Power system inertia online evaluation method, device and equipment and readable storage medium

Abstract

The invention discloses an electric power system inertia online evaluation method, device and equipment and a readable storage medium, and relates to the technical field of novel electric power systems, a unified inertia analysis model comprising network construction type equipment, network following type equipment and a synchronous generator is established through theoretical derivation, then dynamic modeling is performed based on extended Kalman filtering, and the inertia of the electric power system is evaluated. Inertia solution is converted into a parameter identification problem, so that inertia levels of various generators in the system are obtained, continuous tracking of equivalent inertia of the system is realized by adopting a recursive least square algorithm with a forgetting factor, and accurate calculation of inertia of each unit can be realized. The local and system equivalent inertia of the new energy power system under various control strategies is effectively evaluated, and an important reference basis is provided for power grid operation.

Description

Technical Field

[0001] The present application relates to the technical field of novel power systems, and in particular to a method, device, equipment and readable storage medium for online evaluation of inertia of a power system. Background Art

[0002] With the transformation of the global energy structure, the penetration rate of new energy in the power system continues to increase. The output of new energy is volatile. When the output fluctuates on a large scale, the frequency stability of the power system faces serious problems. The inertia of the power system refers to the ability of the system to resist disturbances, that is, the ability of the system to maintain its original state when subjected to external disturbances. Higher inertia means that the system can better maintain stability when the frequency changes. Therefore, accurately assessing the inertia level of the power system is crucial to ensuring the safe operation of the power grid. Current research focuses on a single type of new energy equipment, while there are many types of new energy equipment and control strategies in the actual system, which makes the results of inertia assessment limited. Secondly, due to the different equipment, when disturbances occur, the system inertia levels are different at different times, and the ability to resist disturbances is different. It is necessary to evaluate the system inertia in real time according to the system operation status to ensure the safety and reliability of the system.

[0003] In the related art, there is a new method for measuring inertia of a power system based on an improved extended Kalman filter, which rewrites the rotor motion equation of a synchronous generator into an extended state space model containing parameters to be identified, obtains the disturbance power and frequency data of the node under a disturbance event of the power system, identifies the inertia constant while performing state estimation through the extended Kalman filter algorithm, and applies the gray wolf optimization algorithm and the square root filter algorithm to the extended Kalman filter identification algorithm, thereby realizing a fast and accurate evaluation of the inertia of the power system. However, the applicant realizes that in the process of modeling the inertia of the power system, only the rotor motion equation of the synchronous generator is considered, and the new energy connected to the power grid through the inverter is not modeled, and the entire system cannot be fully evaluated, resulting in low evaluation accuracy. Summary of the invention

[0004] In view of this, the present application provides a method, device, equipment and readable storage medium for online evaluation of the inertia of an electric power system. The main purpose is to solve the problem that in the process of modeling the inertia of the electric power system, only the rotor motion equation of the synchronous generator is considered, and the new energy connected to the power grid through the inverter is not modeled, and the entire system cannot be fully evaluated, resulting in low evaluation accuracy.

[0005] According to a first aspect of the present application, a method for online evaluation of inertia of a power system is provided, the method comprising:

[0006] Acquiring power equipment data, and establishing a unified inertia analytical model using the power equipment data;

[0007] Acquire historical frequency and power change data, and use an extended Kalman filter algorithm to evaluate the unified inertia analytical model and the historical frequency and power change data to obtain a system equivalent inertia online estimation model;

[0008] Obtaining a recursive least squares algorithm with a forgetting factor, and using the recursive least squares algorithm with the forgetting factor to adjust the online estimation model of equivalent inertia of the system to obtain an online estimation model of equivalent inertia of the target system;

[0009] Real-time frequency and power change data are acquired, and the real-time frequency and power change data are input into the target system equivalent inertia online estimation model to obtain an equivalent inertia online evaluation result output by the target system equivalent inertia online estimation model.

[0010] According to a second aspect of the present application, there is provided an online evaluation device for inertia of a power system, the device comprising:

[0011] A construction module is used to obtain power supply equipment data and establish a unified inertia analytical model using the power supply equipment data;

[0012] an estimation module, used for acquiring historical frequency and power change data, and using an extended Kalman filter algorithm to evaluate the unified inertia analytical model and the historical frequency and power change data to obtain an online estimation model of system equivalent inertia;

[0013] An adjustment module, used for obtaining a recursive least squares algorithm with a forgetting factor, and using the recursive least squares algorithm with a forgetting factor to adjust the online estimation model of equivalent inertia of the system to obtain an online estimation model of equivalent inertia of the target system;

[0014] The online evaluation module is used to obtain real-time frequency and power change data, input the real-time frequency and power change data into the target system equivalent inertia online estimation model, and obtain the equivalent inertia online evaluation result output by the target system equivalent inertia online estimation model.

[0015] According to a third aspect of the present application, a device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0016] According to a fourth aspect of the present application, a readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.

[0017] Through the above technical scheme, the present application provides an online evaluation method, device, equipment and readable storage medium for inertia of an electric power system. The present application obtains power supply equipment data, uses the power supply equipment data to establish a unified inertia analytical model, obtains historical frequency and power change data, uses an extended Kalman filter algorithm to evaluate the unified inertia analytical model and the historical frequency and power change data, obtains a system equivalent inertia online estimation model, obtains a recursive least squares algorithm with a forgetting factor, uses a recursive least squares algorithm with a forgetting factor to adjust the system equivalent inertia online estimation model, obtains a target system equivalent inertia online estimation model, obtains real-time frequency and power change data, inputs the real-time frequency and power change data into the target system equivalent inertia online estimation model, and obtains the equivalent inertia online evaluation result output by the target system equivalent inertia online estimation model. Through theoretical derivation, a unified inertia analytical model including grid-building type, grid-following type equipment and synchronous generators is established. Then, dynamic modeling is performed based on the extended Kalman filter, and the inertia solution is converted into a parameter identification problem, so as to obtain the inertia level of various generators in the system. Finally, the recursive least squares algorithm with forgetting factor is used to realize continuous tracking of the system equivalent inertia, which can realize accurate calculation of the inertia of each unit, effectively evaluate the local and system equivalent inertia of the new energy power system under various control strategies, and provide an important reference basis for the operation of the power grid.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0020] Figure 1 A schematic diagram of a method flow for online evaluation of inertia of a power system provided in an embodiment of the present application is shown;

[0021] Figure 2A A schematic diagram of another method flow for online evaluation of inertia of a power system provided in an embodiment of the present application is shown;

[0022] Figure 2B A schematic diagram of the architecture of an online evaluation of inertia of a power system provided in an embodiment of the present application is shown;

[0023] Figure 3A schematic diagram of the structure of an online evaluation of inertia of a power system provided in an embodiment of the present application is shown;

[0024] Figure 4 A schematic diagram of the device structure of a computer device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0026] The present application embodiment provides a method for online evaluation of inertia of a power system, such as Figure 1 As shown, the method includes:

[0027] 101. Obtain power equipment data and use the power equipment data to establish a unified inertia analytical model.

[0028] In the process of modeling the inertia of the power system, some current system inertia assessment methods only consider the rotor motion equation of the synchronous generator, and do not model the new energy sources connected to the grid through the inverter, making it impossible to fully evaluate the entire system; some only evaluate the overall inertia of the power system, and do not calculate the inertia devices at different nodes of the power system in detail, making it impossible to evaluate the ability of local nodes of the power system to resist disturbances.

[0029] To solve this problem, the present application proposes an online evaluation method for inertia of a power system. The unified inertia analytical model of network-building type, network-following type equipment and synchronous generator is established through theoretical derivation. Then, dynamic modeling is performed based on the extended Kalman filter (EKF, Extended Kalman Filter), and the inertia solution is converted into a parameter identification problem. The inertia level of various generators in the system is obtained, and finally the recursive least squares algorithm with a forgetting factor (FFRLS, Forgetting Factor Recursive Least Square) is used to achieve continuous tracking of the equivalent inertia of the system. The execution subject of the present application can be an online evaluation system for inertia of a power system. The online evaluation system for inertia of a power system relies on the computing power of the server to provide services to users. The server can be an independent server, or it can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (Content Delivery Network, CDN), and servers of basic cloud computing such as big data and artificial intelligence platforms, so that the online evaluation system for inertia of a power system can achieve accurate evaluation of the equivalent inertia of the system.

[0030] In an embodiment of the present application, the power system inertia online assessment system obtains power supply equipment data and uses the power supply equipment data to establish a unified inertia analytical model. The main purpose of establishing a unified inertia analytical model including grid-forming type, grid-following type equipment and synchronous generators is to establish an inertia model of different types of power supply equipment in the power system, thereby obtaining the inertia coefficient of each type of generator and describing their inertia response capabilities under frequency fluctuations.

[0031] 102. Obtain historical frequency and power change data, use the extended Kalman filter algorithm to evaluate the unified inertia analytical model and historical frequency and power change data, and obtain the system equivalent inertia online estimation model.

[0032] In an embodiment of the present application, an online evaluation system for the inertia of a power system acquires historical frequency and power change data, and uses an extended Kalman filter algorithm to evaluate a unified inertia analytical model and historical frequency and power change data to obtain an online estimation model for equivalent inertia of the system. By introducing EKF, online evaluation of the equivalent inertia of the power system based on the operating status of the system is achieved, which enables real-time evaluation of the power system's ability to resist disturbances.

[0033] 103. Obtain a recursive least squares algorithm with a forgetting factor, and use the recursive least squares algorithm with a forgetting factor to adjust the online estimation model of the system equivalent inertia to obtain the online estimation model of the target system equivalent inertia.

[0034] In an embodiment of the present application, the power system inertia online assessment system obtains a recursive least squares algorithm with a forgetting factor, and uses the recursive least squares algorithm with a forgetting factor to adjust the system equivalent inertia online estimation model to obtain the target system equivalent inertia online estimation model. Introducing the recursive least squares algorithm with a forgetting factor to identify the model coefficients can improve the accuracy of the model and improve the accuracy of the model estimation.

[0035] 104. Acquire real-time frequency and power change data, input the real-time frequency and power change data into an online estimation model of equivalent inertia of a target system, and obtain an online evaluation result of equivalent inertia output by the online estimation model of equivalent inertia of the target system.

[0036] In an embodiment of the present application, an online evaluation system for inertia of a power system acquires real-time frequency and power change data, inputs the real-time frequency and power change data into an online estimation model of equivalent inertia of a target system, and obtains an online evaluation result of equivalent inertia output by the online estimation model of equivalent inertia of a target system. This system can not only fully evaluate the entire system, but also take into account different types of power supply equipment in the power system. It can effectively evaluate the local and system equivalent inertia of new energy power systems under various control strategies, and provide an important reference for power grid operation.

[0037] The method provided in the embodiment of the present application obtains power supply equipment data, establishes a unified inertia analytical model using the power supply equipment data, obtains historical frequency and power change data, uses an extended Kalman filter algorithm to evaluate the unified inertia analytical model and the historical frequency and power change data, obtains a system equivalent inertia online estimation model, obtains a recursive least squares algorithm with a forgetting factor, uses a recursive least squares algorithm with a forgetting factor to adjust the system equivalent inertia online estimation model, obtains a target system equivalent inertia online estimation model, obtains real-time frequency and power change data, inputs the real-time frequency and power change data into the target system equivalent inertia online estimation model, and obtains an equivalent inertia online evaluation result output by the target system equivalent inertia online estimation model. Through theoretical derivation, a unified inertia analytical model including grid-building type, grid-following type equipment and synchronous generators is established. Then, dynamic modeling is performed based on the extended Kalman filter, and the inertia solution is converted into a parameter identification problem, so as to obtain the inertia level of various generators in the system. Finally, the recursive least squares algorithm with forgetting factor is used to realize continuous tracking of the system equivalent inertia, which can realize accurate calculation of the inertia of each unit, effectively evaluate the local and system equivalent inertia of the new energy power system under various control strategies, and provide an important reference basis for the operation of the power grid.

[0038] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, the embodiment of the present application provides another method for online evaluation of inertia of a power system, such as Figure 2A As shown, the method includes:

[0039] 201. Extract the synchronous unit data from the power supply equipment data, use the synchronous unit data to calculate the motion equation of the synchronous unit, determine the inertia power of the synchronous unit according to the motion equation of the synchronous unit, and construct the dynamic response process function of the synchronous unit based on the motion equation of the synchronous unit and the inertia power of the synchronous unit.

[0040] In the embodiment of the present application, the power system inertia online evaluation system extracts synchronous unit data from the power supply equipment data, and uses the synchronous unit data to calculate the motion equation of the synchronous unit. The calculation formula is the following formula 1:

[0041] Formula 1:

[0042] Where J is the moment of inertia of the synchronous unit, ω is the real-time frequency of the synchronous unit, ω0 is the rated frequency of the synchronous unit, D is the damping coefficient of the synchronous unit, P e is the electromagnetic power of the synchronous unit, P m is the mechanical power of the synchronous unit. It should be noted that for synchronous motors, their inertia support capacity is generally expressed by the swing equation.

[0043] Next, the power system inertia online assessment system determines the inertia power of the synchronous unit according to the motion equation of the synchronous unit. The calculation formula is as follows:

[0044] Formula 2:

[0045] Among them, ΔP sg is the inertia power of the synchronous unit, H sg is the inertia constant of the synchronous unit.

[0046] Subsequently, the power system inertia online assessment system constructs the dynamic response process function of the synchronous unit based on the motion equation of the synchronous unit and the inertia power of the synchronous unit. It should be noted that the process of constructing the dynamic response process function of the synchronous unit is consistent with the process of constructing the dynamic response process function of the grid-forming device.

[0047] 202. Extract the meshing device data from the power supply device data, use the meshing device data to calculate the motion equation of the meshing device, and construct the dynamic response process function of the meshing device based on the motion equation of the meshing device.

[0048] In the embodiment of the present application, for the new energy unit, the first is the grid-type equipment, and the power-frequency characteristics of the virtual synchronous machine are selected to calculate the virtual inertia constant. Specifically, the power system inertia online evaluation system extracts the installed capacity of the grid-type equipment from the grid-type equipment data, and uses the installed capacity of the grid-type equipment to calculate the virtual inertia constant of the grid-type equipment. The calculation formula is the following formula 3:

[0049] Formula 3:

[0050] Among them, H re,GFM is the virtual inertia constant of the networking device, S re,GFM is the installed capacity of the meshing equipment, J is the moment of inertia of the meshing equipment, and ω0 is the rated frequency of the meshing equipment.

[0051] Next, the power system inertia online assessment system uses the grid-forming device data to calculate the motion equation of the grid-forming device, and the calculation formula is the following formula 4:

[0052] Formula 4:

[0053] Among them, J is the moment of inertia of the network-building device, ω0 is the rated frequency of the network-building device, ω is the real-time frequency of the network-building device, D is the damping coefficient of the network-building device, P e is the electromagnetic power of the network-building device, P0 is the specified power of the network-building device, k w is the power regulation factor.

[0054] Subsequently, the power system inertia online assessment system performs Laplace transform on the virtual inertia constant of the grid-forming equipment and the motion equation of the grid-forming equipment, converts the differential equation in the time domain into the frequency domain equation, and finally obtains the dynamic response process function of the grid-forming equipment, that is, the transfer function of the power-frequency characteristic, which is calculated as follows:

[0055] Formula 5:

[0056]

[0057] Among them, J is the moment of inertia of the network-building device, ω0 is the rated frequency of the network-building device, ω is the real-time frequency of the network-building device, D is the damping coefficient of the network-building device, ω * is the normalized frequency of the networking device, is the normalized input mechanical power of the network-forming device, is the normalized output electromagnetic power of the network-building device, k w is the power regulation coefficient, S n is the rated capacity of the network-building device, s is the complex variable under Laplace change, Hre,GFM is the virtual inertia constant of the networking device, Δω * (s) is the node frequency change, ΔP GFM* (s) is the power change of the networking equipment.

[0058] 203. Extract the grid-type equipment data from the power supply equipment data, use the grid-type equipment data to calculate the virtual inertia constant of the grid-type equipment, use the grid-type equipment data to calculate the motion equation of the grid-type equipment, and construct the dynamic response process function of the grid-type equipment based on the motion equation of the grid-type equipment and the virtual inertia constant of the grid-type equipment.

[0059] In the embodiment of the present application, under conventional control, the output power of the grid-following device does not respond to changes in the system frequency. Therefore, in order to simulate the inertia response of the synchronous machine, a component ΔP with frequency as input is added to the output power instruction. ref , the relationship between the two is ΔP ref / Δf=-T j s, and the synchronous inertia response has a similar form. Therefore, the power system inertia online assessment system extracts the grid-following equipment data from the power supply equipment data, and uses the grid-following equipment data to calculate the virtual inertia constant of the grid-following equipment. The calculation formula is the following formula 6:

[0060] Formula 6: H re,GFM =0.5T j

[0061] Among them, H re,GFM is the virtual inertia constant of the network-based device, T j is the inertia time constant, both of which are representations of the physical inertia of the rotor.

[0062] Next, the power system inertia online assessment system uses the grid-following device data to calculate the motion equation of the grid-following device, and constructs the dynamic response process function of the grid-following device based on the motion equation of the grid-following device and the virtual inertia constant of the grid-following device. It should be noted that the process of constructing the dynamic response process function of the grid-following device is consistent with the process of constructing the dynamic response process function of the grid-building device.

[0063] 204. A unified inertia analytical model is constructed based on the dynamic response process function of the synchronous unit, the dynamic response process function of the network-building equipment, and the dynamic response process function of the network-following equipment.

[0064] In the embodiment of the present application, the power system inertia online assessment system is based on the dynamic response process function of the synchronous unit, the dynamic response process function of the grid-forming device, and the dynamic response process function of the grid-following device to construct a unified inertia analytical model, and the calculation formula is the following formula 7:

[0065] Formula 7:

[0066] Among them, ω0 is the rated frequency of the power supply equipment, ω is the real-time frequency of the power supply equipment, D is the damping coefficient of the power supply equipment, k w is the power regulation coefficient, k p is the generator proportional adjustment coefficient, S n is the rated capacity of the power supply equipment, s is the complex variable under Laplace change, H sg is the inertia constant of the synchronous unit, H re,GFM is the virtual inertia constant of the networked device, H re,GFL is the virtual inertia constant of the network-based device, Δω * (s) is the node frequency change, is the power change of the networked device, is the power variation of the synchronous unit, It is the power change of the grid-following equipment. The power equipment includes synchronous units, grid-building equipment, and grid-following equipment. The power system inertia online assessment system builds a unified inertia model including grid-building equipment, grid-following equipment, and synchronous generators according to different types of power equipment in the power system, thereby realizing the online assessment of the inertia of various types of units in the new energy power system.

[0067] 205. Extract the system equivalent inertia, system state vector, system input vector and system observation variables from the historical frequency and power change data.

[0068] In the embodiment of the present application, since the calculation of inertia requires knowing the frequency change and power change of the system, the power system inertia online evaluation system extracts the system equivalent inertia from the historical frequency and power change data, as shown in the following formula 8:

[0069] Formula 8:

[0070] Among them, H sys is the system equivalent inertia, i is the number of power devices, H i is the inertia constant of the power supply equipment, S i It is the installed capacity of the power supply equipment.

[0071] The power system inertia online assessment system extracts the system state vector from the historical frequency and power change data, as shown in the following formula 9:

[0072] Formula 9:

[0073] Among them, X K is the system state vector, ω GFM is the frequency of the networking device, ω SG is the frequency of the synchronous unit, ωGFL is the frequency of the network-type device, H re,GFM is the virtual inertia constant of the networked device, H sg is the inertia constant of the synchronous unit, H re,GFL It is the virtual inertia constant of the network-type device.

[0074] The power system inertia online assessment system extracts the system input vector from the historical frequency and power change data, as shown in the following formula 10:

[0075] Formula 10:

[0076] Among them, U K is the system input vector, ΔP GFM is the power change of the network-building device, ΔP SG is the power change of the synchronous unit, ΔP GFL It is the power change of the network-type equipment.

[0077] The power system inertia online assessment system extracts system observation variables from historical frequency and power change data, as shown in the following formula 11:

[0078] Formula 11:

[0079] Among them, Z K is the system observation variable, Δω GFM is the frequency change of the networking device, Δω SG is the frequency change of the synchronous unit, Δω GFL It is the frequency change of the network-type device.

[0080] 206. Obtain a state transfer function, use the state transfer function to calculate the unified inertia analytical model, and obtain a state transfer equation.

[0081] In the embodiment of the present application, the power system inertia online assessment system obtains a state transfer function, and uses the state transfer function to calculate the unified inertia analytical model to obtain a state transfer equation, and the calculation formula is the following formula 12:

[0082] Formula 12:

[0083] in, is the frequency change of the networking device, is the frequency change of the synchronous unit, To follow the frequency change of the network type equipment, is the power change of the networked device, is the power variation of the synchronous unit, is the power change of the grid-following equipment, ω0 is the rated frequency of the power supply equipment, D is the damping coefficient of the power supply equipment, k w is the power regulation coefficient, k p is the generator proportional adjustment coefficient, S n is the rated capacity of the power supply equipment, s is the complex variable under Laplace change, H sg is the inertia constant of the synchronous unit, H re,GFM is the virtual inertia constant of the networked device, H re,GFL It is the virtual inertia constant of the network-type device.

[0084] 207. The extended Kalman filter algorithm is used to evaluate the state transfer equation and obtain the online estimation model of the system equivalent inertia.

[0085] In the embodiment of the present application, the power system inertia online assessment system uses an extended Kalman filter algorithm to calculate the system equivalent inertia, system state vector, system input vector and system observation variables to obtain a prediction state equation and a covariance prediction matrix, and the calculation formula is the following formula 13:

[0086] Formula 13:

[0087] P k|k-1 =F k P k-1|k-1 F k T +Q k

[0088] Among them, F k is the Jacobian matrix of the state transfer matrix, Q k is the process noise covariance matrix, is the posterior estimation vector at time k-1, is the predicted state estimation vector at time k, P k-1|k-1 is the posterior prediction matrix at time k-1, P k|k-1 is the covariance prediction matrix at time k, U k-1 is the system input vector at time k-1. It needs to be explained that It is the optimal estimate of the state at time k-1. It is the state estimate obtained by updating the observed value at time k-1, so it is the posterior estimate vector at time k-1. It is the predicted estimated value of the state at time k. It is predicted to time k through the state transfer equation based on the state information at time k-1, so it is the predicted state estimation vector at time k.

[0089] Next, the power system inertia online assessment system uses the state transfer equation to update the prediction state equation and the covariance prediction matrix to obtain the Kalman gain, the updated prediction state equation and the updated covariance prediction matrix. The calculation formula is as follows:

[0090] Formula 14: K k =P k|k-1 E k T (E k P k|k-1 E k T +R k ) -1

[0091]

[0092] P k|k =(IK k H k ) k|k-1

[0093] Among them, E k is the Jacobian matrix of the observation matrix, R k is the observation noise covariance matrix, K k is the Kalman gain at time k, P k|k-1 is the covariance prediction matrix at time k, P k|k is the posterior prediction matrix at time k, is the posterior estimation vector at time k, is the predicted state estimation vector at time k, Z k is the system observation variable. It needs to be explained that It is an estimate of the state at time k after updating it by combining the observed value at time k, and is corrected based on the observed data and the predicted state, so it is the posterior estimate vector at time k.

[0094] Subsequently, the power system inertia online evaluation system uses Kalman gain, updated prediction state equation and updated covariance prediction matrix to construct the system equivalent inertia online estimation model. By obtaining the system frequency and power change data and combining the unified inertia analytical model to evaluate the system equivalent inertia, the system equivalent inertia online estimation model can be constructed.

[0095] 208. Obtain a recursive least squares algorithm with a forgetting factor, and use the recursive least squares algorithm with a forgetting factor to adjust the online estimation model of the system equivalent inertia to obtain the online estimation model of the target system equivalent inertia.

[0096] In the embodiment of the present application, the power system inertia online evaluation system obtains the input vector and the parameter vector in the system equivalent inertia online estimation model, and the calculation formula is the following formula 15:

[0097] Formula 15:

[0098] Among them, y(i-1) is the output value at time i-1, u(i-1) is the input value at time i-1, na is the number of lag terms of the output signal in the online estimation model of the system equivalent inertia, a1 is the first coefficient of the lagged first-order term, and b1 is the second coefficient of the lagged first-order term. Specifically, y(i-1) represents the output value of the system at time i-1, which is the output at the previous moment and is used to recursively calculate the estimate at the current moment. u(i-1) represents the input value of the system at time i-1, representing the input signal applied to the system at the previous moment. na represents the number of lag terms (order) of the output signal in the model, that is, the number of output historical data used for parameter identification, which determines the degree of dependence of the historical information of the system output value in the model. a1 represents the parameters of the model, which are associated with the lag terms y(i-1), y(i-2)... of the output variables, a1, a2...a na It reflects the impact of the output history on the current output. b1 represents the parameters of the model, which are associated with the lag terms u(i-1), u(i-2) of the input variables, and reflects the impact of the history of the input signal on the current output.

[0099] Next, the power system inertia online assessment system uses the input vector and parameter vector to construct the error criterion function, and the calculation formula is as follows 16:

[0100] Formula 16:

[0101] Among them, J(θ) is the error criterion function, λ is the forgetting factor, φ T (i) is the input vector, which includes the system frequency change and the system disturbance power, y(i) is the output vector, θ is the parameter vector, and i is the index used to traverse from time step 1 to the current time step t. The role of the error criterion function is to measure the error between the system prediction value and the actual observation value during the system modeling and parameter identification process. In this way, the power system inertia online assessment system can adjust the model parameters according to the error criterion to achieve the optimal parameter estimation effect and improve the accuracy of the model.

[0102] Subsequently, the power system inertia online assessment system obtains a recursive calculation method, uses the recursive calculation method to update the parameter vector, and obtains the parameter estimation value, which is calculated as follows:

[0103] Formula 17: θ(t)=θ(t-1)+L(t)[y(t)-φT (t)θ(t-1)]

[0104]

[0105] Among them, θ(t) is the parameter estimate at time t, L(t) is the gain matrix at time t, P(t) is the covariance matrix at time t, λ is the forgetting factor, φ T (t) is the input vector.

[0106] Then, the power system inertia online assessment system calculates the predicted error change rate according to the error criterion function, and the calculation formula is as follows 18:

[0107] Formula 18:

[0108] Among them, ΔJ(t) is the rate of change of prediction error, J(t) is the prediction error, φ T (i) is the input vector, y(i) is the output vector, θ is the parameter vector, m is the length of the first time window, n is the length of the second time window, that is, the time window length from time t-n+1 to time t is n. The error criterion can evaluate the difference between the model output y(i) and the actual observation value, so as to update the parameters, minimize the model prediction error, and make the model more accurately describe the dynamic behavior of the system.

[0109] The power system inertia online assessment system obtains a fuzzy controller, uses the fuzzy controller to calculate the prediction error change rate, obtains the forgetting factor adjustment change rate, and uses the forgetting factor adjustment change rate to dynamically adjust the forgetting factor. The calculation formula is the following formula 19:

[0110] Formula 19: λ(t)=λ0+Δλ(t)

[0111] Among them, λ0 is the initial forgetting factor, and Δλ(t) is the adjustment change rate of the forgetting factor.

[0112] Finally, the power system inertia online assessment system uses the dynamically adjusted forgetting factor to construct a recursive least squares algorithm with a forgetting factor, and uses the recursive least squares algorithm with a forgetting factor to adjust the system equivalent inertia online estimation model to obtain the target system equivalent inertia online estimation model. The parameters of the system inertia model are continuously corrected by the recursive least squares method with a forgetting factor to ensure the accuracy of real-time assessment.

[0113] In summary, first, we build an inertia model based on different types of power equipment in the power system. The calculation of inertia requires knowing the frequency change and power change of the system. Therefore, we then input the frequency change and power change data into the model, and use EKF to generate the equivalent inertia estimate of the system, reflecting the inertia response capability of the system in disturbance events. Finally, we continuously correct the parameters of the system inertia model through the recursive least squares method with forgetting factor to ensure the accuracy of real-time evaluation.

[0114] 209. Acquire real-time frequency and power change data, input the real-time frequency and power change data into an online estimation model of equivalent inertia of a target system, and obtain an online evaluation result of equivalent inertia output by the online estimation model of equivalent inertia of a target system.

[0115] In an embodiment of the present application, the power system inertia online assessment system obtains real-time frequency and power change data, inputs the real-time frequency and power change data into the target system equivalent inertia online estimation model, and obtains the equivalent inertia online assessment result output by the target system equivalent inertia online estimation model. The present application improves the existing new energy power system inertia assessment method to realize the online assessment of the inertia of various types of units in the new energy power system, and proposes inertia expressions for various power system power equipment including new energy, so as to realize the accurate calculation of the inertia of each unit. At the same time, EKF is introduced to realize the online assessment of the equivalent inertia of the power system based on the system operating status, realize the real-time assessment of the power system's ability to resist disturbances, and improve the accuracy of the online assessment.

[0116] Based on the above process, a schematic diagram of the architecture of an online evaluation of inertia of a power system proposed in an embodiment of the present application is as follows:

[0117] like Figure 2B As shown in the figure, the statutory and active power and frequency fluctuation time series data are normalized, and the inertia coefficient model is constructed. Then the inertia solution is converted into the coefficient online identification, the unit inertia evaluation is performed, and finally the system inertia evaluation is performed.

[0118] The method provided in the embodiment of the present application obtains power supply equipment data, establishes a unified inertia analytical model using the power supply equipment data, obtains historical frequency and power change data, uses an extended Kalman filter algorithm to evaluate the unified inertia analytical model and the historical frequency and power change data, obtains a system equivalent inertia online estimation model, obtains a recursive least squares algorithm with a forgetting factor, uses a recursive least squares algorithm with a forgetting factor to adjust the system equivalent inertia online estimation model, obtains a target system equivalent inertia online estimation model, obtains real-time frequency and power change data, inputs the real-time frequency and power change data into the target system equivalent inertia online estimation model, and obtains an equivalent inertia online evaluation result output by the target system equivalent inertia online estimation model. Through theoretical derivation, a unified inertia analytical model including grid-building type, grid-following type equipment and synchronous generators is established. Then, dynamic modeling is performed based on the extended Kalman filter, and the inertia solution is converted into a parameter identification problem, so as to obtain the inertia level of various generators in the system. Finally, the recursive least squares algorithm with forgetting factor is used to realize continuous tracking of the system equivalent inertia, which can realize accurate calculation of the inertia of each unit, effectively evaluate the local and system equivalent inertia of the new energy power system under various control strategies, and provide an important reference basis for the operation of the power grid.

[0119] Further, as Figure 1 In a specific implementation of the method, the present application provides an online evaluation device for inertia of a power system, such as Figure 3 As shown, the device includes: a construction module 301, an estimation module 302, an adjustment module 303 and an online evaluation module 304.

[0120] A construction module 301 is used to obtain power supply equipment data and establish a unified inertia analytical model using the power supply equipment data;

[0121] An estimation module 302 is used to obtain historical frequency and power change data, and use an extended Kalman filter algorithm to evaluate the unified inertia analytical model and the historical frequency and power change data to obtain an online estimation model of system equivalent inertia;

[0122] An adjustment module 303 is used to obtain a recursive least squares algorithm with a forgetting factor, and use the recursive least squares algorithm with a forgetting factor to adjust the system equivalent inertia online estimation model to obtain a target system equivalent inertia online estimation model;

[0123] The online evaluation module 304 is used to obtain real-time frequency and power change data, input the real-time frequency and power change data into the target system equivalent inertia online estimation model, and obtain the equivalent inertia online evaluation result output by the target system equivalent inertia online estimation model.

[0124] In a specific application scenario, the building module 301 is used to extract synchronous machine data from the power supply equipment data, and use the synchronous machine data to calculate the motion equation of the synchronous machine, wherein:

[0125]

[0126] Wherein, J is the moment of inertia of the synchronous machine, ω is the real-time frequency of the synchronous machine, ω0 is the rated frequency of the synchronous machine, D is the damping coefficient of the synchronous machine, P e is the electromagnetic power of the synchronous unit, P m is the mechanical power of the synchronous unit; the inertia power of the synchronous unit is determined according to the motion equation of the synchronous unit, wherein,

[0127]

[0128] Among them, ΔP sg is the inertia power of the synchronous unit, H sg is the inertia constant of the synchronous unit; constructing the dynamic response process function of the synchronous unit based on the motion equation of the synchronous unit and the inertia power of the synchronous unit; extracting the meshing device data from the power supply device data, using the meshing device data to calculate the motion equation of the meshing device, and constructing the dynamic response process function of the meshing device based on the motion equation of the meshing device; extracting the following mesh device data from the power supply device data, using the following mesh device data to calculate the virtual inertia constant of the following mesh device, wherein,

[0129] H re,GFM =0.5T j

[0130] Among them, H re,GFM is the virtual inertia constant of the network-following device, T j is the inertia time constant; the motion equation of the grid-following device is calculated using the grid-following device data, and the dynamic response process function of the grid-following device is constructed based on the motion equation of the grid-following device and the virtual inertia constant of the grid-following device; the unified inertia analytical model is constructed based on the dynamic response process function of the synchronous unit, the dynamic response process function of the grid-forming device, and the dynamic response process function of the grid-following device, wherein,

[0131]

[0132] Wherein, ω0 is the rated frequency of the power supply device, ω is the real-time frequency of the power supply device, D is the damping coefficient of the power supply device, k w is the power regulation coefficient, k pis the generator proportional adjustment coefficient, S n is the rated capacity of the power supply equipment, s is the complex variable under Laplace change, H sg is the inertia constant of the synchronous unit, H re,GFM is the virtual inertia constant of the networking device, H re,GFL is the virtual inertia constant of the following network device, Δω * (s) is the node frequency change, is the power change of the networked device, is the power variation of the synchronous unit, It is the power change of the grid-following device, and the power supply device includes the synchronous unit, the grid-forming device, and the grid-following device.

[0133] In a specific application scenario, the construction module 301 is used to extract the installed capacity of the networking device from the networking device data, and calculate the virtual inertia constant of the networking device using the installed capacity of the networking device, wherein:

[0134]

[0135] Among them, H re,GFM is the virtual inertia constant of the networking device, S re,GFM is the installed capacity of the network-type equipment, J is the moment of inertia of the network-type equipment, and ω0 is the rated frequency of the network-type equipment; the motion equation of the network-type equipment is calculated using the data of the network-type equipment, wherein,

[0136]

[0137] Wherein, J is the moment of inertia of the mesh-forming device, ω0 is the rated frequency of the mesh-forming device, ω is the real-time frequency of the mesh-forming device, D is the damping coefficient of the mesh-forming device, and P e is the electromagnetic power of the network-building device, P0 is the specified power of the network-building device, k w is the power regulation coefficient; the virtual inertia constant of the networking device and the motion equation of the networking device are Laplace transformed to obtain the dynamic response process function of the networking device, wherein,

[0138]

[0139] Wherein, J is the moment of inertia of the mesh-forming device, ω0 is the rated frequency of the mesh-forming device, ω is the real-time frequency of the mesh-forming device, D is the damping coefficient of the mesh-forming device, ω * is the normalized frequency of the meshing device, is the normalized input mechanical power of the meshing device, is the normalized output electromagnetic power of the networking device, k w is the power regulation coefficient, S n is the rated capacity of the network-building device, s is the complex variable under Laplace change, H re,GFM is the virtual inertia constant of the networking device, Δω * (s) is the frequency change of the node, is the power change of the meshed device.

[0140] In a specific application scenario, the estimation module 302 is used to extract the system equivalent inertia, the system state vector, the system input vector and the system observation variable from the historical frequency and power change data; obtain the state transfer function, and use the state transfer function to calculate the unified inertia analytical model to obtain the state transfer equation, wherein,

[0141]

[0142] in, is the frequency change of the networking device, is the frequency change of the synchronous unit, To follow the frequency change of the network type equipment, is the power change of the networked device, is the power variation of the synchronous unit, is the power change of the grid-following device, ω0 is the rated frequency of the power supply device, D is the damping coefficient of the power supply device, k w is the power regulation coefficient, k p is the generator proportional adjustment coefficient, S n is the rated capacity of the power supply equipment, s is the complex variable under Laplace change, H sg is the inertia constant of the synchronous unit, H re,GFM is the virtual inertia constant of the networking device, H re,GFL is the virtual inertia constant of the grid-following device; the state transfer equation is evaluated by using the extended Kalman filter algorithm to obtain the online estimation model of the system equivalent inertia.

[0143] In a specific application scenario, the estimation module 302 is used to calculate the system equivalent inertia, the system state vector, the system input vector and the system observation variable using the extended Kalman filter algorithm to obtain a prediction state equation and a covariance prediction matrix, wherein:

[0144]

[0145] Pk|k-1 =F k P k-1|k-1 F k T +Q k

[0146] Among them, F k is the Jacobian matrix of the state transfer matrix, Q k is the process noise covariance matrix, is the posterior estimation vector at time k-1, is the predicted state estimation vector at time k, P k-1|k-1 is the posterior prediction matrix at time k-1, P k|k-1 is the covariance prediction matrix at time k, U k-1 is the system input vector at time k-1; the predicted state equation and the covariance prediction matrix are updated using the state transfer equation to obtain the Kalman gain, the updated predicted state equation and the updated covariance prediction matrix, wherein,

[0147] K k =P k|k-1 E k T (E k P k|k-1 E k T +R k ) -1

[0148]

[0149] P k|k =(IK k H k ) k|k-1

[0150] Among them, E k is the Jacobian matrix of the observation matrix, R k is the observation noise covariance matrix, K k is the Kalman gain at time k, P k|k-1 is the covariance prediction matrix at time k, P k|k is the posterior prediction matrix at time k, is the posterior estimation vector at time k, is the predicted state estimation vector at time k, Z k is the system observation variable; the Kalman gain, the updated prediction state equation and the updated covariance prediction matrix are used to construct the system equivalent inertia online estimation model.

[0151] In a specific application scenario, the estimation module 302 is used to extract the system equivalent inertia from the historical frequency and power change data, where:

[0152]

[0153] Among them, H sys is the equivalent inertia of the system, i is the number of power devices, H i is the inertia constant of the power supply device, S i is the installed capacity of the power supply equipment; extracting the system state vector from the historical frequency and power change data, wherein,

[0154]

[0155] Among them, X K is the system state vector, ω GFM is the frequency of the networking device, ω SG is the frequency of the synchronous unit, ω GFL is the frequency of the network-following device, H re,GFM is the virtual inertia constant of the networking device, H sg is the inertia constant of the synchronous unit, H re,GFL is the virtual inertia constant of the grid-following device; and the system input vector is extracted from the historical frequency and power change data, wherein:

[0156]

[0157] Among them, U K The input vector for the system, ΔP GFM is the power change of the networked device, ΔP SG is the power variation of the synchronous unit, ΔP GFL is the power change of the network-following device; extracting the system observation variable from the historical frequency and power change data, wherein,

[0158]

[0159] Among them, Z K is the observed variable of the system, Δω GFM is the frequency change of the networking device, Δω SG is the frequency variation of the synchronous unit, Δω GFL is the frequency change of the network-following device.

[0160] In a specific application scenario, the adjustment module 303 is used to obtain an input vector and a parameter vector in the online estimation model of the system equivalent inertia, wherein:

[0161]

[0162] Wherein, y(i-1) is the output value at time i-1, u(i-1) is the input value at time i-1, na is the number of lag terms of the output signal in the online estimation model of the system equivalent inertia, a1 is the first coefficient of the lag first-order term, and b1 is the second coefficient of the lag first-order term; the error criterion function is constructed using the input vector and the parameter vector, wherein,

[0163]

[0164] Where J(θ) is the error criterion function, λ is the forgetting factor, φ T (i) is the input vector, the input vector includes the system frequency change and the system disturbance power, y(i) is the output vector, and θ is the parameter vector; a recursive calculation method is obtained, and the parameter vector is updated by the recursive calculation method to obtain a parameter estimation value, wherein,

[0165] θ(t)=θ(t-1)+L(t)[y(t)-φ T (t)θ(t-1)]

[0166]

[0167] Among them, θ(t) is the parameter estimate at time t, L(t) is the gain matrix at time t, P(t) is the covariance matrix at time t, λ is the forgetting factor, φ T (t) is the input vector; the prediction error change rate is calculated according to the error criterion function, where,

[0168]

[0169] Wherein, ΔJ(t) is the prediction error change rate, J(t) is the prediction error, φ T (i) is the input vector, y(i) is the output vector, θ is the parameter vector, m is the length of the first time window, and n is the length of the second time window; obtain a fuzzy controller, use the fuzzy controller to calculate the prediction error change rate, and obtain the forgetting factor adjustment change rate; use the forgetting factor adjustment change rate to dynamically adjust the forgetting factor, wherein,

[0170] λ(t)=λ0+Δλ(t)

[0171] Among them, λ0 is the initial forgetting factor, Δλ(t) is the adjustment change rate of the forgetting factor; the dynamically adjusted forgetting factor is used to construct the recursive least squares algorithm with the forgetting factor, and the recursive least squares algorithm with the forgetting factor is used to adjust the system equivalent inertia online estimation model to obtain the target system equivalent inertia online estimation model.

[0172] The device provided in the embodiment of the present application obtains power supply equipment data, establishes a unified inertia analytical model using the power supply equipment data, obtains historical frequency and power change data, uses an extended Kalman filter algorithm to evaluate the unified inertia analytical model and the historical frequency and power change data, obtains a system equivalent inertia online estimation model, obtains a recursive least squares algorithm with a forgetting factor, uses a recursive least squares algorithm with a forgetting factor to adjust the system equivalent inertia online estimation model, obtains a target system equivalent inertia online estimation model, obtains real-time frequency and power change data, inputs the real-time frequency and power change data into the target system equivalent inertia online estimation model, and obtains an equivalent inertia online evaluation result output by the target system equivalent inertia online estimation model. Through theoretical derivation, a unified inertia analytical model including grid-building type, grid-following type equipment and synchronous generators is established. Then, dynamic modeling is performed based on the extended Kalman filter, and the inertia solution is converted into a parameter identification problem, so as to obtain the inertia level of various generators in the system. Finally, the recursive least squares algorithm with forgetting factor is used to realize continuous tracking of the system equivalent inertia, which can realize accurate calculation of the inertia of each unit, effectively evaluate the local and system equivalent inertia of the new energy power system under various control strategies, and provide an important reference basis for the operation of the power grid.

[0173] It should be noted that for other corresponding descriptions of the functional units involved in the online evaluation device for inertia of a power system provided in the embodiment of the present application, reference can be made to Figure 1 and Figure 2A The corresponding description in will not be repeated here.

[0174] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0175] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0176] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

[0177] In an exemplary embodiment, see Figure 4 , and also provides a device, which includes a bus, a processor, a memory and a communication interface, and may also include an input and output interface and a display device, wherein each functional unit can communicate with each other through the bus. The memory stores a computer program, and the processor is used to execute the program stored in the memory and execute the online evaluation method of the power system inertia in the above embodiment.

[0178] A readable storage medium stores a computer program, which implements the steps of the power system inertia online evaluation method when executed by a processor.

[0179] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0180] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present application.

[0181] Those skilled in the art will appreciate that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more devices different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0182] The above application serial numbers are for description only and do not represent the advantages or disadvantages of the implementation scenarios.

[0183] The above disclosure only discloses several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be conceived by technicians in this field should fall within the scope of protection of the present application.

Claims

1. A method for online evaluation of inertia of a power system, characterized in that: include: Acquiring power equipment data, and establishing a unified inertia analytical model using the power equipment data; Acquire historical frequency and power change data, and use an extended Kalman filter algorithm to evaluate the unified inertia analytical model and the historical frequency and power change data to obtain a system equivalent inertia online estimation model; Obtaining a recursive least squares algorithm with a forgetting factor, and using the recursive least squares algorithm with the forgetting factor to adjust the online estimation model of equivalent inertia of the system to obtain an online estimation model of equivalent inertia of the target system; Real-time frequency and power change data are acquired, and the real-time frequency and power change data are input into the target system equivalent inertia online estimation model to obtain an equivalent inertia online evaluation result output by the target system equivalent inertia online estimation model.

2. The method according to claim 1, characterized in that: The method of using the power supply equipment data to establish a unified inertia analytical model includes: Extracting synchronous machine data from the power supply equipment data, and using the synchronous machine data to calculate the motion equation of the synchronous machine, wherein: Wherein, J is the moment of inertia of the synchronous machine, ω is the real-time frequency of the synchronous machine, ω0 is the rated frequency of the synchronous machine, D is the damping coefficient of the synchronous machine, P e is the electromagnetic power of the synchronous unit, P m is the mechanical power of the synchronous unit; The inertia power of the synchronous machine set is determined according to the motion equation of the synchronous machine set, wherein: Where ΔP sg is the inertia power of the synchronous unit, H sg is the inertia constant of the synchronous unit; Constructing a dynamic response process function of the synchronous machine set based on the motion equation of the synchronous machine set and the inertia power of the synchronous machine set; Extracting meshed device data from the power supply device data, using the meshed device data to calculate a motion equation of the meshed device, and constructing a dynamic response process function of the meshed device based on the motion equation of the meshed device; Extracting grid-type equipment data from the power supply equipment data, and using the grid-type equipment data to calculate the virtual inertia constant of the grid-type equipment, wherein: H re,GFM =0.5T j Among them, H re,GFM is the virtual inertia constant of the network-following device, T j is the inertia time constant; The motion equation of the network-following device is calculated using the network-following device data, and a dynamic response process function of the network-following device is constructed based on the motion equation of the network-following device and the virtual inertia constant of the network-following device; The unified inertia analytical model is constructed based on the dynamic response process function of the synchronous unit, the dynamic response process function of the network-building device, and the dynamic response process function of the network-following device, wherein: Wherein, ω0 is the rated frequency of the power supply device, ω is the real-time frequency of the power supply device, D is the damping coefficient of the power supply device, k w is the power regulation coefficient, k p is the generator proportional adjustment coefficient, S n is the rated capacity of the power supply equipment, s is the complex variable under Laplace change, H sg is the inertia constant of the synchronous unit, H re,GFM is the virtual inertia constant of the networking device, H re,GFL is the virtual inertia constant of the following network device, Δω * (s) is the node frequency change, ΔP GFM *(s) is the power change of the network-building equipment, ΔP sG *(s) is the power change of the synchronous unit, ΔP GFL *(s) is the power change of the grid-following device, and the power supply equipment includes the synchronous unit, the grid-forming device, and the grid-following device.

3. The method according to claim 2, characterized in that The method of using the network-following device data to calculate the motion equation of the network-following device, and constructing the dynamic response process function of the network-following device based on the motion equation of the network-following device and the virtual inertia constant of the network-following device, includes: The installed capacity of the meshing device is extracted from the meshing device data, and the virtual inertia constant of the meshing device is calculated using the installed capacity of the meshing device, wherein: Among them, H re,GFM is the virtual inertia constant of the networking device, S re,GFM is the installed capacity of the networking device, J is the moment of inertia of the networking device, and ω0 is the rated frequency of the networking device; The motion equation of the meshing device is calculated using the meshing device data, wherein: Wherein, J is the moment of inertia of the mesh-forming device, ω0 is the rated frequency of the mesh-forming device, ω is the real-time frequency of the mesh-forming device, D is the damping coefficient of the mesh-forming device, and P e is the electromagnetic power of the network-building device, P0 is the specified power of the network-building device, k w is the power regulation factor; The virtual inertia constant of the meshing device and the motion equation of the meshing device are Laplace transformed to obtain the dynamic response process function of the meshing device, wherein: Wherein, J is the moment of inertia of the mesh-forming device, ω0 is the rated frequency of the mesh-forming device, ω is the real-time frequency of the mesh-forming device, D is the damping coefficient of the mesh-forming device, ω * is the normalized frequency of the meshing device, is the normalized input mechanical power of the meshing device, is the normalized output electromagnetic power of the networking device, k w is the power regulation coefficient, S n is the rated capacity of the network-building device, s is the complex variable under Laplace change, H re,GFM is the virtual inertia constant of the networking device, Δω * (s) is the frequency change of the node, ΔP GFM* (s) is the power change of the networking device.

4. The method according to claim 1, characterized in that: The method of using the extended Kalman filter algorithm to evaluate the unified inertia analytical model and the historical frequency and power change data to obtain an online estimation model of system equivalent inertia includes: Extracting system equivalent inertia, system state vector, system input vector and system observation variables from the historical frequency and power change data; A state transfer function is obtained, and the unified inertia analytical model is calculated using the state transfer function to obtain a state transfer equation, wherein: Among them, Δω GFM *(s) is the frequency change of the networking device, Δω SG *(s) is the frequency change of the synchronous unit, Δω GFL *(s) is the frequency change of the network-following device, ΔP GFM *(s) is the power change of the network-building equipment, ΔP SG *(s) is the power change of the synchronous unit, ΔP GFL *(s) is the power change of the grid-following device, ω0 is the rated frequency of the power supply device, D is the damping coefficient of the power supply device, k w is the power regulation coefficient, k p is the generator proportional adjustment coefficient, S n is the rated capacity of the power supply equipment, s is the complex variable under Laplace change, H sg is the inertia constant of the synchronous unit, H re,GFM is the virtual inertia constant of the networking device, H re,GFL is the virtual inertia constant of the network-following device; The extended Kalman filter algorithm is used to evaluate the state transfer equation to obtain the online estimation model of the system equivalent inertia.

5. The method according to claim 4, characterized in that The method of using the extended Kalman filter algorithm to evaluate the state transfer equation to obtain the system equivalent inertia online estimation model includes: The extended Kalman filter algorithm is used to calculate the system equivalent inertia, the system state vector, the system input vector and the system observation variable to obtain a prediction state equation and a covariance prediction matrix, wherein: P k|k-1 =F k P k-1|k-1 F k T +Q k Among them, F k is the Jacobian matrix of the state transfer matrix, Q k is the process noise covariance matrix, is the posterior estimation vector at time k-1, is the predicted state estimation vector at time k, P k-1|k-1 is the posterior prediction matrix at time k-1, P k|k-1 is the covariance prediction matrix at time k, U k-1 is the system input vector at time k-1; The predicted state equation and the covariance prediction matrix are updated using the state transfer equation to obtain the Kalman gain, the updated predicted state equation and the updated covariance prediction matrix, wherein: K k =P k|k-1 E k T (E k P k|k-1 E k T +R k ) -1 P k|k =(I-K k H k )P k|k-1 Among them, E k is the Jacobian matrix of the observation matrix, R k is the observation noise covariance matrix, K k is the Kalman gain at time k, P k|k-1 is the covariance prediction matrix at time k, P k|k is the posterior prediction matrix at time k, is the posterior estimation vector at time k, is the predicted state estimation vector at time k, Z k is the system observation variable; The system equivalent inertia online estimation model is constructed by using the Kalman gain, the updated prediction state equation and the updated covariance prediction matrix.

6. The method according to claim 4, characterized in that The extracting of system equivalent inertia, system state vector, system input vector and system observation variables from the historical frequency and power change data includes: The system equivalent inertia is extracted from the historical frequency and power change data, where: Among them, H sys is the equivalent inertia of the system, i is the number of power devices, H i is the inertia constant of the power supply device, S i is the installed capacity of the power supply equipment; The system state vector is extracted from the historical frequency and power change data, wherein: Among them, X K is the system state vector, ω GFM is the frequency of the networking device, ω SG is the frequency of the synchronous unit, ω GFL is the frequency of the network-following device, H re,GFM is the virtual inertia constant of the networking device, H sg is the inertia constant of the synchronous unit, H re,GFL is the virtual inertia constant of the network-following device; The system input vector is extracted from the historical frequency and power change data, wherein: Among them, U k The input vector for the system, ΔP GFM is the power change of the networked device, ΔP SG is the power variation of the synchronous unit, ΔP GFL is the power change of the network-following device; The system observation variables are extracted from the historical frequency and power change data, wherein: Among them, Z K is the observed variable of the system, Δω GFM is the frequency change of the networking device, Δω SG is the frequency variation of the synchronous unit, Δω GFL is the frequency change of the network-following device.

7. The method according to claim 1, characterized in that The obtaining of the recursive least squares algorithm with a forgetting factor, and using the recursive least squares algorithm with a forgetting factor to adjust the online estimation model of the equivalent inertia of the system to obtain the online estimation model of the equivalent inertia of the target system, comprises: The input vector and parameter vector are obtained in the online estimation model of the system equivalent inertia, wherein: Wherein, y(i-1) is the output value at time i-1, u(i-1) is the input value at time i-1, na is the number of lag terms of the output signal in the online estimation model of the equivalent inertia of the system, a1 is the first coefficient of the lag first-order term, and b1 is the second coefficient of the lag first-order term; The input vector and the parameter vector are used to construct an error criterion function, wherein, Where J(θ) is the error criterion function, λ is the forgetting factor, φ T (i) is the input vector, the input vector includes system frequency change and system disturbance power, y(i) is the output vector, and θ is the parameter vector; Obtain a recursive calculation method, and use the recursive calculation method to update the parameter vector to obtain a parameter estimation value, wherein: θ(t)=θ(t-1)+L(t)[y(t)-φ T (t)θ(t-1)] Among them, θ(t) is the parameter estimate at time t, L(t) is the gain matrix at time t, P(t) is the covariance matrix at time t, λ is the forgetting factor, φ T (t) is the input vector; The prediction error change rate is calculated according to the error criterion function, where: Wherein, ΔJ(t) is the prediction error change rate, J(t) is the prediction error, φ T (i) is the input vector, y(i) is the output vector, θ is the parameter vector, m is the length of the first time window, and n is the length of the second time window; Obtain a fuzzy controller, and use the fuzzy controller to calculate the prediction error change rate to obtain the forgetting factor adjustment change rate; The forgetting factor is dynamically adjusted using the forgetting factor adjustment change rate, wherein: λ(t)=λ0+Δλ(t) Wherein, λ0 is the initial forgetting factor, Δλ(t) is the adjustment change rate of the forgetting factor; The dynamically adjusted forgetting factor is used to construct the recursive least squares algorithm with the forgetting factor, and the recursive least squares algorithm with the forgetting factor is used to adjust the system equivalent inertia online estimation model to obtain the target system equivalent inertia online estimation model.

8. An online evaluation device for inertia of a power system, characterized in that: include: A construction module is used to obtain power supply equipment data and establish a unified inertia analytical model using the power supply equipment data; an estimation module, used for acquiring historical frequency and power change data, and using an extended Kalman filter algorithm to evaluate the unified inertia analytical model and the historical frequency and power change data to obtain an online estimation model of system equivalent inertia; An adjustment module, used for obtaining a recursive least squares algorithm with a forgetting factor, and using the recursive least squares algorithm with a forgetting factor to adjust the online estimation model of equivalent inertia of the system to obtain an online estimation model of equivalent inertia of the target system; The online evaluation module is used to obtain real-time frequency and power change data, input the real-time frequency and power change data into the target system equivalent inertia online estimation model, and obtain the equivalent inertia online evaluation result output by the target system equivalent inertia online estimation model.

9. A device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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