A method and system for determining equivalent inertia of power system nodes

Abstract

The present invention provides a method and system for determining equivalent inertia of power system nodes, belonging to the technical field of power systems. The method includes determining the equivalent inertia of the power system; applying load power disturbance to each node in the power system, and determining a node in the power system as an inertia center node using the frequency of each node after the disturbance; fitting the frequency of each node in the power system after the load power disturbance is applied to a curve of frequency variation over time, so as to determine the frequency change rate of each node after the disturbance occurs; and calculating the equivalent inertia of the target node using the equivalent inertia of the power system according to the relationship between the frequency change rate of the target node after the disturbance occurs and the frequency change rate of the inertia center node after the disturbance occurs. The present invention can more accurately calculate the equivalent inertia of each node in the power system, and better reflect the spatial distribution characteristics of the inertia.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a method and system for determining equivalent inertia of power system nodes. Background Art

[0002] As the penetration rate of renewable energy devices continues to increase, the power system is changing from a traditional structure dominated by synchronous energy to a new model dominated by a high proportion of asynchronous energy. Due to the restrictions of climate and geographical environment on renewable energy devices, renewable energy units are often distributed in clusters, resulting in a significant reduction in the inertia level in local areas and increasingly obvious uneven inertia distribution, which seriously deteriorates the frequency stability of the power system and increases the risk of frequency instability in the power system. Therefore, accurately evaluating the equivalent inertia of power system nodes helps power grid dispatchers to grasp the current actual inertia distribution and reasonably allocate system spare capacity and virtual inertia resources, which is of great significance for improving frequency stability and ensuring safe operation of the power grid.

[0003] At present, the evaluation methods for node equivalent inertia are mainly divided into two categories: model-based analysis and frequency response-based analysis. The model-based analysis method mainly obtains the system real-time topology, inertia parameters of synchronous generators and new energy generators, node voltage amplitude, phase angle and other parameters through synchronous phasor measurement units (PMUs), and derives the node equivalent inertia based on the swing equation and synchronous power equation. The accuracy of this type of evaluation method depends on the various types of data obtained by the PMU. The required data volume is huge and the calculation is complex, and the applicability is poor. The frequency response analysis method is to use the frequency data of each node after the power disturbance to fit, so as to obtain the frequency change rate at the initial moment of the disturbance, and obtain the node equivalent inertia based on the coupling relationship between the node frequency change rate and the node equivalent inertia. However, the current fitting method mainly uses polynomial fitting, which has a single form and cannot fully reflect the frequency dynamic response after the disturbance, resulting in low frequency curve fitting accuracy and difficult to apply in actual engineering. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for determining equivalent inertia of power system nodes.

[0005] In a first aspect, the present invention provides a method for determining equivalent inertia of a power system node, comprising:

[0006] Determine the equivalent inertia of the power system;

[0007] Applying load power disturbance to each node in the power system to determine a node in the power system as an inertia center node using the frequency of each node after the disturbance;

[0008] Fit the frequency of each node after the load power disturbance is applied to the power system into a frequency-time curve to determine the frequency change rate of each node after the disturbance occurs;

[0009] According to the relationship between the frequency change rate of the target node after the disturbance occurs and the frequency change rate of the inertia center node after the disturbance occurs, the equivalent inertia of the target node is calculated using the equivalent inertia of the power system.

[0010] Optionally, determining the equivalent inertia of the power system includes:

[0011] Calculate the equivalent inertia H of the power system according to the following formula sys :

[0012] ;

[0013] Among them, H g,i is the inertia constant of synchronous generator i; S g,i is the rated capacity of synchronous generator i; I is the total number of synchronous generators; H v, j is the inertia constant of the new energy generator j with virtual inertia control technology; S v, j is the rated capacity of the new energy generator j with virtual inertia control technology; J is the total number of new energy generators with virtual inertia control technology; H p is the inertia constant of the load; S p is the rated capacity of the load; S v,k is the rated capacity of the new energy generator k without virtual inertia control technology; K is the total number of new energy generators without virtual inertia control technology.

[0014] Optionally, applying load power disturbance to each node in the power system to determine a node in the power system as an inertia center node by using the frequencies of each node after the disturbance, and using the frequency of the inertia center node as the frequency of the power system, includes:

[0015] The Pearson correlation coefficient of the perturbed frequency of each node and the perturbed frequencies of other nodes is calculated according to the following formula:

[0016] ;

[0017] in, is the frequency f of node m after disturbance in the power system m The frequency f after the disturbance of node n n Pearson correlation coefficient; U is the total number of frequency sequences in the frequency after node perturbation; f mu f m The uth frequency sequence in f nu f nThe uth frequency sequence in ; f m The average value of U frequency sequences in ; f n The average value of U frequency sequences in ;

[0018] The sum of the Pearson correlation coefficients of each node is calculated according to the following formula:

[0019] ;

[0020] Among them, P m is the sum of the Pearson correlation coefficients of node m; s is the total number of nodes in the power system;

[0021] The node with the largest sum of Pearson correlation coefficients in the power system is taken as the inertia center node.

[0022] Optionally, fitting the frequency of each node after the load power disturbance is applied to the power system into a curve of frequency variation over time to determine the frequency change rate of each node after the disturbance occurs, includes:

[0023] Get the frequency f of node m m , f m ={f m1 ,f m2 ,…,f mU}, U is the total number of frequency sequences in the frequency after node perturbation;

[0024] Construct multiple fitting models, including high-order polynomials, exponential functions, logarithmic functions, power functions, Gaussian functions, sigmoid functions, combinations of high-order polynomials and exponential functions, combinations of high-order polynomials and logarithmic functions, combinations of high-order polynomials and power functions, combinations of high-order polynomials and Gaussian functions, and combinations of high-order polynomials and sigmoid functions;

[0025] In the iterative process of the target fitting model, the sum of squares of the residuals is used as the objective function to solve the coefficients of the target fitting model; wherein, when the change of the sum of squares of the residuals is less than a first preset value, or when the absolute value of the gradient is less than a second preset value, or when the maximum allowed number of iterations is reached, the coefficients of the target fitting model under the current number of iterations are output;

[0026] The fitting error ε of the target fitting model is calculated according to the following formula:

[0027] ;

[0028] Among them, f mu f m The uth frequency sequence in ; The uth frequency sequence fitted by the target fitting model at node m;

[0029] Obtain the fitting error of each fitting model under each order polynomial within the target order;

[0030] Within the target order, the fitting model corresponding to the minimum fitting error is taken as the final fitting model; the order of the final fitting model is the order corresponding to the minimum fitting error;

[0031] According to the frequency of node m and the final fitting model of node m, the frequency change rate of node m after the disturbance occurs is determined.

[0032] Optionally, the calculating the equivalent inertia of the target node using the equivalent inertia of the power system according to the relationship between the frequency change rate of the target node after the disturbance occurs and the frequency change rate of the inertia center node after the disturbance occurs includes:

[0033] Calculate the equivalent inertia of the target node according to the following formula:

[0034] ;

[0035] Among them, H q is the equivalent inertia of the target node q in the power system; H sys is the equivalent inertia of the power system; f sys is the frequency of the inertia center node; t is time; f q is the frequency of the target node q after being disturbed by the load power; Represents the frequency change rate of the target node q after the disturbance occurs.

[0036] In a second aspect, the present invention provides a system for determining equivalent inertia of a power system node, comprising:

[0037] A first determination module, used to determine the equivalent inertia of the power system;

[0038] A second determination module is used to apply load power disturbance to each node in the power system to determine a node in the power system as an inertia center node using the frequency of each node after the disturbance;

[0039] The third determination module is used to fit the frequency of each node after the load power disturbance is applied in the power system into a frequency-time variation curve to determine the frequency change rate of each node after the disturbance occurs;

[0040] The calculation module is used to calculate the equivalent inertia of the target node using the equivalent inertia of the power system according to the relationship between the frequency change rate of the target node after the disturbance occurs and the frequency change rate of the inertia center node after the disturbance occurs.

[0041] The present invention provides a method and system for determining equivalent inertia of power system nodes. The method adopts multiple fitting models to calculate the frequency change rate of each node after the disturbance occurs, and according to the relationship between the frequency change rate of the target node after the disturbance occurs and the frequency change rate of the inertia center node after the disturbance occurs, the equivalent inertia of each node is calculated using the equivalent inertia of the power system. In addition to traditional polynomial fitting, the structure of the fitting model also includes an exponential function, a logarithmic function, a power function, a Gaussian function, a Sigmoid function and a combination structure of two models, so as to provide multiple frequency curve fitting results, select the curve with the best fitting result, reduce the error of the fitting frequency curve, improve the accuracy of the equivalent inertia calculation of each node in the power system, so as to better reflect the spatial distribution characteristics of the inertia in the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A schematic flow chart of a method for determining equivalent inertia of a power system node provided by an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of an improved IEEE39 node topology provided in an embodiment of the present invention;

[0045] Figure 3 A schematic diagram of improved IEEE39 node virtual geographic information provided by an embodiment of the present invention;

[0046] Figure 4 A generator parameter diagram provided by an embodiment of the present invention;

[0047] Figure 5 A diagram showing the correspondence between each node and the optimal model structure provided by an embodiment of the present invention;

[0048] Figure 6 A visualization diagram of the spatial distribution of inertia provided by an embodiment of the present invention;

[0049] Figure 7 A schematic diagram of the structure of a power system node equivalent inertia determination system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in 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 creative work are within the scope of protection of the present invention.

[0051] Example 1

[0052] like Figure 1 As shown, this embodiment provides a method for determining equivalent inertia of a power system node, comprising:

[0053] Step 101, determining the equivalent inertia of the power system.

[0054] Exemplarily, the equivalent inertia H of the power system is calculated according to the following formula: sys :

[0055] .

[0056] Among them, H g,i is the inertia constant of synchronous generator i; S g,i is the rated capacity of synchronous generator i; I is the total number of synchronous generators; H v, j is the inertia constant of the new energy generator j with virtual inertia control technology; S v, j is the rated capacity of the new energy generator j with virtual inertia control technology; J is the total number of new energy generators with virtual inertia control technology; H p is the inertia constant of the load; S p is the rated capacity of the load; S v,k is the rated capacity of the new energy generator k without virtual inertia control technology; K is the total number of new energy generators without virtual inertia control technology.

[0057] Step 102 : applying load power disturbance to each node in the power system, so as to determine a node in the power system as an inertia center node by using the frequency of each node after the disturbance.

[0058] For a new power system with s nodes, the frequency data of each node after disturbance are f1, f2, ..., f s ; f1={f 11 ,f 12 ,…,f 1U}、f2={f 21 ,f 22 ,…,f 2U}, ..., f s ={f s1 ,f s2 ,…,fsU}.

[0059] The Pearson correlation coefficient of the perturbed frequency of each node and the perturbed frequencies of other nodes is calculated according to the following formula:

[0060] .

[0061] in, is the frequency f of node m after disturbance in the power system m The frequency f after the disturbance of node n n Pearson correlation coefficient; U is the total number of frequency sequences in the frequency of the node after perturbation (the number of frequency sequences of each node is the same); f mu f m The uth frequency sequence in f nu f n The uth frequency sequence in ; f m The average value of U frequency sequences in ; f n The average value of U frequency sequences in .

[0062] The sum of the Pearson correlation coefficients of each node is calculated according to the following formula:

[0063] .

[0064] Among them, P m is the sum of the Pearson correlation coefficients of node m; s is the total number of nodes in the power system.

[0065] In the power system, if the sum of the Pearson correlation coefficients of a node is larger, it means that the frequency of the node is most similar to the frequencies of other nodes in the power system, and can better represent the frequency f of the power system. sys After finding the sum of the Pearson correlation coefficients of each node, the node with the largest sum of the Pearson correlation coefficients is taken as the inertia center node. The frequency of the inertia center node is the frequency of the power system f sys , the node inertia of the inertia center node is the equivalent inertia H of the power system sys .

[0066] Step 103 , fitting the frequency of each node after the load power disturbance is applied in the power system into a frequency-time variation curve to determine the frequency change rate of each node after the disturbance occurs.

[0067] Assume that the frequency data collected at node m is f m ={f m1 ,f m2 ,…,f mU}, the discrete frequency data points collected by node m are fitted to make them continuous, which is used to calculate the frequency change rate of node m at the initial moment. Construct multiple fitting models, the output of the fitting model is a column vector composed of the frequency data points collected at the node, and the input of the fitting model is a column vector composed of the time corresponding to each frequency data point, that is, A column vector consisting of frequency data points collected by the target fitting model at node m , T represents the transpose of the matrix; is a column vector consisting of the time corresponding to each frequency data point of node m, .

[0068] Exemplarily, this step includes:

[0069] Get the frequency f of node m m , f m ={f m1 ,f m2 ,…,f mU}, U is the total number of frequency sequences in the frequency after node perturbation.

[0070] Construct multiple fitting models, including high-order polynomial, exponential function, logarithmic function, power function, Gaussian function, sigmoid function, combination of high-order polynomial and exponential function, combination of high-order polynomial and logarithmic function, combination of high-order polynomial and power function, combination of high-order polynomial and Gaussian function, and combination of high-order polynomial and sigmoid function.

[0071] Exemplarily, the high-order polynomial is: .

[0072] Among them, a0, a1, ..., a w-1 、a w are the coefficients of the corresponding terms in the high-order polynomial respectively; w is the order of the polynomial, and in this embodiment, 2≤w≤10.

[0073] The exponential function is: .

[0074] Among them, b0, b1 and b2 are preset coefficients respectively; e is a natural constant.

[0075] The logarithmic function is: .

[0076] Among them, b3, b4 and b5 are preset coefficients respectively.

[0077] The power function is: .

[0078] Among them, b6, b7 and b8 are preset coefficients respectively.

[0079] The Gaussian function is: .

[0080] Among them, b9, b 10 and b 11 are preset coefficients respectively; e is a natural constant.

[0081] The Sigmoid function is: .

[0082] Among them, b 12 , b 13 and b 14 are preset coefficients respectively; e is a natural constant.

[0083] The combination of a high-order polynomial and a logarithmic function is a high-order polynomial + a logarithmic function; the combination of a high-order polynomial and a power function is a high-order polynomial + a power function; the combination of a high-order polynomial and a Gaussian function is a high-order polynomial + a Gaussian function; the combination of a high-order polynomial and a Sigmoid function is a combination of a high-order polynomial + a Sigmoid function.

[0084] Take the combination of high-order polynomial and logarithmic function as an example, high-order polynomial + logarithmic function, that is .

[0085] During the iteration process of the target fitting model, the sum of squares of the residuals is used as the objective function to solve the coefficients of the target fitting model; when the change in the sum of squares of the residuals is less than a first preset value, or when the absolute value of the gradient is less than a second preset value, or when the maximum allowed number of iterations is reached, the iteration is stopped and the coefficients of the target fitting model under the current number of iterations are output.

[0086] In order to find the frequency change rate of the node at the initial moment after being disturbed, it is known that the frequency data point at the initial moment has a greater influence on the calculation of the frequency change rate at the initial moment. In order to make the curve fitting more accurate at the initial moment, a gradually decreasing weight is applied to the target fitting model, and the frequency data at the initial moment is fitted first, that is:

[0087] .

[0088] Among them, r is the residual.

[0089] The fitting error ε of the target fitting model is calculated according to the following formula:

[0090] .

[0091] Among them, f mu f m The uth frequency sequence in ; The u-th frequency sequence fitted by the target fitting model at node m.

[0092] Get the fitting error of each fitting model under each order polynomial within the target order.

[0093] Within the target order, the fitting model corresponding to the minimum fitting error is taken as the final fitting model; the order of the final fitting model is the order corresponding to the minimum fitting error.

[0094] In order to save computing resources, for example, in order to make the curve generation speed of the fitting data points faster and with smaller errors, the frequency data of each node after the disturbance is sent to each fitting model. First, let w=2, use the second-order polynomial single model structure and the five multi-model structures of the second-order polynomial combined with the exponential function, logarithmic function, power function, Gaussian function and Sigmoid function to fit the curve, calculate the fitting errors of the six fitting models respectively, save the model structure and model coefficients with the minimum fitting error, and use the fitting error of this model as the minimum model error. Then, let w=3, use the third-order polynomial single model structure and the five multi-model structures of the third-order polynomial combined with the exponential function, logarithmic function, power function, Gaussian function and Sigmoid function to fit the curve, calculate the fitting errors of the six models respectively, if the fitting errors of the six models are all greater than the minimum model error saved in the previous order, then the loop ends, and the model structure and model coefficients saved in the previous order are output. On the contrary, if the minimum error among the six models is less than the minimum model error saved in the previous order, then the model structure and model coefficients are saved, and the fitting error of this model is used as the minimum model error, and enter the next loop, let w=4, until w=10 ends the loop. When the polynomial order is 10, the model structure and model coefficients with the minimum fitting error among the six models under the 10th order are output.

[0095] According to the frequency of node m and the final fitting model of node m, the frequency change rate of node m after the disturbance occurs is determined.

[0096] Step 104, according to the relationship between the frequency change rate of the target node after the disturbance occurs and the frequency change rate of the inertia center node after the disturbance occurs, the equivalent inertia of the power system is used to calculate the equivalent inertia of the target node.

[0097] According to the generator rotor motion equation, the frequency change rate of each node in the power system after being subjected to power disturbance is inversely proportional to the node equivalent inertia, that is:

[0098] .

[0099] Among them, H q is the equivalent inertia of the target node q in the power system; f q is the frequency of the target node q after being disturbed by the load power; △P L is the load disturbance power.

[0100] Apply the same load power disturbance to each node. At the initial moment of the disturbance, the load power disturbance of each node is equal, that is:

[0101] .

[0102] Among them, H o is the equivalent inertia of the target node o in the power system; f o is the frequency of the target node o after being disturbed by the load power.

[0103] Calculate the equivalent inertia of the target node according to the following formula:

[0104] .

[0105] Among them, H q is the equivalent inertia of the target node q in the power system; H sys is the equivalent inertia of the power system; f sys is the frequency of the inertia center node; t is time; f q is the frequency of the target node q after being disturbed by the load power; Represents the frequency change rate of the target node q after the disturbance occurs; It represents the frequency change rate of the inertia center node after the disturbance occurs.

[0106] The method for determining the node equivalent inertia of the power system provided in this embodiment further includes presenting the inertia spatial distribution characteristics of the power system in the form of a heat map according to the node equivalent inertia of each node in the power system obtained in step 104 .

[0107] The spatial distribution characteristics of inertia are visualized in combination with the geographical location information of the power grid. The bicubic interpolation algorithm is used to perform difference processing on each point in the power system network topology, and the Delaunay triangulation method is used to visualize the spatial characteristics of the node equivalent inertia.

[0108] In order to make the solution of this embodiment clearer, this embodiment further discloses specific examples.

[0109] The power system in this embodiment adopts an improved IEEE 39-node system, and the system topology is as follows: Figure 2 As shown, the system has 10 generators, 39 buses and 19 loads, and the rated frequency is 50Hz. In order to verify the effectiveness of the present invention in the power system, the synchronous generators G07 and G09 in the model are replaced by 100 double-fed wind turbines and a virtual inertia control link is set. The capacity of each double-fed wind turbine is 7MW and the output power is 6.3MW. Due to the lack of geographical location information of the 10-machine 39-node system, the coordinates of each node of the system are re-specified here to obtain the virtual geographical information map of the system, as shown in Figure 3 shown.

[0110] To verify the effectiveness of the method for determining the equivalent inertia of power system nodes provided in this embodiment, the equivalent inertia of nodes in the left area of ​​the 10-machine 39-node system is set to be greater than that in the right area. The rated capacity and inertia constant of each generator are as follows: Figure 4 shown.

[0111] A load power disturbance with an amplitude of 200MW is applied to each node respectively, and the frequency data of each node after the disturbance is input into the fitting model to select the optimal model structure and model coefficients, such as Figure 5 shown.

[0112] According to the optimal model structure and model coefficient of each node, the frequency change rate of each node at the initial moment after the disturbance is calculated, so as to calculate the node equivalent inertia of each node. The inertia spatial distribution characteristics of the power system are presented in the form of a heat map, such as Figure 6 shown.

[0113] Depend on Figure 6 It can be seen that the node equivalent inertia of different nodes is obviously different. Since G1 has the largest inertia time constant and large capacity, the node equivalent inertia of node 39 close to G1 is the largest. The inertia time constant of each generator in the right area is smaller than that in the left area, so the node equivalent inertia on the left is obviously larger than that on the right. Figure 6 The above embodiment verifies the effectiveness of the method for determining the equivalent inertia of power system nodes provided by this embodiment.

[0114] In summary, this embodiment adopts multiple fitting models to calculate the frequency change rate of each node at the initial moment after the disturbance, so as to calculate the node equivalent inertia of each node. In addition to the traditional polynomial fitting, the model structure also includes exponential function, logarithmic function, power function and other structures as well as a combined structure of two models. The fitting effect is good, and the error of the fitting frequency curve is greatly reduced. It can more accurately calculate the equivalent inertia of each node in the power system, and better reflect the spatial distribution characteristics of the inertia.

[0115] Example 2

[0116] Based on the same inventive concept as Example 1, this embodiment provides a system for determining the equivalent inertia of power system nodes. Since the principle of solving the problem by this system is similar to the method for determining the equivalent inertia of power system nodes provided in the aforementioned Example 1, the implementation of this system can refer to the implementation of the method for determining the equivalent inertia of power system nodes provided in Example 1.

[0117] like Figure 7 As shown, the power system node equivalent inertia determination system includes:

[0118] The first determination module 10 is used to determine the equivalent inertia of the power system.

[0119] The second determination module 20 is used to apply load power disturbance to each node in the power system, so as to determine a node in the power system as an inertia center node by using the frequency of each node after the disturbance.

[0120] The third determination module 30 is used to fit the frequency of each node after the load power disturbance is applied to the power system into a frequency-time variation curve to determine the frequency change rate of each node after the disturbance occurs.

[0121] The calculation module 40 is used to calculate the equivalent inertia of the target node using the equivalent inertia of the power system according to the relationship between the frequency change rate of the target node after the disturbance occurs and the frequency change rate of the inertia center node after the disturbance occurs.

[0122] Exemplarily, the first determining module includes:

[0123] The first calculation unit is used to calculate the equivalent inertia H of the power system according to the following formula sys :

[0124] .

[0125] Among them, H g,i is the inertia constant of synchronous generator i; S g,i is the rated capacity of synchronous generator i; I is the total number of synchronous generators; H v, j is the inertia constant of the new energy generator j with virtual inertia control technology; S v, j is the rated capacity of the new energy generator j with virtual inertia control technology; J is the total number of new energy generators with virtual inertia control technology; H p is the inertia constant of the load; S p is the rated capacity of the load; S v,k is the rated capacity of the new energy generator k without virtual inertia control technology; K is the total number of new energy generators without virtual inertia control technology.

[0126] Exemplarily, the second determining module includes:

[0127] The second calculation unit is used to calculate the Pearson correlation coefficient between the perturbed frequency of each node and the perturbed frequencies of other nodes according to the following formula:

[0128] .

[0129] in, is the frequency f of node m after disturbance in the power system m The frequency f after the disturbance of node nn Pearson correlation coefficient; U is the total number of frequency sequences in the frequency after node perturbation; f mu f m The uth frequency sequence in f nu f n The uth frequency sequence in ; f m The average value of U frequency sequences in ; f n The average value of U frequency sequences in ;

[0130] The third calculation unit is used to calculate the sum of the Pearson correlation coefficients of each node according to the following formula:

[0131] .

[0132] Among them, P m is the sum of the Pearson correlation coefficients of node m; s is the total number of nodes in the power system;

[0133] The first determining unit is used to take the node with the largest sum of Pearson correlation coefficients in the power system as the inertia center node.

[0134] Exemplarily, the third determining module includes:

[0135] The first acquisition unit is used to acquire the frequency f of the node m m , f m ={f m1 ,f m2 ,…,f mU}, U is the total number of frequency sequences in the frequency after node perturbation.

[0136] The construction unit is used to construct multiple fitting models, including high-order polynomials, exponential functions, logarithmic functions, power functions, Gaussian functions, sigmoid functions, combinations of high-order polynomials and exponential functions, combinations of high-order polynomials and logarithmic functions, combinations of high-order polynomials and power functions, combinations of high-order polynomials and Gaussian functions, and combinations of high-order polynomials and sigmoid functions.

[0137] The coefficient solving unit is used to use the sum of squares of the residuals as the objective function to solve the coefficients of the target fitting model during the iteration process of the target fitting model; wherein, when the change in the sum of squares of the residuals is less than a first preset value, or when the absolute value of the gradient is less than a second preset value, or when the maximum allowed number of iterations is reached, the coefficients of the target fitting model under the current number of iterations are output.

[0138] The fourth calculation unit is used to calculate the fitting error ε of the target fitting model according to the following formula:

[0139] .

[0140] Among them, f mu f m The uth frequency sequence in ; The u-th frequency sequence fitted by the target fitting model at node m.

[0141] The second acquisition unit is used to obtain the fitting errors of each fitting model under each order polynomial within the target order.

[0142] The second determining unit is used to use the fitting model corresponding to the minimum fitting error as the final fitting model within the target order; the order of the final fitting model is the order corresponding to the minimum fitting error.

[0143] The third determining unit is used to determine the frequency change rate of the node m after the disturbance occurs according to the frequency of the node m and the final fitting model of the node m.

[0144] Exemplarily, the calculation module includes:

[0145] The fifth calculation unit is used to calculate the equivalent inertia of the target node according to the following formula:

[0146] .

[0147] Among them, H q is the equivalent inertia of the target node q in the power system; H sys is the equivalent inertia of the power system; f sys is the frequency of the inertia center node; t is time; f q is the frequency of the target node q after being disturbed by the load power; Represents the frequency change rate of the target node q after the disturbance occurs.

[0148] For more specific working processes of the above modules, please refer to the corresponding contents disclosed in Example 1, which will not be repeated here.

[0149] Example 3

[0150] This embodiment provides a computer device, including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, the steps of the method for determining equivalent inertia of a power system node described in Embodiment 1 are implemented.

[0151] For more specific details of the above method, please refer to the corresponding contents disclosed in Example 1, which will not be repeated here.

[0152] Example 4

[0153] This embodiment provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the steps of the method for determining the equivalent inertia of a power system node described in Embodiment 1 are implemented.

[0154] For more specific details of the above method, please refer to the corresponding contents disclosed in Example 1, which will not be repeated here.

[0155] Example 5

[0156] This embodiment provides a computer program product, including computer executable instructions or a computer program. When the computer executable instructions or the computer program are executed by a processor, the steps of the method for determining the equivalent inertia of a power system node described in Embodiment 1 are implemented.

[0157] For more specific details of the above method, please refer to the corresponding contents disclosed in Example 1, which will not be repeated here.

[0158] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the systems, devices, storage media, and computer program products disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0159] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0160] In some embodiments, computer executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0161] As an example, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file storing other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0162] As an example, computer executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed at multiple sites and interconnected by a communication network.

[0163] The present invention has been described in detail above in conjunction with specific implementations and exemplary examples, but these descriptions cannot be understood as limiting the present invention. Those skilled in the art understand that, without departing from the spirit and scope of the present invention, a variety of equivalent substitutions, modifications or improvements may be made to the technical solution of the present invention and its implementation methods, all of which fall within the scope of the present invention. The scope of protection of the present invention shall be subject to the attached claims.

Claims

1. A method for determining equivalent inertia of power system nodes, characterized in that: include: Determine the equivalent inertia of the power system; Applying load power disturbance to each node in the power system to determine a node in the power system as an inertia center node using the frequency of each node after the disturbance; Fit the frequency of each node after the load power disturbance is applied to the power system into a frequency-time curve to determine the frequency change rate of each node after the disturbance occurs; According to the relationship between the frequency change rate of the target node after the disturbance occurs and the frequency change rate of the inertia center node after the disturbance occurs, the equivalent inertia of the target node is calculated using the equivalent inertia of the power system, including: Calculate the equivalent inertia of the target node according to the following formula: Among them, H q is the equivalent inertia of the target node q in the power system; H sys is the equivalent inertia of the power system; f sys is the frequency of the inertia center node; t is time; f q is the frequency of the target node q after being disturbed by the load power; Represents the frequency change rate of the target node q after the disturbance occurs.

2. The method for determining equivalent inertia of power system nodes according to claim 1, characterized in that: The determining of the equivalent inertia of the power system comprises: Calculate the equivalent inertia H of the power system according to the following formula sys : Among them, H g,i is the inertia constant of synchronous generator i; S g,i is the rated capacity of synchronous generator i; I is the total number of synchronous generators; H v,j is the inertia constant of the new energy generator j with virtual inertia control technology; S v,j is the rated capacity of the new energy generator j with virtual inertia control technology; J is the total number of new energy generators with virtual inertia control technology; H p is the inertia constant of the load; S p is the rated capacity of the load; S v,k is the rated capacity of the new energy generator k without virtual inertia control technology; K is the total number of new energy generators without virtual inertia control technology.

3. The method for determining equivalent inertia of power system nodes according to claim 1, characterized in that: The method of applying load power disturbance to each node in the power system to determine a node in the power system as an inertia center node by using the frequency of each node after the disturbance includes: The Pearson correlation coefficient of the perturbed frequency of each node and the perturbed frequencies of other nodes is calculated according to the following formula: in, is the frequency f of node m after disturbance in the power system m The frequency f after the disturbance of node n n Pearson correlation coefficient; U is the total number of frequency sequences in the frequency after node perturbation; f mu f m The uth frequency sequence in f nu f n The uth frequency sequence in ; f m The average value of U frequency sequences in ; f n The average value of U frequency sequences in ; The sum of the Pearson correlation coefficients of each node is calculated according to the following formula: Among them, P m is the sum of the Pearson correlation coefficients of node m; s is the total number of nodes in the power system; The node with the largest sum of Pearson correlation coefficients in the power system is taken as the inertia center node.

4. The method for determining equivalent inertia of power system nodes according to claim 1, characterized in that: The method of fitting the frequency of each node after the load power disturbance is applied to the power system into a frequency-time variation curve to determine the frequency change rate of each node after the disturbance occurs includes: Get the frequency f of node m m , f m ={f m1 ,f m2 ,…,f mU }, U is the total number of frequency sequences in the frequency after node perturbation; Construct multiple fitting models, including high-order polynomials, exponential functions, logarithmic functions, power functions, Gaussian functions, sigmoid functions, combinations of high-order polynomials and exponential functions, combinations of high-order polynomials and logarithmic functions, combinations of high-order polynomials and power functions, combinations of high-order polynomials and Gaussian functions, and combinations of high-order polynomials and sigmoid functions; In the iterative process of the target fitting model, the sum of squares of the residuals is used as the objective function to solve the coefficients of the target fitting model; wherein, when the change of the sum of squares of the residuals is less than a first preset value, or when the absolute value of the gradient is less than a second preset value, or when the maximum allowed number of iterations is reached, the coefficients of the target fitting model under the current number of iterations are output; The fitting error ε of the target fitting model is calculated according to the following formula: Among them, f mu f m The uth frequency sequence in mu The uth frequency sequence fitted by the target fitting model at node m; Obtain the fitting error of each fitting model under each order polynomial within the target order; Within the target order, the fitting model corresponding to the minimum fitting error is taken as the final fitting model; the order of the final fitting model is the order corresponding to the minimum fitting error; According to the frequency of node m and the final fitting model of node m, the frequency change rate of node m after the disturbance occurs is determined.

5. A power system node equivalent inertia determination system, characterized in that: include: A first determination module, used to determine the equivalent inertia of the power system; A second determination module is used to apply load power disturbance to each node in the power system to determine a node in the power system as an inertia center node using the frequency of each node after the disturbance; The third determination module is used to fit the frequency of each node after the load power disturbance is applied in the power system into a frequency-time variation curve to determine the frequency change rate of each node after the disturbance occurs; The calculation module is used to calculate the equivalent inertia of the target node using the equivalent inertia of the power system according to the relationship between the frequency change rate of the target node after the disturbance occurs and the frequency change rate of the inertia center node after the disturbance occurs, including: Calculate the equivalent inertia of the target node according to the following formula: Among them, H q is the equivalent inertia of the target node q in the power system; H sys is the equivalent inertia of the power system; f sys is the frequency of the inertia center node; t is time; f q is the frequency of the target node q after being disturbed by the load power; Represents the frequency change rate of the target node q after the disturbance occurs.

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