Transient voltage key influence factor determination method and device, equipment and medium

Abstract

The invention discloses a transient voltage key influence factor determination method and device, equipment and a medium, and relates to the field of transient voltage analysis, and the method comprises the steps: carrying out the modeling of a power system containing new energy grid connection, and obtaining a power system model; under a set fault type and a set fault position, taking the related input data set as input of the power system model, performing transient time domain simulation on the power system model for multiple times, and calculating a transient voltage stability index output by the power system model in each transient time domain simulation; according to the related input data set and the transient voltage stability index, a non-invasive probability configuration method is adopted to determine a polynomial coefficient of any chaotic polynomial; according to the polynomial coefficient, a Sobol index is adopted to calculate a transient voltage weighted global sensitivity index of each influence factor in the related input data set, so that the transient voltage key influence factors are determined, and the transient voltage key influence factors of the new energy grid-connected power system can be determined.

Description

Technical Field

[0001] The present application relates to the field of transient voltage analysis, and in particular to a method, device, equipment and medium for determining key influencing factors of transient voltage. Background Art

[0002] After a high proportion of new energy is connected to the grid, the characteristics of the power system have changed dramatically, mainly in the following two aspects: First, the operation mode of the power system will become more diverse, and it is necessary to focus on the uncertainty of the source and load of the new energy power system; Second, since new energy is essentially a static component different from traditional equipment, it cannot provide effective reactive support, which leads to changes in the stability mechanism of the power system. The impact of new energy on the power system needs to be taken into account in the analysis. Therefore, the large-scale connection of new energy to the grid increases the complexity of the power system and introduces more uncertainty and dynamics. These factors may have a negative impact on voltage stability. How to use efficient methods to analyze the key factors affecting transient voltage stability in power systems with a high proportion of new energy connected to the grid is crucial to the safe and stable operation and planning and design of the system. Summary of the invention

[0003] The purpose of this application is to provide a method, device, equipment and medium for determining key influencing factors of transient voltage, which can determine the key influencing factors of transient voltage in an electric power system including renewable energy grid connection, thereby determining the stability of transient voltage in an electric power system including renewable energy grid connection.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for determining key influencing factors of transient voltage, comprising:

[0006] Determine a relevant input data set of the power system including the new energy grid-connected power system; the relevant input data set includes the dynamic load power, the output power of each wind farm station and the output power of each photovoltaic power station as an influencing factor;

[0007] Model the power system including the new energy grid connection to obtain the power system model;

[0008] Under the set fault type and the set fault location, the relevant input data set is used as the input of the power system model, the power system model is subjected to multiple transient time domain simulations, and the transient voltage stability index output by the power system model in each transient time domain simulation is calculated; wherein the number of transient time domain simulations is equal to the number of polynomials of any chaotic polynomial;

[0009] Determining polynomial coefficients of an arbitrary chaotic polynomial using a non-invasive probabilistic collocation method according to the relevant input data set and the transient voltage stability index;

[0010] Calculating the transient voltage weighted global sensitivity index of each influencing factor in the relevant input data set using the Sobol index according to the polynomial coefficients;

[0011] The key influencing factors of transient voltage are determined according to the weighted global sensitivity index of transient voltage; the key influencing factors of transient voltage are influencing factors whose weighted global sensitivity index of transient voltage in the relevant input data set is greater than a set value.

[0012] In a second aspect, the present application provides a device for determining key influencing factors of transient voltage, comprising:

[0013] A data set determination module is used to determine the relevant input data set of the power system including the new energy grid-connected power system; the relevant input data set includes the dynamic load power, the output power of each wind farm station and the output power of each photovoltaic power station as an influencing factor;

[0014] A model building module is used to model the power system including the new energy grid connection to obtain the power system model;

[0015] A transient time domain simulation module is used to use the relevant input data set as the input of the power system model under the set fault type and the set fault location, perform multiple transient time domain simulations on the power system model, and calculate the transient voltage stability index output by the power system model in each transient time domain simulation; wherein the number of transient time domain simulations is equal to the number of polynomials of any chaotic polynomial;

[0016] A polynomial coefficient determination module, used for determining the polynomial coefficients of any chaotic polynomial using a non-invasive probability configuration method according to the relevant input data set and the transient voltage stability index;

[0017] A global sensitivity calculation module, used to calculate the transient voltage weighted global sensitivity index of each influencing factor in the relevant input data set using the Sobol index according to the polynomial coefficients;

[0018] A key influencing factor determination module is used to determine the key influencing factors of transient voltage according to the transient voltage weighted global sensitivity index; the transient voltage key influencing factor is an influencing factor whose transient voltage weighted global sensitivity index in the relevant input data set is greater than a set value,

[0019] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for determining key influencing factors of transient voltage.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for determining key influencing factors of transient voltage.

[0021] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0022] The present application provides a method, device, equipment and medium for determining key influencing factors of transient voltage. Based on arbitrary chaotic polynomial (arbitraryPolynomialChaos, aPC), a correlation relationship between influencing factors affecting transient voltage stability of the system and transient voltage stability index characterizing the transient voltage stability of the system is established, the uncertainty between the influencing factors is fully considered, the weighted global sensitivity index of transient voltage of each influencing factor is calculated from a global perspective, the key influencing factors of transient voltage of the power system with new energy grid connection are determined, thereby determining the stability of transient voltage of the power system with new energy grid connection, helping to formulate a voltage control measure strategy to improve the transient voltage stability of the system, and ensuring the safe and stable operation of the system and reasonable planning and design. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 This is an application environment diagram of a method for determining key influencing factors of transient voltage in one embodiment of the present application;

[0025] Figure 2 A schematic diagram of a flow chart of a method for determining key influencing factors of transient voltage provided in one embodiment of the present application;

[0026] Figure 3 A flowchart of a method for determining key influencing factors of transient voltage provided in another specific example of the present application;

[0027] Figure 4 A schematic diagram of a store system topology structure with a high proportion of system energy grid-connected provided in an embodiment of the present application;

[0028] Figure 5 A schematic diagram of transient voltage sensitivity distribution law provided in an embodiment of the present application;

[0029] Figure 6 A schematic diagram of the transient voltage sensitivity distribution law of a wind farm provided in an embodiment of the present application;

[0030] Figure 7 A schematic diagram of the transient voltage sensitivity distribution law of a photovoltaic station provided in an embodiment of the present application;

[0031] Figure 8 A schematic diagram of the transient voltage sensitivity distribution law of a dynamic load point provided in an embodiment of the present application;

[0032] Fig. 9 A schematic diagram of a modified IEEE-39 node system topology structure provided for an embodiment of the present application;

[0033] Fig.10 A schematic diagram of transient voltage sensitivity curves of simulation experiment influencing factors of the present application method and the traditional sensitivity calculation method;

[0034] Fig.11 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0037] In order to overcome the deficiency of traditional voltage stability analysis methods that cannot take into account the uncertain factors of the system including new energy grid connection, researchers have proposed a probabilistic analysis method for voltage stability. By calculating the statistical characteristic values ​​and probability distribution of the load margin and critical power of the power system, the probability of voltage instability of the system under a certain load level is determined. However, this type of method is often used in the static voltage stability analysis of the system.

[0038] In order to quantitatively analyze the impact of random variables on system output and determine the key variables that affect the system, mathematician Sobol proposed a global sensitivity analysis method based on the Monte Carlo sampling idea, which can quantitatively analyze the impact of input random variables on system output, thereby obtaining the key factors that affect the system state. The Monte Carlo (MC) method is also widely used to study the impact of random input on system transient stability. However, this method analyzes the random input of the system by simulating different operating states of the system multiple times, which takes a long time to calculate and occupies a large amount of memory.

[0039] In summary, most of the related methods are based on Monte Carlo simulation to calculate global sensitivity, which makes the computational burden of global sensitivity analysis used for power system analysis very heavy, and the application of global sensitivity in transient voltage stability analysis of grid-connected systems with a high proportion of renewable energy is relatively rare.

[0040] Therefore, for the transient voltage stability analysis of the grid-connected system with a high proportion of renewable energy, it is necessary to use arbitrary chaotic polynomials to introduce an improved Sobol index as the transient voltage global sensitivity index to quantify the correlation between the system transient voltage stability and various influencing factors, and then analyze the key influencing factors of the system transient voltage stability from a global perspective. The main steps include: modeling based on Sklar theorem, using the copula function in the multivariate Gaussian-copula (mvG-copula) method to generate dynamic load power, wind farm output power and photovoltaic power station output power as relevant input data sets of influencing factors; based on arbitrary chaotic polynomial (aPC) expansion, by realizing orthogonal polynomial expansion, the dependence of model output on model parameters can be simulated, and a polynomial expansion of system influencing factors and system transient voltage stability is established; based on the Sobol index, the weighted sum of multivariate orthogonal polynomial basis is introduced to improve the transient voltage global sensitivity of the Sobol index.

[0041] The following is a detailed introduction to the method for determining the key influencing factors of transient voltage in an embodiment of the present application.

[0042] The method for determining the key influencing factors of transient voltage provided in the embodiment of the present application can be applied to the following examples: Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, or it can be integrated on the server 104, or it can be placed on the cloud or other servers. The terminal 102 can send the relevant input data set of the power system including the new energy grid connection to the server 104. After the server 104 receives the relevant input data set of the power system including the new energy grid connection, for the relevant input data set of the power system including the new energy grid connection, the server 104 models the power system including the new energy grid connection to obtain a power system model; under the set fault type and the set fault location, the relevant input data set is used as the input of the power system model, and the power system model is subjected to multiple transient time domain simulations, and the transient voltage stability index output by the power system model in each transient time domain simulation is calculated. wherein the number of transient time domain simulations is equal to the number of polynomials of any chaotic polynomial; the polynomial coefficients of any chaotic polynomial are determined by a non-invasive probability configuration method according to the relevant input data set and the transient voltage stability index; the weighted global sensitivity index of transient voltage of each influencing factor in the relevant input data set is calculated by a Sobol index according to the polynomial coefficients; the key influencing factors of transient voltage are determined according to the weighted global sensitivity index of transient voltage; the key influencing factors of transient voltage are influencing factors whose weighted global sensitivity index of transient voltage in the relevant input data set is greater than a set value.

[0043] The server 104 may feed back the obtained key transient voltage influencing factors to the terminal 102. In addition, in some embodiments, the method for determining the key transient voltage influencing factors may also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 may directly process the relevant input data set of the power system including the new energy grid connection, or the server 104 may obtain the relevant input data set of the power system including the new energy grid connection from the data storage system, and process the relevant input data set of the power system including the new energy grid connection.

[0044] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0045] In an exemplary embodiment, Figure 2As shown, a method for determining key influencing factors of transient voltage is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 201 to 206. Among them:

[0046] Step 201, determining a relevant input data set of a power system including a new energy source connected to the grid; the relevant input data set includes dynamic load power, output power of each wind farm station and output power of each photovoltaic power station as influencing factors.

[0047] Step 202: Modeling the power system including the new energy source connected to the grid to obtain a power system model.

[0048] Step 203, under the set fault type and the set fault location, use the relevant input data set as the input of the power system model, perform multiple transient time domain simulations on the power system model, and calculate the transient voltage stability index output by the power system model in each transient time domain simulation; wherein the number of transient time domain simulations is equal to the number of polynomials of any chaotic polynomial.

[0049] Step 204: According to the relevant input data set and the transient voltage stability index, a non-intrusive probabilistic collocation method (NIPCM) is used to determine the polynomial coefficients of an arbitrary chaotic polynomial.

[0050] Step 205 : Calculate the transient voltage weighted global sensitivity index of each influencing factor in the relevant input data set using the Sobol index according to the polynomial coefficients.

[0051] Step 206, determining the key influencing factors of transient voltage according to the transient voltage weighted global sensitivity index; the transient voltage key influencing factors are the influencing factors whose transient voltage weighted global sensitivity index in the relevant input data set is greater than a set value.

[0052] By implementing the above steps 201 to 206, the key influencing factors of the transient voltage of the power system including the grid-connected renewable energy can be determined, thereby determining the stability of the transient voltage of the power system including the grid-connected renewable energy, helping to formulate voltage control measures and strategies to improve the transient voltage stability of the system, and ensuring the safe and stable operation of the system and reasonable planning and design.

[0053] In another exemplary embodiment of the present application, step 201 specifically includes: generating an initial input data set based on Sklar's theorem and the copula function in the multivariate Gaussian-copula (Multivariate Gaussian-copula, mvG-copula) method; correcting the initial input data set using the inverse of the copula function in the mvG-copula method, and using the corrected initial input data set as the relevant input data set; the dynamic load power in the relevant input data set obeys a normal distribution, the output power of each wind farm station obeys a Weibull distribution, and the output power of each photovoltaic power station obeys a β distribution.

[0054] The specific description is as follows:

[0055] In order to obtain the sequence of variable parameters (including the output power and dynamic load power of new energy stations in the power system with new energy grid connection) of the influencing factors input into the aPC environment, and considering the correlation between the output of new energy stations (including wind farms and photovoltaic power stations) and the dynamic load and the uncertainty between the dynamic load, modeling is carried out based on Sklar theorem, and the copula function of the mvG-copula method is used to generate the initial input data set. The initial input data set is corrected based on the inverse of the copula function in the mvG-copula method to obtain the relevant input data set.

[0056] The copula function in the mvG copula method can be expressed as follows:

[0057] C(u 1 ,u 2 ,....,u n ;Σ)=φ Σ (φ -1 (u 1 ),φ -1 (u 2 ),...,φ -1 (u n ));

[0058] Where Σ is a symmetric positive definite matrix, diag(Σ) = 1; φ(u) is the normal cumulative distribution function with input u, n is the number of input parameters, φ -1 (·) is the inverse of the normal cumulative distribution function.

[0059] The symmetric positive definite matrix Σ in the above formula, also known as the covariance matrix, can be expressed as:

[0060]

[0061] Here, ρ is the linear correlation coefficient between the parameters, indicating the range of different dependency structures. For example, ρ12 is the input parameter u 1 With input parameter u 2 The linear correlation coefficient between .

[0062] The correlation between the same set of data is as follows:

[0063] ρ 11 =ρ 22 =…=ρ nn =1.

[0064] Then the covariance matrix becomes:

[0065]

[0066] The covariance matrix can be composed of Pearson correlation coefficient ρ, Spearman or Kendall rank correlation coefficient τ. This application adopts the inversion method to model each variable (i.e., each influencing factor), and these variables follow different distributions. The system load power, wind farm output power, and photovoltaic power station output power obey normal distribution, Weibull distribution, and beta distribution, respectively.

[0067] In another exemplary embodiment of the present application, step 202 specifically includes: constructing a power system including a new energy grid-connected power system on the CloudPSS platform to obtain a power system model. The power system model is as follows: Figure 3 As shown in the figure, the power system model contains a total of 100 nodes, of which 48 are 500kV nodes. The installed capacity ratio of new energy stations and conventional power sources (such as thermal power units) in the power system model is 1:2.62. There is one DC loop with a DC feed-in power of 800,000 kilowatts. The system contains 20 loads, all of which use dynamic load models. Figure 3 In the figure, bus1-bus22 are bus 1-bus 22, Gen1-Gen8 are thermal power units, GenW1-GenW6 are wind farms, GenPV1-GenPV6 are photovoltaic power stations, LCCReceiver is the DC sending end, and LCCend end is the DC receiving end.

[0068] In another exemplary embodiment of the present application, step 203 specifically includes:

[0069] (1) Determining the order of any chaotic polynomial according to the number of influencing factors in the relevant input data set, and calculating the number of polynomials of the arbitrary chaotic polynomial according to the order and the number of influencing factors in the relevant input data set.

[0070] Specifically, the order d of the arbitrary chaotic polynomial used is determined. The order d is generally 2 or 3, which depends on the number of model input parameters, that is, the number N of influencing factors in the relevant input data set; the value of the number of polynomials M depends on the number N of influencing factors in the relevant input data set and the order d of the polynomial. The calculation formula of the number of polynomials is:

[0071] M = (N+d)! / N!d!-1.

[0072] (2) Determine the fault type and fault location. Specifically, set the AC line bus17~bus21 to have a three-permanent N-2 fault in the power system model, and obtain the bus voltage of all nodes in the system through transient time domain simulation.

[0073] (3) Based on the number of polynomials, under the set fault type and the set fault location, the relevant input data set is used as the input of the power system model, and the power system model is simulated multiple times in transient time domain using differential algebraic equations (DAE) to obtain the bus voltage of all nodes in the power system model under each transient time domain simulation. The above steps determine the number of polynomials M of any chaotic polynomial, that is, the number of transient time domain simulations is M.

[0074] The following is an introduction to the theory of differential algebraic equations.

[0075] The dynamic process of the power system with renewable energy grid connection can be modeled by a set of differential algebraic equations (DAEs), which are expressed as follows:

[0076]

[0077] In the formula: x is the system state variable, such as power angle and speed; y is the system algebraic variable, including the bus voltage U and phase angle q of the system node; f and g are two sets of differential algebraic equations, which characterize the mechanism of transient voltage stability. The following is a detailed analysis of the changes in the system DAE form before and after the power system is disturbed based on the power output characteristics of new energy.

[0078] When the system is disturbed, the output power of the new energy station can generally be expressed as a function of the terminal voltage. During the fault period, the new energy station controls the active current to be zero. Assuming that the new energy station does not provide reactive power before and after the fault, the new energy port power is expressed as a piecewise function, as follows.

[0079] During a fault:

[0080]

[0081] Where: P w The active power output of the new energy station; Qw It is the reactive power output by the new energy station.

[0082] After the failure:

[0083]

[0084] Where: P w0 The active power output of the new energy station before the fault; Q w0 The reactive power output of the new energy station before the fault.

[0085] Furthermore, according to the power output characteristics of the renewable energy station, assuming that node i is a node of the renewable energy station, the algebraic equation in the DAE is modified, and the modified DAE is expressed as:

[0086]

[0087] Where: G ik is the conductance between node i and node k; B ik is the susceptance between node i and node k; θ ik is the phase angle between node i and node k.

[0088] According to the stable boundary theory of DAE, the DAE equations can be expressed as:

[0089]

[0090] Where: L is the constraint surface, and the trajectory of the DAE system is on L; S is the system singular surface, and the points that constitute the S surface are called singular points; the points in L / S are regular points; R n+m represents the state vector defined in the space, which includes an n-dimensional vector x and an m-dimensional vector y; Δ(x,y) represents D y The determinant of g(x,y); D y g(x,y) represents the Jacobian matrix of the function g(x,y) with respect to y. 0 ,y 0 )∈L / S, according to the implicit function theorem, at x=x 0 There is a unique y=y(x) nearby such that (x,y(x))∈L. Substituting into the DAE equations, we get:

[0091] x=f(x,y(x));

[0092] The DAE system is in (x 0 ,y 0 ) has a unique solution, and the singular point (x s ,y s )∈S, the DAE equations are in (x s ,y s ) has no solution nearby.

[0093] It can be seen from the formula that in the power system model, when the DAE equation group encounters a singular point, the system transient voltage becomes unstable, and at this time, the algebraic equation in the modified DAE has no solution. Therefore, the change in the power output characteristics of the new energy station may lead to the occurrence of singular algebraic equations in the transient process after the fault ends, and the voltage of the corresponding new energy power system collapses at this time. It can be seen that the port output power of each device in the power system model will affect the transient voltage stability of the system.

[0094] In summary, the influence of the output power of new energy stations and dynamic load power in the system on the transient voltage stability of the system can be studied.

[0095] The modified DAE is used to perform multiple transient time-domain simulations on the power system model to obtain the bus voltages of all nodes in the power system model under each transient time-domain simulation.

[0096] (4) For any transient time domain simulation, a transient voltage stability index SVDI output by the power system model of the transient time domain simulation is calculated according to the bus voltages of all nodes in the power system model under the transient time domain simulation.

[0097] Specifically, based on the above steps, M groups of input parameter sequences are obtained, and M groups of SVDI are calculated as the model output Ω of aPC through the bus voltage of each node of M transient simulations. The system transient voltage stability index SVDI that traverses the transient voltage change values ​​of all nodes of the system is introduced to quantify the transient voltage stability of the system. The proposed SVDI can quantify the transient voltage stability of the system. The variation range of SVDI(t) is narrow, highlighting a more concentrated variation range, thereby simplifying the interpretation process of the transient voltage stability change of the system. The calculation formula of the transient voltage stability index is:

[0098]

[0099] Where SVDI(t) represents the transient voltage stability index at time t; V i (t) represents the bus voltage of node i at time t; represents the bus voltage before the fault of node i; Indicates the minimum bus voltage allowed at node i; N b Represents the total number of nodes in the power system model.

[0100] In another exemplary embodiment of the present application, the process of determining the polynomial coefficients of any chaotic polynomial in step 204 is as follows:

[0101] It can be seen from the above steps that there are M polynomial coefficients of any chaotic polynomial. Since the essence of calculating polynomial coefficients of any chaotic polynomial is to solve a set of equations, M groups of input parameters and model responses are required when solving the polynomial coefficients, that is, M groups of sequences of each parameter are calculated by step 201, and M groups of transient voltage stability indicators of the system are obtained by M transient time domain simulation calculations according to step 203. Considering that the characteristics of the power system with a high proportion of new energy grid-connected have undergone tremendous changes, based on the principle of arbitrary chaotic polynomial expansion, the dependence of the model output on the model parameters can be simulated by realizing orthogonal polynomial expansion, which can be used to quantitatively characterize the influence of the proportion of new energy and the proportion of dynamic load on the transient voltage stability of the system, and the transient voltage stability indicator in step 203 is introduced to solve the problem of quantifying the transient voltage stability of the system as the model output. It can be seen from the above that SVDI is obtained as the model output sequence of the input sequence of the influencing factors of the corresponding load, wind farm output and photovoltaic farm output.

[0102] In the arbitrary chaotic polynomial expansion, the input parameters are processed by low-dimensional mapping to reduce the data dimension. The arbitrary chaotic polynomial (aPC) expansion expression is as follows:

[0103]

[0104] In the formula, ω represents the influencing factor, i.e., the output power of the new energy station and the system dynamic load power, and an influencing factor is used as a variable parameter; Ω represents the model output, i.e., the transient voltage stability index of the system; c j are polynomial coefficients, used to quantify the impact of each change ω in space x on Ω under time t; Ψ is a simplified representation of the multivariate orthogonal polynomial basis of ω, which is used to map high-dimensional data to low-dimensional space, thereby reducing the dimension of the data.

[0105] Define the set of polynomials of degree d is the orthogonal basis parameter in the parameter space, and the single variable parameter ω j The k-order polynomial It can be written as j Linear combinations of different powers:

[0106]

[0107] In the formula, For polynomial The multivariate orthogonal polynomial basis can be further constructed as a simple product of univariate polynomials, with polynomial coefficients It can be defined by the following matrix equation:

[0108]

[0109] in, It is a multivariate index containing information about the combinations of all possible products of univariate polynomials. The index α is an M×N matrix containing the corresponding order of the parameter j in the expanded term r.

[0110]

[0111] In the formula, μ i,j is the variable parameter ω i The i-th original statistical moment of The polynomials in the formula are made to be exactly orthogonal in the given input distribution of the model parameters.

[0112] Considering a random process with Γ as the probability measure in the probability space, according to the definition of orthogonality, we know that:

[0113]

[0114] The orthogonal polynomial basis can be expressed as:

[0115]

[0116] Among them, the normalization constant of the polynomial is expressed as:

[0117]

[0118] For calculating polynomial coefficients, the non-invasive probabilistic collocation method (NIPCM) is used to calculate the collocation point method to determine the polynomial coefficients, which can reduce the number of M. Its collocation satisfies the linear equation system, that is, the expression of the polynomial coefficients of any chaotic polynomial is:

[0119] M Ψ (ω)V c (x,t)=V Ω (x, t; ω);

[0120] Among them, the configuration point is the parameter set i=1,...,M,V c (x, t) represents the M×1-order vector composed of the polynomial coefficients of any chaotic polynomial in space x at time t and at all transient time-domain simulation times; V Ω (x, t; ω) represents the M×1-order vector composed of the transient voltage stability index output by the power system model under all transient time-domain simulation times when the influencing factor ω in space x at time t is used as the input of the power system model, which contains the model output of each configuration point; M Ψ (ω) represents an M×N matrix of polynomials evaluated at the collocation points, which contains the coefficients of the polynomials evaluated at the collocation points, is independent of space and time, and is generated by the order d and the number of influencing factors ω.

[0121] In the above steps 203 and 204, a new transient voltage stability index is introduced to solve the problem of quantifying the transient voltage stability of the system as the model output. Considering that the characteristics of the power system with a high proportion of new energy connected to the grid have changed dramatically, based on the principle of arbitrary chaotic polynomial (aPC) expansion, the orthogonal polynomial expansion can be used to simulate the dependence of the model output on the model parameters, which can be used to quantitatively characterize the impact of the proportion of new energy and the proportion of dynamic load on the transient voltage stability of the system.

[0122] In another exemplary embodiment of the present application, the specific process of determining the transient voltage weighted global sensitivity index in step 205 is as follows:

[0123] In order to quantify the relationship between the influencing factors and transient voltage stability in the system, the weighted and improved Sobol index of multivariable orthogonal polynomial basis is introduced based on the Sobol index as the global sensitivity of transient voltage. The global sensitivity of transient voltage provides a global sensitivity measurement method similar to the weighted square norm of sensitivity, which reflects the impact of ω (i.e., the output power of the new energy station and the dynamic load power of the system) on Ω (i.e., the transient voltage stability index of the system) when it changes.

[0124] The statistical data of the system output is directly based on the model formula and the specified moment of the input data. The change of the system transient voltage stability is expanded on the basis of the normalized polynomial. The Sobol index used for sensitivity analysis is introduced into any chaotic polynomial. The analysis and calculation can be performed based on the chaotic polynomial expansion (Polynomial Chaos Expansion, PCE) using the following formula. The expression is as follows:

[0125]

[0126] in, is the Sobol index, which indicates the variable parameter ω j Contribution to the total variance of the output space Ω; Summarizes the variable parameters ω j All Sobol indices, including univariate and joint effects; i s The subscript of the input parameter is used to indicate the sth variable among multiple input variables; j Represents the indicator function, which is the judgment variable parameter ω j An indicator of whether it has an effect on system output.

[0127] The improved Sobol index defines the weighted global sensitivity index of transient voltage under the influence of multiple parameters in the system as:

[0128]

[0129] The index reflects the proportion of new energy output and system dynamic load j The statistical distribution of or the square slope of the weighted function, so we have:

[0130]

[0131] Using the orthogonality of polynomials and for all ω l After integration, the above formula can be simplified to the final calculation formula of transient voltage weighted global sensitivity index, which is expressed as:

[0132]

[0133] in, represents the weighted global sensitivity index of transient voltage; M represents the number of polynomials of any chaotic polynomial; c k represents the polynomial coefficient of the kth term in any chaotic polynomial; ω j represents the jth influencing factor; Λ represents the relevant input data set; Represents symbolic parameters; represents the orthogonality factor, whose size is determined by the orthogonality of the polynomials; represents the orthogonal polynomial basis; d represents the order of any chaotic polynomial; Γ(ω j ) represents the probability measure of the jth influencing factor in the probability space; m is used for counting and has no practical meaning.

[0134] In step 205, the weighted and improved Sobol index of the multivariate orthogonal polynomial basis is introduced based on the Sobol index as the transient voltage global sensitivity. The transient voltage global sensitivity provides a global sensitivity measurement method similar to the sensitivity weighted square norm, which reflects the impact of the output power of the new energy station and the dynamic load dynamic law changes on the transient voltage stability of the system.

[0135] In another exemplary embodiment of the present application, step 206 specifically includes: calculating the transient voltage weighted global sensitivity index of each parameter (i.e., influencing factor) and sorting them, and taking the first certain number of influencing factors in the sequence as the key influencing factors of transient voltage according to the set value, and subsequently applying the identified key influencing factors of transient voltage to the formulation of voltage control measures to improve the transient voltage stability of the system.

[0136] In order to overcome the deficiency of traditional voltage stability analysis methods that cannot take into account the uncertain factors of the system containing new energy grid connection, the above-mentioned embodiments of the present application propose a method for determining key influencing factors of transient voltage based on aPC-Sobol transient voltage global sensitivity. The method first processes the uncertainty of the influencing factors in the aPC environment through the mvG-copula method, thereby obtaining a sequence input into the aPC environment as an influencing factor; secondly, an evaluation index for quantifying the transient voltage stability of the system is introduced, and on the basis of mechanism analysis, an arbitrary chaotic polynomial (aPC) is used to fit the correlation between the influencing factors in the large system and the transient voltage stability of the system; finally, an improved Sobol index is introduced in the aPC environment to quantify the correlation between the influencing factors in the system and the transient voltage stability, and the key influencing factors of transient voltage stability in the power system are analyzed.

[0137] The method of the above embodiment of the present application has the following advantages:

[0138] (1) This application uses the copula function in the mvG-copula method to model the correlation and uncertainty between the power system load, wind farm station and photovoltaic station data, and adopts the inversion method to obtain the sequence of each variable as the input of aPC. These variables follow different distributions. The system load, wind power and solar energy data obey the normal distribution, Weibull distribution and beta distribution respectively, which can effectively improve the accuracy of aPC expansion.

[0139] (2) This application establishes a correlation between the factors affecting the transient voltage stability of the system based on aPC and the transient voltage stability of the system, which can fully consider the uncertainty between the various influencing factors and then analyze the transient voltage stability of the system containing a high proportion of new energy from a global perspective.

[0140] (3) This application introduces a global sensitivity measurement method similar to the sensitivity weighted square norm, which reflects the impact of new energy and load changes on the transient voltage stability of the system. The proposed global sensitivity does not rely on the comparison of the impact of different proportions on the transient voltage stability of the system, that is, it is an absolute measure and a quantitative expression of the square slope. Compared with the existing Sobol-based total index, this index can be used as an absolute measure of the transient voltage sensitivity of the system, and because it works on the derivative of the parameter under study, it maintains the original meaning of sensitivity.

[0141] A specific example is given below to further illustrate the above method.

[0142] See also Figure 4 The process of determining the key influencing factors of transient voltage includes three basic steps:

[0143] (1) Based on arbitrary chaotic polynomial expansion, the dependence of model output on model parameters can be simulated by realizing orthogonal polynomial expansion, and the polynomial expansion of system influencing factors and system transient voltage stability can be established.

[0144] (2) Modeling is performed based on Sklar’s ​​theorem and the copula function of the mvG-copula method is used to generate relevant input data sets of load, wind power and solar energy influencing factors.

[0145] (3) The weighted sum of multivariate orthogonal polynomial basis is introduced based on the Sobol index to improve the global sensitivity of transient voltage: the statistical data of the system output is directly based on the model formula and the specified moments of the input data, and the response changes of the transient voltage at the system load point are expanded on the basis of the normalized polynomial.

[0146] Specific examples:

[0147] This embodiment takes the CSEE-VS example published by the China Electric Power Research Institute as an example. The system contains a total of 100 nodes, of which 500kV main grid has 48 nodes. The installed capacity ratio of new energy and conventional power sources is 1:2.62. There is a DC circuit in the system, and the DC input power is 800,000 kilowatts. In the power system model summary, wind farms are numbered 1-6, photovoltaic power stations are numbered 7-12, and dynamic load nodes are numbered 13-33. Calculate the transient voltage sensitivity of the new energy output and load fluctuations at these 33 nodes, such as Figure 5-Figure 8 As shown, Figure 5 The transient voltage sensitivity distribution law is shown. Figure 6 The transient voltage sensitivity distribution law of wind farm is shown. Figure 7 The transient voltage sensitivity distribution law of the photovoltaic station is shown. Figure 8 The transient voltage sensitivity distribution law of dynamic load points is shown. In the modified IEEE-39 node system, such as Fig. 9 As shown in the figure, there are two wind farms and two photovoltaic power stations, each with a capacity of 200MW. The aPC-Sobol sensitivity, FAST sensitivity and Sobol sensitivity are calculated for the same influencing factor, and the ranking results of the influencing factors are compared, as shown in the figure. Fig.10 As shown, Fig.10 Part (a) shows the transient voltage sensitivity distribution of the 39-bus system aPC-sobol, Fig.10 Part (b) shows the transient voltage sensitivity distribution of the 39-bus FAST system. Fig.10 Part (c) shows the transient voltage sensitivity distribution of the 39-bus system SOBOLI.

[0148] The specific implementation is as follows:

[0149] Step 1: Considering the correlation between renewable energy generation and its uncertainty with dynamic load, modeling is performed based on Sklar theorem, and the copula function C of the mvG-copula method is used to generate relevant input data sets of load, wind power and solar energy influencing factors.

[0150] Step 2: Introduce a quantitative system transient voltage stability index that traverses the transient voltage change values ​​of all nodes in the system. The proposed index can quantify the transient voltage stability of the system. In addition, compared with other indicators, the variation range of SVDI(t) is narrower, highlighting a more concentrated variation range, thereby simplifying their interpretation process. The expression of SVDI(t) is not repeated here.

[0151] Taking into account the huge changes in the characteristics of the power system with a high proportion of renewable energy connected to the grid, the principle of arbitrary chaotic polynomial expansion involves the theory of nonlinear dynamics and complex systems. By realizing orthogonal polynomial expansion, the dependence of model output on model parameters can be simulated, which can be used to quantitatively characterize the impact of renewable energy proportion and dynamic load proportion on the transient voltage stability of the system, and a new transient voltage stability index is introduced to quantify the transient voltage stability of the system as the model output.

[0152] In the aPC expansion, the input parameters are low-dimensionally mapped to reduce the data dimension. The aPC expansion expression and the expression of the polynomial coefficients are not repeated here.

[0153] Step 3: Improve the global sensitivity of transient voltage by introducing the weighted sum of multivariate orthogonal polynomial basis based on Sobol index: Provide a global sensitivity measurement method similar to the weighted square norm of sensitivity, which reflects the impact of new energy and load changes on the transient voltage stability of the system. The final expression of transient voltage weighted global sensitivity index will not be repeated here.

[0154] The transient voltage sensitivity distribution law of various influencing factors in this application in a grid-connected system with a high proportion of new energy is shown in the figure below: Figure 5-Figure 8 The average value and the maximum difference are shown in Table 1.

[0155] Table 1 Average values ​​and maximum differences of transient voltage sensitivity of various influencing factors

[0156]

[0157]

[0158] This experiment uses data from one second after the fault for calculation, that is, within 6s-7s; the transient voltage sensitivity of parameters 1-6 is much smaller than that of other parameters, and parameters 7-12 are slightly smaller than those of parameters 13-33; among them, the transient voltage sensitivity of parameter 25 is significantly higher than that of other parameters. Fig.10 It can be seen that the order of aPC-Sobol transient sensitivity of each influencing factor is basically consistent with the order of FAST sensitivity and Sobol sensitivity. This shows the accuracy of the method proposed in this application. The number of simulations of the method proposed in this application is much less than that of the above two comparison methods, and the calculation efficiency is much higher.

[0159] Based on the same inventive concept, the embodiment of the present application also provides a device for determining key influencing factors of transient voltage for implementing the method for determining key influencing factors of transient voltage involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more devices for determining key influencing factors of transient voltage provided below can refer to the limitations of the method for determining key influencing factors of transient voltage above, and will not be repeated here.

[0160] In an exemplary embodiment, a device for determining key influencing factors of transient voltage is provided, comprising:

[0161] The data set determination module is used to determine the relevant input data set of the power system including the new energy grid-connected power system; the relevant input data set includes the dynamic load power, the output power of each wind farm station and the output power of each photovoltaic power station as influencing factors.

[0162] The model building module is used to model the power system including the grid-connected renewable energy sources to obtain the power system model.

[0163] The transient time domain simulation module is used to use the relevant input data set as the input of the power system model under the set fault type and the set fault location, perform multiple transient time domain simulations on the power system model, and calculate the transient voltage stability index output by the power system model in each transient time domain simulation; wherein the number of transient time domain simulations is equal to the number of polynomials of any chaotic polynomial.

[0164] The polynomial coefficient determination module is used to determine the polynomial coefficients of any chaotic polynomial using a non-invasive probability configuration method according to the relevant input data set and the transient voltage stability index.

[0165] A global sensitivity calculation module is used to calculate the transient voltage weighted global sensitivity index of each influencing factor in the relevant input data set using the Sobol index according to the polynomial coefficients.

[0166] The key influencing factor determination module is used to determine the key influencing factors of transient voltage according to the transient voltage weighted global sensitivity index; the transient voltage key influencing factor is the influencing factor whose transient voltage weighted global sensitivity index in the relevant input data set is greater than a set value.

[0167] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.11 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store relevant input data sets of the power system including the grid-connected new energy. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining key influencing factors of transient voltage is implemented.

[0168] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0169] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0170] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0171] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0172] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0173] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, etc., but is not limited thereto.

[0174] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0175] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for determining key influencing factors of transient voltage, characterized in that: The method for determining the key influencing factors of transient voltage includes: Determine a relevant input data set of the power system including the new energy grid-connected power system; the relevant input data set includes the dynamic load power, the output power of each wind farm station and the output power of each photovoltaic power station as an influencing factor; Model the power system including the new energy grid connection to obtain the power system model; Under the set fault type and the set fault location, the relevant input data set is used as the input of the power system model, the power system model is subjected to multiple transient time domain simulations, and the transient voltage stability index output by the power system model in each transient time domain simulation is calculated; wherein the number of transient time domain simulations is equal to the number of polynomials of any chaotic polynomial; Determining polynomial coefficients of an arbitrary chaotic polynomial using a non-invasive probabilistic collocation method according to the relevant input data set and the transient voltage stability index; Calculating the transient voltage weighted global sensitivity index of each influencing factor in the relevant input data set using the Sobol index according to the polynomial coefficients; The key influencing factors of transient voltage are determined according to the weighted global sensitivity index of transient voltage; the key influencing factors of transient voltage are influencing factors whose weighted global sensitivity index of transient voltage in the relevant input data set is greater than a set value.

2. The method for determining key influencing factors of transient voltage according to claim 1, characterized in that: Determine the relevant input data sets for the power system with renewable energy grid connection, including: Generate the initial input data set based on Sklar's theorem and the copula function in the mvG-copula method; The initial input data set is corrected by using the inverse of the copula function in the mvG-copula method, and the corrected initial input data set is used as the relevant input data set; the dynamic load power in the relevant input data set obeys the normal distribution, the output power of each wind farm station obeys the Weibull distribution, and the output power of each photovoltaic power station obeys the β distribution.

3. The method for determining key influencing factors of transient voltage according to claim 1, characterized in that: Under the set fault type and the set fault location, the relevant input data set is used as the input of the power system model, the power system model is subjected to multiple transient time domain simulations, and the transient voltage stability index output by the power system model in each transient time domain simulation is calculated, specifically including: Determining the order of any chaotic polynomial according to the number of influencing factors in the relevant input data set, and calculating the number of polynomials of the arbitrary chaotic polynomial according to the order and the number of influencing factors in the relevant input data set; Determine the set fault type and the set fault location; Based on the number of polynomials, under the set fault type and the set fault location, the relevant input data set is used as the input of the power system model, and the power system model is subjected to multiple transient time domain simulations using differential algebraic equations to obtain the bus voltages of all nodes in the power system model under each transient time domain simulation; For any transient time domain simulation, the transient voltage stability index output by the power system model of the transient time domain simulation is calculated according to the bus voltages of all nodes in the power system model under the transient time domain simulation.

4. The method for determining key influencing factors of transient voltage according to claim 3, characterized in that: The calculation formula of transient voltage stability index is: Where SVDI(t) represents the transient voltage stability index at time t; V i (t) represents the bus voltage of node i at time t; V i pre represents the bus voltage before the fault of node i; V i min Indicates the minimum bus voltage allowed at node i; N b Represents the total number of nodes in the power system model.

5. The method for determining key influencing factors of transient voltage according to claim 1, characterized in that: The expression of the polynomial coefficient of any chaotic polynomial is: M Ψ (ω)V c (x,t)=V Ω (x,t;ω); Among them, V c (x, t) represents the M×1-order vector composed of the polynomial coefficients of any chaotic polynomial in space x at time t and at all transient time-domain simulation times; V Ω (x, t; ω) represents the M×1-order vector composed of the transient voltage stability index output by the power system model under all transient time-domain simulation times when the influencing factor ω in space x at time t is used as the input of the power system model; M Ψ (ω) represents an M×N matrix of polynomials evaluated at the collocation points.

6. The method for determining key influencing factors of transient voltage according to claim 1, characterized in that: The calculation formula of transient voltage weighted global sensitivity index is: in, represents the weighted global sensitivity index of transient voltage; M represents the number of polynomials of any chaotic polynomial; c k represents the polynomial coefficient of the kth term in any chaotic polynomial; ω j represents the jth influencing factor; Λ represents the relevant input data set; Represents symbolic parameters; represents the orthogonality factor; represents the orthogonal polynomial basis; d represents the order of any chaotic polynomial; Γ(ω j ) represents the probability measure of the jth influencing factor in the probability space.

7. The method for determining key influencing factors of transient voltage according to claim 3, characterized in that: The formula for calculating the number of polynomials is: M = (N + d)! / N! d! -1; Among them, M represents the number of polynomials of any chaotic polynomial; N represents the number of influencing factors in the relevant input data set; d represents the order of any chaotic polynomial.

8. A device for determining key influencing factors of transient voltage, characterized in that: The device for determining key influencing factors of transient voltage comprises: A data set determination module is used to determine the relevant input data set of the power system including the new energy grid-connected power system; the relevant input data set includes the dynamic load power, the output power of each wind farm station and the output power of each photovoltaic power station as an influencing factor; A model building module is used to model the power system including the new energy grid connection to obtain the power system model; A transient time domain simulation module is used to use the relevant input data set as the input of the power system model under the set fault type and the set fault location, perform multiple transient time domain simulations on the power system model, and calculate the transient voltage stability index output by the power system model in each transient time domain simulation; wherein the number of transient time domain simulations is equal to the number of polynomials of any chaotic polynomial; A polynomial coefficient determination module, used for determining the polynomial coefficients of any chaotic polynomial using a non-invasive probability configuration method according to the relevant input data set and the transient voltage stability index; A global sensitivity calculation module, used to calculate the transient voltage weighted global sensitivity index of each influencing factor in the relevant input data set using the Sobol index according to the polynomial coefficients; The key influencing factor determination module is used to determine the key influencing factors of transient voltage according to the transient voltage weighted global sensitivity index; the transient voltage key influencing factor is the influencing factor whose transient voltage weighted global sensitivity index in the relevant input data set is greater than a set value.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for determining key influencing factors of transient voltage according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for determining key influencing factors of transient voltage described in any one of claims 1 to 7 is implemented.

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