A method, system and storage medium for evaluating regional equivalent inertia of a power system

Abstract

The present invention discloses a method, system and storage medium for evaluating regional equivalent inertia of a power system, comprising: dividing a power system into different regions, respectively evaluating all generators in each region as one equivalent generator, and obtaining measurement data of each equivalent generator, wherein the measurement data comprises: output power and frequency of the equivalent generator; aggregating the output power of all generators in the region as the output power of the equivalent generator, obtaining the regional inertia center frequency by selecting generators with inertia constants greater than a preset inertia constant threshold from each region, and using the regional inertia center frequency as the frequency of the equivalent generator; filtering the measurement data of each equivalent generator to obtain filtered measurement data; and iteratively solving the inertia constant to be evaluated by using a dichotomy method in combination with the sign of the mean value of the frequency prediction error. H , the inertia constant to be evaluated H To characterize the regional equivalent inertia.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system and storage medium for evaluating regional equivalent inertia of a power system. Background Art

[0002] With the vigorous development of new energy resources such as wind and light, a power system with a high proportion of new energy has become a necessary trend. Photovoltaic and wind power in the system replace synchronous units in the form of inverter grid connection, which leads to a significant decrease in the system inertia level and causes system frequency stability problems. When the system inertia is insufficient, after a large active disturbance occurs, the rate of change of frequency (ROCOF) caused by it is greater, which makes it easier to trigger the action of the low-frequency load reduction relay, exacerbating the risk of system power outages. Such accidents in history were all caused by insufficient inertia support, causing large-scale power outages in the system when there was a shortage of active power. Therefore, it is particularly important to realize the online evaluation of the equivalent inertia of the power system. The actual power system is composed of multiple regions, and the regional inertia sources and their network structures are not the same. By comparing and evaluating the inertia estimation results of each region with the inertia demand threshold, it is helpful for dispatchers to quickly perceive and monitor the inertia level of each region of the system, provide decision-making basis for inertia control strategies, and ensure the safe and stable operation of the power system. At present, the installed capacity of new energy power generation is increasing day by day, and the penetration rate is increasing. It is a problem worthy of in-depth exploration to quickly and accurately grasp the inertia level of the region and the system. Summary of the invention

[0003] In view of this, the present invention provides a method, system and storage medium for evaluating regional equivalent inertia of a power system, so as to at least solve the problem in the prior art that it is difficult to quickly evaluate the regional inertia level of a power system.

[0004] In order to achieve the above object, the present invention adopts the following technical solution:

[0005] A method for evaluating regional equivalent inertia of a power system comprises the following steps:

[0006] S1. Divide the power system into different areas, respectively treat all generators in each area as equivalent generators, and obtain measurement data of each equivalent generator, wherein the measurement data includes: output power and frequency of the equivalent generator; aggregate the output power of all generators in the area as the output power of the equivalent generator, obtain the regional inertia center frequency by selecting generators with inertia constants greater than a preset inertia constant threshold from each area, and use the regional inertia center frequency as the frequency of the equivalent generator;

[0007] S2. Filter the measured data of each equivalent generator to obtain filtered measured data;

[0008] S3. Determine the inertia constants to be evaluated in each area H The initial value interval of a , b ] and intermediate values m , calculated based on the filtered measurement data H for a , b and m Inertia response stage n The frequency prediction value at each time point is calculated, and the mean error between each frequency prediction value and the actual measured frequency measurement value is calculated. Combined with the sign of the mean frequency prediction error, the inertia constant to be evaluated is solved iteratively using the bisection method. H , the inertia constant to be evaluated H To characterize the regional equivalent inertia; in the bisection iteration process, when H Value a and m When the corresponding error means have different signs, let a = m , if the same number, then b = m ;

[0009] S1 mid-region inertial center frequency The specific method of obtaining is:

[0010] ,

[0011] In the formula, and are the inertia constant and frequency of each generator in the region respectively;

[0012] ,

[0013] In the formula, Indicates the start and stop status of the unit, the value is 1 or 0, where 1 means start and 0 means stop. represents the kinetic energy of the unit, Indicates the unit capacity.

[0014] Preferably, the specific content of S2 includes:

[0015] The filter model is:

[0016] ,

[0017] In the formula, Indicates that the filter is n The noise signal output at any time, represents the input signal vector, Represents the weight coefficient vector of the filter;

[0018] Gaussian white noise as the expected output , then the error signal for:

[0019] ,

[0020] The minimum mean square error criterion is used to minimize the objective function to reduce the error Minimize the input signal vector Subtract the value corresponding to the minimum objective function Then the filtered measurement data is obtained, where the objective function is:

[0021] ,

[0022] In the formula, represents the mean square error function, Indicates expected value;

[0023] Calculate the objective function right The derivative of , set the derivative to 0:

[0024] ,

[0025] Get the update formula of the filter weight coefficient vector:

[0026] ,

[0027] In the formula, is the step size factor, The larger the value, the faster the algorithm converges, but the larger the steady-state error; The smaller the value, the slower the algorithm converges, but the smaller the steady-state error. To ensure the steady-state convergence of the algorithm, The value range is:

[0028] .

[0029] Preferably, the specific content of S3 includes:

[0030] S31. Determine the inertia constants to be evaluated in each area H Initial value range[ a , b ], let the inertia constant to be evaluated H The values ​​are a and b The corresponding frequency prediction error value is and Different number, The midpoint value of the interval:

[0031] ,

[0032] S32. Calculate the inertia constant to be evaluated H Take values ​​separately a , b and m The corresponding frequency prediction value , , Three sets of data, among which and Respectively n The inertia constant to be evaluated at time H Take values ​​separately a , b and m The corresponding frequency prediction value when ;

[0033] S33. Calculate frequency prediction value and The error between the measured value of the frequency , and ,in and Respectively n Moment and and the frequency measurement value, and respectively obtain and The mean of : and ;

[0034] S34. If , then let a = m ,like , then let b = m , and jump to step S32 to continue iterating until the error or interval length meets the preset accuracy requirement, output H The estimated value of is after iteration m .

[0035] Preferably, the frequency prediction value is calculated in S32 and The specific method is:

[0036] The equation of motion of the generator rotor is:

[0037] ,

[0038] In the formula, is the frequency deviation of the equivalent generator, is the frequency of the equivalent generator, is the nominal frequency of the power system, Indicates the change in the equivalent generator output power;

[0039] For a sufficiently small time period, the generator rotor motion equation Expressed as ,but:

[0040] ,

[0041] ,

[0042] In the formula, express t The predicted value of the moment frequency, express t -1 frequency measurement value at time;

[0043] Then the inertia constant to be evaluated is H The value of a , b and m Substitute them into the above formula to get the corresponding frequency prediction value and .

[0044] Preferably, in S33, and The specific method is:

[0045] Compute the frequency prediction error:

[0046] ,

[0047] In the formula, represents the error between the predicted frequency value and the measured frequency value, express t The predicted value of the moment frequency, express t The frequency measurement value at time, express t Output power change value at the moment;

[0048] Then the calculated value in S32 and Substitute them into the above formula to get the corresponding and .

[0049] A power system regional equivalent inertia assessment system, comprising:

[0050] The equivalent module is used to divide the power system into different areas, respectively equate all the generators in each area to an equivalent generator, and obtain the measurement data of each equivalent generator, wherein the measurement data includes: the output power and frequency of the equivalent generator; the output power of all the generators in the region is aggregated as the output power of the equivalent generator, and the regional inertia center frequency is obtained by selecting the generator with an inertia constant greater than a preset inertia constant threshold from each region, and the regional inertia center frequency is used as the frequency of the equivalent generator;

[0051] Regional inertial center frequency The specific method of obtaining is:

[0052] ;

[0053] In the formula, and are the inertia constant and frequency of each generator in the region respectively;

[0054] ;

[0055] In the formula, Indicates the start and stop status of the unit, the value is 1 or 0, where 1 means start and 0 means stop. represents the kinetic energy of the unit, Indicates the unit capacity;

[0056] A filtering model is used to filter the measurement data of each equivalent generator to obtain filtered measurement data;

[0057] Inertia constant value module, used to determine the inertia constant to be evaluated in each area H The initial value interval of a , b ] and intermediate values m , calculated based on the filtered measurement data H for a , b and m Inertia response stage n The frequency prediction value at each time point is calculated, and the mean error between each frequency prediction value and the actual measured frequency measurement value is calculated. Combined with the sign of the mean frequency prediction error, the inertia constant to be evaluated is solved iteratively using the bisection method. H , the inertia constant to be evaluated H To characterize the regional equivalent inertia; in the bisection iteration process, when H Value a and m When the corresponding error means have different signs, let a = m , if the same number, then b =m .

[0058] A computer-readable storage medium stores a computer program, which is used to be programmed or configured by a microprocessor to execute a method for evaluating regional equivalent inertia of a power system.

[0059] It can be seen from the above technical solution that, compared with the prior art, the present invention provides a method, system and storage medium for evaluating the regional equivalent inertia of a power system. In view of the fact that the inertia level of the power grid decreases with the development of new energy, a rapid evaluation of the regional inertia level of the power system is achieved based on time series measurement data such as the bus frequency and power change at the generator end of the power system, so as to ensure the safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A schematic diagram of a flow chart of a method for evaluating regional equivalent inertia of a power system provided by the present invention;

[0062] Figure 2 A flow chart of realizing regional inertia estimation based on frequency prediction error and dichotomy in a method for evaluating regional equivalent inertia of a power system provided by the present invention;

[0063] Figure 3 A comparison diagram before and after filtering provided by an embodiment of the present invention; wherein Figure 3 (a) is the frequency data waveform after Gaussian noise is injected. Figure 3 (b) is the frequency data waveform after filtering;

[0064] Figure 4 Schematic diagram of the fluctuation of the inertia center frequency of the entire system under two disturbance scenarios provided by the embodiment of the present invention, wherein: Figure 4 (a) is the fluctuation of large disturbance scene, Figure 4 (b) is the fluctuation situation of small disturbance scene. DETAILED DESCRIPTION

[0065] 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.

[0066] The present invention provides a method for evaluating regional equivalent inertia of a power system, comprising the following steps:

[0067] S1. Divide the power system into different areas, respectively treat all generators in each area as equivalent generators, and obtain measurement data of each equivalent generator, wherein the measurement data includes: output power and frequency of the equivalent generator; aggregate the output power of all generators in the area as the output power of the equivalent generator, obtain the regional inertia center frequency by selecting generators with inertia constants greater than a preset inertia constant threshold from each area, and use the regional inertia center frequency as the frequency of the equivalent generator;

[0068] S2. Filter the measured data of each equivalent generator to obtain filtered measured data;

[0069] S3. Determine the inertia constants to be evaluated in each area H The initial value interval of a , b ] and intermediate values m , calculated based on the filtered measurement data H for a , b and m Inertia response stage n The frequency prediction value at each time point is calculated, and the mean error between each frequency prediction value and the actual measured frequency measurement value is calculated. Combined with the sign of the mean frequency prediction error, the inertia constant to be evaluated is solved iteratively using the bisection method. H , the inertia constant to be evaluated H To characterize the regional equivalent inertia; in the binary iteration process, when H Value a and m When the corresponding error means have different signs, let a = m , if the same number, then b = m .

[0070] It should be noted that:

[0071] In this embodiment, the inertia constants of the generators in the area are obtained from the SCADA and energy management system, the power and frequency data of the generators in the area are collected through the phasor measurement unit PMU, and the frequency sampling points in the area are determined based on the actual situation.

[0072] Since it is difficult to obtain the inertia constants of some generators, the amount of data collected from all generator frequencies is large. Therefore, the functional expression of the regional inertia center frequency is analyzed:

[0073] ,

[0074] In the formula, is the aggregate generator equivalent frequency; and are the inertia time constant and frequency of each generator in the area respectively.

[0075] According to the calculation formula of regional inertia center frequency, The calculation of is actually the weighted frequency of each generator set according to the inertia constant, which means that for a generator, the larger its inertia constant is, the Therefore, when calculating the regional frequency, several units with larger median values ​​of inertia constant generators can be selected to calculate the regional frequency, thereby reducing the data demand and calculation amount. For wind power systems with virtual inertia control, a first-order inertia link is usually added to the design of wind turbine virtual inertia control, so that the wind power output power will not respond immediately to system frequency changes, and it is not suitable to use the wind farm grid-connected bus as a frequency sampling point. H The synchronous generator set export bus frequency with a larger value is taken as the frequency sampling point in this area.

[0076] In order to further implement the above technical solution, the regional inertial center frequency in S1 The specific method of obtaining is:

[0077] ,

[0078] In the formula, and are the inertia constant and frequency of each generator in the region respectively;

[0079] ,

[0080] In the formula, Indicates the start and stop status of the unit, the value is 1 or 0, where 1 means start and 0 means stop. represents the kinetic energy of the unit, Indicates the unit capacity.

[0081] In order to further implement the above technical solution, the specific contents of S2 include:

[0082] The filter model is:

[0083] ,

[0084] In the formula, Indicates that the filter is n The noise signal output at any time, represents the input signal vector, Represents the weight coefficient vector of the filter;

[0085] Gaussian white noise as the expected output , then the error signal for:

[0086] ,

[0087] The minimum mean square error criterion is used to minimize the objective function to reduce the error Minimize the input signal vector Subtract the value corresponding to the minimum objective function Then the filtered measurement data is obtained, where the objective function is:

[0088] ,

[0089] In the formula, represents the mean square error function, Indicates expected value;

[0090] Calculate the objective function right The derivative of , set the derivative to 0:

[0091] ,

[0092] Get the update formula of the filter weight coefficient vector:

[0093] ,

[0094] In the formula, is the step size factor, The larger the value, the faster the algorithm converges, but the larger the steady-state error; The smaller the value, the slower the algorithm converges, but the smaller the steady-state error. To ensure the steady-state convergence of the algorithm, The value range is:

[0095] .

[0096] It should be noted that:

[0097] The key to the LMS algorithm is the step size parameter The choice of step size should be small enough to ensure the convergence of the algorithm, but not too small to avoid slow convergence. Typically set to a function of the maximum eigenvalue of the autocorrelation matrix of the input signal.

[0098] In order to further implement the above technical solutions, the specific contents of S3 include:

[0099] S31. Determine the inertia constants to be evaluated in each area H Initial value range[ a , b ], let the inertia constant to be evaluated H The values ​​are a and b The corresponding frequency prediction error value is and Different number, m The midpoint value of the interval:

[0100] ,

[0101] S32. Calculate the inertia constant to be evaluated H Take values ​​separately a , b and m The corresponding frequency prediction value , , Three sets of data, among which and Respectively n The inertia constant to be evaluated at time H Take values ​​separately a , b and m The corresponding frequency prediction value when ;

[0102] S33. Calculate frequency prediction value and The error between the measured value of the frequency , and ,in and Respectively n Moment and and the frequency measurement value, and respectively obtain and The mean of : and ;

[0103] S34. If , then let a = m ,like , then let b = m , and jump to step S32 to continue iterating until the error or interval length meets the preset accuracy requirement, output H The estimated value of is after iteration m .

[0104] In order to further implement the above technical solution, in the inertia response time scale after the active disturbance, the unit primary frequency regulation does not act, so the frequency prediction value is calculated in S32 and The specific method is:

[0105] The equation of motion of the generator rotor is:

[0106] ,

[0107] In the formula, is the frequency deviation of the equivalent generator, is the frequency of the equivalent generator, is the nominal frequency of the power system, Indicates the change in the equivalent generator output power;

[0108] For a sufficiently small time period, the generator rotor motion equation Expressed as ,but:

[0109] ,

[0110] ,

[0111] In the formula, express t The predicted value of the moment frequency, express t -1 frequency measurement value at time;

[0112] Then the inertia constant to be evaluated is H The value of a , b and m Substitute them into the above formula to get the corresponding frequency prediction value and .

[0113] In order to further implement the above technical solution, when the frequency fluctuation trend is different, the calculated prediction error has different signs. Since the subsequent process needs to be carried out according to the sign of the frequency prediction error mean, the calculation in S33 and The specific method is:

[0114] Compute the frequency prediction error:

[0115] ,

[0116] In the formula, represents the error between the predicted frequency value and the measured frequency value, express t The predicted value of the moment frequency, express t The frequency measurement value at time, express t Output power change value at the moment;

[0117] This ensures that when the estimated inertia constant is H When the value is too large, is a positive number, H When the value is small, Is a negative number.

[0118] Then the calculated value in S32 and Substitute them into the above formula to get the corresponding and .

[0119] A power system regional equivalent inertia assessment system, comprising:

[0120] The equivalent module is used to divide the power system into different areas, respectively equate all the generators in each area to an equivalent generator, and obtain the measurement data of each equivalent generator, wherein the measurement data includes: the output power and frequency of the equivalent generator; the output power of all the generators in the region is aggregated as the output power of the equivalent generator, and the regional inertia center frequency is obtained by selecting the generator with an inertia constant greater than a preset inertia constant threshold from each region, and the regional inertia center frequency is used as the frequency of the equivalent generator;

[0121] A filtering model is used to filter the measurement data of each equivalent generator to obtain filtered measurement data;

[0122] Inertia constant value module, used to determine the inertia constant to be evaluated in each area H The initial value interval of a , b ] and intermediate values m , calculated based on the filtered measurement data H for a , b and m Inertia response stage nThe frequency prediction value at each time point is calculated, and the mean error between each frequency prediction value and the actual measured frequency measurement value is calculated. Combined with the sign of the mean frequency prediction error, the inertia constant to be evaluated is solved iteratively using the bisection method. H , the inertia constant to be evaluated H To characterize the regional equivalent inertia; in the binary iteration process, when H Value a and m When the corresponding error means have different signs, let a = m , if the same number, then b = m .

[0123] A computer-readable storage medium stores a computer program, which is used to be programmed or configured by a microprocessor to execute a method for evaluating regional equivalent inertia of a power system.

[0124] The present invention will be further described below through specific experiments:

[0125] like Figure 2 The figure shows the flow chart of regional inertia estimation based on the dichotomy method.

[0126] Based on the above implementation steps, this example applies the proposed method for evaluating the regional equivalent inertia of a power system based on the dichotomy method to an actual power system to evaluate the inertia level of each area of ​​the power system. The power system is divided into 8 areas (areas 1 to 8), of which area 3 does not contain a generator and DC feeds into area 8. Areas 4 and 6 are connected to the external grid by interconnection lines. Two large and small disturbance scenarios are set for the power system, and the inertia is evaluated based on the two scenarios using the method of this embodiment. The large disturbance is set as a DC bipolar locking fault, and the small disturbance is set as a load shedding disturbance. The disturbance is located in area 7 (the active power of this load accounts for less than 0.1% of the total active load).

[0127] The data required for inertia evaluation are collected in two disturbance scenarios. In order to simulate the measurement noise generated when the PMU device collects data, Gaussian noise is injected into the frequency data, such as Figure 3 (a) As shown in FIG. The LSM algorithm of this embodiment is used to filter the frequency signal. Figure 3 In (b), the blue curve is the frequency data after filtering, and the red curve is the data when the frequency signal is not injected with Gaussian noise. It can be seen that the two curves basically coincide with each other, which shows that the LSM algorithm has a good filtering effect on PMU measurement noise.

[0128] The fluctuation of the inertial center frequency of the entire system under the two disturbance scenarios is as follows: Figure 4 As shown, Figure 4(a) is the fluctuation of large disturbance scene, Figure 4 (b) is the fluctuation of the small disturbance scenario. It can be seen that the frequency fluctuation of large disturbance is within 0.2 Hz, and the frequency fluctuation of small disturbance is within 0.005 Hz. Table 1 shows the regional inertia estimation results under two scenarios.

[0129] ,

[0130] From the estimation results in the above table, it can be seen that the relative error of the equivalent inertia estimation in all regions is below 7%, and the relative error of the estimation results in most regions is below 3%. It can be seen that this embodiment is applicable to both large and small disturbance scenarios, and the equivalent inertia of different regions of the system can be evaluated more accurately under different disturbance scenarios.

[0131] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for evaluating regional equivalent inertia of a power system, characterized in that: The following steps are involved: S1. Divide the power system into different areas, respectively treat all generators in each area as equivalent generators, and obtain measurement data of each equivalent generator, wherein the measurement data includes: output power and frequency of the equivalent generator; aggregate the output power of all generators in the area as the output power of the equivalent generator, obtain the regional inertia center frequency by selecting generators with inertia constants greater than a preset inertia constant threshold from each area, and use the regional inertia center frequency as the frequency of the equivalent generator; The regional inertial center frequency The specific method of obtaining is: ， In the formula, and are the inertia constant and frequency of each generator in the region respectively; ， In the formula, Indicates the start and stop status of the unit, the value is 1 or 0, where 1 means start and 0 means stop. represents the kinetic energy of the unit, Indicates the unit capacity; S2. Filter the measured data of each equivalent generator to obtain filtered measured data; S3. Determine the inertia constants to be evaluated in each area The initial value range of and the median value , calculated based on the filtered measurement data for and Inertia response stage The frequency prediction value at each time point is calculated, and the mean error between each frequency prediction value and the actual measured frequency measurement value is calculated. Combined with the sign of the mean frequency prediction error, the inertia constant to be evaluated is solved iteratively using the bisection method. , the inertia constant to be evaluated To characterize the regional equivalent inertia; in the bisection iteration process, when Value and When the corresponding error means have different signs, let , if the same number, then ; The specific contents of S3 include: S31. Determine the inertia constants to be evaluated in each area Initial value range , let the inertia constant to be evaluated be The values ​​are and The corresponding frequency prediction error value is and Different number, The midpoint value of the interval: ， S32. Calculate the inertia constant to be evaluated Take values ​​separately and The corresponding frequency prediction value , , Three sets of data, among which and Respectively The inertia constant to be evaluated at time Take values ​​separately and The corresponding frequency prediction value when ; S33. Calculate frequency prediction value and The error between the measured value of the frequency , and ,in and Respectively Moment and and the frequency measurement value, and respectively obtain and The mean of : and ; S34. If , then let ,like , then let , and jump to step S32 to continue iterating until the error or interval length meets the preset accuracy requirement, output The estimated value of is after iteration .

2. A method for evaluating regional equivalent inertia of a power system according to claim 1, characterized in that: The specific contents of S2 include: The filter model is: ， In the formula, Indicates that the filter is The noise signal output at any time, represents the input signal vector, Represents the weight coefficient vector of the filter; Gaussian white noise as the expected output , then the error signal for: ， The minimum mean square error criterion is used to minimize the objective function to reduce the error Minimize the input signal vector Subtract the value corresponding to the minimum objective function Then the filtered measurement data is obtained, where the objective function is: ， In the formula, represents the mean square error function, Indicates expected value; Calculate the objective function right The derivative of , set the derivative to 0: ， Get the update formula of the filter weight coefficient vector: ， In the formula, is the step size factor, The larger the value, the faster the algorithm converges, but the larger the steady-state error; The smaller the value, the slower the algorithm converges, but the smaller the steady-state error. To ensure the steady-state convergence of the algorithm, The value range is: 。 3. A method for evaluating regional equivalent inertia of a power system according to claim 1, characterized in that: Calculate the frequency prediction value in S32 and The specific method is: The equation of motion of the generator rotor is: ， In the formula, is the frequency deviation of the equivalent generator, is the frequency of the equivalent generator, is the nominal frequency of the power system, Indicates the change in output power of the equivalent generator; For a sufficiently small time period, the generator rotor motion equation Expressed as ,but: ， ， In the formula, express The predicted value of the moment frequency, express Frequency measurement value at time; Then the inertia constant to be evaluated is The value of and Substitute them into the above formula to get the corresponding frequency prediction value and .

4. A method for evaluating regional equivalent inertia of a power system according to claim 1, characterized in that: Calculation in S33 and The specific method is: Compute the frequency prediction error: ， In the formula, represents the error between the predicted frequency value and the measured frequency value, express The predicted value of the moment frequency, express The frequency measurement value at time, express Output power change value at the moment; Then the calculated value in S32 and Substitute them into the above formula to get the corresponding and .

5. A power system regional equivalent inertia assessment system, characterized in that: A method for evaluating regional equivalent inertia of a power system according to any one of claims 1 to 4, comprising: The equivalent module is used to divide the power system into different areas, respectively equate all the generators in each area to an equivalent generator, and obtain the measurement data of each equivalent generator, wherein the measurement data includes: the output power and frequency of the equivalent generator; the output power of all the generators in the region is aggregated as the output power of the equivalent generator, and the regional inertia center frequency is obtained by selecting the generator with an inertia constant greater than a preset inertia constant threshold from each region, and the regional inertia center frequency is used as the frequency of the equivalent generator; The regional inertial center frequency The specific method of obtaining is: ， In the formula, and are the inertia constant and frequency of each generator in the region respectively; ， In the formula, Indicates the start and stop status of the unit, the value is 1 or 0, where 1 means start and 0 means stop. represents the kinetic energy of the unit, Indicates the unit capacity; A filtering model is used to filter the measurement data of each equivalent generator to obtain filtered measurement data; Inertia constant value module, used to determine the inertia constant to be evaluated in each area The initial value range of and the median value , calculated based on the filtered measurement data for and Inertia response stage The frequency prediction value at each time point is calculated, and the mean error between each frequency prediction value and the actual measured frequency measurement value is calculated. Combined with the sign of the mean frequency prediction error, the inertia constant to be evaluated is solved iteratively using the bisection method. , the inertia constant to be evaluated To characterize the regional equivalent inertia; in the bisection iteration process, when Value and When the corresponding error means have different signs, let , if the same number, then .

6. A computer-readable storage medium, characterized in that: A computer program is stored in a computer-readable storage medium, and the computer program is used to be programmed or configured by a microprocessor to execute a method for evaluating regional equivalent inertia of a power system as described in any one of claims 1 to 5.

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