A method and apparatus for evaluating the probabilistic static voltage stability of a power system

Abstract

This application discloses a method and apparatus for evaluating the probabilistic static stability of a power system. The method includes obtaining the voltage phase angles of each node in a power system containing multiple uncertainty sources. Using the voltage phase angles as input variables, an orthogonalization independent transformation is used to convert the input variables into orthogonal variables. Sensitivity indices for the orthogonal variables are calculated, including the uncorrelated marginal contribution, data correlation contribution, physical interaction contribution, and overall effect contribution of the orthogonal variables. Furthermore, a reasonable uncertainty source management scheme is formulated based on the four contributions of the new sensitivity indices to evaluate the probabilistic static voltage stability of the power system. Therefore, the orthogonalization independent transformation can handle the correlation between uncertainty sources in a power system, and the four contributions of the sensitivity indices can comprehensively quantify the impact of uncertainty sources on the system response, more accurately evaluating the probabilistic static voltage stability of the power system, thereby improving the stability and reliability of the power system.

Description

Technical Field

[0001] This application relates to the field of power system stability assessment, and more specifically, to a method and apparatus for assessing the probabilistic static voltage stability of a power system. Background Technology

[0002] Voltage stability is crucial in the operation and planning of modern power systems. With the rapid increase in the proportion of intermittent renewable energy generation, the randomness and operational constraints of power systems have increased significantly, further exacerbating the challenges of voltage stability.

[0003] Currently, existing probabilistic static voltage stability assessment (PSVSA) methods primarily focus on evaluating the impact of uncertainty sources on power system voltage stability. Sensitivity analysis (SA), as an important tool for exploring the sources of voltage stability uncertainty, aims to quantify the relative importance of uncertainty sources as random inputs to the system response (such as the voltage stability level).

[0004] However, Local Sensitivity Assessment (LSA) can only estimate sensitivity locally around the nominal value, while Global Sensitivity Assessment (GSA), although it can comprehensively consider the changes of all random input variables, fails to adequately explain the impact of the correlation of these sources on the system response when dealing with related uncertainty sources. Furthermore, GSA typically assumes that input variables are independent when quantifying their contribution to the variance of the target system response. However, in real power systems, there are inherent interrelationships and correlations among uncertainty sources, rendering the Sensitivity Index (SI) introduced in traditional GSA inapplicable when considering correlations.

[0005] How to accurately assess the probabilistic static voltage stability of a power system while considering the correlation between uncertain sources is a problem that needs attention. Summary of the Invention

[0006] In view of the above problems, this application provides a method and apparatus for evaluating the probabilistic static voltage stability of a power system, so as to accurately evaluate the probabilistic static voltage stability of a power system by taking into account the correlation between uncertainty sources.

[0007] To achieve the above objectives, the following specific solutions are proposed:

[0008] A method for evaluating the probabilistic static voltage stability of a power system, comprising:

[0009] Obtain the voltage phase angle of each node in a power system containing multiple sources of uncertainty.

[0010] Using the voltage phase angle as the input variable, the input variable is converted into an orthogonal variable using orthogonal independent transformation;

[0011] Calculate the sensitivity index of the orthogonal variables, which includes the uncorrelated marginal contribution, data-related contribution, physical interaction contribution, and overall effect contribution of the orthogonal variables;

[0012] The probabilistic static voltage stability of the power system is evaluated by the unrelated marginal contribution, the data-related contribution, the physical interaction contribution, and the overall effect contribution.

[0013] Optionally, the sensitivity index of the orthogonal variables is calculated, including:

[0014] When the input variable is an input variable with a lower dimension than the preset dimension, a single-layer Global Sensitivity Analysis (GSA) layer is used to calculate the sensitivity index of the orthogonal variable. The single-layer GSA layer is used to screen and refine the orthogonal variable.

[0015] When the input variable is an input variable with a dimension no less than the preset dimension, the sensitivity index of the orthogonal variable is calculated using a two-layer GSA layer. The two-layer GSA layer includes a screening layer and a refining layer. The screening layer is used to screen the orthogonal variable, and the refining layer is used to refine the screened orthogonal variable.

[0016] Optionally, the method further includes:

[0017] Based on the unrelated marginal contribution, the data-related contribution, the physical interaction contribution, and the overall effect contribution, an uncertainty source management scheme for voltage stability optimization of the power system is generated.

[0018] Optionally, the method further includes:

[0019] Construct a simulation scenario of the power system that includes the multiple sources of uncertainty;

[0020] In the simulated scenario, the uncertainty source management scheme is implemented on the power system to obtain the result of optimizing the probabilistic static voltage stability of the power system.

[0021] Optionally, using the voltage phase angle as an input variable, the input variable is converted into an orthogonal variable using an orthogonal independent transformation, including:

[0022] Using the voltage phase angle as the input variable, the input variable is transformed into a standard normal variable in the standard normal space through Nataf transformation;

[0023] Based on the aforementioned standard normal variables, generate the relevant standard normal matrix;

[0024] The relevant standard normal matrix is ​​transformed into independent orthogonal variables using an orthogonalization independent transformation.

[0025] An evaluation device for the probabilistic static voltage stability of a power system, comprising:

[0026] The voltage phase angle acquisition unit is used to acquire the voltage phase angle of each node in a power system containing multiple sources of uncertainty.

[0027] The transformation unit is used to convert the voltage phase angle into an orthogonal variable using orthogonal independent transformation;

[0028] A sensitivity index calculation unit is used to calculate the sensitivity index of the orthogonal variable, wherein the sensitivity index includes the uncorrelated marginal contribution, data correlation contribution, physical interaction contribution, and overall effect contribution of the orthogonal variable;

[0029] A probabilistic static voltage stability assessment unit is used to assess the probabilistic static voltage stability of the power system through the unrelated marginal contribution, the data-related contribution, the physical interaction contribution, and the overall effect contribution.

[0030] Optionally, the sensitivity index calculation unit includes:

[0031] The first sensitivity index calculation subunit is used to calculate the sensitivity index of the orthogonal variable by using a single-layer global sensitivity analysis (GSA) layer when the input variable is an input variable with a lower than preset dimension. The single-layer GSA layer is used to filter and refine the orthogonal variable.

[0032] The second sensitivity index calculation subunit is used to calculate the sensitivity index of the orthogonal variable using a two-layer GSA layer when the input variable is an input variable with a dimension not lower than the preset dimension. The two-layer GSA layer includes a screening layer and a refining layer. The screening layer is used to screen the orthogonal variable, and the refining layer is used to refine the screened orthogonal variable.

[0033] Optionally, the device may also include:

[0034] The management scheme generation unit is used to generate an uncertainty source management scheme for voltage stability optimization of the power system based on the unrelated marginal contribution, the data related contribution, the physical interaction contribution, and the overall effect contribution.

[0035] Optionally, the device may also include:

[0036] The simulation scenario construction unit is used to construct a simulation scenario of the power system that includes the multiple uncertainty sources;

[0037] The scheme application unit is used to execute the uncertainty source management scheme on the power system under the simulated scenario to obtain the result of optimizing the probabilistic static voltage stability of the power system.

[0038] Optionally, the transformation unit includes:

[0039] The first transformation subunit is used to transform the voltage phase angle as the input variable to a standard normal variable in the standard normal space through Nataf transformation.

[0040] The second transformation subunit is used to generate a relevant standard normal matrix based on the standard normal variables;

[0041] The third transformation subunit is used to convert the relevant standard normal matrix into independent orthogonal variables using orthogonalization independent transformation.

[0042] By employing the above technical solution, this application obtains the voltage phase angles of each node in a power system containing multiple uncertainty sources. Using the voltage phase angles as input variables, it utilizes orthogonal independent transformation to convert the input variables into orthogonal variables, calculating the sensitivity indices of the orthogonal variables, including the uncorrelated marginal contribution, data-related contribution, physical interaction contribution, and overall effect contribution. Furthermore, based on these contributions, a reasonable uncertainty source management scheme is formulated to assess the probabilistic static voltage stability of the power system. Therefore, orthogonal independent transformation can handle the correlation between uncertainty sources in a power system, and the uncorrelated marginal contribution, data-related contribution, physical interaction contribution, and overall effect contribution can comprehensively quantify the impact of uncertainty sources on the system response, more accurately assessing the probabilistic static voltage stability of the power system, thereby improving the stability and reliability of the power system. Attached Figure Description

[0043] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0044] Figure 1This is a schematic diagram of a process for implementing probabilistic static voltage stability assessment of a power system, provided in an embodiment of this application.

[0045] Figure 2 This is a schematic flowchart illustrating the implementation of orthogonal independent transformation provided in an embodiment of this application;

[0046] Figure 3 This is a schematic diagram of a device structure for implementing probabilistic static voltage stability assessment of a power system, provided in an embodiment of this application. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] The proposed solution can be implemented based on a terminal with data processing capabilities, such as a computer, server, or cloud platform.

[0049] Next, combined Figure 1 The power system probabilistic static voltage stability assessment method of this application may include the following steps:

[0050] Step S110: Obtain the voltage phase angle of each node in a power system containing multiple sources of uncertainty.

[0051] It is understandable that there are correlations among the various uncertainties in a power system. The voltage phase angle of each node can be used as the input variable for orthogonal independent transformation, and is represented as a random input variable among the uncertainties. The random input variables are mainly information related to active and reactive power.

[0052] Step S120: Using the voltage phase angle as the input variable, the input variable is converted into an orthogonal variable using orthogonal independent transformation.

[0053] Understandably, the Orthogonal Independent Transformation (OIT) technique can effectively handle the correlation between uncertain sources in power systems, transforming related random input variables into independent orthogonal variables, thus providing a new approach for accurately assessing the impact of uncertain sources.

[0054] Specifically, OIT techniques may include steps such as collecting historical data, formalizing correlation matrices, decorating correlation variables, and transforming variables to the original space.

[0055] The process of collecting historical data can involve gathering historical data on uncertainties in the power system, preprocessing this data, and then constructing an uncertainty source correlation matrix based on the collected historical data. A formalized correlation matrix can be represented by dividing the uncertainty source correlation matrix into observable and unobservable system matrices. Decorative correlation variables can represent supplementing the matrix with new data unrelated to existing measurements, such as adding measurable power system nodes, when the matrix is ​​not entirely observable.

[0056] Step S130: Calculate the sensitivity index of the orthogonal variables.

[0057] Understandably, sensitivity indicators are used in power system analysis to consider random factors, such as wind speed related to wind power capacity and solar irradiance related to solar power capacity.

[0058] The sensitivity index may include the uncorrelated marginal contribution, data-related contribution, physical interaction contribution, and overall effect contribution of the orthogonal variables.

[0059] The uncorrelated marginal contribution represents the marginal change in the power system outcome produced by an orthogonal variable alone, without considering the influence of other factors and without any correlation with them. The uncorrelated marginal contribution reflects the degree of independent influence of the orthogonal variable itself, without involving data correlation or physical interaction with other variables.

[0060] Data-related contributions refer to the contributions of orthogonal variables to the power system due to their correlation with other variables at the data level. When changes in orthogonal variables exhibit a certain correlation with other variables at the data level, this correlation can lead to an impact on the system's outcome.

[0061] Physical interaction contribution can represent the contribution of orthogonal variables to physical-level interactions.

[0062] The overall effect contribution can represent the combined effect of orthogonal variables and other factors on the system.

[0063] Specifically, sensitivity indices are calculated by assessing the uncertainty contribution of input variables and calculating the system response, and the final results are output and interpreted.

[0064] In addition, suggestions for managing uncertainty sources can be given based on the calculation results of sensitivity indicators.

[0065] Step S140: Evaluate the probabilistic static voltage stability of the power system using the unrelated marginal contribution, the data-related contribution, the physical interaction contribution, and the overall effect contribution.

[0066] It is understandable that the unrelated marginal contribution, data-related contribution, physical interaction contribution, and overall effect contribution can comprehensively reveal the impact of relevant uncertainty sources on the voltage stability of the power system. Therefore, the calculated sensitivity indicators can be used to support the formulation of reasonable uncertainty source management schemes, thereby applying the uncertainty source management schemes to specific scenarios of the power system and evaluating the probabilistic static voltage stability of the power system.

[0067] The method for evaluating the probabilistic static voltage stability of a power system provided in this embodiment obtains the voltage phase angles of each node in a power system containing multiple uncertainty sources. Using the voltage phase angles as input variables, an orthogonal independent transformation is employed to convert the input variables into orthogonal variables. Sensitivity indices for these orthogonal variables are calculated, including the uncorrelated marginal contribution, data-related contribution, physical interaction contribution, and overall effect contribution. Furthermore, based on these contributions, a reasonable uncertainty source management scheme is formulated to evaluate the probabilistic static voltage stability of the power system. Therefore, the orthogonal independent transformation can handle the correlation between uncertainty sources in a power system, and the uncorrelated marginal contribution, data-related contribution, physical interaction contribution, and overall effect contribution can comprehensively quantify the impact of uncertainty sources on the system response, more accurately assessing the probabilistic static voltage stability of the power system, thereby improving the stability and reliability of the power system.

[0068] In some embodiments of this application, the process of calculating the sensitivity index of the orthogonal variable in step S130 is described, and this process may include the following two cases.

[0069] The first method involves calculating the sensitivity index of the orthogonal variable when the input variable has a dimension lower than a preset dimension, using a single-layer Global Sensitivity Analysis (GSA) layer.

[0070] The single-layer GSA layer is used to filter and refine the orthogonal variables.

[0071] Specifically, a single-layer GSA layer can be applied to power systems with low-dimensional random input variables. Therefore, when the input variables are below the preset dimension, a single-layer GSA layer can be used for calculation to improve computational efficiency and reduce computation time.

[0072] The second method involves using a two-layer GSA layer to calculate the sensitivity index of the orthogonal variable when the input variable is not lower than the preset dimension.

[0073] The two-layer GSA comprises a screening layer and a refining layer. The screening layer filters the orthogonal variables to identify relatively important variables. The refining layer refines the screened orthogonal variables to further analyze the detailed contributions of important variables.

[0074] Specifically, the two-layer GSA layer can be applied to power systems with high-dimensional random input variables. Therefore, when the input variables are of a dimension no less than the preset dimension, the two-layer GSA layer can be used for calculation to reduce the computational burden of each layer and improve the accuracy and reliability of the calculation results.

[0075] The power system probabilistic static voltage stability assessment method provided in this embodiment calculates low-dimensional random input variables using a single-layer GSA layer and calculates low-dimensional random input variables using a double-layer GSA layer, thereby adapting to input variables of different dimensions and improving computational efficiency.

[0076] Considering that sensitivity indicators can comprehensively reveal the impact of relevant uncertainty sources on the voltage stability of the power system, the evaluation method for the probabilistic static voltage stability of the power system provided in this application may further include:

[0077] Based on the unrelated marginal contribution, the data-related contribution, the physical interaction contribution, and the overall effect contribution, an uncertainty source management scheme for voltage stability optimization of the power system is generated.

[0078] Specifically, this uncertainty management scheme is provided to power system operators and planners so that they can implement the uncertainty management scheme for the power system.

[0079] More specifically, the process of implementing an uncertainty source management plan will be further described, which may include:

[0080] S1. Construct a simulation scenario of the power system that includes the multiple sources of uncertainty.

[0081] Specifically, it is possible to identify or acquire various uncertainty sources in the power system, and then construct a simulation scenario of the power system based on these uncertainty sources and the correlations between them.

[0082] S2. Under the simulated scenario, the uncertainty source management scheme is executed on the power system to obtain the result of optimizing the probabilistic static voltage stability of the power system.

[0083] Specifically, since the simulated power system scenario is constructed based on the various uncertainties actually contained in the power system, the same voltage stability problem exists in the simulated power system scenario as in the actual power system. By implementing an uncertainty source management scheme for the power system and updating the state of the power system, an updated result can be obtained.

[0084] It is understandable that the uncertainty source management scheme is generated based on four contributions of the sensitivity index, and it aims to optimize the stability of the power system. Therefore, in the simulation scenario, implementing the uncertainty source management scheme on the power system can result in optimizing the probabilistic static voltage stability of the power system.

[0085] In some embodiments of this application, the process of converting the input variable into an orthogonal variable using the voltage phase angle as the input variable and orthogonal independent transformation is described, such as... Figure 2 As shown, the process may include:

[0086] Step S1201: Using the voltage phase angle as the input variable, transform the input variable to a standard normal variable in the standard normal space through Nataf transformation.

[0087] Specifically, the Nataf transformation process can include:

[0088] S12011. Determine the marginal distribution of input variables: For each input variable, determine its probability density function or distribution function.

[0089] S12012. Calculate the correlation coefficient matrix between input variables: Calculate the correlation coefficient matrix between the input variables based on the sample data or known information.

[0090] The correlation coefficient matrix reflects the linear correlation between input variables.

[0091] S12013. Perform equal probability transformation: Using the marginal distribution of the input variables, transform each input variable into an intermediate variable with a known distribution.

[0092] Equal probability transformations can include Rosenblatt transformations, etc.

[0093] S12014. Determine the joint distribution of intermediate variables: Based on the known distribution of intermediate variables and the correlation coefficient matrix, determine their joint probability density function.

[0094] S12015. Perform the second transformation: convert the intermediate variables into standard normal variables through a specific transformation relationship.

[0095] Step S1202: Generate a relevant standard normal matrix based on the standard normal variables.

[0096] Specifically, the covariance matrix can be used to generate the relevant standard normal matrix. First, based on the known covariances among the standard normal variables, a covariance matrix can be constructed. By performing eigenvalue decomposition on the covariance matrix, a diagonal matrix and an orthogonal matrix are obtained. The elements of the diagonal matrix are the eigenvalues ​​of the covariance matrix, and the column vectors of the orthogonal matrix are the corresponding eigenvectors. Finally, using the orthogonal matrix and the diagonal matrix, the relevant standard normal matrix can be generated.

[0097] Step S1203: Use orthogonalization to transform the relevant standard normal matrix into independent orthogonal variables.

[0098] Specifically, orthogonal independent transformations can be achieved using methods such as principal component analysis (PCA), singular value decomposition (SVD), independent component analysis (ICA), and factor analysis.

[0099] When orthogonalizing the independent variables using principal component analysis (PCA), a set of orthogonal principal components can be obtained by eigenvalue decomposition of the relevant standard normal matrix. These principal components are linear combinations of the original variables, are mutually independent, and are ordered according to their variance. PCA can effectively reduce the dimensionality of the data while retaining its main information.

[0100] When the orthogonalization of independent variables employs singular value decomposition, the relevant standard normal matrix can be decomposed into the product of three matrices: an orthogonal matrix, a diagonal matrix, and the transpose of another orthogonal matrix. By selecting appropriate diagonal matrix elements, independent orthogonal variables can be obtained.

[0101] The power system probabilistic static voltage stability assessment method provided in this embodiment can effectively handle the correlation between uncertainty sources in the power system by introducing orthogonal independent transformation technology, and transform the relevant random input variables into independent orthogonal variables, thereby providing a new approach for accurately assessing the impact of uncertainty sources.

[0102] The apparatus for evaluating the probabilistic static voltage stability of a power system provided in the embodiments of this application will be described below. The apparatus for evaluating the probabilistic static voltage stability of a power system described below can be referred to in correspondence with the method for evaluating the probabilistic static voltage stability of a power system described above.

[0103] See Figure 3 , Figure 3 This is a schematic diagram of a device for evaluating the probabilistic static voltage stability of a power system, as disclosed in an embodiment of this application.

[0104] like Figure 3 As shown, the device may include:

[0105] Voltage phase angle acquisition unit 11 is used to acquire the voltage phase angle of each node in a power system containing multiple sources of uncertainty;

[0106] Transformation unit 12 is used to convert the input variable into an orthogonal variable by using the voltage phase angle as the input variable and orthogonal independent transformation;

[0107] Sensitivity index calculation unit 13 is used to calculate the sensitivity index of the orthogonal variable, the sensitivity index including the uncorrelated marginal contribution, data correlation contribution, physical interaction contribution and overall effect contribution of the orthogonal variable;

[0108] The probabilistic static voltage stability assessment unit 14 is used to assess the probabilistic static voltage stability of the power system through the unrelated marginal contribution, the data-related contribution, the physical interaction contribution, and the overall effect contribution.

[0109] Optionally, the sensitivity index calculation unit includes:

[0110] The first sensitivity index calculation subunit is used to calculate the sensitivity index of the orthogonal variable by using a single-layer global sensitivity analysis (GSA) layer when the input variable is an input variable with a lower than preset dimension. The single-layer GSA layer is used to filter and refine the orthogonal variable.

[0111] The second sensitivity index calculation subunit is used to calculate the sensitivity index of the orthogonal variable using a two-layer GSA layer when the input variable is an input variable with a dimension not lower than the preset dimension. The two-layer GSA layer includes a screening layer and a refining layer. The screening layer is used to screen the orthogonal variable, and the refining layer is used to refine the screened orthogonal variable.

[0112] Optionally, the device may also include:

[0113] The management scheme generation unit is used to generate an uncertainty source management scheme for voltage stability optimization of the power system based on the unrelated marginal contribution, the data related contribution, the physical interaction contribution, and the overall effect contribution.

[0114] Optionally, the device may also include:

[0115] The simulation scenario construction unit is used to construct a simulation scenario of the power system that includes the multiple uncertainty sources;

[0116] The scheme application unit is used to execute the uncertainty source management scheme on the power system under the simulated scenario to obtain the result of optimizing the probabilistic static voltage stability of the power system.

[0117] Optionally, the transformation unit includes:

[0118] The first transformation subunit is used to transform the voltage phase angle as the input variable to a standard normal variable in the standard normal space through Nataf transformation.

[0119] The second transformation subunit is used to generate a relevant standard normal matrix based on the standard normal variables;

[0120] The third transformation subunit is used to convert the relevant standard normal matrix into independent orthogonal variables using orthogonalization independent transformation.

[0121] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0122] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0123] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the probabilistic static voltage stability of a power system, characterized in that, include: Obtain the voltage phase angle of each node in a power system containing multiple sources of uncertainty; Using the voltage phase angle as the input variable, the input variable is converted into an orthogonal variable using orthogonal independent transformation; The sensitivity index of the orthogonal variable is calculated. The sensitivity index includes the uncorrelated marginal contribution, data-related contribution, physical interaction contribution, and overall effect contribution of the orthogonal variable. The uncorrelated marginal contribution represents the marginal change of the orthogonal variable on the power system result when it is not considered to have the influence of other factors and is uncorrelated with other factors. The data-related contribution represents the contribution of the orthogonal variable to the power system due to the data correlation between the orthogonal variable and other variables. The physical interaction contribution represents the contribution of the orthogonal variable based on the interaction at the physical level. The overall effect contribution represents the overall impact of the orthogonal variable and other factors on the system. The probabilistic static voltage stability of the power system is evaluated by the unrelated marginal contribution, the data-related contribution, the physical interaction contribution, and the overall effect contribution. Calculating the sensitivity index of the orthogonal variables includes: When the input variable is an input variable with a lower dimension than the preset dimension, a single-layer Global Sensitivity Analysis (GSA) layer is used to calculate the sensitivity index of the orthogonal variable. The single-layer GSA layer is used to screen and refine the orthogonal variable. When the input variable is an input variable with a dimension no less than the preset dimension, the sensitivity index of the orthogonal variable is calculated using a two-layer GSA layer. The two-layer GSA layer includes a screening layer and a refining layer. The screening layer is used to screen the orthogonal variable, and the refining layer is used to refine the screened orthogonal variable.

2. The method according to claim 1, characterized in that, Also includes: Based on the unrelated marginal contribution, the data-related contribution, the physical interaction contribution, and the overall effect contribution, an uncertainty source management scheme for voltage stability optimization of the power system is generated.

3. The method according to claim 2, characterized in that, Also includes: Construct a simulation scenario of the power system that includes the multiple sources of uncertainty; In the simulated scenario, the uncertainty source management scheme is implemented on the power system to obtain the result of optimizing the probabilistic static voltage stability of the power system.

4. The method according to any one of claims 1-3, characterized in that, Using the voltage phase angle as the input variable, the input variable is converted into an orthogonal variable using an orthogonal independent transformation, including: Using the voltage phase angle as the input variable, the input variable is transformed into a standard normal variable in the standard normal space through Nataf transformation; Based on the aforementioned standard normal variables, generate the relevant standard normal matrix; The relevant standard normal matrix is ​​transformed into independent orthogonal variables using an orthogonalization independent transformation.

5. An evaluation device for the probabilistic static voltage stability of a power system, characterized in that, include: The voltage phase angle acquisition unit is used to acquire the voltage phase angle of each node in a power system containing multiple sources of uncertainty. The transformation unit is used to convert the voltage phase angle into an orthogonal variable using orthogonal independent transformation; The sensitivity index calculation unit is used to calculate the sensitivity index of the orthogonal variable. The sensitivity index includes the uncorrelated marginal contribution, data-related contribution, physical interaction contribution, and overall effect contribution of the orthogonal variable. The uncorrelated marginal contribution represents the marginal change of the orthogonal variable on the power system result when it is not considered to have the influence of other factors and is uncorrelated with other factors. The data-related contribution represents the contribution of the orthogonal variable to the power system due to the data correlation between the orthogonal variable and other variables. The physical interaction contribution represents the contribution of the orthogonal variable based on the interaction at the physical level. The overall effect contribution represents the overall impact of the orthogonal variable and other factors on the system. A probabilistic static voltage stability assessment unit is used to assess the probabilistic static voltage stability of the power system through the unrelated marginal contribution, the data-related contribution, the physical interaction contribution, and the overall effect contribution. The sensitivity index calculation unit includes: The first sensitivity index calculation subunit is used to calculate the sensitivity index of the orthogonal variable by using a single-layer global sensitivity analysis (GSA) layer when the input variable is an input variable with a lower than preset dimension. The single-layer GSA layer is used to filter and refine the orthogonal variable. The second sensitivity index calculation subunit is used to calculate the sensitivity index of the orthogonal variable using a two-layer GSA layer when the input variable is an input variable with a dimension not lower than the preset dimension. The two-layer GSA layer includes a screening layer and a refining layer. The screening layer is used to screen the orthogonal variable, and the refining layer is used to refine the screened orthogonal variable.

6. The apparatus according to claim 5, characterized in that, The device also includes: The management scheme generation unit is used to generate an uncertainty source management scheme for voltage stability optimization of the power system based on the unrelated marginal contribution, the data related contribution, the physical interaction contribution, and the overall effect contribution.

7. The apparatus according to claim 6, characterized in that, The device also includes: The simulation scenario construction unit is used to construct a simulation scenario of the power system that includes the multiple uncertainty sources; The scheme application unit is used to execute the uncertainty source management scheme on the power system under the simulated scenario to obtain the result of optimizing the probabilistic static voltage stability of the power system.

8. The apparatus according to any one of claims 5-7, characterized in that, The transformation unit includes: The first transformation subunit is used to transform the voltage phase angle as the input variable to a standard normal variable in the standard normal space through Nataf transformation. The second transformation subunit is used to generate a relevant standard normal matrix based on the standard normal variables; The third transformation subunit is used to convert the relevant standard normal matrix into independent orthogonal variables using orthogonalization independent transformation.

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