Power system node inertia evaluation method and system based on improved vector matching method

Abstract

The invention discloses a power system node inertia evaluation method and system based on an improved vector matching method, and relates to the field of power system operation control, and the method comprises the steps: S1, obtaining the time sequence data of each node in a power system, and carrying out the normalization processing of the data; s2, representing a transfer function between the node frequency deviation and the active power deviation by using a rational function containing unknown parameters, and performing parameter identification by adopting a vector matching method to calculate the unknown parameters of the rational function to obtain an equivalent active-efficiency transfer function; s3, adopting a particle swarm optimization algorithm to improve a vector matching method, taking a final fitting mean square error obtained by the vector matching method as particle fitness, taking fitness minimization as an optimization target, finding a target initial pole through particle swarm iteration, and obtaining an optimal active-frequency transfer function after iteration is finished; and S4, obtaining an inertia evaluation value according to a mathematical relationship between the optimal active-frequency transfer function and the node inertia. The method improves the accuracy of inertia evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation control, and in particular to a method and system for evaluating power system node inertia based on an improved vector matching method. Background Art

[0002] The large-scale access of new energy has significantly changed the inertia characteristics of new power systems. In areas where new energy is concentrated, the low inertia characteristics are obvious, which is in sharp contrast to the high inertia state in the synchronous power cluster distribution area, resulting in a certain spatial distribution characteristic of inertia. When a system failure occurs, the inertia-weak area will produce a large frequency deviation and frequency change rate, which is more likely to trigger the low-frequency load reduction relay or (Rate of Change of Frequency, RoCoF) relay action, thereby increasing the risk of system power outages. In summary, accurately assessing the inertia level at the node level of the power system is conducive to guiding the grid connection of new energy and planning inertia compensation measures, and avoiding the system from falling into the dilemma of ultra-low inertia operation.

[0003] At present, the power system node inertia assessment methods can be divided into two categories: offline assessment and online assessment: Offline inertia assessment generally conducts in-depth mining of post-accident system operation data, estimates RoCoF based on frequency curve polynomial fitting, and estimates inertia based on the swing equation. However, it is difficult for such methods to eliminate the influence of the oscillation component of the frequency signal, so the assessment accuracy is generally not high. At the same time, these methods also have some limitations: they usually assume that the size of the active disturbance and the time of the accident are known, which may introduce uncertainty in practical applications; the assessment results are highly dependent on the quality of the measurement data, and low-quality data may lead to misjudgment; real-time monitoring and dynamic tracking cannot be achieved.

[0004] Most online evaluations adopt a data-driven approach, using dynamic identification models or prediction models based on machine learning to update to fit the actual system frequency dynamic response characteristics. However, such methods usually require accurate and timely data collection, abundant measurement data, or parameter settings such as model orders with a high degree of fit, and may also have problems such as low accuracy, poor robustness, and large computational complexity. In addition, complex power network structures and interference from multiple factors may also affect the accuracy of the model.

[0005] In general, most of the existing node inertia assessment methods have problems such as reliance on large disturbance information, insufficient adaptability, and large data requirements. Therefore, there is an urgent need for a high-precision inertia assessment method that is easier to implement and applicable to more working conditions. Summary of the invention

[0006] The object of the present invention is to provide a method, system, device and medium for evaluating node inertia of a power system based on an improved vector matching method, so as to solve the technical problems existing in the prior art.

[0007] The present invention is achieved through the following technical solutions: In a first aspect, an embodiment of the present invention provides a method for evaluating node inertia of a power system based on an improved vector matching method, comprising: Step S1: Obtain frequency and power time series data of each node in the power system, and normalize the time series data to obtain a frequency standard data set and a power standard data set respectively; Step S2: The transfer function between the node frequency deviation and the active power deviation is expressed by a rational function containing unknown parameters, and the unknown parameters of the rational function are calculated by using the vector matching method according to the time series data to obtain an equivalent active power-efficiency transfer function; Step S3: The particle swarm optimization algorithm is used to improve the vector matching method, and the final fitting mean square error obtained by the vector matching method is used as the particle fitness. The minimum fitness is taken as the optimization goal, and the target initial pole is found through continuous iteration of the particle swarm. After the iteration, the optimal active power-frequency transfer function is obtained; Step S4: Obtain an inertia evaluation value according to the mathematical relationship between the optimal active power-frequency transfer function and the node inertia.

[0008] Furthermore, in step S2, the specific method of using the vector matching method to perform parameter identification based on the time series data to calculate the unknown parameters of the rational function includes: Step S21: Set the maximum number of iterations and initialize the current number of iterations to 1; Step S22: setting the initial pole and constructing auxiliary functions; Step S23: setting vector matching conditions; Step S24: using a recursive filter and trapezoidal numerical integration to respectively calculate the frequency convolution integral and the power convolution integral of the node; Step S25: according to the set vector matching conditions, the unknown residue of the auxiliary function is obtained by fitting and solving using the least square method, and the identification pole of the rational function is obtained by constructing a matrix calculation; Step S26: performing residue identification of rational functions according to the identified poles; Step S27: Determine whether the current number of iterations is less than the maximum number of iterations. If so, add 1 to the current number of iterations, use the obtained identification pole as the new initial pole, and repeat steps S22-S27. If it is greater than or equal to, obtain an approximate expression of the rational function based on the currently calculated identification pole and residue, use the expression and the node power to calculate the fitting value of the node frequency, and calculate the mean square error based on the difference between the fitting value and the actual node frequency.

[0009] Furthermore, the specific method of using the particle swarm optimization algorithm to improve the vector matching method in step S3 includes: Step S31: setting the particle population size, maximum number of iterations, individual learning factor and social learning factor parameters, and randomly setting the initial position and speed of the particles; Step S32: Calculate the initial fitness, and record the individual best fitness, individual best position, global best fitness and global best position of the particles at the initial time, use the current initial position of each particle as the initial pole of the vector matching method, and use the mean square error as the particle fitness; Step S33: Record the individual best position of each particle and the global best position of the particle at the current moment according to the fitness of each particle, and update the individual best position of the particle and the global best position of the particle; Step S34: Determine whether the number of iterations at the current moment is less than the maximum number of iterations. If so, iterate again, update the position information and velocity information of each particle, and repeat steps S32-S34; if greater than or equal to, record the currently calculated global optimal position of the particle swarm as the optimal position, and use the transfer function obtained at the optimal position as the optimal active-frequency transfer function.

[0010] Furthermore, when calculating the initial fitness, the initial individual best fitness is the fitness of the particle when it is in the initial position, the initial position of each particle is the individual best position, the global best fitness is the minimum value of all particle fitnesses, the global best fitness is the minimum value of all particle fitnesses, and the position of the particle with the smallest fitness is the global best position of the particle.

[0011] Furthermore, in step S33, the best individual position of each particle at the current moment and the best global position of the particle are recorded according to the fitness of each particle. Specifically, the fitness corresponding to the current particle is compared with the previous fitness corresponding to the particle, and the position corresponding to the particle with the smallest fitness is the best individual position of the particle. By comparing the optimal fitness of all individual particles, the one with the smallest fitness is the global best position of the particle group.

[0012] In a second aspect, an embodiment of the present invention provides a power system node inertia assessment system based on an improved vector matching method, comprising: an acquisition module, a calculation module, an optimization module and an assessment module; The acquisition module is used to acquire the frequency and power time series data of each node in the power system, and normalize the time series data to obtain a frequency standard data set and a power standard data set respectively; The calculation module is used to express the transfer function between the node frequency deviation and the active power deviation by a rational function containing unknown parameters, and to perform parameter identification using a vector matching method according to the time series data to calculate the unknown parameters of the rational function, thereby obtaining an equivalent active power-efficiency transfer function; The optimization module uses a particle swarm optimization algorithm to improve the vector matching method, takes the final fitting mean square error obtained by the vector matching method as the particle fitness, takes the minimum fitness as the optimization goal, finds the target initial pole through continuous iteration of the particle swarm, and obtains the optimal active power-frequency transfer function after the iteration is completed; The evaluation module obtains an inertia evaluation value according to a mathematical relationship between an optimal active power-frequency transfer function and a node inertia.

[0013] Furthermore, the calculation module includes a vector matching calculation unit, which is used to set the maximum number of iterations, the initial pole, construct an auxiliary function and a vector matching condition, and initialize the current number of iterations to 1, and use a recursive filter and a trapezoidal numerical integration to respectively calculate the frequency convolution integral and the power convolution integral of the node, and according to the set vector matching condition, use the least squares method to fit and solve to obtain the unknown residue of the auxiliary function, and obtain the approximate pole of the rational function by constructing a matrix calculation, and perform residue identification of the rational function according to the identification pole, and judge whether the current number of iterations is less than the maximum number of iterations. If it is less than, the current number of iterations is increased by 1, and the obtained identification pole is used as the new initial pole, and the identification pole and residue are repeatedly calculated. If it is greater than or equal to, the approximate expression of the rational function is obtained according to the currently calculated identification pole and residue, and the fitting value of the node frequency is calculated using the expression and the node power, and the mean square error is calculated according to the difference between the fitting value and the actual obtained node frequency.

[0014] Furthermore, the optimization module includes an optimization processing unit, which sets the particle population size, the maximum number of iterations, the individual learning factor and the social learning factor parameters, randomly sets the initial position and speed of the particles, calculates the initial fitness, and records the individual best fitness of the particles, the individual best position of the particles, the global best fitness of the particles and the global best position of the particles at the initial time, takes the current initial position of each particle as the initial pole of the vector matching method, takes the mean square error as the particle fitness, records the individual best position of each particle and the global best position of the particles at the current moment according to the fitness of each particle, updates the individual best position of the particles and the global best position of the particles, determines whether the number of iterations at the current moment is less than the maximum number of iterations, if so, iterates again, updates the position information and speed information of each particle, and repeatedly calculates the position information and speed information of each particle; if greater than or equal to, records the currently calculated global best position of the particle swarm as the best position, and uses the transfer function obtained at the best position as the optimal active-frequency transfer function.

[0015] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor, an input device, an output device and a memory, wherein the processor is connected to the input device, the output device and the memory, respectively, the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method described in the above embodiment.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method described in the above embodiment.

[0017] Compared with the prior art, the present invention has the following advantages and beneficial effects: The embodiment of the present invention provides a method, system, device and medium for evaluating the inertia of power system nodes based on the improved vector matching method, which regards the number and position of the initial poles as the dimensions and positions of the particles, and through the collaboration of the particle group, enables it to explore extensively in the search space, significantly enhancing the global optimization ability of the algorithm. At the same time, the mean square error calculated by the vector matching method is used as the fitness index of the particle, and fitness minimization is used as the optimization rule. This process is continuously iterated to find a better initial pole configuration, thereby effectively improving the fitting accuracy of the vector matching method. This improvement not only improves the accuracy of inertia evaluation, but also makes the algorithm more robust and adaptable when processing complex power system data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 A flowchart of a method for evaluating node inertia of a power system based on an improved vector matching method provided in a first embodiment of the present invention; Figure 2 This is a schematic diagram of the geographical location distribution of a provincial power grid node in an embodiment of the present invention; FIG3 (a) is a frequency deviation response curve of each node in a unit tripping fault scenario according to an embodiment of the present invention; FIG3 (b) is a power deviation response curve of each node in a unit tripping fault scenario according to an embodiment of the present invention; FIG4 (a) is a frequency deviation response curve of node 1 obtained by using a power system node inertia assessment method based on an improved vector matching method and a least squares identification algorithm based on an ARMAX model provided by an embodiment of the present invention in an actual application scenario; FIG4 (b) is a frequency deviation response curve of node 3 obtained by using a power system node inertia assessment method based on an improved vector matching method and a least squares identification algorithm based on an ARMAX model provided by an embodiment of the present invention in an actual application scenario; FIG5 (a) is a frequency deviation response curve of node 1 obtained by using a power system node inertia assessment method based on an improved vector matching method and a vector matching method provided by an embodiment of the present invention in an actual application scenario; FIG5( b ) is a frequency deviation response curve of node 3 obtained by using a power system node inertia assessment method based on an improved vector matching method and a vector matching method provided by an embodiment of the present invention in an actual application scenario; FIG5 (c) is a frequency deviation response curve of node 5 obtained by using a power system node inertia assessment method based on an improved vector matching method and a vector matching method provided by an embodiment of the present invention in an actual application scenario; Figure 6 A structural block diagram of a power system node inertia assessment system based on an improved vector matching method provided by another embodiment of the present invention; Figure 7 A structural block diagram of an electronic device provided in another embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention. Example 1

[0020] like Figure 1 As shown, a method for evaluating node inertia of a power system based on an improved vector matching method is provided in a first embodiment of the present invention, comprising the following steps: Step S1: Obtain the time series data of frequency, power, etc. of each node in the system, and normalize the data to obtain a frequency standard data set and a power standard data set respectively; Step S2: The transfer function between the node frequency deviation and the active power deviation is expressed by a rational function containing unknown parameters, and the vector matching method is used to perform parameter identification according to the above measurement data to obtain various parameters, the parameters include the poles and residues of the rational function, and the approximate expression of the rational function is obtained according to the poles and residues, and then the equivalent "active power-frequency" transfer function is obtained; Step S3: Introduce the particle swarm optimization algorithm to improve the vector matching method, take the final fitting mean square error obtained by the vector matching method as the particle fitness, take the minimum fitness as the optimization goal, find the target initial pole through continuous iteration of the particle swarm, and obtain the optimal "active power-frequency" transfer function after the iteration; Step S4: Obtain an inertia evaluation value based on the mathematical relationship between the optimal “active power-frequency” transfer function and the node inertia.

[0021] The working principle of the embodiment of the present invention is as follows: Most existing node inertia assessment methods rely on large disturbance information or a large amount of data, and may have problems of low accuracy, poor robustness and large amount of calculation, making it difficult to adapt to different working conditions. Although the inertia assessment method based on the vector matching method is easy to implement and has strong adaptability, the initial poles are usually roughly distributed within a certain data segment and lack precise selection, resulting in limited fitting accuracy and easy to fall into the local optimal solution, affecting the assessment results.

[0022] Therefore, in order to solve this problem, an embodiment of the present invention provides a method for evaluating the node inertia of a power system by combining a vector matching method and a particle swarm optimization algorithm with global convergence characteristics. The number and position of the initial poles are regarded as the dimensions and positions of the particles, and the search space is extensively explored through the collaboration of the particle swarm to enhance the global optimization capability. At the same time, the mean square error obtained by the vector matching method is used as the fitness index of the particles, and fitness minimization is used as the optimization rule. Through continuous iteration, a better initial pole configuration is found, thereby significantly improving the fitting accuracy of the vector matching method and achieving high-precision evaluation of the node inertia of the power system.

[0023] Furthermore, the data normalization processing formula in step S1 is:

[0024] in, for k Node of time i The standard data obtained after frequency normalization processing, for k Node of time i The standard data obtained after power normalization processing, f i ( k )for k Node of time i The frequency, f i ( k 0) is the node at the initial moment i The frequency, f 0 is the rated frequency of the system, usually 50Hz; P i ( k )for k Node of time i The instantaneous value of active power, P i ( k 0) is the node at the initial moment i The steady-state value of active power; the standard data set obtained after the above normalization process is:

[0025] in, F i , P i Node i The frequency standard data set and power standard data set are as follows, where L is the data length.

[0026] Furthermore, the transfer function between the node frequency deviation and the active power deviation in step S1 is expressed as a rational function containing unknown parameters:

[0027] in, p n is the pole of a rational function, usually a complex pole; c n is the residue corresponding to the pole; d is a constant term, N is the fitting order, and s is the Laplace transform parameter.

[0028] Furthermore, in step S2, the specific method of obtaining the unknown parameters of the above rational function according to the vector matching method includes the following sub-steps: Step S21: Set the maximum number of iterations C max , and initialize the current number of iterations to 1. In this embodiment, C max Set to 100.

[0029] Step S22: Set the initial poles and construct auxiliary functions. First, a set of starting poles is given , and construct auxiliary functions , so that it meets the following conditions:

[0030] in, k n For the unknown remainder, They are zeros and poles.

[0031] Step S23: Establish vector matching conditions. Expression multiplied by the input signal , and perform inverse Laplace transform to get:

[0032] In the above formula, For Node i Frequency convolution integral and power convolution integral.

[0033] Step S24: Solve the convolution integral. Use recursive filter (IIR) and trapezoidal numerical integration to calculate the frequency convolution integral and power convolution integral, and the expressions are as follows: ,

[0034] ,

[0035] in, α , λ are the filter coefficients obtained by trapezoidal numerical integration, Δ t is the sampling time.

[0036] Step S25: Based on the vector matching condition in step S23, the least squares fitting is used to obtain The unknown residue k n , and further operations are performed by constructing the matrix H to obtain The identification point , matrix H and identification poles The expression is as follows:

[0037] Among them, A and B are the points containing the initial poles. remainder k n The block matrix of are the sub-matrices of the corresponding parts of matrices A and B, respectively. The initial poles The real and imaginary parts of They are remainder k n The real and imaginary parts of for The identification point, for The conjugate complex number of ; eig represents the eigenvalue of the matrix.

[0038] Step S26: Based on the identification pole obtained in step S25 , remainder c n It can be calculated by solving the following least squares problem: , In the above formula, By identifying the pole Substitute into step S24 to obtain the result.

[0039] Step S27: Determine whether the current iteration number is less than the maximum iteration number. If it is less than, the current iteration number is increased by 1, and the obtained identification pole is As the new initial pole, repeat steps S22-S27; if it is greater than or equal to, then identify the pole obtained by the current calculation and residue c n Get rational function The approximate expression of is used to calculate the fitting value of the node frequency using the expression and the node power, and the mean square error is calculated based on the difference between the fitting value and the actual node frequency.

[0040] Furthermore, the particle swarm optimization algorithm is introduced in step S3 to improve the vector matching method, which includes the following sub-steps: Step S31: Initialize particle parameters, position, speed, and dimension. Set the particle population size, maximum number of iterations, inertia weight, individual learning factor, and social learning factor parameters; randomly set the initial position and speed of the particles; the position and speed of each particle in the particle swarm are expressed as:

[0041] In the above formula, X j , V j They are the particle swarm j The position and velocity of each particle, d is the particle dimension, which here represents the number of initial poles.

[0042] Furthermore, the particle population size in step S31 is S P =20, that is, there are 20 particles in the particle swarm; the maximum number of iterations I=10, the initial number of iterations is 1; the inertia weight w=0.5, the individual learning factor c1=2, the social learning factor c2=2; the dimension d=5; the initial position and velocity of the particle are expressed as follows:

[0043] In the above formula, Particles j The initial position and velocity of x jm , y jm are all random numbers between 0 and 1; j is the imaginary number symbol.

[0044] Step S32: Calculate the initial fitness and record the individual best fitness, individual best position, global best fitness and global best position of the particle at the initial time. Take the current initial position of each particle as the initial pole of the vector matching method, and obtain a series of mean square error values ​​through steps S21-S27, which is used as the particle fitness, and its expression is:

[0045] In the above formula, For the corresponding node i Particles j The fitness of For particles j The nodes obtained through steps S21-S27 i Frequency prediction values ​​and transfer functions; L aplace -1 Stands for inverse Laplace transform.

[0046] Furthermore, the initial individual optimal fitness is the fitness of the particle at the initial position, and the initial position of each particle is the individual optimal position; the global optimal fitness is the minimum value of all particle fitnesses, and the corresponding initial position of the particle is the global optimal position.

[0047] Step S33: Update individual best and global best. Record the individual best position of each particle and the global best position of each particle at the current moment according to the fitness of each particle. Specifically, compare the current best position of each particle with the best position of the particle at the current moment.j The fitness corresponding to the particle is j The previous fitness corresponding to the particles, the position corresponding to the particle with the smallest fitness is the j The best individual position of each particle; by comparing the individual optimal fitness of all particles, the one with the smallest fitness is the global best position of the particle swarm.

[0048] Step S34: Determine whether the number of iterations at the current moment is less than the maximum number of iterations. If less than, iterate again, update the position information and velocity information of each particle, and repeat steps S32-S34; if greater than or equal to, record the currently calculated global best position of the particle swarm as the best position, and use the transfer function obtained by the best position as the optimal "active power-frequency" transfer function, and update the position information and velocity information of each particle as follows: , In the above formula, V j ( k+ 1) Particles j through k Speed ​​after update; X j,best For particles j The individual best position of Divided into particles j through k The position after and before the update; r1 and r2 are random numbers between 0 and 1.

[0049] Furthermore, the mathematical relationship between the “active power-frequency” transfer function and the node inertia in step S4 is: , In the above formula, H i , D i and F i ( s ) is a node i Inertia, damping coefficient and primary frequency modulation response; b ' 1,i ~ b ' n,i , a ' 1,i ~ a ' n,i They are G i ( s )The coefficients of the numerator and denominator, n is the function order.

[0050] The effectiveness of the power system node inertia evaluation method based on the improved vector matching method provided by the embodiment of the present invention is described below using a specific application scenario: Based on the PSASP simulation software platform, a disturbance event simulation of a provincial power grid under normal operation in summer 2023 is used as a test case to verify the accuracy of the power system node inertia evaluation method based on the improved vector matching method provided by the embodiment of the present invention. The node geographical location distribution of the provincial power grid is as follows: Figure 2 As shown, there are more than 100 units in the system, with a total installed capacity of about 20,000 MW, a rated frequency of 50 Hz, a benchmark capacity of 100 MW, and a sampling time of 0.01 s. Figure 2 In the figure, TS1 and TS2 represent substations in other provinces, while LR1 and LR2 are the interconnection lines between the provincial power grid and the power grid in other provinces. In addition, SS is the switch station, CS is the converter station, and PS represents the power plant. The disturbance fault scenarios during the simulation analysis are: t =1s, the units at nodes 7 and 8 tripped, resulting in a total loss of 3207MW output. The simulation results of the frequency and power deviation of each node are shown in Figure 3(a) and Figure 3(b). The six generator nodes numbered 1, 2, 3, 4, 5 and 6 were selected as test objects, and the proposed method was compared and analyzed with the least squares identification algorithm based on the ARMAX model and the vector matching method, respectively, which shows that the inertia evaluation method proposed in the present invention has better accuracy. The parameters of the above six generator nodes are shown in Table 1, and the comparison and error analysis of the inertia evaluation results of each node obtained based on different methods are shown in Table 2 and Table 3.

[0051]

[0052]

[0053] According to the comparison results in Table 2, the node inertia estimation value obtained by the method of the embodiment of the present invention has a minimum error of 0.455%, a maximum error of 5.622%, and an average error of about 2.3%. The node inertia estimation value obtained based on the least squares method identification has a minimum error of 3.947%, a maximum error of 19.974%, and an average error of about 11%. It can be seen that the node inertia evaluation method provided by the embodiment of the present invention has higher accuracy than the traditional least squares identification method. Specifically, in addition, the frequency fitting mean square error based on the least squares method is significantly higher than the result obtained by the embodiment of the method of the present invention. In order to further highlight the superiority of the method provided by the embodiment of the present invention, node 1 and node 3 are selected as test objects, and the frequency deviation response is obtained by the above two methods, as shown in Figure 4 (a) and Figure 4 (b). It can be seen from Figure 4 (a) and Figure 4 (b) that the frequency deviation response of the inertia evaluation method proposed by the embodiment of the present invention is closer to the actual simulation result than the least squares identification. These test results show that, compared with the traditional system identification algorithm, the method of the embodiment of the present invention performs better in the estimation accuracy of node inertia, and is less sensitive to changes in node position, and therefore has greater robustness and applicability.

[0054]

[0055] As shown in Table 3, in the case of a unit tripping fault, the evaluation errors and mean square errors of nodes 1, 3 and 5 obtained by the method of the embodiment of the present invention are significantly lower than those of the vector matching method, and the evaluation results of other nodes are close to it. The main reason is that there are differences in the frequency response characteristics of different nodes, which may cause some nodes to generate multiple identical local optimal initial solutions when performing vector matching, thereby affecting the existence of the global optimal solution. It is worth noting that in the case of a unit tripping fault, the frequency and power fluctuations of each node are small, which makes the fitting of the transfer function more difficult, which makes the error of the inertia evaluation method based on vector matching exceed 10% on individual nodes. In contrast, the method of the embodiment of the present invention improves the selection of the initial poles by introducing the particle swarm optimization algorithm, thereby significantly reducing the error of each node, and the evaluation errors of all nodes are kept within 6%, ensuring a high degree of accuracy. In order to further verify the superiority of the method of the present invention, nodes 1, 3 and 5 are selected as test objects, and the frequency deviation response is calculated using different methods. The results are shown in Figures 5(a), 5(b) and 5(c). By comparison, it can be seen that the frequency deviation response obtained by the method of the present invention is closer to the actual simulation result than the vector matching method, especially in the comparison of nodes 1 and 3, the effect is more significant, and this phenomenon is further verified by the mean square error. Combined with the comparison results in Table 2, it can be seen that the power system node inertia evaluation method based on the improved vector matching method proposed in the embodiment of the present invention has significant advantages in accuracy, adaptability and stability.

[0056] In summary, the large-scale access of new energy has significantly changed the inertia characteristics of the new power system. In the area where new energy is concentrated, the low inertia characteristics are obvious, which is in sharp contrast to the high inertia state in the area where the synchronous power cluster is distributed, resulting in a certain spatial distribution characteristic of the inertia. When a system fault occurs, the weak inertia area will produce a large frequency deviation and frequency change rate, which is more likely to trigger the action of the low-frequency load reduction relay or RoCoF relay, thereby increasing the risk of system power outage. Therefore, accurately assessing the inertia level at the node level of the power system is conducive to guiding the grid connection of new energy and planning inertia compensation measures, and avoiding the system from falling into the dilemma of ultra-low inertia operation. However, most of the existing inertia evaluation methods rely on large disturbance information or a large amount of data, and may have problems of low accuracy, poor robustness and large amount of calculation at the same time, making it difficult to adapt to different working conditions. Although the inertia evaluation method based on the vector matching method is easy to implement and has strong adaptability, the initial poles are usually roughly distributed in a certain data segment, lacking precise selection, resulting in limited fitting accuracy and affecting the evaluation results. Therefore, in order to solve this problem, the present invention proposes a method for evaluating the node inertia of a power system based on an improved vector matching method, which regards the number and position of the initial poles as the dimensions and positions of the particles, and widely explores the search space through the collaboration of the particle swarm to enhance the global optimization capability. At the same time, the mean square error calculated based on the vector matching method is used as the particle fitness index, and fitness minimization is used as the optimization rule. Through continuous iteration, a better initial pole configuration is found, thereby significantly improving the fitting accuracy of the vector matching method and achieving high-precision evaluation of the node inertia of the power system. Example 2

[0057] like Figure 6 As shown, another embodiment of the present invention provides a power system node inertia evaluation system based on an improved vector matching method, including: an acquisition module, a calculation module, an optimization module and an evaluation module, the acquisition module is used to acquire the frequency and power time series data of each node in the power system, and normalize the time series data to obtain a frequency standard data set and a power standard data set respectively, the calculation module is used to represent the transfer function between the node frequency deviation and the active power deviation with a rational function containing unknown parameters, and perform parameter identification using a vector matching method according to the time series data to calculate the unknown parameters of the rational function to obtain an equivalent active power-efficiency transfer function; the optimization module uses a particle swarm optimization algorithm to improve the vector matching method, and uses the final fitting mean square error obtained by the vector matching method as the particle fitness, takes the minimum fitness as the optimization goal, finds the target initial pole through continuous iteration of the particle swarm, and obtains the optimal active power-frequency transfer function after the iteration; the evaluation module obtains the inertia evaluation value according to the mathematical relationship between the optimal active power-frequency transfer function and the node inertia.

[0058] Among them, the calculation module includes a vector matching calculation unit, which is used to set the maximum number of iterations, the initial pole, construct an auxiliary function and a vector matching condition, and initialize the current number of iterations to 1, and use a recursive filter and a trapezoidal numerical integration to respectively calculate the frequency convolution integral and the power convolution integral of the node, and according to the set vector matching conditions, the least squares method is used to fit and solve the unknown residue of the auxiliary function, and the approximate poles of the rational function are obtained by constructing a matrix calculation, and the residue of the rational function is identified according to the identification poles, and it is judged whether the current number of iterations is less than the maximum number of iterations. If it is less than, the current number of iterations is increased by 1, and the obtained identification pole is used as the new initial pole, and the identification pole and residue are repeatedly calculated. If it is greater than or equal to, the approximate expression of the rational function is obtained according to the currently calculated identification pole and residue.

[0059] Among them, the optimization module includes an optimization processing unit, which sets the particle population size, the maximum number of iterations, the individual learning factor and the social learning factor parameters, randomly sets the initial position and speed of the particles, calculates the initial fitness, and records the individual best fitness, individual best position, global best fitness and global best position of the particles at the initial time, takes the current initial position of each particle as the initial pole of the vector matching method, takes the mean square error value as the particle fitness, records the individual best position of each particle and the global best position of the particle at the current moment according to the fitness of each particle, updates the individual best position of the particle and the global best position of the particle, judges whether the number of iterations at the current moment is less than the maximum number of iterations, if less, iterates again, updates the position information and speed information of each particle, and repeatedly calculates the position information and speed information of each particle; if greater than or equal to, records the currently calculated global best position of the particle group as the best position, and uses the transfer function obtained at the best position as the optimal active-frequency transfer function.

[0060] An embodiment of the present invention provides a power system node inertia assessment system based on an improved vector matching method. It is based on the same inventive concept as the above-mentioned power system node inertia assessment based on an improved vector matching method and has the same beneficial effects, which will not be repeated here. Example 3

[0061] like Figure 7 As shown, a structural block diagram of an electronic device according to another embodiment of the present invention, the device includes a processor, an input device, an output device and a memory, the processor is connected to the input device, the output device and the memory respectively, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method described in the first embodiment above.

[0062] It should be understood that in the embodiments of the present invention, the processor may be a central processing unit (Central Processing Unit), and the processor may also be other general-purpose processors, digital signal processors (Digital Signal Processor), application-specific integrated circuits (Application Specific Integrated Circuit), field-programmable gate arrays (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0063] Input devices may include a touchpad, a fingerprint collection sensor (used to collect the user's fingerprint information and fingerprint direction information), a microphone, etc., and output devices may include a display (LCD, etc.), a speaker, etc.

[0064] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0065] In a specific implementation, the processor, input device, and output device described in the embodiments of the present invention may execute the implementation described in the method embodiment provided in the embodiments of the present invention, or may execute the implementation described in the system embodiment described in the embodiments of the present invention, which will not be described in detail here. Example 4

[0066] Another embodiment of the present invention further provides an embodiment of a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method described in the first embodiment.

[0067] The computer-readable storage medium may be an internal storage unit of the terminal described in the foregoing embodiment, such as a hard disk or memory of the terminal. The computer-readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0068] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0069] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the terminals and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0070] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0071] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating node inertia of a power system based on an improved vector matching method, characterized in that: include: Step S1: Obtain frequency and power time series data of each node in the power system, and normalize the time series data to obtain a frequency standard data set and a power standard data set respectively; Step S2: The transfer function between the node frequency deviation and the active power deviation is expressed by a rational function containing unknown parameters, and the unknown parameters of the rational function are calculated by using the vector matching method according to the time series data to obtain an equivalent active power-efficiency transfer function; Step S3: The particle swarm optimization algorithm is used to improve the vector matching method, and the final fitting mean square error obtained by the vector matching method is used as the particle fitness. The minimum fitness is taken as the optimization goal, and the target initial pole is found through continuous iteration of the particle swarm. After the iteration, the optimal active power-frequency transfer function is obtained; Step S4: Obtain an inertia evaluation value according to the mathematical relationship between the optimal active power-frequency transfer function and the node inertia.

2. The method according to claim 1, characterized in that The specific method of performing parameter identification using the vector matching method according to the time series data in step S2 to calculate the unknown parameters of the rational function includes: Step S21: Set the maximum number of iterations and initialize the current number of iterations to 1; Step S22: setting the initial pole and constructing auxiliary functions; Step S23: setting vector matching conditions; Step S24: using a recursive filter and trapezoidal numerical integration to respectively calculate the frequency convolution integral and the power convolution integral of the node; Step S25: according to the set vector matching conditions, the unknown residue of the auxiliary function is obtained by fitting and solving using the least square method, and the identification pole of the rational function is obtained by constructing a matrix calculation; Step S26: performing residue identification of rational functions according to the identified poles; Step S27: Determine whether the current number of iterations is less than the maximum number of iterations. If so, add 1 to the current number of iterations, use the obtained identification pole as the new initial pole, and repeat steps S22-S27. If it is greater than or equal to, obtain an approximate expression of the rational function based on the currently calculated identification pole and residue, use the expression and the node power to calculate the fitting value of the node frequency, and calculate the mean square error based on the difference between the fitting value and the actual node frequency.

3. The method according to claim 2, characterized in that The specific method of improving the vector matching method by using the particle swarm optimization algorithm in step S3 includes: Step S31: setting the particle population size, maximum number of iterations, individual learning factor and social learning factor parameters, and randomly setting the initial position and speed of the particles; Step S32: Calculate the initial fitness, and record the individual best fitness of the particle, the individual best position of the particle, the global best fitness of the particle, and the global best position of the particle at the initial time, use the current initial position of each particle as the initial pole of the vector matching method, and use the mean square error as the particle fitness; Step S33: Record the best individual position of each particle and the best global position of the particle at the current moment according to the fitness of each particle, and update the best individual position of the particle and the best global position of the particle; Step S34: Determine whether the number of iterations at the current moment is less than the maximum number of iterations. If so, iterate again, update the position information and velocity information of each particle, and repeat steps S32-S34; if greater than or equal to, record the currently calculated global optimal position of the particle swarm as the optimal position, and use the transfer function obtained at the optimal position as the optimal active-frequency transfer function.

4. The method according to claim 3, characterized in that When calculating the initial fitness, the initial individual best fitness is the fitness of the particle at the initial position, the initial position of each particle is the individual best position, the global best fitness is the minimum value of all particle fitnesses, and the position of the particle with the smallest fitness is the global best position of the particle.

5. The method according to claim 3, characterized in that: In step S33, the best individual position of each particle and the best global position of the particle are recorded according to the fitness of each particle at the current moment. Specifically, the fitness corresponding to the current particle is compared with the fitness corresponding to the particle in the past. The position corresponding to the particle with the smallest fitness is the best individual position of the particle. By comparing the optimal fitness of all individual particles, the one with the smallest fitness is the global best position of the particle group.

6. A power system node inertia assessment system based on an improved vector matching method, characterized in that: include: Acquisition module, calculation module, optimization module and evaluation module; The acquisition module is used to acquire the frequency and power time series data of each node in the power system, and normalize the time series data to obtain a frequency standard data set and a power standard data set respectively; The calculation module is used to express the transfer function between the node frequency deviation and the active power deviation by a rational function containing unknown parameters, and to perform parameter identification using a vector matching method according to the time series data to calculate the unknown parameters of the rational function, thereby obtaining an equivalent active power-efficiency transfer function; The optimization module uses a particle swarm optimization algorithm to improve the vector matching method, takes the final fitting mean square error obtained by the vector matching method as the particle fitness, takes the minimum fitness as the optimization goal, finds the target initial pole through continuous iteration of the particle swarm, and obtains the optimal active power-frequency transfer function after the iteration is completed; The evaluation module obtains an inertia evaluation value according to a mathematical relationship between an optimal active power-frequency transfer function and a node inertia.

7. The system according to claim 6, characterized in that The calculation module includes a vector matching calculation unit, which is used to set the maximum number of iterations, the initial pole, construct an auxiliary function and a vector matching condition, and initialize the current number of iterations to 1, use a recursive filter and a trapezoidal numerical integration to respectively calculate the frequency convolution integral and the power convolution integral of the node, according to the set vector matching condition, use the least squares method to fit and solve to obtain the unknown residue of the auxiliary function, and obtain the approximate pole of the rational function by constructing a matrix calculation, perform residue identification of the rational function according to the identification pole, judge whether the current number of iterations is less than the maximum number of iterations, if less than, the current number of iterations is increased by 1, the obtained identification pole is used as the new initial pole, and the identification pole and residue are repeatedly calculated, if greater than or equal to, then the approximate expression of the rational function is obtained according to the currently calculated identification pole and residue, the fitting value of the node frequency is calculated using the expression and the node power, and the mean square error is calculated according to the difference between the fitting value and the actually obtained node frequency.

8. The system according to claim 7, characterized in that The optimization module includes an optimization processing unit, which sets the particle population size, the maximum number of iterations, the individual learning factor and the social learning factor parameters, randomly sets the initial position and speed of the particles, calculates the initial fitness, and records the individual best fitness of the particles, the individual best position of the particles, the global best fitness of the particles and the global best position of the particles at the initial time, takes the current initial position of each particle as the initial pole of the vector matching method, takes the mean square error as the particle fitness, records the individual best position of each particle and the global best position of the particles at the current moment according to the fitness of each particle, updates the individual best position of the particles and the global best position of the particles, judges whether the number of iterations at the current moment is less than the maximum number of iterations, if so, iterates again, updates the position information and speed information of each particle, and repeatedly calculates the position information and speed information of each particle; if greater than or equal to, records the currently calculated global best position of the particle group as the best position, and uses the transfer function obtained at the best position as the optimal active power-frequency transfer function.

9. An electronic device, comprising a processor, an input device, an output device and a memory, wherein the processor is connected to the input device, the output device and the memory respectively, and the memory is used to store a computer program, wherein the computer program comprises program instructions, and wherein: The processor is configured to call the program instructions to execute the method according to any one of claims 1-5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 5.

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