Inertia estimation method, system and device for power system containing new energy

Abstract

The invention relates to an inertia estimation method, system and device for a power system containing new energy. The method comprises the following steps: establishing a system difference equation based on an inertia estimation principle of a system by taking a wind-light-containing power system as a whole; establishing a parameter identification model of the power system containing wind and light by considering the time-varying characteristics of wind and light; a recursive least square method FF-RLS containing a forgetting factor is adopted, and parameters of a difference equation are solved for the parameter identification model; and obtaining the overall inertia of the system based on each parameter of the state equation. And through comparison with the accuracy of an improved difference method, the obtained FF-RLS rapid estimation method has small inertia estimation error of the power system, and the result is more accurate. Compared with the prior art, the inertia estimation error of the power system is small, and the result is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of power system stability assessment, and in particular to a method, system and device for estimating the inertia of a power system partition based on a system identification method. Background Art

[0002] In the context of energy structure transformation, the characteristics brought by the high penetration of renewable energy and power electronic equipment will profoundly reshape the operating characteristics of the power system. In the next few decades, the installed capacity of asynchronous power sources such as wind power and photovoltaics will increase rapidly. Asynchronous power sources are connected to the power grid through electronic converters and are essentially unable to provide inertial support. In recent years, the reduction of inertia has been considered one of the main obstacles to the adoption of more renewable energy in the system. With the transformation and upgrading of traditional power systems to new power systems with new energy as the main body, the number of new energy grid-connected inverters in the system has increased, which has reduced the inertia level of the power grid and reduced the ability to maintain frequency stability. The decline in the system inertia level is considered to be one of the main obstacles to connecting more new energy to the system. The inertia level characterizes the system's ability to suppress frequency changes when unbalanced power occurs. Therefore, conducting research on inertia assessment of new power systems is of great significance for early warning of frequency instability risks and ensuring the safety and stability of the power grid.

[0003] At present, there are mainly the following methods for system inertia assessment: steady-state assessment method, small disturbance assessment method and large disturbance assessment method. The steady-state analysis method analyzes a large amount of historical data to construct the relationship between the grid frequency fluctuation and the inertia level under normal operating conditions. It usually only takes into account the inertia of the synchronous generator, and does not consider the impact of factors such as load on the inertia. The small disturbance assessment method establishes a small signal model for the system, constructs an analytical expression between the oscillation characteristics and inertia to solve the system inertia. The assessment result is affected by the operating state of the system, and the accuracy is difficult to guarantee. The large disturbance assessment method is based on the swing equation. After an accident occurs in the system, the initial frequency change rate depends on the power imbalance and inertia level. The power and frequency data of the system after the disturbance event can be measured to evaluate the system inertia. However, large disturbance events do not occur from time to time. The system frequency amplitude fluctuates greatly and the disturbance period is fixed under small and large disturbances, and its data reliability is difficult to guarantee.

[0004] Virtual inertia is different from the rotational inertia of traditional power systems in terms of form and response characteristics. In recent years, some scholars have also paid attention to the quantitative estimation of virtual inertia. Based on the method of quantitative analytical characterization of the equivalent virtual inertia of wind farms, it is revealed that the equivalent virtual inertia of wind power has fast time-varying characteristics, but this method requires more wind turbine control parameters and operating parameters to solve, and it is difficult to generalize to system-level inertia evaluation. Based on the instantaneous wind speed probability distribution model constructed by the mixed Copula function, the available inertia interval evaluation results of the wind farm under a certain confidence level are obtained. The parameter identification model is used to quantitatively estimate the virtual inertia of the wind farm, but the photovoltaic grid connection is not considered, and the overall inertia estimation of the system is ignored. It can be seen that the inertia evaluation of the system containing wind and solar is worthy of study. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide an inertia estimation method, system and device for an electric power system containing new energy.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] As a first aspect of the present invention, a method for estimating inertia of a power system containing new energy is provided, the method comprising the following steps:

[0008] Establish the system difference equations of the power system including wind and solar power;

[0009] Construct the CAR parameter identification model for wind turbine and solar power systems containing renewable energy;

[0010] The recursive least square method with forgetting factor is used to solve the parameter identification model to obtain the parameters of the difference equation, and then the overall inertia of the system is obtained.

[0011] As a preferred technical solution, the equivalent inertia of the wind-solar power system is expressed as:

[0012] H′ sys =H SG +H PV +H W +H D

[0013]

[0014] Where: H SG , H PV , H W , H D are the inertia provided by the synchronous generator, the virtual inertia provided by the photovoltaic power plant, the virtual inertia provided by the wind turbine, and the load inertia; H i is the inertia of the i-th generator, S Niis the capacity of the i-th generator; N is the number of synchronous generators;

[0015] For a system containing new energy, the electromechanical transient characteristics can be expressed by the rotor motion equation:

[0016]

[0017] Where: ΔP m , ΔP e are the total mechanical power increment and load power increment of the wind farm respectively; D is the damping coefficient; Δω is the angular frequency deviation of the measuring point.

[0018] As a preferred technical solution, the transfer function of the wind-solar power system is expressed as:

[0019]

[0020] Where: ΔP ∑ (s) is the total power increment in frequency domain; D is the damping coefficient;

[0021] The frequency domain expression is converted into the time domain expression:

[0022]

[0023] By finding the initial amplitude of g(t), we can estimate the inertia time constant of the system.

[0024] As a preferred technical solution, the recursive least squares expression of the forgetting factor is as follows:

[0025]

[0026] Its performance indicators are:

[0027]

[0028] In the formula, λ is the forgetting factor, which is 0<λ≤1; h T (k) represents the regression information vector consisting of power increment and frequency deviation measurement data; θ represents the parameter vector to be identified; y(k) is the CAR model in the least squares form, as follows:

[0029] y(k)=h T (k)θ+ξ(k)

[0030] Where ξ(k) represents the noise vector;

[0031] The smaller the forgetting factor λ is, the stronger the tracking ability of the time-varying parameters is, the higher the identification sensitivity is, and the larger the stabilization error is; the larger the forgetting factor λ is, the weaker the tracking ability is, the lower the identification sensitivity is, and the smaller the stabilization error is.

[0032] As a preferred technical solution, the method uses an improved difference method to compare the power system inertia calculated based on the parameter identification model. The improved difference method is specifically:

[0033] Set two measurement points a and b to measure power, make a difference in the measured power group to eliminate the disturbance power term, and get the system inertia as:

[0034]

[0035] Where: P a0 , P b0 are the initial powers of measurement points a and b respectively; P a , P b are the measured powers after disturbances occur at measurement points a and b respectively; r a =df a / dt、r b =df b / dt.

[0036] As a second aspect of the present invention, there is provided an inertia estimation system for a new energy power system, comprising:

[0037] System differential equation building module: building system differential equations for power systems including wind and solar power;

[0038] Parameter identification model building module: build CAR parameter identification model for wind turbine and solar power system containing new energy;

[0039] System inertia acquisition module: The recursive least squares method with forgetting factor is used to solve the parameters of the differential equation of the parameter identification model, and then the overall inertia of the system is obtained.

[0040] As a preferred technical solution, in the system differential equation establishment module, the transfer function of the power system including wind and solar power is expressed as:

[0041]

[0042] Where: ΔP ∑ (s) is the total power increment in frequency domain;

[0043] The frequency domain expression is converted into the time domain expression:

[0044]

[0045] By finding the initial amplitude of g(t), we can estimate the inertia time constant of the system.

[0046] As a preferred technical solution, the recursive least squares expression of the forgetting factor in the system inertia acquisition module is as follows:

[0047]

[0048] Its performance indicators are:

[0049]

[0050] In the formula, λ is the forgetting factor, which is 0<λ≤1; h T (k) represents a regression information vector consisting of power increment and frequency deviation measurement data; θ represents a parameter vector to be identified;

[0051] The smaller the forgetting factor λ is, the stronger the tracking ability of the time-varying parameters is, the higher the identification sensitivity is, and the larger the stabilization error is; the larger the forgetting factor λ is, the weaker the tracking ability is, the lower the identification sensitivity is, and the smaller the stabilization error is.

[0052] As a third aspect of the present invention, an inertia estimation device for a power system containing new energy is provided, comprising a memory, a processor, and a program stored in the memory, wherein when the processor executes the program, the inertia estimation method for the power system containing new energy is implemented as described above.

[0053] As a fourth aspect of the present invention, a storage medium is provided, on which a program is stored, and when the program is executed, the inertia estimation method of the power system containing new energy is implemented as described above.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention uses the CAR identification model and the FF-RLS algorithm to evaluate the overall equivalent inertia of the system, and compares the accuracy with the improved difference method. The fast estimation method of FF-RLS is used to estimate the inertia of the power system with a smaller structural error and a more accurate result. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of an inertia estimation method for a power system containing new energy sources according to the present invention;

[0057] Figure 2 It is a diagram of an improved three-machine nine-node calculation example system in one embodiment of the present invention;

[0058] Figure 3 3a) is the active power and frequency change curve of the system in different scenarios in one embodiment of the present invention; 3b) is the active power change curve of the system in different scenarios;

[0059] Figure 4 It is the frequency result of the CAR model parameter identification output in one embodiment of the present invention; 4a) is the frequency curve after scene 1 identification, 4b) is the frequency curve after scene 2 identification, 4c) is the frequency curve after scene 3 identification, and d) is the frequency curve after scene 4 identification;

[0060] Figure 5 5a) is the active power and frequency change curve of the measuring point b in one embodiment of the present invention; 5b) is the frequency change curve of the measuring point b;

[0061] Figure 6 This is a comparison of inertia estimation accuracy in the same scenario in one embodiment of the present invention. DETAILED DESCRIPTION

[0062] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0063] Example 1

[0064] As a first embodiment of the present invention, the present invention provides a method for estimating the inertia of a power system including wind and solar power. Figure 1 As shown in the figure, this method takes the power system including wind and solar as a whole, establishes the system difference equation based on the inertia estimation principle of the system; considers the time-varying characteristics of wind and solar, establishes the parameter identification model of the power system including wind and solar; uses the recursive least squares method FF-RLS with forgetting factor to solve the parameters of the difference equation for the parameter identification model; and obtains the overall inertia of the system based on the parameters of the state equation. And by comparing the accuracy with the improved difference method, it is concluded that the FF-RLS fast estimation method has a smaller error in estimating the inertia of the power system and a more accurate result.

[0065] S1. System inertia estimation

[0066] 1.1) Traditional power system inertia theory

[0067] For traditional power systems, synchronous generators include steam turbine generators, hydroelectric generators, gas turbines and diesel generators; these different types of generators together provide safe and reliable electricity for various loads in the power system. The inertia of traditional power systems usually refers to the rotational inertia provided by the synchronous generator connected to the power grid. For a single synchronous generator k, the theoretical value of inertia is usually expressed by the rotor kinetic energy of the generator at the rated angular velocity, in units of MW·s. It is related to its own parameters and has nothing to do with the operating state. Its expression is as follows:

[0068]

[0069] Where: E Gk , H k , S k , J k ,ω n They are the theoretical inertia value, inertia time constant, capacity, moment of inertia and rated angular speed of the generator respectively.

[0070] In a traditional multi-machine system, the equivalent theoretical value of the power system inertia is:

[0071]

[0072] Where: E G is the equivalent inertia value, n is the number of traditional generators in the system, H i is the inertia time constant of a generator in the multi-machine.

[0073] 1.2) Inertia theory of power system including new energy

[0074] The equivalent inertia of the wind and solar grid-connected system is divided into three parts, including the inertia of traditional synchronous generators, load inertia, and virtual inertia provided by wind turbines and photovoltaics. There are some differences between the virtual inertia provided by wind turbines and photovoltaics and the inertia of synchronous generators. The virtual inertia time constant (the virtual inertia of new energy) is a parameter simulated by the control method, not an inherent characteristic. The virtual inertia time constant is related to different factors, including control methods, control parameters, energy, and conversion process, so its size is flexible and variable. Equivalent inertia of the system after new energy is connected to the grid:

[0075] H′ sys =H SG +H PV +H W +H D

[0076]

[0077] Where: H SG , H PV , H W , H D are the inertia provided by the synchronous generator, the virtual inertia provided by the photovoltaic power plant, the virtual inertia provided by the wind turbine, and the load inertia; H i is the inertia of the i-th generator, S Ni is the capacity of the i-th generator; N is the number of synchronous generators;

[0078] For a system containing new energy, the electromechanical transient characteristics can be expressed by the rotor motion equation:

[0079]

[0080] Where: ΔP m , ΔP e are the total mechanical power increment and load power increment of the wind farm respectively; D is the damping coefficient; Δω is the angular frequency deviation of the measuring point.

[0081] By performing Laplace transform on the above equation, we can get the transfer function of the system:

[0082]

[0083] Where: ΔP ∑ (s) is the total power increment in frequency domain.

[0084] The frequency domain expression is converted into the time domain expression:

[0085]

[0086] From the above formula, we can see that g(t) decays exponentially with time, and its value is 2H′ sys If the initial amplitude of g(t) is found, the inertia time constant of the system can be estimated.

[0087] S2. Power system inertia estimation method

[0088] 2.1) System parameter identification method

[0089] During the inertia estimation process, the frequency will change with time. Due to the disturbance process, the frequency will produce a sudden change, and then the inertial response stage will stabilize. The batch least squares method (LS) processes a large amount of data each time and cannot be used for online real-time estimation. The recursive least squares method will have a "data saturation" phenomenon as the data grows. In order to avoid the limitations of the above algorithm, the present invention uses the recursive least squares method FF-RLS with a forgetting factor. This algorithm is suitable for parameter identification problems in time-varying systems. First, convert the CAR model into a least squares form:

[0090] y(k)=h T (k)θ+ξ(k)

[0091] Where: h T (k) represents the regression information vector consisting of power increment and frequency deviation measurement data; θ represents the parameter vector to be identified; ξ(k) represents the noise vector.

[0092] The error is obtained by comparing the actual measured value with the predicted value of the construction model, which can be simplified into the following formula:

[0093] E L =Y L-H L θ

[0094] in:

[0095]

[0096] For batch least squares and recursive least squares, the performance metrics are:

[0097]

[0098] Since the principle of the least squares method is to minimize the performance index, the partial derivative of the performance index is calculated as shown in the following formula:

[0099]

[0100] Solving the above formula, we get:

[0101]

[0102] For the convenience of calculation, let but It can be expressed as:

[0103]

[0104] In order to avoid the inverse operation of P(k), the Kalman gain vector K(k)=P(k)h(k) is introduced to obtain:

[0105] P(k)=[IK(k)h T (k)]P(k-1)

[0106] To sum up, the recursive least squares expression is:

[0107]

[0108] However, as time goes by, the influence of early data gradually weakens. A time-varying weighting factor is applied to the power increment / frequency deviation measurement data of the measurement point, with the latest data weighted by 1 and the data of the previous n sampling periods weighted by λ n Weighted (λ is the forgetting factor, the value is 0<λ≤1), so this method is called exponential forgetting method. Due to the existence of the forgetting factor, its performance index becomes:

[0109]

[0110] When the forgetting factor λ is smaller, the tracking ability of time-varying parameters is stronger, the identification sensitivity is higher, and the stability error is larger; when the forgetting factor λ is larger, the tracking ability is weaker, the identification sensitivity is reduced, and the stability error is smaller. The recursive least squares expression of the forgetting factor is as follows:

[0111]

[0112] (3) Improved difference method for calculating power system inertia

[0113] The system inertia is calculated using active power measurement data and RoCoF after a large disturbance. The actual dominant period of the inertial response depends on the system inertia value, which occurs about 2s after the disturbance and dominates the frequency modulation process of the system. However, the traditional power system inertia estimation method using the frequency change rate has large errors and certain limitations. Therefore, the difference method is selected to calculate the inertia of the power system to minimize the error and improve the accuracy. The power change is determined by the real-time power before and after the disturbance and the disturbance power value, as shown in the following formula:

[0114] ΔP i =P i0 -P i +ΔP k

[0115] Where: P i0 , P i , ΔP k are the power before and after the disturbance and the disturbance power respectively.

[0116] Due to unknown reasons such as the fault location and fault type, the disturbance power size cannot usually be directly obtained, which may make the above inertia calculation process difficult to directly apply to the actual system. In order to eliminate the disturbance power term, the difference method is used to calculate the system inertia. Select two measurement points a and b for measurement, and the following expression can be obtained for the power change rate of the measurement point:

[0117]

[0118] Integration gives:

[0119]

[0120] Let r a =df a / dt、r b =df b / dt, and the power term of disturbance can be eliminated by making a difference within the group:

[0121]

[0122] Assuming that the distance between measurement points a and b is relatively close, it can be determined that the difference in initial power between measurement points a and b is approximately equal to 0 (P a0 -P b0 =0), then the simplified formula is:

[0123]

[0124] (4) Example analysis

[0125] In order to verify the effectiveness of the proposed method, this paper sets the following four scenarios to simulate the system and verify its accuracy:

[0126] Scenario 1: No increase in wind and solar grid-connected output;

[0127] Scenario 2: Add wind turbines to G1 and do not connect photovoltaic power to the grid.

[0128] Scenario 3: Select G3 to add PV grid connection and G1 to disconnect the wind turbine from the grid;

[0129] Scenario 4: Increase the grid-connected output of wind and solar power.

[0130] Building an improved three-machine nine-node system simulation verification under Matlab / simulink

[0131] Using the above-mentioned method for estimating the inertia of the power system including wind and solar, Figure 2 The improved three-machine nine-bus system shown in the figure is analyzed, and the overall equivalent inertia of the system is evaluated using the CAR identification model and the FF-RLS algorithm, and a relatively reliable estimate is obtained, as shown in Figure 3 As shown. Figure 5 As shown in Figure 2, the improved difference method is used for accuracy comparison. Figure 6 As shown in the figure, the FF-RLS fast estimation method has a smaller error in estimating the inertia of the power system and a more accurate result. The participation of new energy in the grid connection will also provide a certain amount of inertia for the traditional power grid, which will help the power grid to quickly resume stable operation in the event of instability.

[0132] Example 2

[0133] As another implementation manner of the present invention, this embodiment provides an inertia estimation system for a new energy power system, including:

[0134] System differential equation building module: building system differential equations for power systems including wind and solar power;

[0135] Among them, the transfer function of the power system including wind and solar is expressed as:

[0136]

[0137] Where: ΔP ∑ (s) is the total power increment in frequency domain;

[0138] The frequency domain expression is converted into the time domain expression:

[0139]

[0140] By finding the initial amplitude of g(t), we can estimate the inertia time constant of the system.

[0141] Parameter identification model building module: build CAR parameter identification model for wind turbine and solar power system containing new energy;

[0142] System inertia acquisition module: The recursive least squares method with forgetting factor is used to solve the parameters of the differential equation of the parameter identification model, and then the overall inertia of the system is obtained.

[0143] Among them, the expression of the recursive least squares method of the forgetting factor is as follows:

[0144]

[0145] Its performance indicators are:

[0146]

[0147] In the formula, λ is the forgetting factor, which is 0<λ≤1; h T (k) represents a regression information vector consisting of power increment and frequency deviation measurement data; θ represents a parameter vector to be identified;

[0148] The smaller the forgetting factor λ is, the stronger the tracking ability of the time-varying parameters is, the higher the identification sensitivity is, and the larger the stabilization error is; the larger the forgetting factor λ is, the weaker the tracking ability is, the lower the identification sensitivity is, and the smaller the stabilization error is.

[0149] Example 3

[0150] As a third aspect of the present invention, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned inertia estimation method for a new energy power system. In addition to the above-mentioned processor, memory and interface, any device with data processing capability in which the device in the embodiment is located may also include other hardware according to the actual function of the device with data processing capability, which will not be described in detail.

[0151] Example 4

[0152] As a fourth aspect of the present invention, the present application also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-mentioned inertia estimation method for a new energy power system. The computer-readable storage medium may be an internal storage unit of any device with data processing capability as described in any of the foregoing embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card (Flash Card), etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0153] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for estimating inertia of a power system containing new energy, characterized in that the steps include: Establish the system difference equations of the power system including wind and solar power; Construct the CAR parameter identification model for wind turbine and solar power systems containing renewable energy; The recursive least square method with forgetting factor is used to solve the parameter identification model to obtain the parameters of the difference equation, and then the overall inertia of the system is obtained.

2. The inertia estimation method of a power system containing new energy sources according to claim 1 is characterized in that: The equivalent inertia of the wind-solar power system is expressed as: H′ sys =H SG +H PV +H W +H D Where: H SG , H PV , H W , H D are the inertia provided by the synchronous generator, the virtual inertia provided by the photovoltaic power plant, the virtual inertia provided by the wind turbine, and the load inertia; H i is the inertia of the i-th generator, S Ni is the capacity of the i-th generator; N is the number of synchronous generators; For a system containing new energy, the electromechanical transient characteristics can be expressed by the rotor motion equation: Where: ΔP m , ΔP e are the total mechanical power increment and load power increment of the wind farm respectively; D is the damping coefficient; Δω is the angular frequency deviation of the measuring point.

3. The inertia estimation method of a power system containing new energy according to claim 1, characterized in that: The transfer function of the wind-solar power system is expressed as: Where: ΔP ∑ (s) is the total power increment in frequency domain; D is the damping coefficient; The frequency domain expression is converted into the time domain expression: By finding the initial amplitude of g(t), we can estimate the inertia time constant of the system.

4. The inertia estimation method of a power system containing new energy sources according to claim 1 is characterized in that: The recursive least squares expression of the forgetting factor is as follows: Its performance indicators are: In the formula, λ is the forgetting factor, which is 0<λ≤1; h T (k) represents the regression information vector consisting of power increment and frequency deviation measurement data; θ represents the parameter vector to be identified; y(k) is the CAR model in the least squares form, as follows: y(k)=h T (k)θ+ξ(k) Where ξ(k) represents the noise vector; The smaller the forgetting factor λ is, the stronger the tracking ability of the time-varying parameters is, the higher the identification sensitivity is, and the larger the stabilization error is; the larger the forgetting factor λ is, the weaker the tracking ability is, the lower the identification sensitivity is, and the smaller the stabilization error is.

5. The inertia estimation method of a power system containing new energy sources according to claim 1 is characterized in that: The method uses an improved difference method to compare the power system inertia calculated based on the parameter identification model. The improved difference method is specifically: Set two measurement points a and b to measure power, make a difference in the measured power group to eliminate the disturbance power term, and get the system inertia as: Where: P a0 , P b0 are the initial powers of measurement points a and b respectively; P a , P b are the measured powers after disturbances occur at measurement points a and b respectively; r a =df a / dt、r b =df b / dt.

6. An inertia estimation system for a new energy power system, characterized in that: include: System differential equation building module: building system differential equations for power systems including wind and solar power; Parameter identification model building module: build CAR parameter identification model for wind turbine and solar power system containing new energy; System inertia acquisition module: The recursive least squares method with forgetting factor is used to solve the parameters of the differential equation of the parameter identification model, and then the overall inertia of the system is obtained.

7. The inertia estimation system of the power system containing new energy as claimed in claim 6, characterized in that: In the system differential equation establishment module, the transfer function of the power system including wind and solar power is expressed as: Where: ΔP ∑ (s) is the total power increment in frequency domain; The frequency domain expression is converted into the time domain expression: By finding the initial amplitude of g(t), we can estimate the inertia time constant of the system.

8. The inertia estimation system for a new energy power system according to claim 6, characterized in that: The recursive least squares expression of the forgetting factor in the system inertia acquisition module is as follows: Its performance indicators are: In the formula, λ is the forgetting factor, which is 0<λ≤1; h T (k) represents a regression information vector consisting of power increment and frequency deviation measurement data; θ represents a parameter vector to be identified; The smaller the forgetting factor λ is, the stronger the tracking ability of the time-varying parameters is, the higher the identification sensitivity is, and the larger the stabilization error is; the larger the forgetting factor λ is, the weaker the tracking ability is, the lower the identification sensitivity is, and the smaller the stabilization error is.

9. An inertia estimation device for a new energy power system, comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, the inertia estimation method for the power system containing new energy sources as described in any one of claims 1-5 is implemented.

10. A storage medium having a program stored thereon, characterized in that: When the program is executed, the inertia estimation method for the electric power system containing new energy sources as described in any one of claims 1 to 5 is implemented.

Images