A comprehensive evaluation method, system and medium for a multi-source power grid in an integrated power system

Abstract

The present invention provides a comprehensive evaluation method, system and medium for a multi-source power grid of an integrated power system, relating to the technical field of power grid evaluation, including: constructing a training data set for the comprehensive evaluation of the multi-source power grid of the integrated power system according to the evaluation indexes and original data of the multi-source power grid of the integrated power system; taking the classification accuracy of the 5-fold cross-validation SVM of the training data set as the objective function; according to the objective function, using an improved sooty tern optimization algorithm to search for the optimal penalty factor C and RBF kernel function of the SVM; obtaining a comprehensive evaluation model for the multi-source power grid of the integrated power system according to the optimal penalty factor C and RBF kernel function as the SVM parameters; obtaining the data of the current multi-source power grid of the integrated power system, and predicting and obtaining an evaluation prediction result through the comprehensive evaluation model for the multi-source power grid of the integrated power system. This method can accurately evaluate the operation state of the multi-source power grid of the integrated power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid evaluation, and particularly to a comprehensive evaluation method, system and medium for a multi-source power grid of an integrated power system. Background Art

[0002] The load types of the multi-source power grid of the vehicle-mounted integrated power system are diversified, and the operation mechanism of multi-source networking and the characteristics of constant power load and pulse load are complex. Therefore, in-depth research on the comprehensive evaluation method of the multi-source power grid of the vehicle-mounted integrated power system to evaluate the operation health status and fault warning of the multi-source power grid of the integrated power system has great theoretical significance and practical significance for improving the stability margin of the integrated power system.

[0003] As one of the research contents of machine learning, the support vector machine (SVM) is widely used in comprehensive evaluations such as power systems. During the SVM training process, the selection quality of the penalty factor C and the RBF kernel function parameter g of the SVM directly affects the accuracy of the final comprehensive evaluation result of the multi-source power grid of the integrated power system. The intelligent optimization algorithm is an effective method for optimizing SVM model parameters. The sooty tern optimization algorithm (STOA) is a new intelligent optimization algorithm that simulates the foraging behavior of sooty terns and can also be applied to the comprehensive evaluation problem of the multi-source power grid of the integrated power system. However, the sooty tern optimization algorithm still has some defects, which makes the algorithm prone to falling into local optima and having low convergence accuracy, reducing the effect of optimizing SVM model parameters, and resulting in inaccurate evaluation and prediction effects when using this model for comprehensive evaluation and prediction of the multi-source power grid of the integrated power system. Summary of the Invention

[0004] The present invention provides a comprehensive evaluation method for a multi-source power grid of an integrated power system, which can form an effective grid comprehensive evaluation and prediction model and obtain more accurate evaluation and prediction results.

[0005] The present invention proposes a comprehensive evaluation method for a multi-source power grid of an integrated power system, specifically including the following steps:

[0006] Construct a training data set for the comprehensive evaluation of the multi-source power grid of the integrated power system according to the evaluation indexes and original data of the multi-source power grid of the integrated power system;

[0007] Determine the upper and lower limits of the penalty factor C and the RBF kernel function parameter g;

[0008] Take the grid evaluation accuracy rate of the 5-fold cross-validation SVM of the training data set as the objective function, and calculate the fitness value according to the objective function;

[0009] The improved Sooty Tern optimization algorithm is used to search for the optimal penalty factor C and RBF kernel function of the SVM corresponding to the optimal fitness value. Among them, the improved Sooty Tern optimization algorithm introduces the position update mechanism of the lotus effect algorithm and the escape optimization algorithm, and updates the position of the Sooty Tern by synthesizing the optimal position of the Sooty Tern in this iteration, different position update modes, and the cohesion and panic index factors of the Sooty Tern.

[0010] According to the optimal penalty factor C and RBF kernel function as SVM parameters, a comprehensive evaluation model of the multi-source power grid of the integrated power system is obtained.

[0011] Obtain the data of the multi-source power grid of the current integrated power system, and predict and obtain the evaluation and prediction results through the comprehensive evaluation model of the multi-source power grid of the integrated power system.

[0012] Preferably, the evaluation indexes of the multi-source power grid of the integrated power system include stability, security and adaptability, and the stability, security and adaptability are divided into three evaluation levels: high, medium and low, which are used as the evaluation results of the comprehensive evaluation model of the multi-source power grid of the integrated power system.

[0013] Preferably, the original data includes: energy benchmark parameters, electric energy production data, operation monitoring data and equipment maintenance data in the power generation link, transmission line parameters, transmission loss data and transmission stability data in the transmission link, substation operation data, substation equipment status data and substation loss data in the transformation link, distribution network parameters, distribution loss data and distribution reliability data in the distribution link, user power consumption data, smart meter data and user power consumption behavior data in the power consumption link.

[0014] Preferably, the step of using the improved Sooty Tern optimization algorithm to search for the optimal penalty factor C and RBF kernel function of the SVM includes the following steps:

[0015] Take the penalty factor C and RBF kernel function of the SVM as the individuals of the Sooty Tern, determine the upper and lower limits of the penalty factor C and RBF kernel function of the SVM, initialize the position of the Sooty Tern population through Gaussian mapping, and calculate the optimal fitness value and the optimal position of the Sooty Tern according to the objective function.

[0016] Introduce the position update mechanism of the lotus effect algorithm and the escape optimization algorithm to improve the position update method in the original Sooty Tern optimization algorithm; perform position update through the improved position update method, and record the optimal fitness value and position after the current iteration.

[0017] Perform two-way sine mutation on the optimal position of the Sooty Tern, and take the position of the Sooty Tern with the optimal fitness value before and after mutation as the updated optimal position of the Sooty Tern.

[0018] Update the optimal sooty tern position according to the preset maximum number of iterations in sequence to determine the optimal sooty tern position; determine the penalty factor C and the RBF kernel function of the optimal SVM according to the optimal sooty tern position.

[0019] Preferably, the initialization of the sooty tern population position by Gaussian mapping includes the following steps:

[0020] Determine the population size N, the lower boundary LB of sooty tern optimization, and the upper boundary UB of sooty tern optimization;

[0021] Generate the next random number x through Gaussian mapping t+1 :

[0022]

[0023] where mod(·) is the remainder function and x t is the current random number;

[0024] Initialize the sooty tern position using the generated Gaussian random number:

[0025] P s (t) = (UB - LB) × x t + LB.

[0026] Preferably, the position update is performed by the improved position update method, and the optimal fitness value and position after the current iteration are recorded, including the following steps:

[0027] Simulate the migration behavior of sooty terns, including collision avoidance, aggregation, and update;

[0028] Collision avoidance: Simulate the process of the collision avoidance behavior of sooty terns, which is expressed by the following formula:

[0029] C s (t) = S A × P s (t)

[0030] where: P s (t) represents the position of the sooty tern at the current t-th iteration; C s (t) represents the new position of the sooty tern without colliding with other sooty terns; S A represents a variable factor for collision avoidance, which is used to calculate the position after collision avoidance, and its constraint condition formula is as follows:

[0031] S A = C f -(t × C f / Miter)

[0032] where: C f is used to adjust S AControl variable; t represents the current iteration number; S A As the number of iterations increases, from C f Gradually decreases to 0; assume C f Is 2, S A Will gradually decrease from 2 to 0; Miter is the number of iterations;

[0033] Aggregation: Aggregation means that the current sooty tern moves closer to the best position among adjacent sooty terns on the premise of avoiding collisions, that is, moves closer to the optimal position. Its mathematical expression is as follows:

[0034] M s (t) = C B ×(P bs (t) - P s (t))

[0035] In the formula: P bs (t) is the optimal position of the sooty tern at the t-th iteration; M s (t) represents the process of moving from different positions P s (t) to the optimal position P bs (t); C B Is a random variable and changes according to the following formula:

[0036] C B = 0.5 × rand

[0037] In the formula: rand is a random number from 0 to 1;

[0038] Update: Make the current sooty tern move in the direction of the optimal position and update the position. Its mathematical expression is:

[0039] D s (t) = C s (t) + M s (t)

[0040] In the formula: D s (t) is the distance that the sooty tern moves from the current position to the optimal position direction;

[0041] Attack behavior: During the migration process, the sooty tern increases its flight altitude by flapping its wings, adjusts its own speed and attack angle. When attacking prey, their hovering behavior in the air is defined by the following mathematical model:

[0042]

[0043] In the formula: x, y, and z are the coordinates in three directions respectively; r is the radius of each helix; θ is a random angle value within the range of [0, 2π]; u and v are related constants defining the helix shape, both set to 1; e is the base of the natural logarithm;

[0044] Introduce the position update mechanism of the lotus effect algorithm and the escape optimization algorithm, and update the position of the sooty tern by integrating the optimal position of the sooty tern in this iteration, different position update modes, and the cohesion and panic index factors of the sooty tern:

[0045]

[0046] Where:

[0047]

[0048] In the formula: P s (t + 1) is the position of the sooty tern at the (t + 1)-th iteration after update; P bs (t) represents the best position at the t-th iteration; P s (t) is the current position of the sooty tern; P i (t) is the position of the sooty tern with index i at the t-th iteration; C i (t) is the cohesion of the sooty tern with index i at the t-th iteration; Z(t) is the panic index of the sooty tern at the t-th iteration; α is a random number between [0, 1]; λ1 is a random number within [0, 1];

[0049] Calculate the fitness value:

[0050] fitness(t) = F f (P s (t + 1))

[0051] In the formula, F f (·) is the fitness value calculation function;

[0052] Record the optimal sooty tern in the current iteration.

[0053] Preferably, perform two-way sine mutation on the position of the optimal sooty tern, and use the position of the sooty tern with the best fitness value before and after mutation as the updated optimal sooty tern position, including the following steps:

[0054] For dimension j, calculate the sine chaos value according to the current iteration number and switch the positive and negative directions with equal probability:

[0055] sinValue = sin(πx0)

[0056]

[0057] In the formula, rand is a random number from 0 to 1; x0 is the iteration sequence value;

[0058] Perform mutation perturbation on the optimal position:

[0059] Pbs(j) (t + 1)' = P bs(j) (t + 1) + SinValue × P bs(j) (t + 1)

[0060] Where: P bs(j) (t + 1) represents the optimal position P at the (t + 1)-th iteration bs the j-th dimension of (t + 1); P bs(j) (t + 1)' is the optimal position P after mutation perturbation bs the j-th dimension of (t + 1);

[0061] Greedy update:

[0062]

[0063] Where, after mutation in each dimension, the mutation stops.

[0064] The present invention also provides a comprehensive evaluation system for a multi-source power grid of an integrated power system, the system includes:

[0065] A processor;

[0066] A memory, on which a computer program that can run on the processor is stored;

[0067] Wherein, when the computer program is executed by the processor, the steps of the comprehensive evaluation method for the multi-source power grid of the integrated power system are implemented.

[0068] The present invention also provides a computer-readable storage medium, on which a data processing program is stored, and when the data processing program is executed by a processor, the steps of the comprehensive evaluation method for the multi-source power grid of the integrated power system are implemented.

[0069] Advantages of the present invention:

[0070] The present invention provides a comprehensive evaluation method for a multi-source power grid of an integrated power system. By improving the position update method of the Chinese crested tern, introducing the position update mechanisms of the lotus effect algorithm and the escape optimization algorithm to improve the position update method of the Chinese crested tern, comprehensively considering factors such as the optimal position of the Chinese crested tern in the current iteration, different position update modes, the cohesion of the Chinese crested tern, and the panic index, etc., to update the position of the Chinese crested tern, the search range of the algorithm is increased, the adaptability of the algorithm is enhanced, the optimal parameters of the SVM model are obtained to construct an evaluation prediction model with a higher prediction accuracy, further accurately evaluate the operating health status of the multi-source power grid of the integrated power system, and provide a basis for fault early warning, which helps to improve the stability margin of the integrated power system subsequently. Description of the Drawings

[0071] Figure 1It is the flowchart of the comprehensive evaluation method for multi-source power grids in the integrated power system of the embodiments of the present invention. Detailed implementation manners

[0072] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0073] Embodiment 1

[0074] There are still some defects in the current Sooty tern optimization algorithm, which makes the algorithm prone to falling into local optimum and having low convergence accuracy, reducing the effect of optimizing the SVM model parameters, resulting in inaccurate evaluation and prediction effects when using this model for comprehensive evaluation and prediction of multi-source power grids in the integrated power system. For this reason, in combination with the basic steps of STOA and aiming at several problems existing in STOA, the present invention proposes an improved Sooty tern optimization algorithm (Improve Sooty tern optimization algorithm, ISTOA) and uses it for the comprehensive evaluation of multi-source power grids in the integrated power system. The process of the comprehensive evaluation method for multi-source power grids in the integrated power system is as Figure 1 shown, and the specific steps are as follows:

[0075] S1: According to the evaluation indexes and original data of the multi-source power grid in the integrated power system, construct a training data set for the comprehensive evaluation of the multi-source power grid in the integrated power system.

[0076] Among them, the evaluation indexes of the multi-source power grid in the integrated power system include stability, security and adaptability. The stability, security and adaptability are divided into three evaluation levels: high, medium and low, as the evaluation results of the comprehensive evaluation model of the multi-source power grid in the integrated power system. The original data of the multi-source power grid in the integrated power system include: energy benchmark parameters, electric energy production data, operation monitoring data and equipment maintenance data in the power generation link, transmission line parameters, transmission loss data and transmission stability data in the transmission link, substation operation data, substation equipment status data and substation loss data in the transformation link, distribution network parameters, distribution loss data and distribution reliability data in the distribution link, user power consumption data, smart meter data and user power consumption behavior data in the power consumption link, etc.

[0077] S2: Take the classification accuracy of the 5-fold cross-validation SVM of the training data set as the objective function, that is, the fitness function; at the same time, set the corresponding constraint conditions: the upper and lower limits of the penalty factor C and the RBF kernel function parameter g of the SVM.

[0078] S3: Perform parameter settings, mainly including: the size N of the sooty tern population, i.e., the number of sooty tern individuals; the maximum number of iterations Miter, i.e., the condition for stopping the iteration; the lower bound LB for sooty tern optimization; and the upper bound UB for sooty tern optimization.

[0079] S4: Initialize the sooty tern population position using the Gaussian mapping:

[0080] Gaussian mapping random number generation:

[0081]

[0082] Initialize the sooty tern position using the generated Gaussian random numbers:

[0083] P s (t) = (UB - LB) × x t + LB

[0084] S5: The migration behavior of sooty terns:

[0085] The migration behavior of sooty terns, i.e., the exploration part of the algorithm, is mainly divided into three stages: collision avoidance, aggregation, and update.

[0086] a) Collision avoidance.

[0087] Simulate the process of the collision avoidance behavior of sooty terns, which is expressed by the following formula:

[0088] C s (t) = S A × P s (t) [[ID=4l]]

[0089] In the formula: P s (t) represents the position of the sooty terns at the current t-th iteration; C s (t) represents the new position of the sooty terns without colliding with other sooty terns; S A represents a variable factor for collision avoidance, which is used to calculate the position after collision avoidance. Its constraint condition formula is as follows: [[ID=SO]]

[0090] S A = C f -(t × C f / Miter)

[0091] In the formula: C f is the control variable used to adjust S A ; t represents the current iteration number; S A gradually decreases from C f to 0 as the number of iterations increases; for example, assuming C f is 2, S A will gradually decrease from 2 to 0.

[0092] b) Aggregation.

[0093] Aggregation means that the current Sooty Tern moves closer to the best position among adjacent Sooty Terns while avoiding conflicts, that is, moves closer to the optimal position. Its mathematical expression is as follows:

[0094] M s (t) = C B × (P bs (t) - P s (t))

[0095] In the formula: P bs (t) is the optimal position of the Sooty Tern at the t-th iteration; M s (t) represents the process of moving from different positions P s (t) to the optimal position P bs (t); C B is a random variable that makes the exploration more comprehensive and changes according to the following formula:

[0096] C B = 0.5 × rand

[0097] In the formula: rand is a random number within the range of [0, 1].

[0098] c) Update.

[0099] Update means that the current Sooty Tern moves in the direction of the optimal position and updates its position. Its mathematical expression is:

[0100] D s (t) = C s (t) + M s (t)

[0101] In the formula: D s (t) is the distance that the Sooty Tern moves from the current position to the optimal position.

[0102] S6: Attack behavior:

[0103] During migration, the Sooty Tern can increase its flight altitude by flapping its wings, and can also adjust its own speed and attack angle. When attacking prey, their hovering behavior in the air can be defined by the following mathematical model:

[0104]

[0105] In the formula: r is the radius of each helix; θ is a random angle value within the range of [0, 2π]; u and v are related constants that define the shape of the helix and can both be set to 1; e is the base of the natural logarithm.

[0106] In the original Sooty Tern algorithm, only the optimal Sooty Tern position is used to guide the update of the Sooty Tern position. In order to more effectively improve the global search ability of the Sooty Tern, the position update mechanisms of the Lotus Effect algorithm and the Escape Optimization algorithm are introduced to improve the Sooty Tern position update method. When updating the Sooty Tern position, factors such as the optimal position of the Sooty Tern in this iteration, different position update modes, the cohesion of the Sooty Tern, and the panic index are comprehensively considered to avoid local optima in each iteration, thereby improving the global search ability of the Sooty Tern algorithm.

[0107] The Lotus Effect algorithm and the Escape Optimization algorithm are introduced, and the improved Sooty Tern position update formula is as follows:

[0108]

[0109] Where:

[0110]

[0111]

[0112] In the formula: P s (t + 1) is the position of the Sooty Tern at the (t + 1)-th iteration after update; P bs (t) represents the best position at the t-th iteration; P s (t) is the current position of the Sooty Tern; P i (t) is the position of the Sooty Tern with index i at the t-th iteration; C i (t) is the cohesion of the Sooty Tern with index i at the t-th iteration; Z(t) is the panic index of the Sooty Tern at the t-th iteration; α is a random number between [0, 1]; λ1 is a random number within [0, 1].

[0113] S7: Calculate the fitness value.

[0114] fitness(t) = F f (P s (t + 1))

[0115] In the formula, F f (·) is the fitness function when calculating the fitness value.

[0116] S8: Record information. Record the optimal Sooty Tern in the current iteration.

[0117] S9: Perform two-dimensional bidirectional sine mutation on the optimal Sooty Tern.

[0118] For dimension j, first calculate the sine chaotic value according to the current iteration number and switch the positive and negative directions with equal probability.

[0119] SinValue = sin(πx0)

[0120]

[0121] Then, perform mutation perturbation on the optimal position:

[0122] P bs(j) (t + 1)' = P bs(j) (t + 1) + SinValue × P bs(j) (t + 1)

[0123] Where: P bs(j) (t + 1) represents the optimal position P bs of the (t + 1)-th iteration, and the j-th dimension of P

[0124] Greedy update:

[0125]

[0126] After mutation in each dimension, stop the mutation.

[0127] S10: Record information. Record the optimal sooty tern in the current iteration.

[0128] S11: Repeat steps S5 - S11. After reaching the maximum number of iterations Miter, the algorithm stops, outputs the optimal sooty tern result, and outputs the optimal sooty tern result, that is, obtain the optimal penalty factor C and RBF kernel function of SVM.

[0129] S12: Obtain the comprehensive evaluation model of the multi-source power grid of the integrated power system according to the optimal penalty factor C and RBF kernel function as the SVM parameters.

[0130] S13: Obtain the data of the multi-source power grid of the current integrated power system, and predict and obtain the evaluation prediction result through the comprehensive evaluation model of the multi-source power grid of the integrated power system.

[0131] In this embodiment:

[0132] Select 1000 groups of samples of the multi-source power grid of the integrated power system, and randomly take 800 groups of them as training samples, and the remaining 200 groups as test samples. Use STOA - SVM and ISTOA - SVM to evaluate the multi-source power grid of the integrated power system respectively. Taking MATLAB as the simulation platform, the parameters in the STOA algorithm are: N = 50, Maxiter = 200, and the search ranges of C and g are both between 0 and 100, that is, LB = 0, UB = 100; the parameters in the ISTOA algorithm are: N = 50, Maxiter = 200, and the search ranges of C and g are both between 0 and 100, that is, LB = 0, UB = 100.

[0133] The evaluation metrics for the STOA-SVM model and the ISTOA-SVM model can be selected as: Mean Absolute Error (MAE), Mean Relative Error (MRE), and Root Mean Square Error (RMSE). As shown in Table 1, compared with STOA-SVM, the accuracy of ISTOA-SVM in evaluating the multi-source power grid of the integrated power system is higher. That is to say, the SVM parameters obtained by ISTOA search are better than those obtained by STOA search. The simulation results show that the ISTOA algorithm has stronger search ability than the STOA algorithm, and the evaluation accuracy of ISTOA-SVM is higher than that of STOA-SVM, verifying the effectiveness of the method.

[0134] Table 1 Comparison of evaluation methods

[0135]

[0136] The above is the comprehensive evaluation method for the multi-source power grid of the integrated power system provided by an embodiment of this embodiment. Based on the same idea, this embodiment also provides a corresponding comprehensive evaluation system for the multi-source power grid of the integrated power system. For the specific limitations of the comprehensive evaluation system for the multi-source power grid of the integrated power system, reference can be made to the limitations of the comprehensive evaluation method for the multi-source power grid of the integrated power system in the above text, which will not be elaborated here. Each module in the above comprehensive evaluation system for the multi-source power grid of the integrated power system can be implemented in whole or in part through software, hardware, and their combinations. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0137] This embodiment also provides a computer-readable storage medium, which stores a computer program that can be used to execute the above Figure 1 provided comprehensive evaluation method for the multi-source power grid of the integrated power system.

[0138] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0139] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for multi-source power grids in an integrated power system, characterized in that, It includes the following steps: Construct a training dataset for the comprehensive evaluation of the multi-source power grid of the integrated power system according to the evaluation indicators and original data of the multi-source power grid of the integrated power system; Determine the upper and lower limits of the penalty factor C and the RBF kernel function parameter g; Take the grid evaluation accuracy of the 5-fold cross-validation SVM of the training dataset as the objective function and calculate the fitness value according to the objective function; Use an improved sooty tern optimization algorithm to search for the optimal penalty factor C and RBF kernel function of the SVM; wherein, the improved sooty tern optimization algorithm introduces the position update mechanism of the lotus effect algorithm and the escape optimization algorithm, and updates the position of the sooty tern by integrating the optimal position of the sooty tern in this iteration, different position update modes, and the cohesion and panic index of the sooty tern; Obtain a comprehensive evaluation model of the multi-source power grid of the integrated power system with the optimal penalty factor C and RBF kernel function as the SVM parameters; Obtain the data of the current multi-source power grid of the integrated power system and predict the evaluation and prediction results through the comprehensive evaluation model of the multi-source power grid of the integrated power system.

2. The comprehensive evaluation method for multi-source power grids of an integrated power system according to claim 1, characterized in that The evaluation indicators of the multi-source power grid of the integrated power system include stability, security, and adaptability, and the stability, security, and adaptability are divided into three evaluation levels: high, medium, and low, which are used as the evaluation results of the comprehensive evaluation model of the multi-source power grid of the integrated power system.

3. The comprehensive evaluation method for multi-source power grids of an integrated power system according to claim 1, characterized in that The original data includes: energy benchmark parameters, power production data, operation monitoring data, and equipment maintenance data in the power generation link, transmission line parameters, transmission loss data, and transmission stability data in the transmission link, substation operation data, substation equipment status data, and substation loss data in the transformation link, distribution network parameters, distribution loss data, and distribution reliability data in the distribution link, user power consumption data, smart meter data, and user power consumption behavior data in the power consumption link.

4. The comprehensive evaluation method for a multi-source power grid of an integrated power system according to claim 1, wherein The step of using the improved sooty tern optimization algorithm to search for the optimal penalty factor C and RBF kernel function of the SVM includes the following steps: Take the penalty factor C and RBF kernel function of the SVM as the sooty tern individuals, determine the upper and lower limits of the penalty factor C and RBF kernel function of the SVM, initialize the position of the sooty tern population through Gaussian mapping, and calculate the optimal fitness value and the optimal sooty tern position according to the objective function; Introduce the position update mechanism of the lotus effect algorithm and the escape optimization algorithm to improve the position update method in the original sooty tern optimization algorithm; perform position update through the improved position update method, and record the optimal fitness value and position after the current iteration; Perform two-way sine mutation on the optimal sooty tern position, and take the sooty tern position with the optimal fitness value before and after mutation as the updated optimal sooty tern position; Update the optimal sooty tern position according to the preset maximum number of iterations in turn to determine the optimal sooty tern position; determine the optimal penalty factor C and RBF kernel function of the SVM according to the optimal sooty tern position.

5. The comprehensive power grid integrated assessment method for an integrated power system according to claim 4, characterized in that The step of initializing the position of the sooty tern population through Gaussian mapping includes the following steps: Determine the size N of the population, the lower boundary LB of the sooty tern optimization, and the upper boundary UB of the sooty tern optimization; Generate the next random number x through the Gaussian mapping t+1 : where mod(·) is the remainder function and x t is the current random number; Initialize the position of the sooty tern using the generated Gaussian random numbers: P s P(t) = (UB - LB) × x t + LB 6. The comprehensive evaluation method for multi-source power grids of an integrated power system according to claim 4, characterized in that The position update is performed by the improved position update method, and the optimal fitness value and position after the current iteration are recorded, including the following steps: Simulate the migration behavior of Sooty Terns, including conflict avoidance, aggregation, and update; Conflict avoidance: Simulate the conflict avoidance behavior process of Sooty Terns, which is expressed by the following formula: C s (t) = S A × P s (t) Wherein: P s (t) represents the position of the sooty tern at the current t-th iteration; C s (t) represents the new position of the sooty tern without colliding with other sooty terns; S A represents a variable factor for collision avoidance, used to calculate the position after collision avoidance, and its constraint condition formula is as follows: S A = C f - (t × C f / Miter) Wherein: C f is a control variable used to adjust S A ; t represents the current iteration number; S A As the number of iterations increases, it gradually decreases from C f to 0; Assume C f is 2, S A will gradually decrease from 2 to 0; Miter is the number of iterations; Aggregation: Aggregation means that the current Sooty Tern moves closer to the best position among adjacent Sooty Terns on the premise of avoiding conflicts, that is, closer to the optimal position. Its mathematical expression is as follows: M s M(t) = C B ×(P bs (t) - P s (t)) In the formula: P bs \((t)\) is the optimal position of the sooty tern at the \(t\) - th iteration; M s \((t)\) represents at different positions P s \((t)\) moves towards the optimal position P bs \((t)\) during the moving process; C B is a random variable and changes according to the following formula: C B = 0.5 × rand In the formula: rand is a random number from 0 to 1; Update: Make the current Sooty Tern move in the direction of the optimal position and update the position. Its mathematical expression is: D s (t) = C s (t) + M s (t) In the formula: D s (t) is the distance that the Sooty Tern moves from the current position towards the optimal position; Attack behavior: During the migration process, Sooty Terns increase their flight altitude by flapping their wings, adjust their speed and attack angle. When attacking prey, their hovering behavior in the air is defined by the following mathematical model: In the formula: x, y, and z are the coordinates in three directions respectively; r is the radius of each helix; θ is a random angle value within the range of [0, 2π]; u and v are related constants defining the helix shape, both set to 1; e is the base of the natural logarithm; Introduce the position update mechanism of the lotus effect algorithm and the escape optimization algorithm, and update the position of the Sooty Tern by integrating the optimal position of the Sooty Tern in this iteration, different position update modes, and the cohesion and panic index factors of the Sooty Tern: Among them: Where: P s (t + 1) is the position of the sooty tern at the (t + 1)-th updated iteration; P bs (t) represents the best position at the t-th iteration; P s (t) is the current position of the sooty tern; P i (t) is the position of the sooty tern with index i at the t-th iteration; C i (t) is the cohesion of the sooty tern with index i at the t-th iteration; Z(t) is the panic index of the sooty terns at the t-th iteration; α is a random number between [0, 1]; λ1 is a random number within [0, 1]; Calculate the fitness value: fitness(t) = F f (P s (t + 1)) where F f (·) is a fitness value calculation function; Record the optimal Sooty Tern in the current iteration.

7. The comprehensive evaluation method for a multi-source power grid of an integrated power system according to claim 6, wherein The two-way sine mutation is performed on the optimal position of the Sooty Tern, and the position of the Sooty Tern with the optimal fitness value before and after mutation is used as the updated optimal position of the Sooty Tern, including the following steps: For dimension j, calculate the sine chaos value according to the current iteration number and switch the positive and negative directions with equal probability: sin Value = sin(πx0) In the formula, rand is a random number from 0 to 1; x0 is the iteration sequence value; Perform mutation perturbation on the optimal position: P bs(j) (t + 1)' = P bs(j) (t + 1) + sin Value × P bs(j) (t + 1) Where: P bs(j) (t + 1) represents the optimal position P at the (t + 1)-th iteration bs of the j-th dimension of P(t + 1); P bs(j) (t + 1)' is the optimal position P after mutation perturbation bs of the j-th dimension of P(t + 1); Greedy update: In the formula, after mutation in each dimension, stop the mutation.

8. A comprehensive evaluation system for a multi-source power grid in an integrated power system, characterized in that, The system includes: A processor; A memory, on which a computer program that can run on the processor is stored; Among them, when the computer program is executed by the processor, it implements the steps of the comprehensive power system multi-source power grid comprehensive evaluation method described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, A data processing program is stored on the computer-readable storage medium, and when the data processing program is executed by the processor, it implements the steps of the comprehensive power system multi-source power grid comprehensive evaluation method described in any one of claims 1 to 7.

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