Power system forward uncertainty quantization method based on reverse uncertainty quantization

Abstract

The invention discloses an electric power system forward uncertainty quantization method based on reverse uncertainty quantization, which comprises the following steps: firstly, establishing an uncertainty model of transient parameters by adopting reverse uncertainty quantization, and obtaining a parameter sample conforming to the model by adopting an MCMC method; unification of the number of reverse uncertainty quantization samples and the number of samples needed by forward uncertainty quantization is achieved, and finally forward uncertainty quantization is conducted on system output. According to the method, a reasonable transient parameter uncertainty model is constructed through reverse uncertainty quantification and is applied to uncertainty quantification of the output result of the power system, so that the accuracy of the uncertainty quantification result of the novel power system is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of power system dynamics and uncertainty quantification, and in particular relates to a power system forward uncertainty quantification method based on inverse uncertainty quantification. Background Art

[0002] The new power system is transforming into a strong uncertainty system, and the uncertainty comes from model parameters, simulation algorithms, etc. The uncertainty of model parameters will affect the dynamic behavior of the system and then affect the system decision-making. Therefore, it is necessary to quantify its uncertainty. The premise for obtaining accurate uncertainty quantification results is to establish a correct uncertainty model.

[0003] For some model parameters, such as generator output and load, their uncertainty models can be fitted through historical data. However, for transient parameters of the power system, such as inertia and damping, their uncertainty models cannot be accurately characterized due to the lack of historical data. Most of the existing methods are model assumption methods, which will lead to inaccurate uncertainty quantification results. Therefore, how to improve the accuracy of power system uncertainty quantification results has become a technical problem and difficulty that technicians in this field want to overcome. Summary of the invention

[0004] The present invention is aimed at the difficulty of obtaining accurate uncertainty quantification results of power systems in the prior art, and provides a method for quantifying forward uncertainty of power systems based on inverse uncertainty quantification. First, the uncertainty model of transient parameters is established by inverse uncertainty quantification, and the parameter samples that conform to the model are obtained by MCMC method, and then the number of samples of inverse uncertainty quantification is unified with the number of samples required for forward uncertainty quantification, and finally the system output is forwardly quantified. The method of the present invention constructs a reasonable transient parameter uncertainty model through inverse uncertainty quantification, and applies it to the uncertainty quantification of power system output results, thereby improving the accuracy of uncertainty quantification results of new power systems.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: a forward uncertainty quantification method of a power system based on inverse uncertainty quantification, comprising the following steps:

[0006] S1. Model establishment: Use inverse uncertainty quantification to establish the uncertainty model of transient parameters;

[0007] S2, parameter sample acquisition: use MCMC method to obtain parameter samples that conform to the model in step S1;

[0008] S3, sample number unification: realize the unification of the number of samples required for reverse uncertainty quantification and forward uncertainty quantification;

[0009] S4. Forward uncertainty quantification: Quantify the forward uncertainty of the power system output.

[0010] As an improvement of the present invention, the establishment of the uncertainty model in step S1 specifically includes the following steps:

[0011] S11: Building a framework for Bayesian inference:

[0012] d=h(z)+e

[0013] Where d is a measurement vector of dimension m; z is a vector of dimension N, whose components are transient parameters that need to be quantified; h(·) is a function vector that maps z to d; e is a measurement error vector, including active and reactive errors, whose joint probability density distribution is:

[0014]

[0015] S12: The expression of the posterior probability density function is:

[0016] π post (z|d)∝π like (d|z)π prior (z)

[0017] Among them, π prior (z) is the prior probability distribution of z, π like (d|z) is the likelihood function, π post (z|d) is the posterior probability distribution of z;

[0018] S13: Select simulation time t0 to t f , the logarithmic form of π post (z|d) is:

[0019]

[0020] in, and are the likelihood, measurement and simulation results at time t respectively;

[0021] S14: Calculate the maximum a posteriori value of z, expressed as:

[0022]

[0023] As another improvement of the present invention, the likelihood function in step S12 is specifically:

[0024]

[0025] Among them, d i is the i-th component of the measurement vector d;

[0026] Assuming that the components of z are independent of each other, the calculation formula of the prior probability distribution function is:

[0027]

[0028] As another improvement of the present invention, the logarithmic form of π in step S13 post (z|d) is formed, comprising the following steps:

[0029] S131: To avoid the dimensionality curse problem of subsequent MCMC sampling, a decentralized model is adopted. Assume that the voltage and phase of the generator node are directly obtained from the PMU device, and the algebraic variable V di ,V qi ,I di ,I qi The calculation expression is:

[0030] V di =V i sin(δ i -θ vi )

[0031] V qi =V i cos(δ i -θ vi )

[0032]

[0033] Among them, V di ,V qi are the direct-axis and quadrature-axis components of the voltage at the i-th node, respectively; I di ,I qi are the direct-axis and quadrature-axis components of the current at the i-th node, respectively; V i ,θ vi are the amplitude and phase angle of the voltage at the ith generator node; E di ′,E qi ′ are the direct-axis and quadrature-axis transient electromotive forces of the i-th generator respectively; X di ′,X qi ′ are the direct-axis and quadrature-axis subtransient reactances of the i-th generator respectively; δ i is the power angle of the ith generator;

[0034] S132: Calculate the active and reactive outputs of the i-th generator, which are:

[0035] P ei =V di I di +V qi I qi +ePi

[0036] Q ei =-V di I qi +V qi I di +e Qi

[0037] Among them, P ei ,Q ei are the active and reactive outputs of the ith generator respectively; e Pi ,e Qi are the errors of active power and reactive power respectively;

[0038] S133: Logarithmic form of π based on a decentralized model post (z|d) can be rewritten as:

[0039]

[0040] As another improvement of the present invention, the parameter sample acquisition in step S2 specifically includes the following steps:

[0041] S21: for k=0,...,N inv -1, from the assumed proposal distribution q(z k ,·) Generate a new sample z * , where N inv The number of samples generated that meet the inverse uncertainty quantification results;

[0042] S22: Calculate log(π post (z * |d));

[0043] S23: Calculate the reception rate α(z k ,z * ), the expression is:

[0044]

[0045] S24: randomly generate a number u from a uniform distribution in the interval [0,1];

[0046] S25: If u<α(z k ,z * ), then z k+1 =z * Otherwise z k+1 =z k ;

[0047] S26: Repeat steps S21-S25 until k=N inv -1, and we get the result that obeys π postN of (z|d) inv parameter samples.

[0048] As another improvement of the present invention, the step S3 of unifying the number of samples specifically includes the following steps:

[0049] S31: Get the marginal cumulative density function of z with the help of kernel density estimation:

[0050]

[0051] Among them, P i For z i Cumulative density function of K i For z i The kernel function of B i is bandwidth; Yes i The jth sample of inv The number of samples generated that meet the inverse uncertainty quantification results;

[0052] S32: Generate samples for forward uncertainty quantification with the help of Gaussian Cupula, the number of samples is set to N for , convert the samples obtained by MCMC sampling in step S2 into samples under Gaussian distribution:

[0053] z i ′=φ -1 (z i )

[0054] Among them, z i ′ is a sample under Gaussian distribution; φ is the cumulative density function of standard normal distribution;

[0055] Perform Cholesky decomposition on the correlation matrix of z′:

[0056] D=L·L T

[0057] Where D is the correlation matrix;

[0058] Generate N for Samples M under Gaussian distribution that conform to the correlation matrix D:

[0059] M=L·R

[0060] Where R is a randomly generated N for It is composed of standard normal distribution samples, with dimension N;

[0061] Get samples that obey their respective marginal distributions:

[0062] M i ′=P i-1 (φ(M i ))

[0063] Among them, M i ' is to obey z i The number of samples from the marginal distribution is N for .

[0064] As a further improvement of the present invention, step S4 specifically includes the following steps:

[0065] S41: Substitute the transient parameter z in the power system transient model with the corresponding N for Sample replacement;

[0066] S42: Modeling the uncertainty of generator output and load in power systems using stochastic differential equations:

[0067] S43: Perform dynamic simulation of power systems and quantify the uncertainty of output results.

[0068] Compared with the prior art, the technical advantages and technical effects of the present invention are as follows: in view of the era characteristics of strong uncertainty of new power systems, the present invention proposes a method for quantifying forward uncertainty of power systems based on inverse uncertainty quantification. The method constructs a reasonable transient parameter uncertainty model through inverse uncertainty quantification, and applies it to the uncertainty quantification of the output results, thereby improving the accuracy of the uncertainty quantification results of the power system. At the same time, in order to avoid the dimensionality curse problem faced by MCMC sampling, the present invention performs inverse uncertainty quantification based on a decentralized model. In addition, in view of the fact that the number of samples for inverse uncertainty quantification is generally much larger than the number of samples required for forward uncertainty quantification, the present invention combines kernel density estimation and Gaussian Copula to achieve the unification of the number of samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a flowchart of the steps of the method for quantifying forward uncertainty of a power system based on inverse uncertainty quantification of the present invention;

[0070] Figure 2 It is a schematic diagram of the structure of the New England 39 busbar 10 generator system to which the method of the present invention is applied;

[0071] Figure 3 is a schematic diagram of the inverse uncertainty quantification result in Test Example 1 of the present invention;

[0072] Figure 4 It is a schematic diagram of the comparison results of the mean values ​​obtained by the forward uncertainty quantification based on the method of the present invention and the model assumption method in Test Example 1 of the present invention;

[0073] Figure 5It is a schematic diagram of the comparison results of the standard deviations obtained by the forward uncertainty quantification based on the method of the present invention and the model assumption method in Test Example 1 of the present invention;

[0074] Figure 6 It is a schematic diagram of the comparison results of the mean values ​​obtained by the forward uncertainty quantification based on the method of the present invention and the improved model assumption method in Test Example 2 of the present invention;

[0075] Figure 7 It is a schematic diagram of the comparison results of the standard deviations obtained by the forward uncertainty quantification based on the method of the present invention and the improved model assumption method in Test Example 2 of the present invention. DETAILED DESCRIPTION

[0076] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0077] Example 1

[0078] The present invention is mainly aimed at the power system industry to realize the importance of uncertainty model for uncertainty quantification. Taking into account the problem that accurate uncertainty model cannot be constructed for transient parameters due to lack of historical data, a forward uncertainty quantification method for power system based on inverse uncertainty quantification is proposed, aiming to improve the accuracy of uncertainty quantification results of new power systems.

[0079] The method of this embodiment is applied to Figure 2 The power system shown, Figure 2 The schematic diagram of the classic New England 39-bus 10-generator system is shown in Figure 1. The forward uncertainty quantification method of the power system based on the inverse uncertainty quantification is shown in Figure 2. Figure 1 As shown, the following steps are included:

[0080] Step S1, using inverse uncertainty quantification to establish an uncertainty model of transient parameters;

[0081] S11. Establish the framework of Bayesian reasoning, the expression is:

[0082] d=h(z)+e

[0083] Where d is a measurement vector of dimension m, whose data comes from the PMU device; z is a vector of dimension N, whose components are transient parameters that need to be quantified; h(·) is a function vector that maps z to d; e is a measurement error vector, which mainly consists of active and reactive errors. It is usually assumed that its components are independent of each other, so its joint probability density distribution can be expressed as:

[0084]

[0085] S12. The expression of the posterior probability density function is obtained as follows:

[0086] π post (z|d)∝π like (d|z)π prior (z)

[0087] Among them, π prior (z) is the prior probability distribution of z, π like (d|z) is the likelihood function, π post (z|d) is the posterior probability distribution of z;

[0088] The calculation formula of the likelihood function is:

[0089]

[0090] Among them, d i is the i-th component of the measurement vector d;

[0091] In this embodiment, assuming that the components of z are independent of each other, the calculation formula of the prior probability distribution function is:

[0092]

[0093] S13, select simulation time t0 to t f , the logarithmic form of π post (z|d) can be further expressed as:

[0094]

[0095] in, and are the likelihood, measurement and simulation results at time t respectively;

[0096] The logarithmic form of π formed in step S13 post In (z|d), in order to avoid the dimensionality curse problem of subsequent MCMC sampling, a decentralized model is adopted; it is assumed that the voltage and phase of the generator node are directly obtained from the PMU device, and the algebraic variable V di ,V qi ,I di ,I qi The calculation expression is:

[0097] V di =V i sin(δ i -θ vi )

[0098] V qi =V i cos(δ i -θvi )

[0099]

[0100] Among them, V di ,V qi are the direct-axis and quadrature-axis components of the voltage at the i-th node, respectively; I di ,I qi are the direct-axis and quadrature-axis components of the current at the i-th node, respectively; V i ,θ vi are the amplitude and phase angle of the voltage at the ith generator node; E di ′,E qi ′ are the direct-axis and quadrature-axis transient electromotive forces of the i-th generator respectively; X di ′,X qi ′ are the direct-axis and quadrature-axis subtransient reactances of the i-th generator respectively; δ i is the power angle of the ith generator;

[0101] Calculate the active and reactive output of the i-th generator, the expression is:

[0102] P ei =V di I di +V qi I qi +e Pi

[0103] Q ei =-V di I qi +V qi I di +e Qi

[0104] Among them, P ei ,Q ei are the active and reactive outputs of the ith generator respectively; e Pi ,e Qi are the errors of active power and reactive power respectively;

[0105] Based on the decentralized model, the logarithmic form of π post (z|d) can be further rewritten as:

[0106]

[0107] S14, the maximum a posteriori value of z can be expressed as:

[0108]

[0109] Step S2, using the MCMC method to obtain a parameter sample that conforms to the model;

[0110] S21, for k = 0, ..., N inv -1, from the assumed proposal distribution q(z k ,·) Generate a new sample z * ;

[0111] S22. Calculate log(π post (z * |d));

[0112] S23, calculate the receiving rate α(z k ,z * ), the expression is:

[0113]

[0114] S24. Randomly generate a number u from a uniform distribution in the interval [0,1];

[0115] S25. If u<α(z k ,z * ), then z k+1 =z * Otherwise z k+1 =z k ;

[0116] S26: Repeat steps S21, S22, S23, S24, and S25 in sequence until k=N inv -1, then the MCMC method can be used to obtain the post N of (z|d) inv parameter samples.

[0117] Step S3, unifying the number of samples required for reverse uncertainty quantization and the number of samples required for forward uncertainty quantization;

[0118] The marginal cumulative density function of z is obtained by kernel density estimation:

[0119]

[0120] Among them, P i For z i Cumulative density function of K i For z i The kernel function of B i is bandwidth; Yes i The jth sample of inv The number of samples generated that meet the inverse uncertainty quantification results;

[0121] Use Gaussian Cupula to generate samples for forward uncertainty quantification, and the number of samples is set to N for; Convert the samples obtained by MCMC sampling into samples under Gaussian distribution, the expression is:

[0122] z i ′=φ -1 (z i )

[0123] Among them, z i ′ is a sample under Gaussian distribution; φ is the cumulative density function of standard normal distribution;

[0124] Perform Cholesky decomposition on the correlation matrix of z′, the expression is:

[0125] D=L·L T

[0126] Where D is the correlation matrix;

[0127] Generate N for Samples M under Gaussian distribution that conform to the correlation matrix D:

[0128] M=L·R

[0129] Where R is a randomly generated N for It is composed of standard normal distribution samples, with dimension N;

[0130] Further obtain samples that obey their respective marginal distributions:

[0131]

[0132] Among them, M i ' is to obey z i The number of samples from the marginal distribution is N for .

[0133] Step S4, quantifying the forward uncertainty of the system output;

[0134] S41, the transient parameter z in the power system transient model is converted into the corresponding N for Sample replacement;

[0135] S42. Modeling the uncertainty of generator output and load in power system with the help of stochastic differential equations;

[0136] S43. Perform dynamic simulation of the power system to quantify the uncertainty of the system output results.

[0137] Test Case

[0138] Test Example 1

[0139] based on Figure 2 For the generator system shown in the figure, we select the inertia H of the motor connected to the system node 31 and the excitation parameter KA ,K E ,K F As the research object. Assume that the prior probability of these parameters follows a Gaussian distribution, where the mean is the value provided by the manufacturer and the standard deviation is 10% of the mean. The proposed distribution q follows a multidimensional Gaussian distribution, with the mean being the last accepted value and the standard deviation being 0.03% of that value. The components of the measurement error vector e follow a Gaussian distribution with a mean of 0 and a standard deviation of 0.01. Assume that the external disturbance is to remove the transmission line between nodes 15-16 after 0.05s. Assume that the number of samples sampled by MCMC is 100,000.

[0140] First, the parameters H,K are obtained by inverse uncertainty quantification A ,K E ,K F The uncertainty model is then used to obtain parameter samples using the MCMC method to characterize the uncertainty model. Figure 3 The uncertainty model of each parameter is shown. As can be seen from the figure, H, K A ,K E ,K F It does not follow some common distributions, for example, K E There are two peaks in the probability distribution diagram. In addition, the relationship between the parameters can also be obtained from the diagram, for example, K A and K E There is a positive correlation between F and K A ,K E There is a negative correlation between H and K A ,K E ,K F They are all relatively independent. This fully proves that the inverse uncertainty quantification can obtain the accurate probability distribution of each parameter and the correlation between parameters, which is not obtained by the parameter uncertainty model assumption method currently used. This also explains the rationality of the present invention using inverse uncertainty quantification to obtain the transient parameter uncertainty model.

[0141] This test case uses the Monte Carlo method to quantify the forward uncertainty of the active output of the motor connected to node 31, and sets the number of Monte Carlo simulations to 5000 times. The simulation is the transient process of cutting off line 15-16 after 2s, and the total simulation time is 20s. This test case selects the model assumption method widely used in the current transient parameter uncertainty quantification research for comparison. This method assumes that the uncertainty model of the transient parameter is characterized by some common distributions. In this test case, it is assumed that the parameters obey two distributions, one is a Gaussian distribution, whose mean is the value given by the manufacturer and the standard deviation is 10% of the mean; the other is a uniform distribution, whose mean is the value given by the manufacturer and the interval length is 60% of the mean.

[0142] Based on the parameter uncertainty models obtained by the method of this case and the model assumption method, the Monte Carlo method is used to obtain the forward uncertainty quantification results of the active output of the motor connected to node 31. When analyzing the results, the mean and standard deviation are selected as the research objects. Figure 4 , Figure 5 The comparison results of the mean and standard deviation obtained by the method of this case and the model assumption method are shown respectively. Figure 4 It can be seen that the mean obtained by the method proposed in this case is basically the same as the mean obtained by using the Gaussian and uniform assumptions in the first 6 seconds, but as time evolves, the mean results differ greatly. Figure 5 It can be seen that the standard deviation of the results obtained by the method of the present invention is very different from the results obtained by the model assumption method after 2 seconds. This reflects that different assumed models will lead to different final uncertainty quantification results, which also confirms the importance of uncertainty modeling and further proves the rationality of the method proposed in this case.

[0143] Test Example 2

[0144] In order to ensure the comprehensiveness of the results, this test case improves the transient parameter uncertainty model assumed in the model assumption method of test case 1, replacing the mean of Gaussian and uniform distributions with the maximum a posteriori value obtained by inverse uncertainty quantification, and the other parameters remain unchanged. This test case quantifies the uncertainty of the active output of the motor connected to node 31, and selects its mean and standard deviation as the research objects when analyzing the results. Figure 6 , Figure 7 The comparison results of the mean and standard deviation obtained by this method and the improved model assumption method are shown respectively. Figure 6 It can be seen that the difference between the mean result obtained by the proposed method and the mean obtained by the improved model assumption method is Figure 4 Significantly reduced, but Figure 7 The standard deviation results are still very different. These simulation results confirm that even if the mean of the assumed model is improved, its confidence interval cannot be determined, and the uncertainty model of the transient parameters cannot be accurately characterized, and thus the correct transient parameter uncertainty quantification results cannot be obtained. This confirms the importance of transient parameter uncertainty modeling, and further demonstrates the rationality of the method of the present invention.

[0145] In summary, the present invention proposes a forward uncertainty quantification method for an electric power system based on inverse uncertainty quantification. Inverse uncertainty quantification is used to obtain a reasonable transient parameter uncertainty model, and the system output results are quantified to improve the accuracy of the uncertainty quantification results of the new electric power system, ensure the rationality of system decision-making, and provide a guarantee for the accuracy of the uncertainty quantification results of the new electric power system.

[0146] It should be noted that the above content only illustrates the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications all fall within the protection scope of the claims of the present invention.

Claims

1. A forward uncertainty quantification method for power systems based on inverse uncertainty quantification, characterized in that: The steps include: S1. Model establishment: Use inverse uncertainty quantification to establish the uncertainty model of transient parameters; S2, parameter sample acquisition: use MCMC method to obtain parameter samples that conform to the model in step S1; S3, sample number unification: realize the unification of the number of samples required for reverse uncertainty quantification and forward uncertainty quantification; S4. Forward uncertainty quantification: Quantify the forward uncertainty of the power system output.

2. The method for quantifying forward uncertainty of a power system based on inverse uncertainty quantification according to claim 1, characterized in that: The establishment of the uncertainty model in step S1 specifically includes the following steps: S11: Building a framework for Bayesian inference: d=h(z)+e Where d is a measurement vector of dimension m; z is a vector of dimension N, whose components are transient parameters that need to be quantified; h(·) is a function vector that maps z to d; e is a measurement error vector, including active and reactive errors, whose joint probability density distribution is: S12: The expression of the posterior probability density function is: π post (z|d)∝π like (d|z)π prior (With) Among them, π prior (z) is the prior probability distribution of z, π like (d|z) is the likelihood function, π post (z|d) is the posterior probability distribution of z; S13: Select simulation time t0 to t f , the logarithmic form of π post (z|d) is: in, and are the likelihood, measurement and simulation results at time t respectively; S14: Calculate the maximum a posteriori value of z, expressed as:

3. The method for quantifying forward uncertainty of a power system based on inverse uncertainty quantification according to claim 2, characterized in that: The likelihood function in step S12 is specifically: Among them, d i is the i-th component of the measurement vector d; Assuming that the components of z are independent of each other, the calculation formula of the prior probability distribution function is:

4. The method for quantifying forward uncertainty of a power system based on inverse uncertainty quantification according to claim 2, characterized in that: The logarithmic form of π in step S13 post (z|d) is formed, comprising the following steps: S131: Using a decentralized model, it is assumed that the voltage and phase of the generator node are directly obtained from the PMU device, and the algebraic variable V di ,V qi ,I di ,I qi The calculation expression is: V di =V i sin(δ i -θ vi ) V qi =V i cos(δ i -θ vi ) Among them, V di ,V qi are the direct-axis and quadrature-axis components of the voltage at the i-th node, respectively; I di ,I qi are the direct-axis and quadrature-axis components of the current at the i-th node, respectively; V i ,θ vi are the amplitude and phase angle of the voltage at the ith generator node; E di ′,E qi ′ are the direct-axis and quadrature-axis transient electromotive forces of the i-th generator respectively; X di ′,X qi ′ are the direct-axis and quadrature-axis subtransient reactances of the i-th generator respectively; δ i is the power angle of the ith generator; S132: Calculate the active and reactive outputs of the i-th generator, which are: P ei =V di I di +V qi I qi +e Pi Q ei =-V di I qi +V qi I di +e Qi Among them, P ei ,Q ei are the active and reactive outputs of the ith generator respectively; e Pi ,e Qi are the errors of active power and reactive power respectively; S133: In the decentralized model, the logarithmic form of π post (z|d) can be rewritten as:

5. The method for quantifying forward uncertainty of a power system based on inverse uncertainty quantification according to claim 3 or 4, characterized in that: The parameter sample acquisition in step S2 specifically includes the following steps: S21: for k=0,...,N inv -1, from the assumed proposal distribution q(z k ,·) Generate a new sample z * , where N inv The number of samples generated that meet the inverse uncertainty quantification results; S22: Calculate log(π post (z * |d)); S23: Calculate the reception rate α(z k ,z * ), the expression is: S24: randomly generate a number u from a uniform distribution in the interval [0,1]; S25: If u<α(z k ,z * ), then z k+1 =z * Otherwise z k+1 =z k ; S26: Repeat steps S21-S25 until k=N inv -1, and we get the result that obeys π post N of (z|d) inv parameter samples.

6. The method for quantifying forward uncertainty of a power system based on inverse uncertainty quantification according to claim 5, characterized in that: The step S3 of unifying the number of samples specifically includes the following steps: S31: Get the marginal cumulative density function of z with the help of kernel density estimation: Among them, P i For z i Cumulative density function of K i For z i The kernel function of B i is the bandwidth; z i j Yes i The jth sample of inv The number of samples generated that meet the inverse uncertainty quantification results; S32: Generate samples for forward uncertainty quantification with the help of Gaussian Cupula, the number of samples is set to N for , convert the samples obtained by MCMC sampling in step S2 into samples under Gaussian distribution: With i ′=φ -1 (With i ) Among them, z i ′ is a sample under Gaussian distribution; φ is the cumulative density function of standard normal distribution; Perform Cholesky decomposition on the correlation matrix of z′: D=L·L T Where D is the correlation matrix; Generate N for Samples M under Gaussian distribution that conform to the correlation matrix D: M=L·R Where R is a randomly generated N for It is composed of standard normal distribution samples, with dimension N; Get samples that obey their respective marginal distributions: Among them, M i ' is to obey z i The number of samples from the marginal distribution is N for .

7. The method for quantifying forward uncertainty of a power system based on inverse uncertainty quantification according to claim 6, characterized in that: The step S4 specifically includes the following steps: S41: Substitute the transient parameter z in the power system transient model with the corresponding N for Sample replacement; S42: Modeling the uncertainty of generator output and load in power systems using stochastic differential equations: S43: Perform dynamic simulation of power systems and quantify the uncertainty of output results.

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