Power system frequency control method and system based on weight factor-free model

Abstract

The invention relates to the technical field of power system frequency control, in particular to a power system frequency control method and system based on a weight factor-free model, and solves the problems of rapid development of a renewable energy system and reduction of inertia and damping of the power system by adopting an inverter and a voltage source converter on a large scale. A target function is converted into a linear quadratic cost function by adopting weight factor-free model predictive control, so that the calculation amount is reduced; an inner ring of finite control set model predictive control uses an SW factor to control a voltage source inverter without a weight factor; optimal control is realized through optimal switching control of power grid current, balance of voltage of a neutral point clamping capacitor and geometric fast frequency control; by managing the power injection of the inverter, the operation within the frequency limit is ensured, the optimal power flow is realized during the disturbance period, the frequency control of the power system of the voltage source inverter in the low-inertia environment is realized, and the calculation time and the calculation amount are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system frequency control, and in particular to a power system frequency control method and system based on a non-weighted factor model. Background Art

[0002] With the rapid development of renewable energy power generation in my country, the proportion of renewable energy in the power system continues to increase. Due to the lack of inertia and primary frequency regulation capability of renewable energy power generation units, the large-scale use of inverters and voltage source converters has brought a series of problems to the operation and management of real-time power systems. The reduction of inertia and damping has led to large-scale frequency deviations, and frequency stability events in high-proportion renewable energy systems have occurred frequently, causing people to worry about the stability of system frequency.

[0003] Fast frequency control ancillary services are implemented by utilizing DC energy storage systems and voltage source inverters to effectively adjust the output power to cope with frequency fluctuations. Control methods for grid-forming voltage source inverters and voltage source converters include virtual synchronous machine (VSG)-based control, which simulates the dynamic behavior of asynchronous machines, and droop control-based strategies, which exploit the active and reactive power output of the inverter and its droop control characteristics. However, most control methods only focus on the AC side of the converter and ignore the dynamic behavior of the DC link, such as the energy storage system (ESS). These methods simplify by assuming that the energy and power of the DC side capacitors are finite. In addition, the constant droop gain leads to poor performance of the VSI / VSC under small frequency deviations, making it impossible for the inverter / converter to fully utilize the maximum power capacity when dealing with disturbances and faults.

[0004] Existing studies have applied model predictive control (MPC) to centralized, distributed, and hierarchical power system control, and found that compared with PI control, MPC performs better in robust frequency control and frequency uncertainty regulation. However, there is still little discussion on the application of MPC in fast frequency control. Some studies have used explicit MPC for real-time optimal frequency and inertial response control. However, the study did not demonstrate converter-based power generation, and used a simplified power system model for fast frequency control. In addition, relevant scholars have used voltage source inverters combined with model predictive control for frequency control of low-inertia power systems. However, this method does not use multi-level inverter control in the inner loop and is not suitable for large-scale power systems. In summary, the existing fast frequency control methods based on model predictive control still have many defects, which restrict and limit the frequency regulation of power systems. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention is proposed.

[0006] Therefore, the technical problem to be solved by the present invention is: in order to solve the problem that the rapid development of renewable energy systems and the large-scale adoption of inverters and voltage source converters reduce the inertia and damping of power systems, resulting in large-scale frequency deviations and difficulties in real-time power system operation and management.

[0007] To solve the above technical problems, the present invention provides the following technical solutions, a power system frequency control method based on a weight-free factor model, including: establishing an outer loop VSG model based on MPC to improve the frequency regulation capability of the power system; using the swing equation to simulate the rotor inertia of the asynchronous generator to optimize the output power; applying the Tustin approximation to obtain a discrete model; designing an incremental discrete model to reduce static errors and improve control accuracy; predicting the inverter reference voltage vector to determine the optimal switching state.

[0008] As a preferred scheme of the power system frequency control method based on the unweighted factor model described in the present invention, wherein: the establishment of the MPC-based outer loop VSG model includes considering the dual MPC scheme based on the unweighted MPC-based VSG and the unweighted FCS-MPCSW factor to improve the frequency regulation capability and the accuracy of frequency control, and eliminate the oscillation problem caused by the traditional virtual synchronous generator frequency returning to normal value under inertia.

[0009] As a preferred solution of the power system frequency control method based on the weight-free factor model described in the present invention, the swing equation includes a rotor inertia provided by the asynchronous generator for simulating the inertia, as a component of the VSG, which is expressed as:

[0010]

[0011] Where J represents the virtual moment of inertia, P out represents output power, D represents damping coefficient, ω represents angular frequency, ω0 represents initial angular frequency, and P represents virtual active power.

[0012] As a preferred solution of the power system frequency control method based on the model without weight factor described in the present invention, wherein: the Tustin approximation includes a discrete MPC model using VSG, and a discrete model is obtained by using Tustin approximation, which is expressed as:

[0013] Jω0[ω(k+1)-ω(k)]=T s [P(k)-P out (k)]-T s D[ω(k)-ω0]

[0014] Among them, T s represents the sampling interval, and k represents the index variable in iterations and loops.

[0015] As a preferred solution of the power system frequency control method based on the model without weight factors described in the present invention, the incremental discrete model includes the difference between the optimal measurement value and the true value of the quantity, which is expressed as:

[0016] Δω(k+1)=A1Δω(k)+A2ΔP out (k)+A3ΔP(k)

[0017] in:

[0018]

[0019] Here, j represents the imaginary part.

[0020] As a preferred solution of the power system frequency control method based on the weight-free factor model described in the present invention, the predicted inverter reference voltage vector includes the predicted reference current Actual grid current The current value and predicted grid voltage Get, expressed as:

[0021]

[0022] Among them, T s is the sampling time, n represents the sampling moment, and represents, Predicted grid voltage, represents the predicted reference current, Indicates the actual grid current, represents the predicted inverter reference voltage vector;

[0023] The predicted grid voltage is derived as follows:

[0024]

[0025] As a preferred solution of the power system frequency control method based on the weight-free factor model described in the present invention, the optimal switch state is expressed as:

[0026]

[0027]

[0028]

[0029] Where SW is the switching factor; V DC is the DC bus voltage;

[0030] Through optimal switching, a reduced cost function without weighting factor correction for reducing harmonics is obtained, and the inverter can be quickly controlled to respond quickly to frequency changes.

[0031] Another object of the present invention is to provide an electric power system frequency control system based on a weight-free factor model, which can achieve rapid and precise control of the electric power system frequency in a low-inertia environment through precise prediction and control technology, effectively cope with frequency fluctuations caused by the grid connection of renewable energy, and at the same time reduce computing resource consumption and improve system response speed and operating efficiency.

[0032] In order to solve the above technical problems, the present invention provides the following technical solutions: A power system frequency control system based on a model without weight factors, comprising: an outer loop VSG module, a swing equation module, a discrete model module, an incremental control module and a voltage vector module;

[0033] The outer loop VSG module establishes an outer loop VSG model based on MPC to improve the frequency regulation capability of the power system;

[0034] The swing equation module uses the swing equation to simulate the rotor inertia of the asynchronous generator and optimize the output power;

[0035] The discrete model module applies Tustin approximation to obtain a discrete model;

[0036] The incremental control module designs an incremental discrete model to reduce static errors and improve control accuracy;

[0037] The voltage vector module predicts the inverter reference voltage vector and determines the optimal switching state.

[0038] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the power system frequency control method based on a model without weight factors as described above are implemented.

[0039] A computer-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 power system frequency control method based on a weight-free factor model as described above are implemented.

[0040] Beneficial effects of the present invention: The dual MPC scheme based on the VSG of the unweighted MPC and the unweighted FCS-MPC SW factor is comprehensively considered to improve the frequency regulation capability and the accuracy of frequency control, and eliminate the oscillation problem caused by the frequency of the traditional virtual synchronous generator returning to the normal value under small inertia;

[0041] By finding the optimal voltage vector closest to the predicted reference inverter voltage vector, the optimal switching state of the three-level neutral point clamped inverter is determined, and the inverter is quickly controlled to respond quickly to frequency changes, thereby performing fast frequency control;

[0042] Centralized model predictive control (MPC) realizes primary and secondary frequency control, minimizes operating costs during power flow optimization, and finally reflects the optimization results in tertiary frequency control. MPC makes full use of a variety of inverter-based devices for fast frequency control, and the power output of each inverter participates in fast frequency control. The frequency control of the voltage source inverter power system in a low-inertia environment is realized, which significantly reduces the calculation time and amount. The proposed method has universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0044] Figure 1 An overall flow chart of a method for controlling power system frequency based on a model without weight factors is provided for one embodiment of the present invention.

[0045] Figure 2 A control strategy diagram of a power system frequency control method based on a model without weight factors provided by an embodiment of the present invention.

[0046] Figure 3 A voltage control VSG model diagram of a power system frequency control method based on a weight-free factor model provided by an embodiment of the present invention.

[0047] Figure 4 A complete method principle flow chart of a power system frequency control method based on a weight-free factor model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0049] Example 1, reference Figure 1-Figure 3, is an embodiment of the present invention, which provides a method for controlling the frequency of a power system based on a model without weight factors, comprising:

[0050] S1: Establish an outer loop VSG model based on MPC to improve the frequency regulation capability of the power system.

[0051] It should be noted that if Figure 1 As shown in S1, the establishment of the MPC-based outer loop VSG model includes considering the dual MPC scheme based on the VSG of the unweighted MPC and the unweighted FCS-MPC SW factor to improve the frequency regulation capability and the accuracy of frequency control, and eliminate the oscillation problem caused by the frequency of the traditional virtual synchronous generator returning to the normal value under inertia. The corresponding principle is as follows Figure 2 shown.

[0052] Furthermore, the speed governor is a component of the turbine control system and can adjust the speed according to the changes in load conditions; since reactive power is related to the AC bus voltage, reactive-droop is used for output voltage control; the speed governor and reactive-droop form a droop control technology, and the corresponding principle is as follows Figure 3 No.

[0053] Further:

[0054]

[0055] Where P and ω are the virtual active power of the speed regulator and the angular frequency generated by the VSG, respectively; P N 、V N is the nominal active power and nominal voltage; K ω and K q are frequency deviation gain and reactive power-droop coefficient respectively; V ref and Q out are the reference voltage and instantaneous output reactive power respectively; ω0 and Q N are the nominal angular frequency and nominal reactive power respectively.

[0056] Example 2, reference Figure 1-Figure 3 , is an embodiment of the present invention, which provides a method for controlling the frequency of a power system based on a model without weight factors, comprising:

[0057] S2: Use the swing equation to simulate the rotor inertia of the asynchronous generator and optimize the output power.

[0058] It should be noted that if Figure 1 As shown in S2, the swing equation includes the rotor inertia provided by the asynchronous generator for simulating the inertia of the asynchronous generator as a component of the VSG and is expressed as:

[0059]

[0060] Where J represents the virtual moment of inertia, P out represents output power, D represents damping coefficient, ω represents angular frequency, ω0 represents initial angular frequency, and P represents virtual active power.

[0061] S3: Apply Tustin approximation to obtain a discrete model.

[0062] It should be noted that if Figure 1 As shown in S3, the Tustin approximation includes a discrete MPC model using VSG. The discrete model is obtained using the Tustin approximation and is expressed as:

[0063] Jω0[ω(k+1)-ω(k)]=T s [P(k)-P out (k)]-T s D[ω(k)-ω0]

[0064] Among them, T s represents the sampling interval, and k represents the index variable in iterations and loops.

[0065] Furthermore, in order to reduce the static error, that is, the difference between the optimal measurement value and the true value of the quantity, an incremental discrete model can be obtained by algebraic processing of the formula of S3, as shown in S4.

[0066] Example 3, reference Figure 1-Figure 4 , is an embodiment of the present invention, which provides a method for controlling the frequency of a power system based on a model without weight factors, comprising:

[0067] S4: Design an incremental discrete model to reduce static error and improve control accuracy.

[0068] It should be noted that if Figure 1 As shown in S4, the incremental discrete model includes the difference between the best measured value and the true value of the quantity, expressed as:

[0069] Δω(k+1)=A1Δω(k)+A2ΔP out (k)+A3ΔP(k)

[0070] in:

[0071]

[0072] Among them, j represents the imaginary part, which is used to represent the value of the imaginary part of the complex number.

[0073] Furthermore, the output follows dynamic changes:

[0074] y(k+1)=y(k)+Δω(k+1)

[0075] Set the model prediction range to n steps, and assume that it takes l steps to reach a steady state, then the output Y n (k+1|k) can be expressed as:

[0076] Y n (k+1|k)=Iy(k)+S x Δω(k)+S u ΔP(k)+S e ΔP out (k)

[0077] Among them, y(k) is the current output; S x is the state of the x matrix; Δω(k) is the angular frequency deviation; S u is the state of the input matrix; ΔP(k) is the deviation of the virtual active power; S e is the state of the error matrix; P out (k) is the output power deviation; Y n Represents the total output matrix; I represents a 1*n-order matrix with elements of 1;

[0078] in:

[0079]

[0080] Among them, the output Y n (k+1|k) is the representation of future output, Y n (k+1|k) can be optimized by modulating ΔP(k), where T represents the transpose of the matrix.

[0081] Furthermore, the MPC strategy without weighted FCS-MPC SW factor: Its purpose is to determine the optimal switching state of the three-level neutral point clamped inverter by finding the optimal voltage vector that is closest to the predicted reference inverter voltage vector, and to quickly control the inverter so that it responds quickly to frequency changes and perform fast frequency control;

[0082] According to the dynamic model of the three-level inverter:

[0083]

[0084] in Represents the grid current, Indicates the inverter voltage, represents the grid voltage; L is the inductance, and the voltage and current with the subscript x or y are expressed as quantities obtained from the original values ​​by Clarke transformation;

[0085] The predicted grid current at the next sampling moment obtained by discretizing the above formula is as follows:

[0086]

[0087] Among them, T s is the sampling time, n is the sampling moment, and R is the resistance.

[0088] S5: Predict the inverter reference voltage vector and determine the optimal switching state.

[0089] It should be noted that if Figure 1 As shown in S5, the predicted inverter reference voltage vector includes the predicted reference current Actual grid current The current value and predicted grid voltage Get, expressed as:

[0090]

[0091] Among them, T s is the sampling time, n represents the sampling moment, and represents, Predicted grid voltage, represents the predicted reference current, Indicates the actual grid current, represents the predicted inverter reference voltage vector;

[0092] The predicted grid voltage is derived as follows:

[0093]

[0094] The predicted reference current is derived as:

[0095]

[0096] in, is the grid current reference value;

[0097] The set of switching states of the neutral point clamped inverter can be divided into two categories: redundant switching states (v0 to v 14 ) and non-redundant switch state (v 15 to v 26 ); positive non-zero redundant switch states (v3, v5, v7, v 10 , v 11 and v 14 ) will increase the neutral point voltage (v c2 ), while the negative non-zero redundant switch states (v4, v6, v8, v9, v 12 and v 13) will reduce the neutral point voltage; therefore, the neutral point voltage balance can be achieved by selecting the appropriate non-zero redundant switch state according to the predicted capacitor voltage; after predicting all the required variables, the next step is to determine the optimal switching state of the inverter to obtain the minimum grid current cost function; the cost function C(n+1) for the grid current is as follows:

[0098]

[0099] The cost function represents the square of the distance between two vectors, and the cost function is minimized by finding the optimal voltage vector that is closest to the predicted reference inverter voltage vector.

[0100] Furthermore, each possible switching state of the three-level inverter is separated by its optimal region, each optimal region contains only one switching state, and the optimal switching state is expressed as:

[0101]

[0102] Where SW is the switching factor; V DC is the DC bus voltage;

[0103] Through the optimal switch, the cost reduction function without weighted factor correction is obtained to reduce harmonics, and the inverter can be quickly controlled to respond quickly to frequency changes. The entire prediction method is as follows Figure 4 shown.

[0104] Furthermore, the centralized frequency control strategy: centralized model predictive control (MPC) aims to achieve primary and secondary frequency control and minimize the operating cost in the process of power flow optimization, and finally reflects the optimization results in tertiary frequency control; MPC for fast frequency control can make full use of a variety of inverter-based devices and determine the power output of each inverter to participate in fast frequency control;

[0105] The core of the objective function is to minimize the total control cost over the entire time domain and all equipment units:

[0106]

[0107] Where k is the time domain step size, is the total generation cost of the voltage source inverter (VSI) with optimized charging cost (for simplicity, only the charging cost is considered); is the total cost of the energy storage system (ESS) SOC management cost, is the bus management cost of the reference bus, is the total cost of tie line management cost;

[0108] Calculate the rate of change of frequency ROCOF for all generators:

[0109]

[0110] Where, f is the generator frequency;

[0111] To ensure the charging power of each VSI, define the following constraints:

[0112]

[0113] Among them, P chi (k) is the charging power of VSI, is the initial charging power of the VSI, is the charging power deviation of the VSI, is the resistive power of the VSI, which is the resistance multiplied by the deviation of the angular frequency;

[0114] Active and reactive power of distributed generation bus, charging and discharging power of energy storage system, and load power

[0115] They are:

[0116]

[0117] Among them, P Gik is the generated power, P chik is the charging power, P dik is the discharge power, P Lik is the load power; P Lik is the initial bus power, A ij ΔV jk is the product of A coefficient and voltage deviation, B ij θ jk It is the product of B coefficient and phase deviation; Q Gik is the reactive power of the generator, Q Lik is the load reactive power, is the initial bus reactive power, C ij ΔV jk is the product of the C coefficient and the voltage deviation, D ij θ jk It is the product of the D coefficient and the phase deviation;

[0118] The charging and discharging active power limits of the VSI and the SOC limits of the battery energy storage system are:

[0119]

[0120] in, The upper limit of charging power; is the upper limit of discharge power; SOC 1 and They are the lower and upper limits of the state of charge SOC respectively;

[0121] The upper and lower frequency limits are:

[0122]

[0123] in, is the VSI resistance; is the initial charging power deviation of VSI;

[0124] The upper and lower frequency limits are composed of the current frequency plus the product of the VSI resistance and the charging power deviation;

[0125] Figure 2 The dual MPC scheme is shown to improve the accuracy of frequency control, which includes the unweighted MPC-based VSG and the unweighted FCS-MPC SW factor scheme; P N is the nominal active power, Q N is the nominal reactive power, ω is the angular frequency, V ref is the reference voltage, SW is the switching factor, v is the output voltage of the voltage source inverter, i0 is the output current of the voltage source inverter;

[0126] Figure 3 Simple modeling of VSG is shown, providing inertia for stability support and voltage reference through reactive power droop control, and active power calculation through active power droop control.

[0127] The above is a schematic scheme of a method for controlling the frequency of an electric system based on a model without weight factors in this embodiment. It should be noted that the technical scheme of the system of the method for controlling the frequency of an electric system based on a model without weight factors belongs to the same concept as the technical scheme of the method for controlling the frequency of an electric system based on a model without weight factors mentioned above. For details not described in detail in the technical scheme of the method for controlling the frequency of an electric system based on a model without weight factors in this embodiment, please refer to the description of the technical scheme of the method for controlling the frequency of an electric system based on a model without weight factors mentioned above.

[0128] Embodiment 4 is an embodiment of the present invention, which provides a power system frequency control system based on a model without weight factors, including: an outer loop VSG module, a swing equation module, a discrete model module, an incremental control module and a voltage vector module;

[0129] The outer loop VSG module establishes an outer loop VSG model based on MPC to improve the frequency regulation capability of the power system;

[0130] The swing equation module uses the swing equation to simulate the rotor inertia of the asynchronous generator and optimize the output power;

[0131] The discrete model module applies Tustin approximation to obtain a discrete model;

[0132] The incremental control module designs an incremental discrete model to reduce static errors and improve control accuracy;

[0133] The voltage vector module predicts the inverter reference voltage vector and determines the optimal switching state.

[0134] This embodiment further provides a computing device, which is applicable to the case of a power system frequency control method based on a model without weight factors, and includes:

[0135] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the power system frequency control method based on the weight-free factor model proposed in the above embodiment.

[0136] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the power system frequency control method based on the non-weighted factor model proposed in the above embodiment is implemented.

[0137] The storage medium proposed in this embodiment and the power system frequency control method based on the weight-free factor model proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0138] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0139] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0140] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A power system frequency control method based on a model without weight factors, characterized in that: include: Establish an outer loop VSG model based on MPC to improve the frequency regulation capability of the power system; Use the swing equation to simulate the rotor inertia of asynchronous generators and optimize the output power; Applying Tustin approximation, a discrete model is obtained; Design incremental discrete models to reduce static errors and improve control accuracy; Predict the inverter reference voltage vector and determine the optimal switching state.

2. The method for controlling the frequency of a power system based on a model without weight factors according to claim 1, characterized in that: The establishment of the MPC-based outer loop VSG model includes considering a dual MPC scheme based on an unweighted MPC-based VSG and an unweighted FCS-MPC SW factor to improve the frequency regulation capability and the accuracy of frequency control, and eliminate the oscillation problem caused by the frequency of the traditional virtual synchronous generator returning to a normal value under inertia.

3. The power system frequency control method based on the non-weighted factor model according to claim 2, characterized in that: The swing equation includes the rotor inertia provided for simulating the asynchronous generator, as an integral part of the VSG, and is expressed as: Where J represents the virtual moment of inertia, P out represents output power, D represents damping coefficient, ω represents angular frequency, ω0 represents initial angular frequency, and P represents virtual active power.

4. The method for controlling the frequency of a power system based on a model without weight factors according to claim 3, characterized in that: The Tustin approximation includes a discrete MPC model using VSG. The discrete model is obtained by using the Tustin approximation, which is expressed as: Jω0[ω(k+1)-ω(k)]=T s [P(k)-P out (k)]-T s D[ω(k)-ω0] Among them, T s represents the sampling interval, and k represents the index variable in iterations and loops.

5. The method for controlling the frequency of a power system based on a model without weight factors according to claim 4, characterized in that: The incremental discrete model includes the difference between the best measurement and the true value of the quantity, expressed as: Δω(k+1)=A1Δω(k)+A2ΔP out (k)+A3ΔP(k) in: Here, j represents the imaginary part.

6. The method for controlling the frequency of a power system based on a model without weight factors according to claim 5, characterized in that: The predicted inverter reference voltage vector includes a predicted reference current Actual grid current The current value and predicted grid voltage Get, expressed as: Among them, T s is the sampling time, n represents the sampling moment, and represents, Predicted grid voltage, represents the predicted reference current, Indicates the actual grid current, represents the predicted inverter reference voltage vector; The predicted grid voltage is derived as follows:

7. The method for controlling the frequency of a power system based on a model without weight factors according to claim 6, characterized in that: The optimal switching state is expressed as: Where SW is the switching factor; V DC is the DC bus voltage; Through optimal switching, a reduced cost function without weighting factor correction for reducing harmonics is obtained, and the inverter can be quickly controlled to respond quickly to frequency changes.

8. A system for power system frequency control based on a model without weight factors according to any one of claims 1 to 7, characterized in that: include: Outer loop VSG module, swing equation module, discrete model module, incremental control module and voltage vector module; The outer loop VSG module establishes an outer loop VSG model based on MPC to improve the frequency regulation capability of the power system; The swing equation module uses the swing equation to simulate the rotor inertia of the asynchronous generator and optimize the output power; The discrete model module applies Tustin approximation to obtain a discrete model; The incremental control module designs an incremental discrete model to reduce static errors and improve control accuracy; The voltage vector module predicts the inverter reference voltage vector and determines the optimal switching state.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power system frequency control method based on a model without weight factors described in any one of claims 1 to 7 are implemented.

10. A computer-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 power system frequency control method based on a model without weight factors described in any one of claims 1 to 7 are implemented.

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