Power grid strength evaluation method and device for multi-type synchronous control equipment, equipment and medium

Abstract

The invention relates to the technical field of power grid strength evaluation, in particular to a power grid strength evaluation method, device and equipment for multi-type synchronous control equipment and a medium, and the method comprises the steps: constructing an evaluation index system of a multi-type synchronous control equipment grid-connected system, and the evaluation index system comprises a frequency stability margin index and a voltage stability margin index; calculating objective weights and subjective weights of the evaluation indexes based on an independent information entropy weight method, an analytic hierarchy process and a principal component analysis method, and generating final weights through an improved hierarchical combination weight distribution method; obtaining evaluation data, and performing abnormal data elimination and standardization processing; calculating a power grid strength comprehensive evaluation result in combination with the preprocessed data; judging whether a threshold value is reached or not according to the evaluation result classification, and if not, generating early warning and providing improvement measures; and if yes, outputting an evaluation result, realizing synchronous accurate evaluation on the frequency and the voltage support strength of the regional power grid, and providing technical support for optimized operation and planning construction of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid strength assessment, and particularly to a method, device, equipment and medium for assessing the power grid strength of multi-type synchronous control equipment. Background Art

[0002] In response to the "dual carbon" goal, a large number of new energy generating units have been connected in places such as Jining and Haixi in China, and the annual connection volume continues to grow. At the same time, due to the long distance between new energy resources and urban electricity load centers, multiple high-voltage direct current (HVDC) transmission projects have been built in places such as Haixi and Tianzhong. As a result, a regional power grid system coexists with wind turbines, photovoltaics, energy storage, and flexible HVDC transmission that adopt various different synchronous control strategies. Affected by new energy output fluctuations, DC transmission faults, etc., the power grid frequency and voltage in this regional power grid are vulnerable to disturbances, the corresponding stability margin is low, and the support ability is not strong. It is urgent to evaluate the power grid frequency and voltage support strength of such regional power grid systems to adapt to the future development trends of power grid in aspects such as new energy access construction planning and system safety and stable operation ability assessment.

[0003] Existing methods mainly focus on the extended research of the short circuit ratio (SCR), which can evaluate the voltage support strength and static voltage stability margin, but have limited ability to evaluate the frequency support strength or comprehensively evaluate both. For example, the Chinese invention patent with the authorization announcement number CN118100258A discloses a method, system and medium for evaluating the strength of the receiving-end power grid considering flexible DC control mode on May 28, 2024. It considers flexible DC systems adopting different control modes, equivalent the DC access with different control modes as the perturbation of the eigenmatrix of the system Jacobian matrix, defines the voltage stability margin evaluation index of the equivalent generalized SCR, and realizes the accurate evaluation of the receiving-end voltage support strength of the DC system, but fails to effectively evaluate the frequency and voltage support strengths at the same time.

[0004] The information disclosed in this background art section is only intended to deepen the understanding of the overall background art of the present invention, and should not be regarded as an admission or any form of implication that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a method, device, equipment and medium for assessing the power grid strength of multi-type synchronous control equipment, thus effectively solving the problems in the background art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: A method for assessing the power grid strength of multi-type synchronous control equipment, including the following steps:

[0007] S10: Construct an evaluation index system for the frequency and voltage support strength of a multi-type synchronous control device grid-connected system, where the evaluation index system includes a frequency stability margin index and a voltage stability margin index;

[0008] S20: Calculate the objective weight and subjective weight of each evaluation index based on the independent information entropy weight method, the analytic hierarchy process, and the principal component analysis method, and fuse and generate the final weight through an improved hierarchical combined weight allocation method;

[0009] S30: Obtain the evaluation data of the multi-type synchronous control device grid-connected system, and preprocess the evaluation data, where the preprocessing includes abnormal data rejection and data standardization processing;

[0010] S40: Based on the frequency and voltage support strength evaluation index system and the final weight, combined with the preprocessed evaluation data, calculate the comprehensive evaluation result of the grid strength;

[0011] S50: Classify and evaluate the grid strength according to the comprehensive evaluation result of the grid strength, and judge whether the evaluation result reaches the set threshold; if not, generate a warning and provide improvement measures to optimize the grid strength; if so, output the grid strength evaluation result.

[0012] Further, in step S10, the frequency stability margin index includes an equivalent inertia improvement factor and a steady-state frequency deviation reduction factor; the voltage stability margin index includes voltage stiffness and steady-state voltage deviation;

[0013] The equivalent inertia improvement factor is used to quantify the improvement degree of the total system inertia after the access of multi-type synchronous control devices;

[0014] The steady-state frequency deviation reduction factor is used to describe the steady-state frequency deviation improvement ability after multi-type synchronous control devices participate in primary frequency regulation;

[0015] The voltage stiffness is used to characterize the ability of multi-type synchronous control devices to improve the node voltage stability through control strategies;

[0016] The steady-state voltage deviation is used to quantify the voltage deviation level of the system after the voltage disturbance recovery.

[0017] Further, the calculation model of the equivalent inertia improvement factor includes:

[0018]

[0019] In the formula, H pro is the equivalent inertia improvement factor; is the RoCoF when all non-synchronous machine synchronous control devices adopt a non-grid-forming inertia-free support control strategy; RoCoF when adopting the grid-forming inertia support control strategy; J eq 1 The equivalent inertia time constant of the system when all the non-synchronous machine synchronization control devices adopt the non-grid-forming non-inertia support control strategy; J eq 0 The equivalent inertia time constant of the system when all the non-synchronous machine synchronization control devices adopt the grid-forming inertia support control strategy.

[0020] Furthermore, the model of the steady-state frequency deviation reduction factor includes:

[0021]

[0022] In the formula, R Δ is the steady-state frequency deviation reduction factor; Δf ss 0 and Δf ss 1 are the steady-state frequency deviations before and after all the non-synchronous machine synchronization control devices adopt the primary frequency regulation control strategy respectively; β 0 and β 1 are the steady-state frequency deviation factors before and after all the non-synchronous machine synchronization control devices adopt the primary frequency regulation control strategy respectively.

[0023] Furthermore, the model of the voltage stiffness includes:

[0024]

[0025] In the formula, K volt is the voltage stiffness; U node is the node voltage magnitude; U node0 is the no-load voltage of the node; Z dev is the impedance value of the non-synchronous machine synchronization control device connected to the power grid, which is a complex number; Z gr is the Thevenin equivalent impedance value of the power grid, which is a complex number; μ SCR is the short-circuit ratio of the device; is the impedance angle of the device; is the Thevenin equivalent impedance angle of the power grid.

[0026] Furthermore, in step S20, the model of the steady-state voltage deviation includes:

[0027] ΔU nodei =(U nodei,taf -U nodei,N ) / U nodei,N ×100%;

[0028] In the formula, ΔU nodeiis the steady-state voltage deviation of node i; U nodei,taf is the voltage of node i after the voltage control action when the system is disturbed; U nodei,N is the rated voltage of node i.

[0029] Furthermore, in step S20, based on the independent information entropy weight method, the analytic hierarchy process, and the principal component analysis method, calculate the objective weight and subjective weight of each evaluation index, and fuse and generate the final weight through an improved hierarchical combination weight allocation method. The steps include:

[0030] S21: Based on the principal component analysis method, screen the primary evaluation indexes from the secondary evaluation indexes, extract the main features, reduce the correlation between the evaluation indexes, and optimize the data basis for weight allocation;

[0031] S22: Based on the main features, use the independent information entropy weight method to calculate the objective weight of the evaluation indexes, reflecting the fluctuation degree and independence of each evaluation index;

[0032] S23: Use the analytic hierarchy process to calculate the subjective weight of the evaluation indexes, and combine expert experience to determine the importance of the evaluation indexes;

[0033] S24: Fuse the objective weight and the subjective weight, use the improved hierarchical combination weight allocation method, comprehensively consider the fluctuation degree, the independence, and the expert experience of the evaluation indexes, calculate the combination weight based on the sum of squared deviations, and determine the final weight of each evaluation index.

[0034] Furthermore, in step S21, based on the principal component analysis method, screen the primary evaluation indexes from the secondary evaluation indexes, extract the main features, reduce the correlation between the evaluation indexes, and optimize the data basis for weight allocation. The steps include:

[0035] S211: Calculate and obtain the correlation coefficient matrix of the secondary evaluation index matrix in each primary evaluation index;

[0036] S212: Based on the correlation coefficient matrix, calculate the x eigenvalue and eigenvector of the matrix;

[0037] S213: Calculate the variance contribution rate, arrange the variance contribution rate in descending order, and calculate the sum of the variance contribution rates of the first n principal components;

[0038] When the sum of the variance contribution rates of the n principal components is greater than the set ratio, use the first n secondary evaluation indexes to form the primary evaluation index system to replace the original evaluation index system;

[0039] S214: Based on the eigenvector and the first n principal components, calculate the principal component matrix composed of the x secondary evaluation indexes in the y regional power grids;

[0040] S215: Calculate the first-level evaluation index values based on the principal component matrix.

[0041] Further, in step S22, based on the main features, the objective weights of the evaluation indexes are calculated by using the independent information entropy weight method to reflect the fluctuation degree and independence of each evaluation index. The steps include:

[0042] S221: Based on the frequency stability margin index and the voltage stability margin index, establish a judgment matrix for two first-level evaluation indexes of y regional power grids;

[0043] S222: Calculate the entropy value of each first-level evaluation index based on the judgment matrix;

[0044] S223: Calculate the entropy weight according to the entropy value;

[0045] S224: Take each of the first-level evaluation indexes as the dependent variable and the other first-level evaluation indexes as the independent variables to conduct a regression analysis. Based on the regression analysis results, eliminate the evaluation indexes with insignificant statistical tests, and calculate the goodness of fit of the remaining evaluation indexes to obtain the corresponding independent information ratio;

[0046] S225: Standardize the independent information ratio to obtain the standardized independent information ratio, and calculate the independent information entropy value of each index;

[0047] S226: Calculate the independent information entropy weight of each evaluation index based on the independent information entropy value to quantify the objective contribution degree of each evaluation index to the power grid strength evaluation result.

[0048] Further, in step S40, based on the frequency and voltage support strength evaluation index system and the final weights, combined with the preprocessed evaluation data, calculate the comprehensive evaluation result of the power grid strength. The steps include:

[0049] S41: Calculate the status value of each evaluation index, where the status value is the ratio of the actual value of the evaluation index to the reference value;

[0050] S42: Multiply the status value of each evaluation index by the final weight and accumulate to obtain the comprehensive evaluation result of the power grid strength.

[0051] Further, in step S42, the model for obtaining the comprehensive evaluation result of the power grid strength includes:

[0052]

[0053] In the formula, G is the comprehensive evaluation result of the power grid strength; W T is the transpose of the combined weight vector; ω mi is the i-th element in the combined weight vector W, sstat,i the \(i\)-th element in the evaluation index status vector \(s\) stat ; \(s\) stat = [s stat,1 , s stat,2 , …, s stat,n T is the evaluation index status vector.

[0054] The present invention further includes a power grid strength evaluation device for multi-type synchronous control equipment, which uses the method as described above and includes:

[0055] A construction module, configured to construct an evaluation index system for the frequency and voltage support strength of a multi-type synchronous control equipment grid-connected system, and the evaluation index system includes a frequency stability margin index and a voltage stability margin index;

[0056] A weight calculation module, configured to calculate the objective weight and subjective weight of each evaluation index based on the independent information entropy weight method, the analytic hierarchy process, and the principal component analysis method, and generate a final weight through an improved hierarchical combination weight allocation method;

[0057] A data processing module, configured to obtain the evaluation data of a multi-type synchronous control equipment grid-connected system, and preprocess the evaluation data, and the preprocessing includes abnormal data elimination and data standardization processing;

[0058] An evaluation calculation module, configured to calculate the comprehensive evaluation result of the power grid strength based on the frequency and voltage support strength evaluation index system and the final weight, in combination with the preprocessed evaluation data;

[0059] An evaluation result judgment module, configured to classify and evaluate the power grid strength according to the comprehensive evaluation result of the power grid strength, and judge whether the evaluation result reaches a set threshold; if not, generate a warning and provide improvement measures for optimizing the power grid strength; if so, output the power grid strength evaluation result.

[0060] The present invention further includes a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method as described above is implemented.

[0061] The present invention further includes a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method as described above is implemented.

[0062] ​The beneficial effects of the present invention are as follows: The present invention comprehensively considers frequency and voltage support strength, and establishes an evaluation index system for the grid-connected system of multi-type synchronous control devices such as wind-solar-storage flexibility, including the equivalent inertia improvement factor, the steady-state frequency deviation reduction factor, the voltage stiffness, and the steady-state voltage deviation. Considering that IIEWM and AHPM can respectively achieve objective and subjective weighting, and combined with PCAM, an improved hierarchical combined weight allocation method based on IIEWM-AHPM-PCAM is designed. A multi-objective comprehensive evaluation model of grid strength is established, and a grid strength evaluation method is proposed, which realizes the accurate evaluation of the frequency and voltage support strength of the grid-connected system of multi-type synchronous control devices, and provides a technical method for research and analysis for the optimal operation, planning and construction of regional power grid systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0064] Figure 1 Schematic diagram of the evaluation index system for the grid-connected system of multi-type synchronous control devices;

[0065] Figure 2 Flow chart of the multi-objective comprehensive evaluation of grid strength;

[0066] Figure 3 Regional power grid structure of the modified IEEE-39 bus;

[0067] Figure 4 Results based on IIEWM, AHPM, and the proposed method after standardization;

[0068] Figure 5 Schematic diagram of the structure of the grid strength evaluation device for multi-type synchronous control devices;

[0069] Figure 6 Schematic diagram of the structure of a computer device. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0071] As Figures 1 to 4 shown: A grid strength evaluation method for multi-type synchronous control devices includes the following steps:

[0072] S10: Construct an evaluation index system for the frequency and voltage support strength of a multi-type synchronous control device grid connection system. The evaluation index system includes a frequency stability margin index and a voltage stability margin index. Among them: The frequency stability margin index is used to characterize the system's ability to regulate frequency fluctuations; The voltage stability margin index is used to quantify the system's ability to maintain voltage stability.

[0073] S20: Based on the Independent Information Entropy Weight Method (IIEWM), the Analytic Hierarchy Process (AHPM), and the Principal Component Analysis Method (PCAM), calculate the objective weight and subjective weight of each evaluation index, and generate the final weight through an improved hierarchical combination weight allocation method. The improved hierarchical combination weight allocation method combines the volatility and independence of the indicators to ensure the rationality of weight allocation.

[0074] S30: Obtain the evaluation data of the multi-type synchronous control device grid connection system, and preprocess the evaluation data. The preprocessing includes abnormal data elimination and data standardization processing.

[0075] S40: Based on the frequency and voltage support strength evaluation index system and the final weight, combined with the preprocessed evaluation data, calculate the comprehensive evaluation result of the grid strength.

[0076] S50: Classify and evaluate the grid strength according to the comprehensive evaluation result of the grid strength, and judge whether the evaluation result reaches the set threshold. If not, generate a warning and provide improvement measures to optimize the grid strength. If so, output the grid strength evaluation result.

[0077] This invention patent will fill the technical gap of "grid strength evaluation of multi-type synchronous control device grid connection system", construct an evaluation index system for frequency and voltage support strength, and propose a grid strength evaluation method for multi-type synchronous control device grid connection system based on the improved hierarchical combination weight allocation method of IIEWM-AHPM-PCAM, realizing the accurate evaluation of the frequency and voltage support strength of the regional power grid, and providing a research and analysis technical means for the optimal operation and planning and construction of the regional power grid.

[0078] This method combines the frequency stability margin index and the voltage stability margin index, simultaneously evaluates the frequency support ability and voltage support ability of the power grid, makes up for the deficiency of only single focus on one aspect (such as frequency or voltage support) in the existing technology, and makes the evaluation result more comprehensive.

[0079] Adopted a weight allocation method combining the independent information entropy weight method (IIEWM), the analytic hierarchy process (AHPM), and the principal component analysis method (PCAM): IIEWM ensures that the calculation of objective weights takes into account the volatility and independence of data; AHPM subjectively assigns weights to the importance of indicators using expert experience; PCAM reduces the correlation between indicators and improves the representativeness of indicators. Through the improved hierarchical combined weight allocation method, the objective and subjective weights are reasonably integrated to ensure more scientific weight allocation and improve the evaluation accuracy.

[0080] Introduced data preprocessing steps, including abnormal data elimination and standardization processing, to ensure the accuracy and uniformity of evaluation data; Abnormal data elimination: Remove outliers or invalid data to avoid interfering with the evaluation results; Data standardization: Eliminate the dimensional differences between different indicators and enhance the generality of the evaluation method.

[0081] By combining the indicator system, the final weights, and the processed evaluation data, the comprehensive evaluation result of the grid strength can accurately reflect the frequency and voltage support strength of the grid. Compared with traditional methods: It can better reflect the mutual relationship between different indicators; It helps to identify potential weak links in the grid.

[0082] Through the judgment logic of classification evaluation and setting thresholds, the grid evaluation has dynamic adaptability: Warning and improvement: When the result does not reach the threshold, generate a warning and provide optimization measures to improve the safety and stability of the grid; Result output: When the result reaches the threshold, directly output the evaluation result for facilitating the decision-making of grid operation management.

[0083] The proposed method can provide a scientific basis for the optimal operation and planning and construction of regional power grids, which helps to: Discover problems in advance and avoid grid operation failures caused by insufficient frequency and voltage support; Provide quantitative analysis support for the access design of multi-type synchronous control equipment and the evaluation of grid safe operation.

[0084] As a preference of the above embodiment, in step S10, as Figure 1 shown, the frequency stability margin indicators include the equivalent inertia improvement factor and the steady-state frequency deviation reduction factor; the voltage stability margin indicators include voltage stiffness and steady-state voltage deviation;

[0085] The equivalent inertia improvement factor is used to quantify the improvement degree of the total system inertia after the access of multi-type synchronous control equipment;

[0086] The steady-state frequency deviation reduction factor is used to describe the improvement ability of the steady-state frequency deviation after the participation of multi-type synchronous control equipment in primary frequency regulation;

[0087] Voltage stiffness is used to characterize the ability of multi-type synchronous control equipment to improve the node voltage stability through control strategies;

[0088] The steady-state voltage deviation is used to quantify the voltage deviation level of the system after the voltage disturbance is restored.

[0089] By quantifying the improvement of the total inertia of the system after the access of multiple types of synchronous control devices, the frequency support ability of the devices in the initial stage of the disturbance is evaluated, which makes up for the defect of insufficient inertia description in the traditional power grid evaluation method. By describing the improvement ability of the devices on the steady-state frequency deviation after participating in primary frequency regulation, the response effect of the frequency regulation process is quantified, enhancing the accuracy of the evaluation; by characterizing the ability of multiple types of synchronous control devices to improve the voltage stability of the nodes, a more refined way to quantify the voltage support ability than the traditional short-circuit ratio (SCR) is provided, which helps to more comprehensively reflect the voltage stability of the power grid; by quantifying the voltage deviation level of the system during the recovery process after the disturbance, the suppression effect of the devices on the voltage fluctuation of the system is evaluated, meeting the requirements of the voltage recovery speed and deviation control in the new energy power grid.

[0090] To accurately evaluate the frequency and voltage support strengths of the regional power grid system with multiple types of synchronous control devices connected, it is first necessary to construct a frequency and voltage evaluation index system and define the evaluation indexes related to the frequency support strength.

[0091] The frequency support strength can be defined from the perspectives of the inertia support ability and primary frequency regulation ability of the system. The inertia of the traditional synchronous generator is fixed, and for the non-synchronous machine synchronous control device, its control strategy and parameters determine its inertia size. At the same time, the steady-state operating point of the device and the change of the system operating mode will change the total inertia size. The inertia size can reflect the rate of change of frequency (RoCoF) of the system. During the initial response period of large and small disturbances, only inertia and disturbance type will affect the size of RoCoF.

[0092] To describe the inertia support ability of the non-synchronous machine synchronous control device, an equivalent inertia improvement factor is defined. When the system is subjected to the specified maximum active power disturbance, the RoCoF of a node in the system during the initial response period of the disturbance, the model includes:

[0093]

[0094] In the formula, K op is a constant coefficient determined by the system operating mode; ΔP unb is the unbalanced power of the system during the maximum active power disturbance; J eq is the equivalent inertia time constant of the system.

[0095] In this embodiment, the calculation model of the equivalent inertia improvement factor includes:

[0096]

[0097] In the formula, Hpro is the equivalent inertia improvement factor; is the RoCoF when all the non-synchronous machine synchronous control devices adopt the non-network-forming inertia-free support control strategy; is the RoCoF when adopting the network-forming inertia support control strategy; J eq 1 is the equivalent inertia time constant of the system when all the non-synchronous machine synchronous control devices adopt the non-network-forming inertia-free support control strategy; J eq 0 is the equivalent inertia time constant of the system when all the non-synchronous machine synchronous control devices adopt the network-forming inertia support control strategy. It can quantify the improvement degree of the total system inertia after the inertia support control strategy is put into use, accurately describe the inertia support ability of the non-synchronous machine synchronous control devices, and at the same time, this index can be obtained through numerical simulation differential calculation.

[0098] To describe the primary frequency regulation ability of the non-synchronous machine synchronous control devices, first define the frequency deviation factor γ, and the model includes:

[0099]

[0100] In the formula, R sg is the equivalent droop rate of the traditional synchronous generator governor; D l is the active power frequency regulation coefficient of the load; K fre is the equivalent frequency regulation coefficient of the non-synchronous machine synchronous control devices.

[0101] The active power disturbance ΔP dis and the frequency deviation Δf ss at the steady state of the power grid can be characterized by the frequency deviation factor γ, and the model includes:

[0102]

[0103] Among them, the model of the steady-state frequency deviation reduction factor includes:

[0104]

[0105] In the formula, R Δ is the steady-state frequency deviation reduction factor; Δf ss 0 and Δf ss 1 are the steady-state frequency deviations before and after all the non-synchronous machine synchronous control devices adopt the primary frequency regulation control strategy respectively; β 0 and β 1 are the steady-state frequency deviation factors before and after all the non-synchronous machine synchronous control devices adopt the primary frequency regulation control strategy respectively. R ΔThe smaller it is, the more effectively the asynchronous machine synchronous control device can reduce the steady-state frequency deviation of the power grid after the primary frequency regulation control strategy is put into operation. It can accurately describe the primary frequency regulation ability of the asynchronous machine synchronous control device, and it can also be obtained through numerical simulation differential calculation.

[0106] Then, an evaluation index related to the voltage support strength is defined. The voltage support strength can be defined from the voltage support ability of the system. To accurately describe the voltage support ability of the asynchronous machine synchronous control device, the voltage stiffness K is defined. volt It represents the ability of the asynchronous machine synchronous control device to make the node voltage modulus close to the no-load voltage of the node through the control strategy.

[0107] As an optimization of the above embodiment, the model of the voltage stiffness includes:

[0108]

[0109] In the formula, K volt is the voltage stiffness; U node is the modulus of the node voltage; U node0 is the no-load voltage of the node; Z dev is the impedance value of the asynchronous machine synchronous control device connected to the power grid, which is a complex number; Z gr is the Thevenin equivalent impedance value of the power grid, which is a complex number; μ SCR is the short-circuit ratio of the device; is the impedance angle of the device; is the Thevenin equivalent impedance angle of the power grid. K volt The value range is 0 to 1. The larger its value, the greater the voltage support ability of the device. Compared with the short-circuit ratio, it more reflects the mathematical relationship among the impedance values and impedance angles of the device and the power grid.

[0110] The voltage support strength can also be evaluated from the voltage fluctuation level. To accurately evaluate the support ability of the asynchronous machine synchronous control device for the power grid voltage recovery after the new energy output fluctuates and after the load disturbance, the steady-state voltage deviation after the voltage control action when the system is disturbed is defined.

[0111] In this embodiment, in step S20, the model of the steady-state voltage deviation includes:

[0112] ΔU nodei =(U nodei,taf -U nodei,N ) / U nodei,N ×100%;

[0113] In the formula, ΔU nodei is the steady-state voltage deviation of node i; U nodei,taf is the voltage of node i after the voltage control action when the system is disturbed; Unodei,N is the rated voltage of node i. Through the calculation of the steady-state voltage deviation, the voltage deviation degree of each node after being disturbed can be accurately quantified, providing a reliable quantitative basis for evaluating the voltage stability of the system.

[0114] The above evaluation indexes of frequency and voltage support strength can be applied to the evaluation of the grid strength of the grid-connected system of the asynchronous machine synchronous control equipment, and can also evaluate the grid support ability of the synchronous machine equipment. Combining the above indexes, a schematic diagram of the evaluation index system of the grid-connected system of multiple types of synchronous control equipment can be formed. Continue to refer to Figure 1 .

[0115] In summary, four evaluation indexes are defined, which can effectively judge the strength of the system frequency and voltage support. After defining the evaluation indexes, it is necessary to study the weight allocation method among the indexes. This invention patent will design an improved hierarchical combination weight allocation method that combines the common advantages of objective weight assignment method and subjective weight assignment method based on the Independent Information Entropy Weighing Method (IIEWM), Analytic Hierarchy Process Method (AHPM), and Principal Component Analysis Method (PCAM).

[0116] Among them, in step S20, based on the Independent Information Entropy Weighing Method, Analytic Hierarchy Process Method, and Principal Component Analysis Method, calculate the objective weight and subjective weight of each evaluation index, and fuse and generate the final weight through the improved hierarchical combination weight allocation method. The steps include:

[0117] S21: Based on the Principal Component Analysis Method (PCAM), screen the first-level evaluation indexes from the second-level evaluation indexes, extract the main features, reduce the correlation among the evaluation indexes, and optimize the data basis for weight allocation; reduce redundant information, and at the same time retain the key information of the evaluation indexes, providing an optimized index set for subsequent weight calculation;

[0118] S22: Based on the main features, use the Independent Information Entropy Weighing Method (IIEWM) to calculate the objective weight of the evaluation indexes, reflecting the fluctuation degree and independence of each evaluation index; ensure the objectivity of weight allocation;

[0119] S23: Use the Analytic Hierarchy Process Method (AHPM) to calculate the subjective weight of the evaluation indexes, and determine the importance of the evaluation indexes in combination with expert experience; reflect the relative importance of the evaluation indexes in actual business requirements;

[0120] S24: Integrate the objective weight and the subjective weight, and use the improved hierarchical combined weight allocation method to comprehensively consider the fluctuation degree, independence and expert experience of the evaluation indicators, calculate the combined weight based on the sum of squared deviations, and determine the final weights of each evaluation indicator. Ensure the rationality and scientificity of the weight allocation.

[0121] In this embodiment, in step S21, based on the principal component analysis method (PCAM), the primary evaluation indicators are screened from the secondary evaluation indicators, the main features are extracted, the correlation between the evaluation indicators is reduced, and the data basis for weight allocation is optimized. Suppose there are y regional power grids and x secondary evaluation indicators. The steps include:

[0122] S211: Calculate and obtain the correlation coefficient matrix X of the secondary evaluation indicator matrix S in each primary evaluation indicator y,x ;

[0123] S212: Based on the correlation coefficient matrix X, calculate the x eigenvalue λ j (j = 1, 2, …, x) and eigenvector e j (j = 1, 2, …, x);

[0124] S213: Calculate the variance contribution rate and sort the variance contribution rates in descending order, and calculate the sum of the variance contribution rates of the first n principal components

[0125] When the sum φ n of the variance contribution rates of the n principal components is greater than the set occupancy ratio, then use the first n secondary evaluation indicators to form the primary evaluation indicator system to replace the original evaluation indicator system;

[0126] S214: Based on the eigenvector e j (j = 1, 2, …, x) and the first n principal components, calculate the principal component matrix M y,x = S y,x × [e 1 e 2 …e x ';

[0127] S215: Based on the principal component matrix M y,x = S y,x × [e 1 e 2 …e x ', calculate the primary evaluation indicator value F 1k :

[0128]

[0129] In the formula, φ kj is the jth variance contribution rate; Mkj is the element in the k-th row and j-th column of the principal component matrix, λ i (i = 1, 2, …, x) are the i-th eigenvalues; φ j is the j-th variance contribution rate.

[0130] Among them, in step S22, based on the main features, the objective weights of the evaluation indicators are calculated using the independent information entropy weight method (IIEWM), which reflects the fluctuation degree and independence of each evaluation indicator. The steps include:

[0131] S221: Based on the frequency stability margin index and the voltage stability margin index, establish a judgment matrix for two first-level evaluation indicators of y regional power grids; where the rows of the judgment matrix correspond to the regional power grid samples, and the columns correspond to the first-level evaluation indicators;

[0132]

[0133] In the formula, j ij (i = 1, 2, j = 1, 2, …, y) is the element in the i-th row and j-th column of the judgment matrix;

[0134] S222: Based on the judgment matrix, calculate the entropy value of each first-level evaluation indicator; used to characterize the information uncertainty of each indicator;

[0135]

[0136] In the formula, j ij (i = 1, 2; j = 1, 2, …, y) is the element in the i-th row and j-th column of the judgment matrix; q ij (i = 1, 2; j = 1, 2, …, y) is the intermediate variable in the i-th row and j-th column; y is the number of regional power grids; H i (i = 1, 2) is the entropy value of the first-level evaluation indicator.

[0137] S223: Calculate the entropy weight according to the entropy value; the entropy weight is used to reflect the fluctuation degree of the indicator;

[0138]

[0139] In the formula, H i (i = 1, 2) is the entropy value of the first-level evaluation indicator; ω Ei is the entropy weight;

[0140] S224: Take each first-level evaluation indicator as the dependent variable one by one, and other first-level evaluation indicators as the independent variables to conduct regression analysis. Based on the regression analysis results, eliminate the evaluation indicators with insignificant statistical tests, and calculate the goodness of fit R i 2 of the remaining evaluation indicators, and obtain the corresponding independent information ratio O i ;

[0141] O i = 1 - R i 2 ;

[0142] S225: Standardize the independent information ratio O i to obtain the standardized independent information ratio O i ', and calculate the independent information entropy value E i of each index; used to reflect the independence of the index;

[0143]

[0144] E i = ω Ei O i ';

[0145] In the formula, O i is the independent information ratio; O i ' is the standardized independent information ratio; E i is the independent information entropy value of each index;

[0146] S226: Based on the independent information entropy value E i , calculate the independent information entropy weight ω IEi of each evaluation index to quantify the objective contribution degree of each evaluation index to the power grid strength evaluation result.

[0147]

[0148] In the formula, E j (j = 1, 2) is the independent information entropy value of each index; ω IEi is the independent information entropy weight of each evaluation index.

[0149] This invention patent will be improved based on IIEWM and combined with PCAM, and then a combined weighting method will be adopted to reasonably allocate the weights of each evaluation index.

[0150] In the subjective weighting method, AHPM is a method widely used in systems engineering. It can quantify the differences and consistencies among the numerical values of each evaluation index and can reasonably give the weights of each evaluation index. The specific process is as follows:

[0151] a) Suppose there are m sample data for x secondary evaluation indexes. To eliminate the influence of different dimensions among each evaluation index and improve the evaluation accuracy, standardize the large-value superior type indexes and small-value superior type indexes. The models respectively include:

[0152] t(i, j) = s(i, j) / [s min (i) + s max (i)](i = 1, 2,..., x; j = 1, 2,..., m);

[0153] t(i,j) = [s min (i) + s max (i) - s(i,j)] / [s min (i) + s max (i)];

[0154] Wherein, t(i,j) is the value of the secondary evaluation index after standardization; s(i,j) is the original value of the secondary evaluation index; s min (i) and s max (i) are the minimum and maximum values of the secondary evaluation index i in the sample data respectively; i = 1, 2, …, x, j = 1, 2, …, m. All t(i,j) constitute the evaluation matrix T x,m .

[0155] b) Establish a judgment matrix B through the evaluation matrix x,x , which is used to determine the weight ω i of each evaluation index. First, calculate the sample standard deviation σ(i) of each evaluation index. The model is as follows, quantifying the influence of each evaluation index on the comprehensive evaluation index.

[0156]

[0157] Wherein, is the mean value of the evaluation index i in the sample data. Based on Equation (19), the judgment matrix B x,x of 9-scale is derived as follows:

[0158]

[0159] Wherein, σ min and σ max are the minimum and maximum values of the sample standard deviation σ(i) respectively; ε m is the relative importance degree parameter, equal to min{9, int[σ max / σ min + 0.5]}, and min{} and int[] are the minimum function and the integer function respectively.

[0160] It can be seen from the definition of the judgment matrix B x,x that there exists b ij = ω i / ω j . At the same time, it has three properties: unitarity: b ii = ω i / ω i = 1, reciprocity: b ji = ω j / ω i = 1 / b ij and consistency condition: bij b jk = (ω i / ω j )·(ω j / ω k ) = ω i / ω k = b ik . Let ω i > 0 and hold, and then complete the consistency check, correction of the judgment matrix B x,x and the assignment of the weight ω i .

[0161] c) Let C x,x be the corrected judgment matrix, and its corresponding weight is also ω i . Obtain the optimization model that makes the judgment matrix B x,x achieve the optimal consistency. The model includes:

[0162]

[0163] s.t. c ii = 1 (i = 1, 2,..., x)

[0164] 1 / c ji = c ij ∈ [b ij - γb ij , b ij + γb ij (i = 1, 2,..., x, j = 1, 2,..., m);

[0165] ω i > 0 (i = 1, 2,..., x)

[0166]

[0167] In the formula, c ij (i = 1, 2,..., x; j = 1, 2,..., m) is the element in the i-th row and j-th column of the corrected judgment matrix C x,x ; b ij (i = 1, 2,..., x; j = 1, 2,..., m) is the element in the i-th row and j-th column of the judgment matrix B x,x ; ω i (i = 1, 2,..., x) is the i-th weight; ω j (j = 1, 2,..., m) is the j-th weight.

[0168] In the formula, the optimization objective function CIC(x) is the consistency index coefficient; γ is a non-negative parameter, and its value range is determined by engineering experience to be [0, 0.5]. The improved grey wolf algorithm or the like can be used to solve this non-linear global optimization model. If the matrix Bx,x If CIC(x) < 0.1, it indicates that the consistency of the judgment matrix is relatively good, and the weight distribution of each evaluation index obtained is reasonable. Otherwise, the parameter γ is changed until the judgment matrix C after correction x,x meets the consistency requirement and reasonable weights are obtained.

[0169] d) Multiply the standardized secondary evaluation index value t(i,j) of the j-th sample data by the corresponding evaluation index weight and sum them up to calculate the comprehensive evaluation index r(j) of the j-th sample data, as shown in Equation (22). The larger its value, the better the comprehensive efficiency of the sample data j.

[0170]

[0171] The above AHPM combines the subjective experience of people and the objective correction process of the judgment matrix, can reasonably give the weights of each evaluation index, effectively evaluate the comprehensive efficiency, and its simple steps make it more beneficial to be applied in the evaluation and analysis of grid strength.

[0172] Based on the combination weighting method of the sum of squared deviations, this invention patent combines the above two weighting methods of IIEWM and AHPM, designs an improved hierarchical combination weight distribution method, and gives reasonable weights to each evaluation index. When the following model optimization problem is solved, the combined weight meets the optimal condition, and at this time, the deviation sum between the combined weight and the original weight is the smallest.

[0173]

[0174] In the formula, l is the total number of weighting methods; n is the total number of evaluation indexes; ω jk is the weight of the k-th weighting method of the evaluation index j; ω mj is the combined weight of the evaluation index j; j ij is the element in the judgment matrix J; y is the number of regional power grids.

[0175] Using the Lagrange method, the above model is transformed into a problem of solving conditional extreme value, and a Lagrange objective function is established as follows:

[0176]

[0177] In the formula, y is the number of regional power grids; η is the Lagrange coefficient.

[0178] When the extreme value of the above model exists, the first-order partial derivatives of ω mj and η are zero, as shown in the following model:

[0179]

[0180] Convert the above formula into matrix form:

[0181]

[0182] where \(v = [1, 1, \ldots, 1]\) T is a unit column vector; \(W = [\omega m1 , \omega m2 , \ldots, \omega mn T is a combined weight vector;

[0183]

[0184] Solving the above equation gives the combined weight value:

[0185]

[0186] As an optimization of the above embodiment, in step S40, based on the frequency and voltage support strength evaluation index system and the final weights, combined with the preprocessed evaluation data, calculate the comprehensive evaluation result of the grid strength. The steps include:

[0187] S41: Calculate the status value of each evaluation index. The status value is the ratio of the actual value of the evaluation index to the reference value. The model includes:

[0188]

[0189] where \(s stat,i is the status value of evaluation index \(i\); \(s inti,i is the original value of evaluation index \(i\); \(s base,i is the reference value of evaluation index \(i\);

[0190] S42: Multiply the status value of each evaluation index by the final weight and accumulate to obtain the comprehensive evaluation result of the grid strength.

[0191] In this embodiment, in step S42, the model for obtaining the comprehensive evaluation result of the grid strength includes:

[0192]

[0193] where \(G\) is the comprehensive evaluation result of the grid strength; \(W T is the transpose of the combined weight vector; \(\omega mi is the \(i\)-th element in the combined weight vector \(W\), \(s stat,i is the \(i\)-th element in the evaluation index status vector \(s stat ; \(s stat = [s stat,1 , s stat,2 , \ldots, s stat,n T ​​It is the evaluation index status vector. The comprehensive evaluation results are divided into the set {[0, 15], [15, 40], [40, 60], [60, 85], [85, 100]}, and the five elements respectively correspond to the five evaluation grades of excellent, relatively excellent, medium, poor, and very poor. In summary, the design of the multi-objective comprehensive evaluation process for grid strength is completed, and the effective evaluation of the system grid strength can be realized.

[0194] Figure 2 It is the multi-objective comprehensive evaluation process for grid strength. The specific process is as follows: First, establish the system configuration model and parameters, then obtain the sample data of the indicators and preprocess the data. Subsequently, calculate the combined weights based on the improved hierarchical combined weight allocation, and then calculate the comprehensive evaluation result of the grid strength. Judge the evaluation result. If it is relatively excellent or above, display the evaluation result; otherwise, give an early warning and measures to improve the grid strength, and finally complete the evaluation.

[0195] Subsequently, carry out the simulation analysis work on the grid strength evaluation method proposed in this invention patent (hereinafter referred to as "the proposed method" for short). Figure 3 It is the regional power grid structure of the modified IEEE-39 bus after connecting each new energy power generation unit. Based on this structure, the effectiveness verification of the proposed method is completed. There are 10 power generation unit buses and 29 load buses in this structure, and bus 31 is used to balance the system power. The system setting parameters are shown in Table 1, and the secondary evaluation index data are shown in Table 2. Except that the steady-state voltage deviation in Table 2 is a reverse index, the other indexes are forward indexes, that is, the larger the value, the better the index; the equivalent inertia improvement factor, frequency deviation factor, and voltage stiffness of the samples with larger serial numbers are larger, while the steady-state voltage deviation is smaller, indicating that the frequency and voltage support strength of the corresponding system are stronger, and theoretically the comprehensive evaluation result should be better.

[0196] Table 1 System setting parameters

[0197]

[0198] Table 2 Secondary evaluation index data

[0199]

[0200]

[0201] Calculate the sample comprehensive evaluation results based on IIEWM and AHPM, and standardize the results. In addition, use the proposed method to complete the comprehensive evaluation of the sample data and carry out the comparative analysis of the results. Table 3 Figure 4 Corresponds to the results based on IIEWM, AHPM, and the proposed method after standardization. Analyze Table 3 and Figure 4It can be seen that the comprehensive evaluation results based on the proposed method after standardization are 0.2065, 0.2191, 0.2516, and 0.3228 respectively. The result values gradually increase, indicating that the frequency and voltage support strength of the system corresponding to the sample data are gradually rising, which is consistent with the theoretical analysis results in Table 2 and the results obtained by IIEWM and AHPM, proving the effectiveness and credibility of the proposed method and enabling the accurate analysis of the strength of the power grid support of the system corresponding to the sample data.

[0202] Table 3 Results after standardization based on IIEWM, AHPM, and the proposed method

[0203]

[0204] Comparative analysis of the results obtained by the three methods shows that the difference between the data of the results obtained by the proposed method is smaller than that of IIEWM, and the difference between the data of the results obtained by the proposed method is not much different from that of AHPM. The result data of the proposed method corresponding to sample 4 is larger than that of AHPM, indicating that the proposed method can objectively and accurately find the sample with the best evaluation result, and the difference of the result data is relatively reasonable.

[0205] Table 4 Weights corresponding to IIEWM, AHPM, and the proposed method. Analyzing Table 4, it can be seen that the equivalent inertia improvement factor and the frequency deviation factor reflect the system frequency support strength, and the independence between the two is relatively low. IIEWM assigns relatively reasonable weights to indicators 1 and 2, while the proposed method assigns relatively smaller weights of 0.1845 and 0.1852 to indicators 1 and 2. Theoretically, the higher the independence between the data, the greater the assigned weight should be, indicating that the proposed method can more effectively reflect the independence between the data; IIEWM and the proposed method respectively assign higher weights to indicator 1 and indicator 4 with larger data fluctuations. Among them, the proposed method assigns a relatively larger weight of 0.3423 to indicator 4 with greater fluctuations, indicating that the proposed method can more effectively reflect the data fluctuation degree; the weight assignment of AHPM depends more on subjective setting, and its ability to reflect the independence degree and data fluctuation degree of the data is poor; therefore, the proposed method is better at reflecting both the data independence degree and the data fluctuation degree.

[0206] Table 4 Weights based on IIEWM, AHPM, and the proposed method

[0207]

[0208] As can be seen from the above, this invention patent has invented a method for evaluating and analyzing the grid strength of a multi-type synchronous control equipment grid connection system. An evaluation index system for the multi-type synchronous control equipment grid connection system is constructed, specifically including: equivalent inertia improvement factor, steady-state frequency deviation reduction factor, voltage stiffness, and steady-state voltage deviation. Considering that IIEWM and AHPM can respectively achieve objective and subjective weighting, and combining with PCAM, an improved hierarchical combined weight allocation method based on IIEWM-AHPM-PCAM is designed. A multi-objective comprehensive evaluation model of grid strength is built, and a method for evaluating grid strength is proposed, which can accurately evaluate the frequency and voltage support strength of the grid connection system for multi-type synchronous control equipment. The effectiveness of the proposed method is analyzed using Matlab software, and the result data shows that the proposed method has better ability to reflect the data independence degree and fluctuation degree, and can provide a technical solution for the grid strength evaluation, optimal operation and planning construction of the regional power grid system.

[0209] This invention also includes a grid strength evaluation device for multi-type synchronous control equipment, which uses the method as described above, including:

[0210] A construction module, used to construct an evaluation index system for the frequency and voltage support strength of the multi-type synchronous control equipment grid connection system, and the evaluation index system includes a frequency stability margin index and a voltage stability margin index;

[0211] A weight calculation module, used to calculate the objective weight and subjective weight of each evaluation index based on the independent information entropy weight method, the analytic hierarchy process and the principal component analysis method, and generate the final weight through an improved hierarchical combined weight allocation method;

[0212] A data processing module, used to obtain the evaluation data of the multi-type synchronous control equipment grid connection system, and preprocess the evaluation data, and the preprocessing includes abnormal data elimination and data standardization processing;

[0213] An evaluation calculation module, used to calculate the comprehensive evaluation result of grid strength based on the frequency and voltage support strength evaluation index system and the final weight, in combination with the preprocessed evaluation data;

[0214] An evaluation result judgment module, used to classify and evaluate the grid strength according to the comprehensive evaluation result of grid strength, and judge whether the evaluation result reaches the set threshold; if not, generate a warning and provide improvement measures to optimize the grid strength; if so, output the grid strength evaluation result.

[0215] Please refer to Figure 6Schematic structural diagram of a computer device provided by an embodiment of the present application. A computer device 400 provided by an embodiment of the present application includes: a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, the above method is executed.

[0216] An embodiment of the present application also provides a storage medium 430. A computer program is stored on the storage medium 430. When the computer program is run by the processor 410, the above method is executed.

[0217] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (abbreviated as SRAM), electrically erasable programmable read-only memory (abbreviated as EEPROM), erasable programmable read-only memory (abbreviated as EPROM), programmable read-only memory (abbreviated as PROM), read-only memory (abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0218] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "plurality" is two or more, unless otherwise specifically defined.

[0219] In the present invention, unless otherwise clearly specified and defined, terms such as "installation", "connection", "connection", "fixation" and the like should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0220] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0221] Any process or method description represented in a flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a manner other than shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0222] The logic and / or steps represented in a flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in connection with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0223] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0224] Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0225] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the power grid strength of multiple types of synchronous control equipment, characterized in that: The steps include: S10: constructing a frequency and voltage support strength evaluation index system for a multi-type synchronous control device grid-connected system, wherein the evaluation index system includes a frequency stability margin index and a voltage stability margin index; S20: Based on the independent information entropy weight method, hierarchical analysis method and principal component analysis method, the objective weight and subjective weight of each evaluation indicator are calculated, and the final weight is generated by fusion through the improved hierarchical combination weight allocation method; S30: Acquire evaluation data of a multi-type synchronous control device grid-connected system, and preprocess the evaluation data, wherein the preprocessing includes abnormal data elimination and data standardization processing; S40: Calculating a comprehensive evaluation result of power grid strength based on the frequency and voltage support strength evaluation index system and the final weight, combined with the pre-processed evaluation data; S50: Classify and evaluate the grid strength according to the comprehensive grid strength evaluation result to determine whether the evaluation result reaches a set threshold; if not, generate an early warning and provide improvement measures for optimizing the grid strength; if so, output the grid strength evaluation result.

2. The power grid strength assessment method for multi-type synchronous control equipment according to claim 1, characterized in that: In step S10, the frequency stability margin index includes an equivalent inertia improvement factor and a steady-state frequency deviation reduction factor; the voltage stability margin index includes voltage stiffness and a steady-state voltage deviation; The equivalent inertia improvement factor is used to quantify the degree of improvement of the total inertia of the system after the multi-type synchronous control equipment is connected; The steady-state frequency deviation reduction factor is used to describe the steady-state frequency deviation improvement capability after multiple types of synchronous control devices participate in a frequency modulation; The voltage stiffness is used to characterize the ability of multiple types of synchronous control devices to improve node voltage stability through control strategies; The steady-state voltage deviation is used to quantify the voltage deviation level of the system after the voltage disturbance is recovered.

3. The power grid strength assessment method for multiple types of synchronous control equipment according to claim 2, characterized in that: The calculation model of the equivalent inertia improvement factor includes: In the formula, H pro is the equivalent inertia improvement factor; RoCoF when a non-grid type non-inertia support control strategy is adopted for all asynchronous machine synchronous control devices; RoCoF when the control strategy of network-type inertia support is adopted; J eq 1 is the equivalent inertia time constant of the system when all asynchronous machine synchronous control devices adopt the non-grid type non-inertia support control strategy; J eq 0 It is the equivalent inertia time constant of the system when the network-type inertia support control strategy is adopted for all asynchronous machine synchronous control devices.

4. The power grid strength assessment method for multiple types of synchronous control equipment according to claim 2, characterized in that: The model of the steady-state frequency deviation reduction factor includes: In the formula, R Δ is the steady-state frequency deviation reduction factor; Δf ss 0 and Δf ss 1 are the steady-state frequency deviations before and after all asynchronous machine synchronous control devices adopt a frequency modulation control strategy; β 0 and β 1 They are the steady-state frequency deviation factors before and after the primary frequency modulation control strategy is adopted for all non-synchronous machine synchronous control devices.

5. The power grid strength assessment method for multiple types of synchronous control equipment according to claim 2, characterized in that: The voltage stiffness model includes: In the formula, K volt is the voltage stiffness; U node is the node voltage modulus; U node0 is the node no-load voltage; Z dev is the impedance value of the synchronous control device of the non-synchronous machine connected to the power grid, which is a complex number; Z gr is the Thevenin equivalent impedance of the power grid, which is a complex number; μ SCR is the short circuit ratio of the device; is the impedance angle of the device; is the Thevenin equivalent impedance angle of the power grid.

6. The method for evaluating the power grid strength of multiple types of synchronous control equipment according to claim 2, characterized in that: In step S20, the model of the steady-state voltage deviation includes: ΔU nodei =(U nodei,t | af -U nodei,N ) / U nodei,N ×100%; In the formula, ΔU nodei is the steady-state voltage deviation of node i; U nodei,t | af is the voltage of node i after voltage control when the system is disturbed; U nodei,N is the rated voltage of node i.

7. The power grid strength assessment method for multiple types of synchronous control equipment according to claim 1, characterized in that: In step S20, based on the independent information entropy weight method, the hierarchical analysis method and the principal component analysis method, the objective weight and the subjective weight of each evaluation index are calculated, and the final weight is generated by fusing through the improved hierarchical combination weight allocation method. The steps include: S21: Based on the principal component analysis method, the primary evaluation indicators are selected from the secondary evaluation indicators, the main features are extracted, the correlation between the evaluation indicators is reduced, and the data basis for weight distribution is optimized; S22: Based on the main features, the objective weights of the evaluation indicators are calculated using the independent information entropy weight method to reflect the fluctuation degree and independence of each evaluation indicator; S23: The subjective weights of evaluation indicators are calculated using the analytic hierarchy process and the importance of evaluation indicators is determined based on expert experience; S24: The objective weight and the subjective weight are integrated, and an improved hierarchical combined weight allocation method is used to comprehensively consider the volatility, independence and expert experience of the evaluation indicators, and the combined weight is calculated based on the sum of squared deviations to determine the final weight of each evaluation indicator.

8. The method for evaluating the power grid strength of multiple types of synchronous control equipment according to claim 7, characterized in that: In step S21, based on the principal component analysis method, the primary evaluation indicators are selected from the secondary evaluation indicators, the main features are extracted, the correlation between the evaluation indicators is reduced, and the data basis for weight distribution is optimized. The steps include: S211: Calculate and obtain the correlation coefficient matrix of the secondary evaluation indicator matrix in each primary evaluation indicator; S212: Based on the correlation coefficient matrix, calculate x eigenvalues ​​and eigenvectors of the matrix; S213: Calculate variance contribution rates, arrange the variance contribution rates in descending order, and calculate the sum of the variance contribution rates of the first n principal components; When the sum of the variance contribution rates of the n principal components is greater than the set proportion value, the first n secondary evaluation indicators are used to form a primary evaluation indicator system to replace the original evaluation indicator system; S214: Calculating a principal component matrix consisting of x secondary evaluation indicators in y regional power grids based on the eigenvector and the first n principal components; S215: Calculate the primary evaluation index value based on the principal component matrix.

9. The method for evaluating the power grid strength of multiple types of synchronous control equipment according to claim 7, characterized in that: In step S22, based on the main features, the objective weights of the evaluation indicators are calculated using the independent information entropy weight method to reflect the fluctuation degree and independence of each evaluation indicator. The steps include: S221: Based on the frequency stability margin index and the voltage stability margin index, establish a judgment matrix of two primary evaluation indicators of y regional power grids; S222: Calculate the entropy value of each first-level evaluation index based on the evaluation matrix; S223: Calculate entropy weight according to the entropy value; S224: performing regression analysis using the first-level evaluation indicators as dependent variables and other first-level evaluation indicators as independent variables one by one, eliminating evaluation indicators with insignificant statistical tests based on the regression analysis results, and calculating the goodness of fit of the remaining evaluation indicators to obtain corresponding independent information ratios; S225: performing standardization processing on the independent information ratio to obtain a standardized independent information ratio, and calculating the independent information entropy value of each indicator; S226: Based on the independent information entropy value, the independent information entropy weight of each evaluation indicator is calculated to quantify the objective contribution of each evaluation indicator to the power grid strength evaluation result.

10. The power grid strength assessment method for multiple types of synchronous control equipment according to claim 1, characterized in that: In step S40, based on the frequency and voltage support strength evaluation index system and the final weight, combined with the pre-processed evaluation data, a comprehensive evaluation result of the power grid strength is calculated, the steps comprising: S41: Calculating the status value of each evaluation indicator, wherein the status value is the ratio of the actual value of the evaluation indicator to the reference value; S42: multiplying the state value of each evaluation index by the final weight and adding them up to obtain a comprehensive evaluation result of the power grid strength.

11. The method for evaluating the power grid strength of multiple types of synchronous control equipment according to claim 10, characterized in that: In step S42, the model for obtaining the comprehensive evaluation result of the power grid strength includes: Where G is the comprehensive evaluation result of power grid strength; W T is the transpose of the combined weight vector; ω mi is the i-th element in the combined weight vector W, s stat,i is the evaluation indicator state vector s stat The i-th element in s stat =[s stat,1 ,s stat,2 ,…,s stat,n ] T is the evaluation indicator state vector.

12. A power grid strength assessment device for multiple types of synchronous control equipment, characterized in that: Using the method according to any one of claims 1 to 11, comprising: A construction module is used to construct a frequency and voltage support strength evaluation index system for a multi-type synchronous control device grid-connected system, wherein the evaluation index system includes a frequency stability margin index and a voltage stability margin index; The weight calculation module is used to calculate the objective weight and subjective weight of each evaluation index based on the independent information entropy weight method, hierarchical analysis method and principal component analysis method, and generate the final weight by fusing them through the improved hierarchical combination weight allocation method; A data processing module, used to obtain evaluation data of a multi-type synchronous control device grid-connected system and pre-process the evaluation data, wherein the pre-processing includes abnormal data elimination and data standardization processing; An evaluation calculation module, used to calculate a comprehensive evaluation result of power grid strength based on the frequency and voltage support strength evaluation index system and the final weight, combined with the pre-processed evaluation data; The evaluation result judgment module is used to classify and evaluate the grid strength according to the comprehensive evaluation result of the grid strength, and judge whether the evaluation result reaches the set threshold; if not, generate an early warning and provide improvement measures for optimizing the grid strength; if so, output the grid strength evaluation result.

13. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 11 is implemented.

14. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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