Alternating current-direct current power grid probability static voltage stability margin calculation method

Abstract

The invention discloses an AC / DC power grid probability static voltage stability margin calculation method, and relates to the technical field of AC / DC power grid safety assessment. Based on historical data, an AC / DC power grid uncertain source load output probability model is constructed, and the uncertain source load output is used as input; and taking the probability static voltage stability margin of the alternating current-direct current power grid as output, and establishing a probability static voltage stability margin calculation model of the alternating current-direct current power grid. Through a small-scale model input-output sample, an agent model for calculating the probability static voltage stability margin of the AC / DC power grid is constructed. And based on the agent model and in combination with a Monte Carlo simulation method, obtaining a probability static voltage stability margin result of the AC-DC power grid, and quantitatively evaluating the probability static voltage stability of the AC-DC power grid. Probability static voltage safety analysis is achieved, and therefore safe and reliable operation of an alternating current and direct current power grid is guaranteed. By introducing the proxy model, the calculation efficiency of the probability static voltage stability margin of the AC / DC power grid is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of AC / DC power grid security assessment, and in particular to a method for calculating the probabilistic static voltage stability margin of an AC / DC power grid. Background Art

[0002] With the rapid development of society, the number of grid-connected loads is increasing, and grid operation is becoming more and more close to the limit operation state. Under the limit operation state, the grid may suffer voltage instability due to the sudden increase in load, which may cause more serious voltage collapse, resulting in huge economic and livelihood losses. The static voltage stability margin of AC / DC power grid reflects the gap between the current operation state of the system and the limit operation state, and can be used to quantitatively evaluate the static voltage safety of AC / DC power grid. As the proportion of renewable energy units in AC / DC power grids continues to increase, the uncertainty factors in AC / DC power grids have also brought great challenges to the static voltage safety analysis of power grids. The traditional deterministic AC / DC power grid static voltage stability margin calculation method has been difficult to adapt to the needs of AC / DC power grid static voltage safety analysis under strong randomness and volatility scenarios. In addition, the continuous increase in the scale of AC / DC power grids has also made online safety assessment increasingly complex, and the heavy computational burden will become a bottleneck hindering the practical application of online static voltage safety assessment methods for AC / DC power grids.

[0003] Therefore, a method is needed to take into account the influence of source and load uncertainty factors and quickly and accurately calculate the probabilistic static voltage stability margin of AC and DC power grids. Summary of the invention

[0004] The purpose of the present invention is to provide a method for calculating the probabilistic static voltage stability margin of an AC or DC power grid, which takes into account the uncertainty factors of source and load, realizes the rapid and accurate calculation of the probabilistic static voltage stability margin of an AC or DC power grid, and is of great significance to the static voltage safety analysis of an AC or DC power grid.

[0005] In order to achieve the above object, the method for calculating the probabilistic static voltage stability margin of AC and DC power grids proposed in the present invention comprises the following steps:

[0006] Step S1: Acquire basic data such as grid structure and line parameters of AC and DC power grids and historical data of uncertain source and load outputs such as renewable energy units and loads;

[0007] Step S2: establishing a calculation model for the probabilistic static voltage stability margin of an AC or DC power grid, the model taking the uncertain source and load output as input and the probabilistic static voltage stability margin of an AC or DC power grid as output;

[0008] Step S3: constructing a probability model of uncertain source and load output of AC and DC power grids based on historical data;

[0009] Step S4: Based on the established uncertain source-load output probability model, an input random variable sample of the AC / DC power grid probabilistic static voltage stability margin calculation model is obtained by a low-discrepancy sequence sampling method;

[0010] Step S5: for each input random variable sample, the AC / DC power grid probabilistic static voltage stability margin calculation model is applied to calculate the corresponding output variable to obtain a small-scale model input-output sample;

[0011] Step S6: constructing an agent model for calculating the probabilistic static voltage stability margin of AC and DC power grids through small-scale model input-output samples;

[0012] Step S7: Based on a large-scale input random variable sample and the agent model, the Monte Carlo simulation (MCS) method is combined to obtain the probabilistic static voltage stability margin result of the AC and DC power grids.

[0013] In step S2, the essence of the AC / DC power grid probabilistic static voltage stability margin calculation model is to calculate the maximum value λ of the load growth factor λ when the source load uncertainty is considered. max The model is as follows:

[0014] min-λ

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] U dRi =U dIi +I di R dc

[0022]

[0023]

[0024] Q Gimin ≤Q Gi ≤Q Gimax

[0025] (Q Gi -Q Gimin )V ai =0

[0026] (Q Gi -Q Gimax )V bi =0

[0027] V Gi -(V Giref +V ai -V bi )=0

[0028] V ai ≥0,V bi ≥0

[0029] Where: P Li0 and Q Li0 is the initial active and reactive loads of node i; and is a constant, which is related to the growth mode of active and reactive power of node i load. and K pi is the number of pole pairs of the DC line connected to node i; U di ,I di and K is the DC side voltage, DC current and power factor angle of the DC converter station connected to node i; di and i X is the converter transformer ratio and converter control angle of the DC converter station connected to node i; ci is the commutation reactance of the DC converter station connected to node i; R dc is the DC line resistance; U dRi and U dIi is the DC voltage on the rectifier and inverter sides; Q Gi is the reactive power output of the i-th generator, Q Gimax and Q Gimin Q Gi The upper and lower bounds of V Gi and V Giref are the terminal voltage of the i-th generator and its given reference value respectively; V ai With V bi It is an auxiliary variable used to reflect the offset of the terminal voltage when the reactive output of the generator reaches the lower or upper limit.

[0030] In the above optimization model, the first four constraints are the power balance equations of the pure AC node and the AC node connected to the LCC DC converter station. The sign of the first constraint is negative for the rectifier station and positive for the inverter station. If node i is connected to a renewable energy generator, then P Gi =P REi , Q Gi =Q REi , P REiand Q REi is the uncertain active and reactive output of the renewable energy generator at node i. The 5th to 7th constraints are the operating characteristics of the LCC DC line. The 8th constraint is the equation describing the growth mode of the generator active output, is a constant, which is related to the growth mode of the output of the generator at node i. The ninth constraint indicates that the total increase of the load active power is distributed according to the proportion of the active power margin of the generators other than the balancing node generators to the total active power margin. The last five constraints are the complementary constraints of the generator terminal voltage and reactive power output.

[0031] In actual operation, the control mode of LCC DC transmission line is generally fixed DC current and fixed rectifier side DC voltage control mode. As the load increases, the AC node voltage on the inverter side of LCC DC decreases. In order to maintain the DC voltage unchanged, it is necessary to reduce the inverter control angle ξ Ii . Because Ii There is a minimum limit, and the control mode of the LCC DC transmission line needs to be changed to fixed inverter side minimum control angle control. Therefore, the constraints need to be added in the optimization model as follows:

[0032] (cosξ Iimin -cosξ Ii )V di =0

[0033] U dRi =U dRiref -V di

[0034] U dIi =U dIiref -V di

[0035] V di ≥0

[0036] Where: Iimin For Ii The minimum value of U dRiref and U dIiref For U dRi and U dIi The given reference value of V di is an auxiliary variable that reflects the offset of the DC voltage.

[0037] In step S3, the probability model construction method of the uncertain source and load output of the AC and DC power grid includes a parametric model method and a non-parametric model method. The parametric model method presupposes that the random variable obeys a specific probability distribution function through empirical rules, and uses the historical data of the random variable to derive the parameters of the probability distribution function. The non-parametric model method is based on the kernel density estimation method, and obtains the probability distribution function of the random variable through the linear superposition of multiple kernel functions. In the process of constructing the probability model of the uncertain source and load output of the actual AC and DC power grid, it is necessary to take into account the actual experience and the distribution characteristics of the random variables, appropriately select the probability model construction method, and improve the modeling accuracy as much as possible.

[0038] In step S4, the low-discrepancy sequence sampling method includes Latin hypercube sampling (LHS) and Sobol sequence sampling. The principle of LHS is stratified sampling, which optimizes the generation process of random variable samples to ensure that the probability distribution area of ​​the system input random variable can be evenly covered by the sampled samples, and ensure that the sampled samples can more accurately reflect the probability characteristics of the system input random variable. The Sobol sequence sampling method can also achieve effective coverage of the input random variable sampling space, improve sample quality, reduce the sample size required for MCS convergence, and thus improve computational efficiency.

[0039] In step S6, the proxy model is constructed by small-scale model input-output sample data, and is used to simulate the AC / DC power grid probabilistic static voltage stability margin calculation model with high precision. The proxy model is a mathematical model with small calculation amount and fast calculation speed, which can replace the complex and time-consuming original model calculation. The proxy model includes an artificial neural network (ANN) model, a support vector regression (SVR) model, a polynomial chaos expansion (PCE) model, a Gaussian process regression (GPR) model, etc.

[0040] In step S7, a large-scale source-load sample is obtained by the low-difference sequence sampling method, and then the source-load sample is used as the input of the proxy model, and the MCS method is used to repeatedly calculate the output data of the proxy model under each source-load sample data input, and the output data is the static voltage stability margin data of the AC / DC power grid under the source-load input scenario, and then the data result is used to calculate the probabilistic static voltage stability margin result of the AC / DC power grid. In the process, the proxy model replaces the solution calculation of the original model, which can significantly improve the computational efficiency of the probabilistic static voltage stability margin calculation process of the AC / DC power grid under the large-scale source-load input scenario.

[0041] In step S7, the AC / DC power grid probabilistic static voltage stability margin result is the statistical characteristic quantity of the model output variable sample, such as mean, variance, probability density function (PDF), cumulative distribution function (CDF), etc.

[0042] The beneficial effects of the present invention are as follows: considering the coupling characteristics of AC and DC power grids and the uncertainty factors of source and load, probabilistic static voltage safety analysis is realized, thereby ensuring the safe and reliable operation of AC and DC power grids; by introducing an agent model, the calculation efficiency of the probabilistic static voltage stability margin of AC and DC power grids is greatly improved, meeting the needs of online application and practical promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The present invention is a flow chart of a method for calculating a probabilistic static voltage stability margin of an AC or DC power grid.

[0044] Figure 2 The present invention is: an AC / DC power grid line topology diagram containing a renewable energy unit.

[0045] Figure 3 The present invention is a probability density function diagram of the probabilistic static voltage stability margin of AC and DC power grids. DETAILED DESCRIPTION

[0046] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0047] Reference Figure 1 The flowchart of the method for calculating the probabilistic static voltage stability margin of AC and DC power grids is shown in FIG. 1 , and the specific process of the method includes:

[0048] Step S1: Acquire basic data such as grid structure and line parameters of AC and DC power grids and historical data of uncertain source and load outputs such as renewable energy units and loads;

[0049] Step S2: establishing a calculation model for the probabilistic static voltage stability margin of an AC or DC power grid, the model taking the uncertain source and load output as input and the probabilistic static voltage stability margin of an AC or DC power grid as output;

[0050] Preferably, in step S2, the essence of the AC / DC power grid probabilistic static voltage stability margin calculation model is to calculate the maximum value λ of the load growth factor λ when the uncertainty of the source load is considered. max The model is as follows:

[0051] min-λ

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] U dRi =U dIi +I di R dc

[0059]

[0060]

[0061] Q Gimin ≤Q Gi ≤Q Gimax

[0062] (Q Gi -Q Gimin )V ai =0

[0063] (Q Gi -Q Gimax )V bi =0

[0064] V Gi -(V Giref +V ai -V bi )=0

[0065] V ai ≥0,V bi ≥0

[0066] Where: P Li0 and Q Li0 is the initial active and reactive loads of node i; and is a constant, which is related to the growth mode of active and reactive power of node i load. and K pi is the number of pole pairs of the DC line connected to node i; U di ,I di and K is the DC side voltage, DC current and power factor angle of the DC converter station connected to node i; di and i X is the converter transformer ratio and converter control angle of the DC converter station connected to node i; ci is the commutation reactance of the DC converter station connected to node i; Rdc is the DC line resistance; U dRi and U dIi is the DC voltage on the rectifier and inverter sides; Q Gi is the reactive power output of the i-th generator, Q Gimax and Q Gimin Q Gi The upper and lower bounds of V Gi and V Giref are the terminal voltage of the i-th generator and its given reference value respectively; V ai With V bi It is an auxiliary variable used to reflect the offset of the terminal voltage when the reactive output of the generator reaches the lower or upper limit.

[0067] In the above optimization model, the first four constraints are the power balance equations of the pure AC node and the AC node connected to the LCC DC converter station. The sign of the first constraint is negative for the rectifier station and positive for the inverter station. If node i is connected to a renewable energy generator, then P Gi =P REi , Q Gi =Q REi , P REi and Q REi is the uncertain active and reactive output of the renewable energy generator at node i. The 5th to 7th constraints are the operating characteristics of the LCC DC line. The 8th constraint is the equation describing the growth mode of the generator active output, is a constant, which is related to the growth mode of the output of the generator at node i. The ninth constraint indicates that the total increase of the load active power is distributed according to the proportion of the active power margin of the generators other than the balancing node generators to the total active power margin. The last five constraints are the complementary constraints of the generator terminal voltage and reactive power output.

[0068] In actual operation, the control mode of LCC DC transmission line is generally fixed DC current and fixed rectifier side DC voltage control mode. As the load increases, the AC node voltage on the inverter side of LCC DC decreases. In order to maintain the DC voltage unchanged, it is necessary to reduce the inverter control angle ξ Ii . Because Ii There is a minimum limit, and the control mode of the LCC DC transmission line needs to be changed to fixed inverter side minimum control angle control. Therefore, the constraints need to be added in the optimization model as follows:

[0069] (cosξ Iimin -cosξ Ii )V di =0

[0070] U dRi =U dRiref -Vdi

[0071] U dIi =U dIiref -V di

[0072] V di ≥0

[0073] Where: Iimin For Ii The minimum value of U dRiref and U dIiref For U dRi and U dIi The given reference value of V di is an auxiliary variable that reflects the offset of the DC voltage.

[0074] Step S3: constructing a probability model of uncertain source and load output of AC and DC power grids based on historical data;

[0075] Preferably, in step S3, the probability model construction method of the uncertain source and load output of the AC and DC power grid includes a parametric model method and a non-parametric model method. The parametric model method presupposes that the random variable obeys a specific probability distribution function through empirical rules, and uses the historical data of the random variable to derive the parameters of the probability distribution function. The non-parametric model method is based on the kernel density estimation method, and obtains the probability distribution function of the random variable through the linear superposition of multiple kernel functions. In the process of constructing the probability model of the uncertain source and load output of the actual AC and DC power grid, it is necessary to take into account the actual experience and the distribution characteristics of the random variables, appropriately select the probability model construction method, and improve the modeling accuracy as much as possible.

[0076] Step S4: Based on the established uncertain source-load output probability model, an input random variable sample of the AC / DC power grid probabilistic static voltage stability margin calculation model is obtained by a low-discrepancy sequence sampling method;

[0077] Preferably, in step S4, the low-discrepancy sequence sampling method includes Latin hypercube sampling (LHS) and Sobol sequence sampling. The principle of LHS is stratified sampling, which ensures that the probability distribution area of ​​the system input random variable can be evenly covered by the sampled samples by optimizing the generation process of the random variable samples, and ensures that the sampled samples can more accurately reflect the probability characteristics of the system input random variable. The Sobol sequence sampling method can also achieve effective coverage of the input random variable sampling space, improve sample quality, reduce the sample amount required for MCS convergence, and thus improve computational efficiency.

[0078] Step S5: for each input random variable sample, the AC / DC power grid probabilistic static voltage stability margin calculation model is applied to calculate the corresponding output variable to obtain a small-scale model input-output sample;

[0079] Step S6: constructing an agent model for calculating the probabilistic static voltage stability margin of AC and DC power grids through small-scale model input-output samples;

[0080] Preferably, in step S6, the proxy model is constructed by a small-scale model input-output sample data, and is used to perform high-precision simulation on the AC / DC power grid probabilistic static voltage stability margin calculation model. The proxy model is a mathematical model with a small amount of calculation and a fast calculation speed, which can replace the complex and time-consuming original model calculation. The proxy model includes an artificial neural network (ANN) model, a support vector regression (SVR) model, a polynomial chaos expansion (PCE) model, a Gaussian process regression (GPR) model, etc.

[0081] Step S7: Based on a large-scale input random variable sample and the agent model, the Monte Carlo simulation (MCS) method is combined to obtain the probabilistic static voltage stability margin result of the AC and DC power grids;

[0082] Preferably, in step S7, a large-scale source-load sample is obtained by the low-difference sequence sampling method, and then the source-load sample is used as the input of the proxy model, and the MCS method is used to repeatedly calculate the output data of the proxy model under each source-load sample data input, and the output data is the static voltage stability margin data of the AC / DC power grid under the source-load input scenario, and then the data result is used to calculate the probabilistic static voltage stability margin result of the AC / DC power grid. In the process, the proxy model replaces the solution calculation of the original model, which can significantly improve the computational efficiency of the probabilistic static voltage stability margin calculation process of the AC / DC power grid under the large-scale source-load input scenario.

[0083] Preferably, in step S7, the AC / DC power grid probabilistic static voltage stability margin result is a statistical characteristic quantity of a model output variable sample, such as mean, variance, probability density function (PDF), cumulative distribution function (CDF), etc.

[0084] In order to demonstrate the applicability of the present invention in practical applications, it is applied to the problem of calculating the probabilistic static voltage stability margin of an AC / DC power grid containing actual renewable energy sources:

[0085] The AC / DC grid in the example model consists of an IEEE 118-node AC grid (sending grid, including multiple wind farms) and an IEEE 39-node AC grid (receiving grid). The two AC grids are connected by a DC line (sending grid node 88-receiving grid node 9). The DC line connection method is a conventional LCC DC transmission line. The AC / DC grid topology is shown in Figure 1. Figure 2As shown, the sending-end power grid has wind turbines at nodes 23, 33, 75, 84, and 88; the receiving-end power grid load is divided into four areas, covering nodes 1-9, nodes 10-18, nodes 19-24, and nodes 25-29, respectively, and it is assumed that the random fluctuations of the loads in the same area are the same.

[0086] The sampling scale of the MCS method is J = 10000. The system input random variables set are: the output power P of the five wind turbines in the sending grid R,i (i=23、33、75、84、88); random load fluctuations in the four areas of the receiving power gridξ D,i (i=1,2,3,4). The proxy model adopts the SPCE proxy model, the highest polynomial order p=5, and the sample size J used in model construction S = 600. Calculate the probabilistic static voltage stability margin of AC and DC power grids as follows Figure 3 shown.

[0087] Depend on Figure 3 The results of the probabilistic static voltage stability margin of the AC / DC power grid show that due to the influence of uncertain sources and loads, the static voltage stability margin of the AC / DC system fluctuates between 0.14 and 0.24. The above probabilistic static voltage stability margin results can quantitatively characterize the probabilistic characteristics of the static voltage stability of the AC / DC power grid, and provide more comprehensive information reference for the joint operation analysis, operation status perception and early warning of the AC / DC power grid.

[0088] The beneficial effects of the present invention are as follows: considering the coupling characteristics of AC and DC power grids and the uncertainty factors of source and load, probabilistic static voltage safety analysis is realized, thereby ensuring the safe and reliable operation of AC and DC power grids; by introducing an agent model, the calculation efficiency of the probabilistic static voltage stability margin of AC and DC power grids is greatly improved, meeting the needs of online application and practical promotion.

[0089] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

[0090] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for calculating the probabilistic static voltage stability margin of an AC / DC power grid, characterized in that: The following steps are involved: Step S1: Acquire basic data of AC and DC power grids and historical data of uncertain source and load output; the basic data include grid structure and line parameters, and the historical data of uncertain source and load output include renewable energy units and loads; Step S2: establishing a calculation model for the probabilistic static voltage stability margin of an AC or DC power grid, the model taking the uncertain source and load output as input and the probabilistic static voltage stability margin of an AC or DC power grid as output; Step S3: constructing a probability model of uncertain source and load output of AC and DC power grids based on historical data; Step S4: Based on the established uncertain source-load output probability model, an input random variable sample of the AC / DC power grid probabilistic static voltage stability margin calculation model is obtained by a low-discrepancy sequence sampling method; Step S5: for each input random variable sample, the AC / DC power grid probabilistic static voltage stability margin calculation model is applied to calculate the corresponding output variable to obtain a small-scale model input-output sample; Step S6: constructing an agent model for calculating the probabilistic static voltage stability margin of AC and DC power grids through small-scale model input-output samples; Step S7: Based on a large-scale input random variable sample and the agent model, the Monte Carlo Simulation (MCS) method is combined to obtain the probabilistic static voltage stability margin result of the AC and DC power grids.

2. The method for calculating the probabilistic static voltage stability margin of an AC / DC power grid according to claim 1, characterized in that: In step S2, the AC / DC power grid probabilistic static voltage stability margin calculation model is specifically as follows: min-λ U dRi =U dIi +I di R dc Q Gimin ≤Q Gi ≤Q Gimax (Q Gi -Q Gimin )V ai =0 (Q Gi -Q Gimax )V bi =0 V Gi -(V Giref +V ai -V bi )=0 V ai ≥0,V bi ≥0 Where: P Li0 and Q Li0 is the initial active and reactive loads of node i; and As a constant, take and K pi is the number of pole pairs of the DC line connected to node i; U di ,I di and K is the DC side voltage, DC current and power factor angle of the DC converter station connected to node i; di and i X is the converter transformer ratio and converter control angle of the DC converter station connected to node i; ci is the commutation reactance of the DC converter station connected to node i; R dc is the DC line resistance; U dRi and U dIi is the DC voltage on the rectifier and inverter sides; Q Gi is the reactive power output of the i-th generator, Q Gimax and Q Gimin Q Gi The upper and lower bounds of V Gi and V Giref are the terminal voltage of the i-th generator and its given reference value respectively; V ai With V bi It is an auxiliary variable, used to reflect the offset of the terminal voltage when the reactive output of the generator reaches the lower or upper limit; In the above optimization model, the first four constraints are the power balance equations of the pure AC node and the AC node connected to the LCC DC converter station. The sign of the first constraint is negative for the rectifier station and positive for the inverter station. If node i is connected to a renewable energy generator, then P Gi =P REi , Q Gi =Q REi , P REi and Q REi is the uncertain active and reactive output of the renewable energy generator set at node i, the 5th to 7th constraints are the operating characteristics of the LCC DC line, and the 8th constraint is the equation describing the growth mode of the active output of the generator. is a constant. The ninth constraint indicates that the total increase in load active power is distributed according to the proportion of the active power margin of other generators except the balancing node generators to the total active power margin. The last five constraints are the complementary constraints of the generator terminal voltage and reactive power output. The constraints added to the optimization model are as follows: (cosξ Iimin -cosξ Ii )V di =0 U dRi =U dRiref -V di U dIi =U dIiref -V di V di ≥0 Where: Iimin For Ii The minimum value of U dRiref and U dIiref For U dRi and U dIi The given reference value of V di It is an auxiliary variable, reflecting the offset of DC voltage.

3. The method for calculating the probabilistic static voltage stability margin of an AC / DC power grid according to claim 1, characterized in that: In step S3, the probability model construction method of the uncertain source and load output of the AC / DC power grid includes a parametric model method and a non-parametric model method. The parametric model method pre-assumes that the random variable obeys a specific probability distribution function through empirical rules, and uses the historical data of the random variable to derive the parameters of the probability distribution function. The non-parametric model method is based on the kernel density estimation method and obtains the probability distribution function of the random variable through the linear superposition of multiple kernel functions.

4. The method for calculating the probabilistic static voltage stability margin of an AC / DC power grid according to claim 1, characterized in that: In the step S4, the low-discrepancy sequence sampling method includes Latin Hypercube Sampling (LHS) and Sobol sequence sampling.

5. The method for calculating the probabilistic static voltage stability margin of an AC / DC power grid according to claim 1, characterized in that: In the step S6, the proxy model is constructed by small-scale model input-output sample data, and is used to perform high-precision simulation on the probabilistic static voltage stability margin calculation model of the AC and DC power grids. The proxy model includes an artificial neural network (ANN) model, a support vector regression (SVR) model, a polynomial chaos expansion (PCE) model, and a Gaussian process regression (GPR) model.

6. The method for calculating the probabilistic static voltage stability margin of an AC / DC power grid according to claim 1, characterized in that: In step S7, a large-scale source-load sample is obtained by the low-discrepancy sequence sampling method, and then the source-load sample is used as the input of the proxy model. The MCS method is used to repeatedly calculate the output data of the proxy model under each source-load sample data input. The output data is the static voltage stability margin data of the AC / DC power grid under the source-load input scenario, and the data result is then used to calculate the probabilistic static voltage stability margin result of the AC / DC power grid.

7. The method for calculating the probabilistic static voltage stability margin of an AC / DC power grid according to claim 1, characterized in that: In the step S7, the AC / DC power grid probabilistic static voltage stability margin result is the statistical characteristic quantity of the model output variable sample, including mean, variance, probability density function (Probability Density Function, PDF), and cumulative distribution function (Cumulative Distribution Function, CDF).

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