difficult to apply to actual power grids.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for evaluating the maximum transmission capacity of a multi-circuit direct current system based on extreme gradient boosting includes the following steps:

[0007] A multi-circuit DC limit transmission capacity assessment model is constructed to evaluate the multi-circuit DC limit transmission capacity. The specific contents of constructing the multi-circuit DC limit transmission capacity assessment model include:

[0008] A time-domain simulation model is built based on the multi-circuit DC grid structure of the target power grid, and regional generator output and load level parameters are adjusted in batches to generate simulation files for evaluating the multi-circuit DC transmission capacity.

[0009] Determine the static safety and stability constraints that affect the maximum transmission capacity of multi-circuit DC power transmission, including equality constraints and inequality constraints. Equality constraints include power flow constraints for pure AC nodes, AC nodes connected to LCC converter stations, and AC nodes connected to MMC converter stations. Inequality constraints include generator output constraints, converter station capacity constraints, converter transformer ratio constraints, and node voltage constraints.

[0010] The optimal multi-circuit DC transmission capacity of each simulation file under static safety and stability constraints is calculated based on the particle swarm algorithm, and the sample set S is formed by combining the adjusted generator output and load level parameters. p ;

[0011] Determine the hyperparameters of the extreme gradient boosting XGboost algorithm;

[0012] The sample set S p The data are input into the XGboost algorithm in batches for learning until the XGboost model obtained through learning converges. The converged XGboost model is used as the multi-circuit DC limit transmission capability evaluation model.

[0013] Preferably, the specific contents of batch adjustment of regional generator processing and load level parameters include:

[0014] In accordance with the principle that the power factor remains unchanged before and after the change, the output and load levels of all generators in the area are multiplied by a random correction coefficient between 0.95 and 1.05; the principles for adjusting the generator output and load levels are:

[0015]

[0016] Where s is the correction coefficient, 0.95≤s≤1.05, φ u is the power factor angle, P Gu0 and Q Gu0 are respectively the active power and reactive power of the generator output before adjustment in the u-th region, P Gu and Q Gu are respectively the active power and reactive power of the generator output after adjustment in the uth region; P Lu and Q Lu are the active power consumption and reactive power consumption of the load in the u-th area, u=1,2…a, and a is the number of areas included in the actual power grid.

[0017] Optimally, the power flow constraints for pure AC nodes are:

[0018]

[0019] Power flow constraints for AC nodes connected to LCC converter stations:

[0020]

[0021] Power flow constraints for AC nodes connected to MMC converter stations:

[0022]

[0023] Among them, P i and Q i are the active power and reactive power of AC node i respectively; φ k is the power factor angle of the kth LCC converter station; U dk and I dk are the DC voltage and current of the kth LCC converter station or MCC converter station respectively; θ k is the power factor angle of the kth MMC converter station; δ k Y is the fundamental voltage phase output by the kth MMC converter station; k is the equivalent admittance of the kth MMC converter station; M k is the modulation ratio of the kth MMC converter station; n is the number of pure AC nodes in the power grid; m is the number of LCC converter stations; N is the number of MMC converter stations;

[0024] Generator output constraints:

[0025]

[0026] Converter station capacity constraints:

[0027]

[0028] Converter transformer ratio constraints:

[0029] K vmin ≤K v ≤K vmax (7)

[0030] Node voltage constraints:

[0031] U imin ≤U i ≤U imax (8).

[0032] Among them, P Gi is the active power output of the generator; Q Gi is the reactive power of the generator output; K v is the transformation ratio of the converter transformer; the subscripts min and max represent the maximum and minimum values ​​of the corresponding parameters respectively.

[0033] Preferably, the optimal multi-circuit DC limit transmission capacity of each simulation file under the static safety and stability constraints is calculated according to the particle swarm algorithm, and the sample set S is formed by combining the adjusted generator output and load level parameters. p The specific contents include:

[0034] The maximum transmission capacity of multi-circuit DC is reflected by the active power transmitted by the multi-circuit DC, which is expressed as x1, x2, ..., x m , where m is the number of DC transmission times;

[0035] The fitness function f(x) of the particle swarm algorithm is expressed as: f(x) = -(x1+x2+...+x m );

[0036] After iterative optimization using the particle swarm algorithm, the minimum value of f(x) under the static safety and stability constraints is calculated and recorded as y;

[0037] Combine y with the adjusted generator active output P G1 ,P G2 ,…,P Ga , load active power consumption P L1 ,P L2 ,…,P La , generator reactive output Q G1 ,Q G2 ,…,Q Ga , reactive power consumption of load QL1 ,Q L2 ,…,Q La Combined to form the sample set S p :

[0038] S p ={[P G1 ,Q G1 ,P L1 ,Q L1 ...,P Ga ,Q Ga ,P La ,Q La ,y] (p)},p=1,2,...,q

[0039] Where q is the number of samples.

[0040] Preferably, the loss function used by the XGboost algorithm to construct the t-th decision tree model is the mean square error loss function. The goal of training each decision tree model is to minimize the loss function:

[0041]

[0042] Among them, y p is the true value of the p-th sample, The model prediction value obtained by inputting the p-th sample into the t-th decision tree, q is the number of samples, γ is the regularization coefficient in the loss function, and the number of regression tree leaf nodes T that is greater than the preset threshold is penalized;

[0043] The converged XGboost model is tuned using the grid search method. After giving the search range of each hyperparameter, the grid search traverses the given hyperparameter value combinations and performs ten-fold cross-validation. All data are randomly divided into ten non-overlapping subsets. Nine of these subsets are selected as training sets in each iteration. Multiple regression prediction models are constructed using multiple sets of hyperparameter combinations. The remaining subset is used as a test set to evaluate the accuracy of these models, thereby selecting the optimal hyperparameters and obtaining the final multi-circuit DC limit transmission capacity assessment model.

[0044] Preferably, the accuracy of the multi-circuit DC limit transmission capability assessment model is evaluated by the coefficient of determination and the root mean square error:

[0045] The coefficient of determination is:

[0046]

[0047] The root mean square error is:

[0048]

[0049] in, is the predicted value of the p-th sample by the XGboost-based limit transmission capacity regression prediction model; y p is the true value of the sample's ultimate transmission capacity; where p = 1, 2, 3, ..., q, q is the number of samples; R 2 ∈[0,1], the closer it is to 1, the better the prediction effect of the multi-circuit DC limit transmission capacity assessment model; the smaller the RMSE, the better the prediction effect.

[0050] A multi-circuit DC limit transmission capacity assessment system based on extreme gradient boosting, comprising: a model building module;

[0051] The model building module is used to construct a multi-circuit DC limit transmission capacity assessment model, and to evaluate the multi-circuit DC limit transmission capacity through the multi-circuit DC limit transmission capacity assessment model. The specific contents of constructing the multi-circuit DC limit transmission capacity assessment model include:

[0052] A time-domain simulation model is built based on the multi-circuit DC grid structure of the target power grid, and regional generator output and load level parameters are adjusted in batches to generate simulation files for evaluating the multi-circuit DC transmission capacity.

[0053] Determine the static safety and stability constraints that affect the maximum transmission capacity of multi-circuit DC power transmission, including equality constraints and inequality constraints. Equality constraints include power flow constraints for pure AC nodes, AC nodes connected to LCC converter stations, and AC nodes connected to MMC converter stations. Inequality constraints include generator output constraints, converter station capacity constraints, converter transformer ratio constraints, and node voltage constraints.

[0054] The optimal multi-circuit DC transmission capacity of each simulation file under static safety and stability constraints is calculated based on the particle swarm algorithm, and the sample set S is formed by combining the adjusted generator output and load level parameters. p ;

[0055] Determine the hyperparameters of the extreme gradient boosting XGboost algorithm;

[0056] The sample set S p The data are input into the XGboost algorithm in batches for learning until the XGboost model obtained through learning converges. The converged XGboost model is used as the multi-circuit DC limit transmission capability evaluation model.

[0057] A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0058] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method, system, and computer-readable storage medium for evaluating the maximum transmission capacity of a multi-circuit DC power system based on extreme gradient boosting, which has the following beneficial effects:

[0059] Compared with existing methods such as the continuous power flow method and the optimal power flow method, the present invention constructs a limit transmission capacity regression prediction model based on the XGboost algorithm through a large number of samples. When the generator output or load level changes, there is no need to use a time-consuming optimization algorithm to solve the limit transmission capacity. Instead, the generator output and load level related data are directly input into the regression prediction model to obtain the result, which improves the flexibility and speed of multi-circuit DC limit transmission capacity assessment. At the same time, the present invention adopts ten-fold cross-validation as a parameter tuning method to improve the generalization performance of the model. In addition, the simulation file generation method of the present invention is based on actual engineering applications, and its accuracy also meets the needs of actual power grid operation planning, which has practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 A flowchart of the method for constructing a multi-circuit DC limit transmission capability assessment model provided by the present invention;

[0062] Figure 2 This is a structural diagram of the XGboost-based regression prediction model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0064] The present invention provides a method for evaluating the maximum transmission capacity of a multi-circuit direct current (DC) system based on extreme gradient boosting, comprising the following steps:

[0065] A multi-circuit DC limit transmission capacity evaluation model is constructed to evaluate the multi-circuit DC limit transmission capacity; among them, Figure 1As shown in Figure 2, the specific contents of constructing a multi-circuit DC limit transmission capacity assessment model include:

[0066] A time-domain simulation model is built based on the multi-circuit DC grid structure of the target power grid, and regional generator output and load level parameters are adjusted in batches to generate simulation files for evaluating the multi-circuit DC transmission capacity.

[0067] Determine the static safety and stability constraints that affect the maximum transmission capacity of multi-circuit DC power transmission, including equality constraints and inequality constraints. Equality constraints include power flow constraints for pure AC nodes, AC nodes connected to LCC converter stations, and AC nodes connected to MMC converter stations. Inequality constraints include generator output constraints, converter station capacity constraints, converter transformer ratio constraints, and node voltage constraints.

[0068] The optimal multi-circuit DC transmission capacity of each simulation file under static safety and stability constraints is calculated based on the particle swarm algorithm, and the sample set S is formed by combining the adjusted generator output and load level parameters. p ;

[0069] Determine the hyperparameters of the extreme gradient boosting XGboost algorithm;

[0070] The sample set S p The data are input into the XGboost algorithm in batches for learning until the XGboost model obtained through learning converges. The converged XGboost model is used as the multi-circuit DC limit transmission capability evaluation model.

[0071] It should be noted that:

[0072] In this embodiment, PSD-BPA is used to perform time domain simulation, and the obtained simulation result file contains the power system flow calculation results: the voltage amplitude and phase of each node, and the active and reactive power transmitted on each line.

[0073] To further implement the above technical solution, the specific contents of batch adjustment of regional generator processing and load level parameters include:

[0074] In order to simulate the changes in generator output and load level fluctuations and make the constructed model applicable to various scenarios, according to the principle of unchanged power factor before and after the change, all generator outputs and load levels in the area are multiplied by a random correction coefficient between 0.95 and 1.05; the principles for adjusting generator output and load levels are as follows:

[0075]

[0076] Where s is the correction coefficient, 0.95≤s≤1.05, φ uis the power factor angle, P Gu0 and Q Gu0 are respectively the active power and reactive power of the generator output before adjustment in the u-th region, P Gu and Q Gu are respectively the active power and reactive power of the generator output after adjustment in the uth region; P Lu and Q Lu are the active power consumption and reactive power consumption of the load in the u-th area, u=1,2…a, and a is the number of areas included in the actual power grid.

[0077] In order to further implement the above technical solution, the power flow constraints of pure AC nodes are:

[0078]

[0079] Power flow constraints for AC nodes connected to LCC converter stations:

[0080]

[0081] Power flow constraints for AC nodes connected to MMC converter stations:

[0082]

[0083] Among them, P i and Q i are the active power and reactive power of AC node i respectively; φ k is the power factor angle of the kth LCC converter station; U dk and I dk are the DC voltage and current of the kth LCC converter station or MCC converter station respectively; θ k is the power factor angle of the kth MMC converter station; δ k Y is the fundamental voltage phase output by the kth MMC converter station; k is the equivalent admittance of the kth MMC converter station; M k is the modulation ratio of the kth MMC converter station; n is the number of pure AC nodes in the power grid; m is the number of LCC converter stations; N is the number of MMC converter stations;

[0084] Generator output constraints:

[0085]

[0086] Converter station capacity constraints:

[0087]

[0088] Converter transformer ratio constraints:

[0089] K vmin≤K v ≤K vmax (7)

[0090] Node voltage constraints:

[0091] U imin ≤U i ≤U imax (8).

[0092] Among them, P Gi is the active power output of the generator; Q Gi is the reactive power of the generator output; K v is the transformation ratio of the converter transformer; the subscripts min and max represent the maximum and minimum values ​​of the corresponding parameters respectively.

[0093] In order to further implement the above technical solution, the optimal multi-circuit DC limit transmission capacity of each simulation file under static safety and stability constraints is calculated according to the particle swarm algorithm, and the sample set S is formed by combining the adjusted generator output and load level parameters. p The specific contents include:

[0094] The active power of multiple DC transmission is used to reflect the maximum transmission capacity of multiple DC transmission lines. That is, the variable to be optimized in the particle swarm algorithm is the active power of multiple DC transmission lines, which are expressed as x1, x2,…, x m , where m is the number of DC transmission times;

[0095] The fitness function f(x) of the particle swarm algorithm is expressed as: f(x) = -(x1+x2+...+x m );

[0096] By continuously calling the power flow calculation program and checking whether the constraints in the results are satisfied, the particle swarm algorithm iteratively searches for the optimal solution within the search range defined by the constraints, and finally calculates the minimum value of f(x) under the static safety and stability constraints, which is recorded as y.

[0097] Combine y with the adjusted generator active output P G1 ,P G2 ,…,P Ga , load active power consumption P L1 ,P L2 ,…,P La , generator reactive output Q G1 ,Q G2 ,…,Q Ga , reactive power consumption of load Q L1 ,Q L2 ,…,Q La Combined to form the sample set S p :

[0098] Sp ={[P G1 ,Q G1 ,P L1 ,Q L1 ...,P Ga ,Q Ga ,P La ,Q La ,y] (p)},p=1,2,...,q

[0099] Where q is the number of samples.

[0100] It should be noted that:

[0101] Since the generator output and load level are fixed in each simulation file, the particle swarm algorithm calculates the maximum transmission capacity of multi-circuit DC power in a specific scenario.

[0102] To further implement the above technical solution, the loss function used by the XGboost algorithm in S5 to construct the t-th decision tree model is the mean square error loss function. The goal of training each decision tree model is to minimize the loss function:

[0103]

[0104] Among them, y p is the true value of the p-th sample, The model prediction value obtained by inputting the p-th sample into the t-th decision tree, q is the number of samples, γ is the regularization coefficient in the loss function, and the number of regression tree leaf nodes T that is greater than the preset threshold is penalized;

[0105] The converged XGboost model is tuned using the grid search method. After giving the search range of each hyperparameter, the grid search traverses the given hyperparameter value combinations and performs ten-fold cross-validation. All data are randomly divided into ten non-overlapping subsets. Nine of these subsets are selected as training sets in each iteration. Multiple regression prediction models are constructed using multiple sets of hyperparameter combinations. The remaining subset is used as a test set to evaluate the accuracy of these models, thereby selecting the optimal hyperparameters and obtaining the final multi-circuit DC limit transmission capacity assessment model.

[0106] It should be noted that:

[0107] XGboost is an ensemble learning algorithm based on decision trees. The core idea of ​​the XGboost algorithm is to first train a decision tree model, then add more decision trees to fit the residuals of the first tree, and finally combine the results of each decision tree into a set of k (x) and weight coefficient α kAfter multiplication, sum is taken to obtain the final regression prediction result. The structure diagram of the regression prediction model based on XGboost constructed in this embodiment is as follows: Figure 2 shown.

[0108] In this embodiment, the meaning, search range, and search results of each hyperparameter of the XGboost algorithm are as follows:

[0109]

[0110] In this example, the XGboost algorithm ensembles 50 decision trees, using a learning rate of 0.4 for gradient descent of the loss function. Each decision tree is constructed using 60% of the sample size, and sampling with replacement is used to ensure diversity among the trees. Each tree is split up to a maximum of six levels. The decision tree stops splitting when the loss function value of a node decreases by less than 0.1. The final prediction output is the weighted sum of the predictions from all trees.

[0111] In order to further implement the above technical solution, the accuracy of the multi-circuit DC limit transmission capacity assessment model is evaluated by the coefficient of determination and root mean square error:

[0112] The coefficient of determination is:

[0113]

[0114] The root mean square error is:

[0115]

[0116] in, is the predicted value of the p-th sample by the XGboost-based limit transmission capacity regression prediction model; y p is the true value of the sample's ultimate transmission capacity; where p = 1, 2, 3, ..., q, q is the number of samples; R 2 ∈[0,1], the closer it is to 1, the better the prediction effect of the multi-circuit DC limit transmission capacity assessment model; the smaller the RMSE, the better the prediction effect.

[0117] A multi-circuit DC limit transmission capacity assessment system based on extreme gradient boosting, comprising: a model building module;

[0118] The model building module is used to construct a multi-circuit DC limit transmission capacity assessment model, and to evaluate the multi-circuit DC limit transmission capacity through the multi-circuit DC limit transmission capacity assessment model. The specific contents of constructing the multi-circuit DC limit transmission capacity assessment model include:

[0119] A time-domain simulation model is built based on the multi-circuit DC grid structure of the target power grid, and regional generator output and load level parameters are adjusted in batches to generate simulation files for evaluating the multi-circuit DC transmission capacity.

[0120] Determine the static safety and stability constraints that affect the maximum transmission capacity of multi-circuit DC power transmission, including equality constraints and inequality constraints. Equality constraints include power flow constraints for pure AC nodes, AC nodes connected to LCC converter stations, and AC nodes connected to MMC converter stations. Inequality constraints include generator output constraints, converter station capacity constraints, converter transformer ratio constraints, and node voltage constraints.

[0121] The optimal multi-circuit DC transmission capacity of each simulation file under static safety and stability constraints is calculated based on the particle swarm algorithm, and the sample set S is formed by combining the adjusted generator output and load level parameters. p ;

[0122] Determine the hyperparameters of the extreme gradient boosting XGboost algorithm;

[0123] The sample set S p The data are input into the XGboost algorithm in batches for learning until the XGboost model obtained through learning converges. The converged XGboost model is used as the multi-circuit DC limit transmission capability evaluation model.

[0124] A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0125] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for evaluating the maximum transmission capacity of multi-circuit DC power based on extreme gradient boosting, characterized in that: The following steps are involved: A multi-circuit DC limit transmission capacity assessment model is constructed to evaluate the multi-circuit DC limit transmission capacity. The specific contents of constructing the multi-circuit DC limit transmission capacity assessment model include: A time-domain simulation model is built based on the multi-circuit DC grid structure of the target power grid, and regional generator output and load level parameters are adjusted in batches to generate simulation files for evaluating the multi-circuit DC transmission capacity. Determine the static safety and stability constraints that affect the maximum transmission capacity of multi-circuit DC power transmission, including equality constraints and inequality constraints. Equality constraints include power flow constraints for pure AC nodes, AC nodes connected to LCC converter stations, and AC nodes connected to MMC converter stations. Inequality constraints include generator output constraints, converter station capacity constraints, converter transformer ratio constraints, and node voltage constraints. The optimal multi-circuit DC transmission capacity of each simulation file under static safety and stability constraints is calculated based on the particle swarm algorithm, and the sample set S is formed by combining the adjusted generator output and load level parameters. p ; Determine the hyperparameters of the extreme gradient boosting XGboost algorithm; The sample set S p The data are input into the XGboost algorithm in batches for learning until the XGboost model obtained through learning converges. The converged XGboost model is used as the multi-circuit DC limit transmission capability evaluation model.

2. The method for evaluating the maximum transmission capacity of multi-circuit DC power transmission based on extreme gradient boosting according to claim 1, characterized in that: The specific contents of batch adjustment of regional generator processing and load level parameters include: In accordance with the principle that the power factor remains unchanged before and after the change, the output and load levels of all generators in the area are multiplied by a random correction coefficient between 0.95 and 1.05; the principles for adjusting the generator output and load levels are: Where s is the correction coefficient, 0.95≤s≤1.05, φ u is the power factor angle, P Gu0 and Q Gu0 are respectively the active power and reactive power of the generator output before adjustment in the u-th region, P Gu and Q Gu are respectively the active power and reactive power of the generator output after adjustment in the uth region; P Lu and Q Lu are the active power consumption and reactive power consumption of the load in the u-th area, u=1,2…a, and a is the number of areas included in the actual power grid.

3. The method for evaluating the maximum transmission capacity of multi-circuit DC power transmission based on extreme gradient boosting according to claim 1, characterized in that: Power flow constraints for pure AC nodes: Among them, U i and U j are the node voltages of node i and node j respectively; G ij is the mutual conductance between node i and node j; B ij is the mutual susceptance between node i and node j; Power flow constraints for AC nodes connected to LCC converter stations: Power flow constraints for AC nodes connected to MMC converter stations: Among them, P i and Q i are the active power and reactive power of AC node i respectively; φ k is the power factor angle of the kth LCC converter station; U dk and I dk are the DC voltage and current of the kth LCC converter station or MCC converter station respectively; θ k is the power factor angle of the kth MMC converter station; δ k Y is the fundamental voltage phase output by the kth MMC converter station; k is the equivalent admittance of the kth MMC converter station; M k is the modulation ratio of the kth MMC converter station; n is the number of pure AC nodes in the power grid; m is the number of LCC converter stations; N is the number of MMC converter stations; Generator output constraints: Converter station capacity constraints: Converter transformer ratio constraints: K vmin ≤K v ≤K vmax (7) Node voltage constraints: IN imin ≤U i ≤U imax (8) Among them, P Gi is the active power output of the generator; Q Gi is the reactive power of the generator output; K v is the transformation ratio of the converter transformer; the subscripts min and max represent the maximum and minimum values ​​of the corresponding parameters respectively.

4. The method for evaluating the maximum transmission capacity of multi-circuit DC power transmission based on extreme gradient boosting according to claim 1, characterized in that: The optimal multi-circuit DC transmission capacity of each simulation file under static safety and stability constraints is calculated based on the particle swarm algorithm, and the sample set S is formed by combining the adjusted generator output and load level parameters. p The specific contents include: The maximum transmission capacity of multi-circuit DC is reflected by the active power transmitted by the multi-circuit DC, which is expressed as x1, x2, ..., x m , where m is the number of DC transmission times; The fitness function f(x) of the particle swarm algorithm is expressed as: f(x) = -(x1+x2+...+x m ); After iterative optimization using the particle swarm algorithm, the minimum value of f(x) under the static safety and stability constraints is calculated and recorded as y; Combine y with the adjusted generator active output P G1 ,P G2 ,…,P Ga , load active power consumption P L1 ,P L2 ,…,P La , generator reactive output Q G1 ,Q G2 ,…,Q Ga , reactive power consumption of load Q L1 ,Q L2 ,…,Q La Combined to form the sample set S p : S p ={[P G1 ,Q G1 ,P L1 ,Q L1 ...,P Ga ,Q Ga ,P La ,Q La ,y] (p) },p=1,2,...,q Where q is the number of samples.

5. The method for evaluating the maximum transmission capacity of multi-circuit DC power transmission based on extreme gradient boosting according to claim 1, characterized in that: The loss function used by the XGboost algorithm to construct the t-th decision tree model is the mean square error loss function. The goal of training each decision tree model is to minimize the loss function: Among them, y p is the true value of the p-th sample, The model prediction value obtained by inputting the p-th sample into the t-th decision tree, q is the number of samples, γ is the regularization coefficient in the loss function, and the number of regression tree leaf nodes T that is greater than the preset threshold is penalized; The converged XGboost model is tuned using the grid search method. After giving the search range of each hyperparameter, the grid search traverses the given hyperparameter value combinations and performs ten-fold cross-validation. All data are randomly divided into ten non-overlapping subsets. Nine of these subsets are selected as training sets in each iteration. Multiple regression prediction models are constructed using multiple sets of hyperparameter combinations. The remaining subset is used as a test set to evaluate the accuracy of these models, thereby selecting the optimal hyperparameters and obtaining the final multi-circuit DC limit transmission capacity assessment model.

6. The method for evaluating the maximum transmission capacity of multi-circuit DC power transmission based on extreme gradient boosting according to claim 1, characterized in that: The accuracy of the multi-circuit DC transmission capacity assessment model is evaluated by the coefficient of determination and root mean square error: The coefficient of determination is: The root mean square error is: in, is the predicted value of the p-th sample by the XGboost-based limit transmission capacity regression prediction model; y p is the true value of the sample's ultimate transmission capacity; where p = 1, 2, 3, ..., q, q is the number of samples; R 2 ∈[0,1], the closer it is to 1, the better the prediction effect of the multi-circuit DC limit transmission capacity assessment model; the smaller the RMSE, the better the prediction effect.

7. A multi-circuit DC limit transmission capacity assessment system based on extreme gradient boosting, based on the multi-circuit DC limit transmission capacity assessment method based on extreme gradient boosting according to any one of claims 1 to 6, characterized in that: include: Model building module; The model building module is used to construct a multi-circuit DC limit transmission capacity assessment model, and to evaluate the multi-circuit DC limit transmission capacity through the multi-circuit DC limit transmission capacity assessment model. The specific contents of constructing the multi-circuit DC limit transmission capacity assessment model include: A time-domain simulation model is built based on the multi-circuit DC grid structure of the target power grid, and regional generator output and load level parameters are adjusted in batches to generate simulation files for evaluating the multi-circuit DC transmission capacity. Determine the static safety and stability constraints that affect the maximum transmission capacity of multi-circuit DC power transmission, including equality constraints and inequality constraints. Equality constraints include power flow constraints for pure AC nodes, AC nodes connected to LCC converter stations, and AC nodes connected to MMC converter stations. Inequality constraints include generator output constraints, converter station capacity constraints, converter transformer ratio constraints, and node voltage constraints. The optimal multi-circuit DC transmission capacity of each simulation file under static safety and stability constraints is calculated based on the particle swarm algorithm, and the sample set S is formed by combining the adjusted generator output and load level parameters. p ; Determine the hyperparameters of the extreme gradient boosting XGboost algorithm; The sample set S p The data are input into the XGboost algorithm in batches for learning until the XGboost model obtained through learning converges. The converged XGboost model is used as the multi-circuit DC limit transmission capability evaluation model.

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

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