Linear modeling method and device for new energy grid-connected system based on TS fuzzy theory

Abstract

A method and device for linear modeling of a new energy grid-connected system based on T-S fuzzy theory. The method comprises: obtaining system parameters and control parameters of the new energy grid-connected system; defining a membership function and a coefficient matrix based on the system parameters and control parameters; constructing a fuzzy set based on the coefficient matrix; and building a linear model based on the membership function and the fuzzy set. The method and device for linear modeling of a new energy grid-connected system provided by an embodiment of the present invention can be used for broadband oscillation risk assessment and early warning of power systems. In particular, in scenarios where new energy output frequently changes and system operating modes frequently switch, the linear model of the new energy grid-connected system established based on this method does not require repeated modeling and parameter adjustment, significantly improving the accuracy and efficiency of stability analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and device for linear modeling of a new energy grid-connected system based on TS fuzzy theory. Background Art

[0002] Voltage source converters (VSCs) are the primary interface between renewable energy sources and the AC grid. The control system characteristics of VSCs are deeply involved in the stability of power systems. In recent years, numerous power system incidents involving renewable energy disconnections have occurred internationally. Renewable energy power systems are facing the dual challenges of random fluctuations in renewable energy output and random grid-side system failures.

[0003] However, existing technologies can often only model and analyze the stability of power systems under several specific scenarios and parameters. There are few linear modeling methods for new energy grid-connected systems that take into account the randomness of new energy output and the randomness of grid fault levels. Summary of the Invention

[0004] In view of this, the present invention proposes a linear modeling method and device for a new energy grid-connected system based on TS fuzzy theory, aiming to solve the above problems.

[0005] In the first aspect, an embodiment of the present invention provides a linear modeling method for a new energy grid-connected system based on TS fuzzy theory, including: obtaining system parameters and control parameters of the new energy grid-connected system; defining a membership function and a coefficient matrix based on the system parameters and control parameters; constructing a fuzzy set based on the coefficient matrix; and building a linearization model based on the membership function and the fuzzy set.

[0006] Furthermore, based on the system parameters and control parameters, a membership function is defined, including: constructing a new energy grid-connected system model based on the system parameters and control parameters; defining premise variables based on the new energy grid-connected system model; and defining a membership function based on the premise variables.

[0007] Furthermore, based on the new energy grid-connected system model, premise variables are defined, including: based on the new energy grid-connected system model, premise variables ξ1, ξ2, ξ3 and ξ4 are defined as follows: ξ1 = ω, ξ2 = sin(θ), ξ4=cos(θ); where θ and ω are the phase-locked angle and virtual frequency of the phase-locked loop.

[0008] Further, based on the premise variables, defining a member function includes: based on the premise variables, defining a member function as follows:

[0009]

[0010] in,

[0011] Among them, ξ1, ξ2, ξ3 and ξ4 are premise variables, and p1, p2, q1, q2, j1, j2, r1 and r2 are coefficients.

[0012] Furthermore, based on the system parameters and the control parameters, a coefficient matrix is ​​defined, including: based on the system parameters and the control parameters, a coefficient matrix is ​​constructed as follows:

[0013]

[0014]

[0015]

[0016] in,

[0017] In the formula, R is the grid-connected resistance of the inverter power supply, L is the grid-connected reactance of the inverter power supply, K p1 is the gain coefficient of the active power control link, K i1 is the integral coefficient of the active power control link, K p2 is the gain coefficient of the reactive power control link, K i2 is the integral coefficient of the reactive power control link, K p3 is the gain coefficient of the phase-locked loop control link, K i2 is the integral coefficient of the phase-locked loop control link, u g is the amplitude of the infinite power supply voltage, and i, p, g, n, v, and m are all positive integers.

[0018] Furthermore, constructing a fuzzy set based on the coefficient matrix includes: constructing a fuzzy set based on the coefficient matrix as follows: A i =Q i F i ,B i =Q i G, C=E i ;

[0019] in,

[0020] w=(1+c1j v )(1+c2j v ),w1=-a1c2p m q n +a2(1+c1j v ),w2=-b1c2p m q n +b2(1+c1j v ).

[0021] Furthermore, constructing a linearized model based on the membership function and the fuzzy set includes: constructing a linearized model based on the membership function and the fuzzy set as follows:

[0022]

[0023] Among them, μ i is the membership function, x(t) is the state variable of the new energy grid-connected system, u(t) is the input of the new energy grid-connected system, y(t) is the output of the new energy grid-connected system, A i 、B i and C are fuzzy sets, and ξ(t) is the premise variable.

[0024] In the second aspect, an embodiment of the present invention also provides a linear modeling device for a new energy grid-connected system based on TS fuzzy theory, including: an acquisition unit for acquiring system parameters and control parameters of the new energy grid-connected system; a definition unit for defining member functions and coefficient matrices based on the system parameters and control parameters; a first processing unit for constructing a fuzzy set based on the coefficient matrix; and a second processing unit for constructing a linearization model based on the member function and the fuzzy set.

[0025] Furthermore, based on the system parameters and control parameters, a membership function is defined, including: constructing a new energy grid-connected system model based on the system parameters and control parameters; defining premise variables based on the new energy grid-connected system model; and defining a membership function based on the premise variables.

[0026] Furthermore, based on the new energy grid-connected system model, premise variables are defined, including:

[0027] Based on the new energy grid-connected system model, the premise variables ξ1, ξ2, ξ3 and ξ4 are defined as follows:

[0028] ξ1=ω,ξ2=sin(θ), ξ4=cos(θ);

[0029] Where θ and ω are the phase-locked angle and virtual frequency of the phase-locked loop.

[0030] Further, based on the premise variables, defining a member function includes: based on the premise variables, defining a member function as follows:

[0031]

[0032] in,

[0033] Among them, ξ1, ξ2, ξ3 and ξ4 are premise variables, and p1, p2, q1, q2, j1, j2, r1 and r2 are coefficients.

[0034] Furthermore, based on the system parameters and the control parameters, a coefficient matrix is ​​defined, including: based on the system parameters and the control parameters, a coefficient matrix is ​​constructed as follows:

[0035]

[0036]

[0037]

[0038] in,

[0039] In the formula, R is the grid-connected resistance of the inverter power supply, L is the grid-connected reactance of the inverter power supply, K p1 is the gain coefficient of the active power control link, K i1 is the integral coefficient of the active power control link, K p2 is the gain coefficient of the reactive power control link, K i2 is the integral coefficient of the reactive power control link, K p3 is the gain coefficient of the phase-locked loop control link, K i2 is the integral coefficient of the phase-locked loop control link, u g is the amplitude of the infinite power supply voltage, and i, p, g, n, v, and m are all positive integers.

[0040] Furthermore, the first processing unit is further configured to construct a fuzzy set based on the coefficient matrix as follows:

[0041] A i =Q i F i ,B i =Q i G, C=E i ;

[0042] in,

[0043] w=(1+c1j v )(1+c2j v ),w1=-a1c2p m q n +a2(1+c1j v ),w2=-b1c2p m q n +b2(1+c1j v ).

[0044] Furthermore, the second processing unit is further configured to construct a linearized model based on the membership function and the fuzzy set as follows:

[0045]

[0046] Among them, μ i is the membership function, x(t) is the state variable of the new energy grid-connected system, u(t) is the input of the new energy grid-connected system, y(t) is the output of the new energy grid-connected system, A i 、B i and C are fuzzy sets, and ξ(t) is the premise variable.

[0047] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the linear modeling method of the new energy grid-connected system based on TS fuzzy theory provided in the above embodiments is implemented.

[0048] In a fourth aspect, an embodiment of the present invention further provides an electronic device, comprising: a processor; a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the executable instructions to implement the linear modeling method of the new energy grid-connected system based on TS fuzzy theory provided in the above embodiments.

[0049] The embodiment of the present invention provides a linear modeling method and device for a new energy grid-connected system based on TS fuzzy theory, which obtains system parameters and control parameters of the new energy grid-connected system, defines membership functions and coefficient matrices based on the system parameters and control parameters, constructs fuzzy sets based on the coefficient matrices, and constructs a linear model based on the membership functions and fuzzy sets. The linear model obtained by the linear modeling method for a new energy grid-connected system provided by the above embodiment can be used for broadband oscillation risk assessment and early warning of power systems. In particular, for scenarios where new energy output frequently changes and system operation modes frequently switch, the linear model of the new energy grid-connected system established based on this method does not require repeated modeling and parameter adjustment, which significantly improves the accuracy and efficiency of stability analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 An exemplary flow chart of a linear modeling method for a new energy grid-connected system based on TS fuzzy theory according to an embodiment of the present invention is shown;

[0051] Figure 2 It shows a schematic structural diagram of a new energy grid-connected system according to an embodiment of the present invention;

[0052] Figure 3A structural diagram of a linear modeling device for a new energy grid-connected system based on TS fuzzy theory according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to provide a thorough and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments shown in the accompanying drawings is not intended to limit the present invention. In the accompanying drawings, identical elements are denoted by the same reference numerals.

[0054] Unless otherwise specified, the terms used herein (including technical terms) have the meanings commonly understood by those skilled in the art. In addition, it is understood that terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0055] Figure 1 An exemplary flow chart of a linear modeling method for a new energy grid-connected system based on TS fuzzy theory according to an embodiment of the present invention is shown.

[0056] like Figure 1 As shown in FIG, the linear modeling method of the new energy grid-connected system based on TS fuzzy theory includes:

[0057] Step S101: Obtain system parameters and control parameters of the new energy grid-connected system.

[0058] Specifically, obtaining system parameters and control parameters includes: phase-locked loop control parameter K p3 and K i3 , active outer loop control parameter K p1 and K i1 , reactive outer loop parameter K p2 and K i2 , active power command value P ref , reactive power command value Q ref , system grid resistance R, system grid inductance L, active current i d , reactive current i q .

[0059] Step S102: Based on the system parameters and control parameters, define the membership function and coefficient matrix.

[0060] Furthermore, based on the system parameters and control parameters, member functions are defined, including:

[0061] Build a new energy grid-connected system model based on system parameters and control parameters;

[0062] Based on the new energy grid-connected system model, define the premise variables;

[0063] Define member functions based on premise variables.

[0064] Furthermore, based on the new energy grid-connected system model, premise variables are defined, including:

[0065] Based on the new energy grid-connected system model, the premise variables ξ1, ξ2, ξ3 and ξ4 are defined as follows:

[0066] ξ1=ω,ξ2=sin(θ), ξ4=cos(θ);

[0067] Where θ and ω are the phase-locked angle and virtual frequency of the phase-locked loop.

[0068] Furthermore, based on the premise variables, member functions are defined, including:

[0069] Based on the premise variables, define the member functions as follows:

[0070]

[0071] in,

[0072] Among them, ξ1, ξ2, ξ3 and ξ4 are premise variables, and p1, p2, q1, q2, j1, j2, r1 and r2 are coefficients.

[0073] Specifically, it includes the following:

[0074] Step 1: Establish the state space equation of the new energy grid-connected system.

[0075] Figure 2 FIG. 1 shows a schematic structural diagram of a new energy grid-connected system according to an embodiment of the present invention. Figure 2 As shown in Figure 1, a new energy grid-connected system model is constructed based on system parameters and control parameters. The mathematical expression of the established new energy grid-connected system model is as follows. This model considers the dynamics of the inverter phase-locked loop and outer loop controller on the grid side of the new energy:

[0076]

[0077] Where θ and ω are the phase-locked angle and virtual frequency of the phase-locked loop, and χ1 and χ2 are the state variables of the power outer loop.

[0078] Step 2: Define the premise variables

[0079] A fuzzy set premise variable identification method suitable for modeling new energy grid-connected systems is proposed. The component form of the state variables of the grid-connected system is defined to determine the premise variables. The premise variables are composed of state variables or trigonometric functions of state variables:

[0080] ξ1=ω,ξ2=sin(θ), ξ4=cos(θ).

[0081] Step 3: Define member functions

[0082] Based on the premise variables in step 2, a fuzzy set membership function identification method suitable for renewable energy grid-connected system modeling is proposed. The component form of the membership function is defined and the membership function is determined. The membership function is composed of premise variables:

[0083]

[0084] Where,

[0085]

[0086] Furthermore, based on the system parameters and control parameters, a coefficient matrix is ​​defined, including:

[0087] Based on the system parameters and control parameters, the coefficient matrix is ​​constructed as follows:

[0088]

[0089]

[0090]

[0091] in,

[0092] In the formula, R is the grid-connected resistance of the inverter power supply, L is the grid-connected reactance of the inverter power supply, K p1 is the gain coefficient of the active power control link, K i1 is the integral coefficient of the active power control link, K p2 is the gain coefficient of the reactive power control link, K i2 is the integral coefficient of the reactive power control link, K p3 is the gain coefficient of the phase-locked loop control link, K i2 is the integral coefficient of the phase-locked loop control link, u g is the amplitude of the infinite power supply voltage, and i, p, g, n, v, and m are all positive integers.

[0093] Specifically, define the coefficient matrix, including:

[0094] Based on the phase-locked loop control parameter K p3and K i3 , active outer loop control parameter K p1 and K i1 , reactive outer loop parameter K p2 and K i2 , active power command value P ref , reactive power command value Q ref , system grid resistance R, system grid inductance L, active current i d , reactive current i q Parameters such as , construct the coefficient matrix based on the new energy grid-connected system model

[0095]

[0096] In the formula, various parameters can be obtained from the new energy grid-connected system:

[0097]

[0098] Step S103: constructing a fuzzy set based on the coefficient matrix.

[0099] Furthermore, step S103 includes:

[0100] Based on the coefficient matrix, the fuzzy set is constructed as follows:

[0101] A i =Q i F i ,B i =Q i G, C=E i ;

[0102] in,

[0103] w=(1+c1j v )(1+c2j v ),w1=-a1c2p m q n +a2(1+c1j v ),w2=-b1c2p m q n +b2(1+c1j v ).

[0104] Step S104: constructing a linearized model based on the membership function and the fuzzy set.

[0105] Furthermore, step S104 includes:

[0106]

[0107] Among them, μ iis the membership function, x(t) is the state variable of the new energy grid-connected system, u(t) is the input of the new energy grid-connected system, y(t) is the output of the new energy grid-connected system, A i 、B i and C are fuzzy sets, and ξ(t) is the premise variable.

[0108] In the above embodiment, system parameters and control parameters of the new energy grid-connected system are obtained, membership functions and coefficient matrices are defined based on the system parameters and control parameters, fuzzy sets are constructed based on the coefficient matrices, and linearization models are constructed based on the membership functions and fuzzy sets. The linearization model obtained by the linearization modeling method of the new energy grid-connected system provided by the above embodiment can be used for broadband oscillation risk assessment and early warning of the power system. In particular, for scenarios where the output of new energy sources frequently changes and the system operation mode frequently switches, the linearization model of the new energy grid-connected system established based on this method does not require repeated modeling and parameter adjustment, which significantly improves the accuracy and efficiency of stability analysis.

[0109] Figure 3 A structural diagram of a design device for a sliding mode fault-tolerant controller of a new energy grid-connected system based on TS fuzzy theory according to an embodiment of the present invention is shown.

[0110] like Figure 3 As shown, the linear modeling device of the new energy grid-connected system based on TS fuzzy theory includes:

[0111] An acquisition unit 301 is used to acquire system parameters and control parameters of a new energy grid-connected system;

[0112] A definition unit 302, configured to define a membership function and a coefficient matrix based on system parameters and control parameters;

[0113] A first processing unit 303 is configured to construct a fuzzy set based on a coefficient matrix;

[0114] The second processing unit 304 is configured to construct a linearization model based on the membership function and the fuzzy set.

[0115] Furthermore, based on the system parameters and control parameters, member functions are defined, including:

[0116] Build a new energy grid-connected system model based on system parameters and control parameters;

[0117] Based on the new energy grid-connected system model, define the premise variables;

[0118] Define member functions based on premise variables.

[0119] Furthermore, based on the new energy grid-connected system model, premise variables are defined, including:

[0120] Based on the new energy grid-connected system model, the premise variables ξ1, ξ2, ξ3 and ξ4 are defined as follows:

[0121] ξ1=ω,ξ2=sin(θ), ξ4=cos(θ);

[0122] Where θ and ω are the phase-locked angle and virtual frequency of the phase-locked loop.

[0123] Furthermore, based on the premise variables, member functions are defined, including:

[0124] Based on the premise variables, define the member functions as follows:

[0125]

[0126] in,

[0127] Among them, ξ1, ξ2, ξ3 and ξ4 are premise variables, and p1, p2, q1, q2, j1, j2, r1 and r2 are coefficients.

[0128] Furthermore, based on the system parameters and control parameters, a coefficient matrix is ​​defined, including:

[0129] Based on the system parameters and control parameters, the coefficient matrix is ​​constructed as follows:

[0130]

[0131]

[0132]

[0133] in,

[0134] In the formula, R is the grid-connected resistance of the inverter power supply, L is the grid-connected reactance of the inverter power supply, K p1 is the gain coefficient of the active power control link, K i1 is the integral coefficient of the active power control link, K p2 is the gain coefficient of the reactive power control link, K i2 is the integral coefficient of the reactive power control link, K p3 is the gain coefficient of the phase-locked loop control link, K i2 is the integral coefficient of the phase-locked loop control link, u g is the amplitude of the infinite power supply voltage, and i, p, g, n, v, and m are all positive integers.

[0135] Furthermore, the first processing unit 303 is further configured to:

[0136] Based on the coefficient matrix, the fuzzy set is constructed as follows:

[0137] A i =Q i F i ,B i =Q i G, C=E i ;

[0138] in,

[0139] w=(1+c1j v )(1+c2j v ),w1=-a1c2p m q n +a2(1+c1j v ),w2=-b1c2p m q n +b2(1+c1j v ).

[0140] Furthermore, the second processing unit 304 is further configured to:

[0141] Based on the membership function and fuzzy set, the linearization model is constructed as follows:

[0142]

[0143] Among them, μ i is the membership function, x(t) is the state variable of the new energy grid-connected system, u(t) is the input of the new energy grid-connected system, y(t) is the output of the new energy grid-connected system, A i 、B i and C are fuzzy sets, and ξ(t) is the premise variable.

[0144] In the above embodiment, system parameters and control parameters of the new energy grid-connected system are obtained, membership functions and coefficient matrices are defined based on the system parameters and control parameters, fuzzy sets are constructed based on the coefficient matrices, and linearization models are constructed based on the membership functions and fuzzy sets. The linearization model obtained by the linearization modeling device for the new energy grid-connected system provided by the above embodiment can be used for broadband oscillation risk assessment and early warning of the power system. In particular, for scenarios where the output of new energy sources frequently changes and the system operation mode frequently switches, the linearization model of the new energy grid-connected system established based on this method does not require repeated modeling and parameter adjustment, which significantly improves the accuracy and efficiency of stability analysis.

[0145] It should be noted that the apparatus provided in the above embodiments is merely illustrated by the division of the above functional modules when implementing its functions. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0146] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the linear modeling method of the new energy grid-connected system based on TS fuzzy theory provided in the above-mentioned embodiments is implemented.

[0147] An embodiment of the present invention also provides an electronic device, comprising: a processor; a memory for storing processor executable instructions; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the linear modeling method of the new energy grid-connected system based on TS fuzzy theory provided in the above-mentioned embodiments.

[0148] The invention has been described above with reference to a few embodiments. However, it is readily apparent to a person skilled in the art that other embodiments than the ones disclosed above are equally within the scope of the invention, as defined by the appended patent claims.

[0149] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a / the [means, component, etc.]" are to be interpreted openly as referring to at least one instance of the means, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not necessarily need to be performed in the exact order disclosed, unless explicitly stated otherwise.

[0150] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0152] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A linear modeling method for a new energy grid-connected system based on TS fuzzy theory, characterized by: include: Obtain system parameters and control parameters of new energy grid-connected systems; Based on the system parameters and control parameters, defining a membership function and a coefficient matrix; constructing a fuzzy set based on the coefficient matrix; constructing a linearized model based on the membership function and the fuzzy set; Wherein, based on the system parameters and control parameters, a coefficient matrix is ​​defined, including: Based on the system parameters and control parameters, the coefficient matrix is ​​constructed as follows: in, ; Where, R is the grid-connected resistance of the inverter power supply, L The grid-connected reactance of the inverter power supply, K p1 is the gain coefficient of the active power control link, K i1 is the integral coefficient of the active power control link, K p2 is the gain coefficient of the reactive power control link, K i2 is the integral coefficient of the reactive power control link, K p3 is the gain coefficient of the phase-locked loop control link, K i2 is the integral coefficient of the phase-locked loop control link, u g is the magnitude of the infinite power supply voltage, i 、 p 、 g 、 n 、 v 、 m All are positive integers.

2. The linear modeling method for new energy grid-connected system based on TS fuzzy theory according to claim 1 is characterized in that: Based on the system parameters and control parameters, a member function is defined, including: Based on the system parameters and control parameters, a new energy grid-connected system model is constructed; Based on the new energy grid-connected system model, defining premise variables; Based on the premise variables, a membership function is defined.

3. The linear modeling method for new energy grid-connected system based on TS fuzzy theory according to claim 2 is characterized in that: Based on the new energy grid-connected system model, premise variables are defined, including: Based on the new energy grid-connected system model, the premise variables ξ1, ξ2, ξ3 and ξ4 are defined as follows: ； in, and is the phase-locked loop phase-locked angle and virtual frequency.

4. The linear modeling method for new energy grid-connected system based on TS fuzzy theory according to claim 3 is characterized in that: Based on the premise variables, define the member functions, including: Based on the premise variables, the member functions are defined as follows: ； in, ; Among them, ξ1, ξ2, ξ3 and ξ4 are premise variables, p 1. p 2. q 1. q 2. j 1. j 2. r 1. r 2 are coefficients.

5. The linear modeling method for new energy grid-connected system based on TS fuzzy theory according to claim 1 is characterized in that: Based on the coefficient matrix, a fuzzy set is constructed, including: Based on the coefficient matrix, the fuzzy set is constructed as follows: ； in, ; 。 6. The linear modeling method for new energy grid-connected system based on TS fuzzy theory according to claim 1 is characterized in that: Based on the membership function and the fuzzy set, a linearization model is constructed, comprising: Based on the membership function and the fuzzy set, a linearized model is constructed as follows: ； in, μ i is a member function, x ( t ) is the state variable of the new energy grid-connected system, u ( t ) is the input of the new energy grid-connected system, y ( t ) is the output of the new energy grid-connected system, A i 、 B i and C is a fuzzy set, ξ ( t ) is the premise variable.

7. A linear modeling device for a new energy grid-connected system based on TS fuzzy theory, characterized in that: include: An acquisition unit, used to acquire system parameters and control parameters of the new energy grid-connected system; A definition unit, configured to define a membership function and a coefficient matrix based on the system parameters and the control parameters; A first processing unit, configured to construct a fuzzy set based on the coefficient matrix; A second processing unit is configured to construct a linearized model based on the membership function and the fuzzy set; Wherein, based on the system parameters and control parameters, a coefficient matrix is ​​defined, including: Based on the system parameters and control parameters, the coefficient matrix is ​​constructed as follows: in, ; Where, R is the grid-connected resistance of the inverter power supply, L The grid-connected reactance of the inverter power supply, K p1 is the gain coefficient of the active power control link, K i1 is the integral coefficient of the active power control link, K p2 is the gain coefficient of the reactive power control link, K i2 is the integral coefficient of the reactive power control link, K p3 is the gain coefficient of the phase-locked loop control link, K i2 is the integral coefficient of the phase-locked loop control link, u g is the magnitude of the infinite power supply voltage, i 、 p 、 g 、 n 、 v 、 m All are positive integers.

8. The linear modeling device for new energy grid-connected system based on TS fuzzy theory according to claim 7 is characterized in that: Based on the system parameters and control parameters, a member function is defined, including: Based on the system parameters and control parameters, a new energy grid-connected system model is constructed; Based on the new energy grid-connected system model, defining premise variables; Based on the premise variables, a membership function is defined.

9. The linear modeling device for new energy grid-connected system based on TS fuzzy theory according to claim 8 is characterized in that: Based on the new energy grid-connected system model, premise variables are defined, including: Based on the new energy grid-connected system model, the premise variables ξ1, ξ2, ξ3 and ξ4 are defined as follows: ； in, and is the phase-locked loop phase-locked angle and virtual frequency.

10. The linear modeling device for new energy grid-connected system based on TS fuzzy theory according to claim 9 is characterized in that: Based on the premise variables, define the member functions, including: Based on the premise variables, the member functions are defined as follows: ； in, ; Among them, ξ1, ξ2, ξ3 and ξ4 are premise variables, p 1. p 2. q 1. q 2. j 1. j 2. r 1. r 2 are coefficients.

11. The linear modeling device for new energy grid-connected system based on TS fuzzy theory according to claim 7, characterized in that: The first processing unit is further configured to: Based on the coefficient matrix, the fuzzy set is constructed as follows: ； in, ; 。 12. The linear modeling device for new energy grid-connected system based on TS fuzzy theory according to claim 7, characterized in that: The second processing unit is further configured to: Based on the membership function and the fuzzy set, a linearized model is constructed as follows: ； in, μ i is a member function, x ( t ) is the state variable of the new energy grid-connected system, u ( t ) is the input of the new energy grid-connected system, y ( t ) is the output of the new energy grid-connected system, A i 、 B i and C is a fuzzy set, ξ ( t ) is the premise variable.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the linear modeling method of the new energy grid-connected system based on TS fuzzy theory as described in any one of claims 1-6.

14. An electronic device comprising: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the linear modeling method of the new energy grid-connected system based on TS fuzzy theory as described in any one of claims 1-6.

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