Nonlinear fuzzy large signal modeling method for grid-connected stability control of new energy

Abstract

The application discloses a nonlinear fuzzy large-signal modeling method for grid-connected stable control of new energy, and the method comprises the following steps: fuzzy linearization is performed on a nonlinear system, a linear system is used to approximate the nonlinear system, the properties of the original nonlinear system are characterized by studying the equivalent linear system, so that the LMI toolbox can be used to conveniently find a Lyapunov function for stability analysis and the stable operation domain of the system is given, and thus the problem that the traditional grid-connected system control of new energy is difficult to use the Lyapunov direct method for large-signal stability analysis and design is solved.

Description

Technical Field

[0001] This invention belongs to the technical field of high-proportion new energy power systems, and particularly relates to a nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy. Background Technology

[0002] New energy sources such as photovoltaics and wind power will inevitably replace traditional energy sources as the mainstream energy for human society's production and daily life. Traditional power systems are developing towards a high proportion of new energy sources, primarily driven by power electronic converters. However, due to the nonlinear and multi-coupling characteristics of converter control, system stability faces significant challenges. Currently, grid-connected control of new energy sources is mostly based on small-signal model analysis and design. After experiencing large system disturbances, instability phenomena such as frequency divergence, voltage instability, and oscillations easily occur. Therefore, it is necessary to conduct research on large-signal control. In reality, new energy sources are a typical type of nonlinear system, and grid-connected systems exhibit very complex nonlinear characteristics during stability control, making it difficult to perform large-signal stability analysis of new energy grid-connected systems using the Lyapunov direct method. Summary of the Invention

[0003] This invention provides a nonlinear fuzzy large-signal modeling method for stable control of new energy grid-connected systems. It addresses the technical problem that new energy grid-connected systems exhibit highly complex nonlinear characteristics during stable control, making it difficult to perform large-signal stability analysis of these systems using the Lyapunov direct method.

[0004] In a first aspect, the present invention provides a nonlinear fuzzy large-signal modeling method for stable control of new energy grid connection, comprising: obtaining a second-order nonlinear model of a new energy grid-connected system; dividing the sinusoidal quantity sinδ in the second-order nonlinear model by the phase angle difference δ between the phase angle output of the phase control unit and the grid, and then multiplying it by the phase angle difference δ between the phase angle output of the phase control unit and the grid; and using the phase angle difference δ between the phase angle output of the phase control unit and the grid and the integrator output value α as a first state variable x1 and a second state variable x2, respectively; rewriting the second-order nonlinear model according to the first state variable x1 and the second state variable x2, and determining the state space model of the new energy grid-connected system according to the second-order differential equation system to obtain the state space matrix of the new energy grid-connected system; taking the nonlinear term sinx1 / x1 in the state space matrix as a fuzzy object, and determining the first state variable x1 / x2 according to the fuzzy object. The universe of discourse of the first state variable x1 is (-a, a), and x1 ≠ 0. a is the fuzzy boundary, and the initial value of a is any value within the range (-π, π). The fuzzy boundary of the fuzzy object is determined based on the universe of discourse (-a, a) of the first state variable x1, and the first fuzzy state matrix A1 and the second fuzzy state matrix A2 are determined. A system of linear matrix inequalities is written based on the first fuzzy state matrix A1 and the second fuzzy state matrix A2, and it is determined whether there is a common positive definite matrix M in the solution of the system of linear matrix inequalities. If there is a common positive definite matrix M, the universe of discourse (-a, a) of the first state variable x1 is expanded to obtain the maximum universe of discourse of the first state variable, and the membership function and fuzzy rule are determined based on the maximum universe of discourse. The fuzzy equivalent model of the new energy grid-connected system is determined based on the first fuzzy state matrix, the second fuzzy state matrix, the membership function, and the fuzzy rule.

[0005] Secondly, the present invention provides a nonlinear fuzzy large-signal modeling system for stable control of new energy grid connection, comprising: an acquisition module configured to acquire a second-order nonlinear model of a new energy grid-connected system, divide the sinusoidal quantity sinδ in the second-order nonlinear model by the phase angle difference δ between the phase angle output of the phase control unit and the grid, multiply it by the phase angle difference δ between the phase angle output of the phase control unit and the grid, and use the phase angle difference δ between the phase angle output of the phase control unit and the grid and the integrator output value α as a first state variable x1 and a second state variable x2, respectively; a rewriting module configured to rewrite the second-order nonlinear model according to the first state variable x1 and the second state variable x2, and determine the state space model of the new energy grid-connected system according to the second-order differential equation system to obtain the state space matrix of the new energy grid-connected system; and a first determination module configured to take the nonlinear term sinx1 / x1 in the state space matrix as a fuzzy object, and determine the argument of the first state variable x1 according to the fuzzy object. The system has three fuzzy state matrix A1 and A2. The first fuzzy state variable x1 has a domain of (-a, a) and x1 ≠ 0, where a is the fuzzy boundary and its initial value is any value within the range (-π, π). The second determining module is configured to determine the fuzzy boundary of the fuzzy object based on the domain of the first state variable x1 (-a, a) and to determine the first fuzzy state matrix A1 and the second fuzzy state matrix A2. The judging module is configured to write a system of linear matrix inequalities based on the first fuzzy state matrix A1 and the second fuzzy state matrix A1, and to determine whether there is a common positive definite matrix M in the solution of the system of linear matrix inequalities. The expanding module is configured to expand the domain of the first state variable x1 (-a, a) if there is a common positive definite matrix M, to obtain the maximum domain of the first state variable, and to determine the membership function and fuzzy rules based on the maximum domain. The third determining module is configured to determine the fuzzy equivalent model of the new energy grid-connected system based on the first fuzzy state matrix, the second fuzzy state matrix, the membership function, and the fuzzy rules.

[0006] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the nonlinear fuzzy large-signal modeling method for stable grid-connected control of new energy sources according to any embodiment of the present invention.

[0007] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources according to any embodiment of the present invention.

[0008] The nonlinear fuzzy large-signal modeling method for stable control of new energy grid connection proposed in this application can accurately model the grid-connected system, accurately describe the dynamics of the new energy grid-connected system, and quickly solve the Lyapunov function of the system using the LMI toolbox, which greatly reduces the difficulty of using the Lyapunov direct method to perform stability analysis of the system and facilitates the stability analysis of new energy grid-connected systems. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating a nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources, provided in an embodiment of the present invention;

[0011] Figure 2 A schematic diagram of the training phase process of a new energy grid-connected system is provided for one embodiment of the present invention;

[0012] Figure 3 A schematic diagram of a phase control unit system according to a specific embodiment of the present invention is provided;

[0013] Figure 4 A schematic diagram of the maximum stability region of a specific embodiment of the present invention;

[0014] Figure 5 A schematic diagram of the membership function of a specific embodiment provided in this invention;

[0015] Figure 6 In the equivalent model of a specific embodiment provided for an embodiment of the present invention Schematic diagram of dynamic characteristic verification;

[0016] Figure 7 In the equivalent model of a specific embodiment provided for an embodiment of the present invention Schematic diagram of dynamic characteristic verification;

[0017] Figure 8 This is a structural block diagram of a nonlinear fuzzy large-signal modeling system for grid-connected stability control of new energy sources, provided in an embodiment of the present invention.

[0018] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 The diagram shows a flowchart of a nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources, as presented in this application.

[0021] like Figure 1 As shown, the nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources specifically includes the following steps:

[0022] In step S101, the sinusoidal quantity sinδ is divided by the difference between the phase angle output of the phase control unit and the phase angle of the power grid, δ, and then multiplied by the difference between the phase angle output of the phase control unit and the phase angle of the power grid, δ. The difference between the phase angle output of the phase control unit and the phase angle of the power grid, δ, and the integrator output value α are respectively used as the first state variable x1 and the second state variable x2.

[0023] Step S102: Rewrite the second-order nonlinear model based on the first state variable x1 and the second state variable x2, and determine the state-space model of the new energy grid-connected system based on the second-order differential equation system to obtain the state-space matrix of the new energy grid-connected system.

[0024] In this step, the expression for the second-order nonlinear model is:

[0025]

[0026] In the formula, The differential of the phase angle output by the phase control unit and the phase angle difference of the power grid. k is the derivative of the integrator output value in the PI controller of the phase control unit. p V is the proportional-integral coefficient in the phase control unit PI controller. g L represents the voltage amplitude of the power grid. g For line inductance, I s ω is the grid-connected current, δ is the difference between the phase angle output by the phase control unit and the grid phase angle, and ω is the grid-connected current. g Let k be the grid voltage angular velocity. i α is the integral coefficient in the phase control unit PI controller, and α is the integrator output value of the phase control unit PI controller;

[0027] The expression for the second-order nonlinear model rewritten based on the first state variable x1 and the second state variable x2 is as follows:

[0028]

[0029] In the formula, The derivative of the first state variable, It is the differential of the second state variable.

[0030] It should be noted that the expression for the state space matrix is:

[0031]

[0032] In the formula, A is the state space matrix, k p V is the proportional-integral coefficient in the phase control unit PI controller. g L represents the voltage amplitude of the power grid. g For line inductance, I s For grid-connected current, ω g Let k be the grid voltage angular velocity. i The integral coefficient in the phase control unit PI controller.

[0033] Step S103: The nonlinear term sinx1 / x1 in the state space matrix is ​​taken as a fuzzy object, and the universe of discourse (-a, a) of the first state variable x1 is determined according to the fuzzy object, and x1≠0, a is the fuzzy boundary, and the initial value of a is any value in the range (-π, π).

[0034] In this step, the maximum range of variation for the first state variable x1 is determined. in, Let x1 be the static operating phase angle of the first state variable. The expression for the static operating phase angle is:

[0035]

[0036] In the formula, I s Z represents the grid-connected current amplitude. g For grid impedance, The impedance angle is V. g This refers to the voltage amplitude of the power grid.

[0037] Due to the new energy grid connection system, The proportion in a cycle is small, therefore Ignoring this, we obtain the universe of discourse x1∈(-a, a) for the first state variable x1.

[0038] Step S104: Determine the fuzzy boundary of the fuzzy object based on the universe of discourse (-a, a) of the first state variable x1, and determine the first fuzzy state matrix A1 and the second fuzzy state matrix A2.

[0039] In this step, sina / a and 1 are used as the fuzzy boundaries of the fuzzy object;

[0040] The expression for the first fuzzy state matrix A1 is:

[0041]

[0042] The expression for the second fuzzy state matrix A2 is:

[0043]

[0044] In the formula, k p V is the proportional-integral coefficient in the phase control unit PI controller. g L represents the voltage amplitude of the power grid. g For line inductance, I s ω is the amplitude of the grid-connected current. g Let k be the grid voltage angular velocity. i The integral coefficient in the phase control unit PI controller.

[0045] Step S105: Write a set of linear matrix inequalities based on the first fuzzy state matrix A1 and the second fuzzy state matrix A1, and determine whether there is a common positive definite matrix M in the solution of the set of linear matrix inequalities.

[0046] In this step, the expression for the system of linear matrix inequalities written based on the first fuzzy state matrix A1 and the second fuzzy state matrix A2 is as follows:

[0047]

[0048] In the formula, T is the transpose symbol, and M is the common positive definite matrix.

[0049] If a common positive definite matrix M cannot be found, then the universe of discourse of the first state variable x1 in step S103 is reduced; if a positive definite matrix M is not found at all, then the modeling method is not applicable.

[0050] Step S106: If a common positive definite matrix M exists, the universe of discourse (-a, a) of the first state variable x1 is expanded to obtain the maximum universe of discourse of the first state variable, and the membership function and fuzzy rule are determined based on the maximum universe of discourse.

[0051] In this step, the expression for the membership function is:

[0052]

[0053]

[0054] In the formula, ω1 and ω2 are the membership functions of the first and second fuzzy subspaces, respectively, and ω1+ω2=1.

[0055] The fuzzy rule is: if x1≠0, then the state space matrix A=A1; if x1=0, then the state space matrix A=A2.

[0056] Step S107: Determine the fuzzy equivalent model of the new energy grid-connected system based on the first fuzzy state matrix, the second fuzzy state matrix, the membership function, and the fuzzy rules.

[0057] In this step, the expression for the fuzzy equivalent model of the new energy grid-connected system is:

[0058]

[0059] in,

[0060] In the formula, Let x be the differential matrix of the state variables, and ω be the state variable matrix. i Let A be the membership function of the i-th fuzzy subspace. i Let k be the state subspace matrix of the i-th state. p V is the proportional-integral coefficient in the phase control unit PI controller. g L represents the voltage amplitude of the power grid. g For line inductance, I s ω is the amplitude of the grid-connected current. g Let k be the grid voltage angular velocity. i The integral coefficient in the phase control unit PI controller.

[0061] In summary, the method of this application can accurately model the grid-connected system, accurately describe the dynamics of the new energy grid-connected system, and quickly solve the Lyapunov function of the system using the LMI toolbox, which greatly reduces the difficulty of using the Lyapunov direct method to perform stability analysis of the system and facilitates the stability analysis of the new energy grid-connected system.

[0062] In one specific embodiment, the new energy grid-connected system is as follows: Figure 2 As shown, in grid-connected control, the grid-connected converter is equivalent to a current source, I c For the reference current amplitude, θ pll The phase angle is output by the phase control unit, R g For the parasitic resistance of the power grid, L g For the parasitic inductance of the power grid, V g For the power grid amplitude, θg This refers to the phase angle of the power grid. The phase control unit system is as follows: Figure 3 As shown.

[0063] In this embodiment, the reference value I of the grid-connected current of the system c The current is 1512A, the reference current phase angle is the output phase angle of the phase control unit, and the magnitude of the grid voltage V. g The voltage is 220V, the mains frequency is 50Hz, and the parasitic resistance of the mains is Z. g The resistance is 0.5Ω, and the parasitic inductance of the power grid is L. g The value is 0.1mH, and the proportionality coefficient is k. p The integral coefficient k is 0.7. i It is 78.4.

[0064] according to Figure 1 The nonlinear model of the phase control unit in the traditional rotating coordinate system is derived as follows:

[0065]

[0066] In the formula, The differential of the phase angle output by the phase control unit, θ g Let θ be the phase angle of the power grid. pll Output phase angle for the phase control unit;

[0067] Let δ = θ pll -θ g The second-order nonlinear model of a traditional renewable energy grid-connected system is obtained as follows:

[0068]

[0069] Divide the sinusoidal quantity sinδ in the model by δ, then multiply by δ, and use δ and α as the first state variable x1 and the second state variable x2, respectively, to obtain the following equation:

[0070]

[0071] The state-space matrix A is determined based on the system of second-order differential equations as follows:

[0072]

[0073] Select sinx1 / x1 in the state-space matrix A as the fuzzy object. Determine the maximum range of variation for x1. Let x1 be the static operating phase angle of the first state variable. The expression for the static operating phase angle is:

[0074]

[0075] In the formula, I s This is the grid-connected current amplitude. The impedance angle is V. g Z represents the grid voltage amplitude. g Z is the power grid impedance. g =R g +jX L X L =ω pll L g , where ω pll The phase control unit outputs the rotational angular velocity. Because the inductance in the power grid lines is very small in new energy grid-connected systems, this makes... The proportion in a cycle is very small, so it can be Ignoring this, we obtain the universe of discourse for the first state variable x1 as x1∈(-a, a), and x1≠0.

[0076] First, we choose a = 0.5π as the boundary of the universe of discourse. Then x1 ∈ (-0.5π, 0.5π) and x1 ≠ 0. We then define the fuzzy boundary of sinx1 / x1 as sin0.5π / 0.5π, 1.

[0077] Based on the first fuzzy state matrix A1 and the second fuzzy state matrix A2, write a system of linear matrix inequalities:

[0078]

[0079] In the formula, T is the transpose sign, and M is the common positive definite matrix;

[0080] The positive definite matrix is ​​obtained by solving using the LMI toolbox. Further expanding the universe of discourse, using sin0.8π / 0.8π, 1 as the fuzzy boundary, and failing to solve for the stability boundary using the LMI toolbox, the universe of discourse is reduced. This process is repeated until the universe of discourse with the largest state variable is found to be x1∈(-0.78π, 0.78π). The resulting common positive definite matrix is... The corresponding Lyapunov function V(x) = x T Mx, let The maximum stability region of the system can then be determined. The maximum stability region of the system is as follows: Figure 4 As shown.

[0081] The membership function is determined based on the maximum universe of discourse. The expression for the membership function is as follows:

[0082]

[0083]

[0084] In the formula, ω1 and ω2 are the membership functions of the first and second fuzzy subspaces, respectively, and ω1+ω2=1.

[0085] Membership function diagram as shown below Figure 5 As shown.

[0086] The final fuzzy equivalent model of the new energy grid-connected system is determined as follows:

[0087]

[0088] in,

[0089] In the formula, Let x be the differential matrix of the state variables, and ω be the state variable matrix. i Let A be the membership function of the i-th fuzzy subspace. i Let k be the state subspace matrix of the i-th state. p V is the proportional-integral coefficient in the phase control unit PI controller. g L represents the voltage amplitude of the power grid. g For line inductance, I s ω is the amplitude of the grid-connected current. g Let k be the grid voltage angular velocity. i The integral coefficient in the phase control unit PI controller.

[0090] like Figure 6 and Figure 7 To verify the dynamic relationship between the grid-connected system and the fuzzy equivalent model, phase angle jumps of 0.3π and -0.5π were added at 0.15s and 0.3s, respectively. It can be seen that the fuzzy equivalent model can maintain the same dynamic characteristics as the grid-connected model, that is, the phase control unit model constructed in this invention is correct.

[0091] Please see Figure 8 The diagram shows a structural block diagram of a nonlinear fuzzy large-signal modeling system for grid-connected stability control of new energy sources, according to this application.

[0092] like Figure 8 As shown, the nonlinear fuzzy large signal modeling system 200 includes an acquisition module 210, a rewriting module 220, a first determination module 230, a second determination module 240, a judgment module 250, an amplification module 260, and a third determination module 270.

[0093] The acquisition module 210 is configured to acquire a second-order nonlinear model of the new energy grid-connected system, divide the sinusoidal quantity sinδ in the second-order nonlinear model by the phase angle difference δ between the phase angle output of the phase control unit and the grid, and then multiply it by the phase angle difference δ between the phase angle output of the phase control unit and the grid. The phase angle difference δ between the phase angle output of the phase control unit and the grid and the integrator output value α are respectively used as the first state variable x1 and the second state variable x2. The rewriting module 220 is configured to rewrite the second-order nonlinear model according to the first state variable x1 and the second state variable x2, and determine the state space model of the new energy grid-connected system according to the second-order differential equation system to obtain the state space matrix of the new energy grid-connected system. The first determination module 230 is configured to take the nonlinear term sinx1 / x1 in the state space matrix as a fuzzy object, and determine the universe of discourse (-a, a) of the first state variable x1 according to the fuzzy object, where x1≠0 and a is the fuzzy boundary. The initial value of 'a' is any value within the range of (-π, π); the second determining module 240 is configured to determine the fuzzy boundary of the fuzzy object based on the universe of discourse (-a, a) of the first state variable x1, and to determine the first fuzzy state matrix A1 and the second fuzzy state matrix A2; the judging module 250 is configured to write a set of linear matrix inequalities based on the first fuzzy state matrix A1 and the second fuzzy state matrix A1, and to determine whether there is a common positive definite matrix M in the solution of the set of linear matrix inequalities; the expanding module 260 is configured to expand the universe of discourse (-a, a) of the first state variable x1 if there is a common positive definite matrix M, to obtain the maximum universe of discourse of the first state variable, and to determine the membership function and fuzzy rules based on the maximum universe of discourse; the third determining module 270 is configured to determine the fuzzy equivalent model of the new energy grid-connected system based on the first fuzzy state matrix, the second fuzzy state matrix, the membership function and the fuzzy rules.

[0094] It should be understood that Figure 8 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 8 The various modules in the document will not be described in detail here.

[0095] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources in any of the above method embodiments.

[0096] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0097] Obtain the second-order nonlinear model of the new energy grid-connected system, divide the sinusoidal quantity sinδ in the second-order nonlinear model by the phase angle difference δ between the phase angle output of the phase control unit and the grid, and then multiply it by the phase angle difference δ between the phase angle output of the phase control unit and the grid. The phase angle difference δ between the phase angle output of the phase control unit and the grid and the integrator output value α are respectively used as the first state variable x1 and the second state variable x2.

[0098] The second-order nonlinear model is rewritten based on the first state variable x1 and the second state variable x2, and the state space model of the new energy grid-connected system is determined based on the second-order differential equation system to obtain the state space matrix of the new energy grid-connected system.

[0099] The nonlinear term sinx1 / x1 in the state space matrix is ​​taken as a fuzzy object, and the universe of discourse (-a, a) of the first state variable x1 is determined according to the fuzzy object, and x1≠0, a is the fuzzy boundary, and the initial value of a is any value in the range (-π, π).

[0100] The fuzzy boundary of the fuzzy object is determined based on the universe of discourse (-a, a) of the first state variable x1, and the first fuzzy state matrix A1 and the second fuzzy state matrix A2 are determined.

[0101] Write a set of linear matrix inequalities based on the first fuzzy state matrix A1 and the second fuzzy state matrix A2, and determine whether there is a common positive definite matrix M in the solution of the set of linear matrix inequalities;

[0102] If a common positive definite matrix M exists, the universe of discourse (-a, a) of the first state variable x1 is expanded to obtain the maximum universe of discourse of the first state variable, and the membership function and fuzzy rules are determined based on the maximum universe of discourse.

[0103] The fuzzy equivalent model of the new energy grid-connected system is determined based on the first fuzzy state matrix, the second fuzzy state matrix, the membership function, and the fuzzy rules.

[0104] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of a nonlinear fuzzy large-signal modeling system for grid-connected stability control of new energy sources. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely configured relative to a processor, which can be connected via a network to the nonlinear fuzzy large-signal modeling system for grid-connected stability control of new energy sources. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0105] Figure 9 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 9 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 9 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the nonlinear fuzzy large-signal modeling method for new energy grid-connected stability control described in the above-described method embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the nonlinear fuzzy large-signal modeling system for new energy grid-connected stability control. The output device 340 may include a display screen or other display device.

[0106] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0107] In one implementation, the above-described electronic device is applied in a nonlinear fuzzy large-signal modeling system for grid-connected stability control of new energy sources. As a client, it includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to:

[0108] Obtain the second-order nonlinear model of the new energy grid-connected system, divide the sinusoidal quantity sinδ in the second-order nonlinear model by the phase angle difference δ between the phase angle output of the phase control unit and the grid, and then multiply it by the phase angle difference δ between the phase angle output of the phase control unit and the grid. The phase angle difference δ between the phase angle output of the phase control unit and the grid and the integrator output value α are respectively used as the first state variable x1 and the second state variable x2.

[0109] The second-order nonlinear model is rewritten based on the first state variable x1 and the second state variable x2, and the state space model of the new energy grid-connected system is determined based on the second-order differential equation system to obtain the state space matrix of the new energy grid-connected system.

[0110] The nonlinear term sinx1 / x1 in the state space matrix is ​​taken as a fuzzy object, and the universe of discourse (-a, a) of the first state variable x1 is determined according to the fuzzy object, and x1≠0, a is the fuzzy boundary, and the initial value of a is any value in the range (-π, π).

[0111] The fuzzy boundary of the fuzzy object is determined based on the universe of discourse (-a, a) of the first state variable x1, and the first fuzzy state matrix A1 and the second fuzzy state matrix A2 are determined.

[0112] Write a set of linear matrix inequalities based on the first fuzzy state matrix A1 and the second fuzzy state matrix A2, and determine whether there is a common positive definite matrix M in the solution of the set of linear matrix inequalities;

[0113] If a common positive definite matrix M exists, the universe of discourse (-a, a) of the first state variable x1 is expanded to obtain the maximum universe of discourse of the first state variable, and the membership function and fuzzy rules are determined based on the maximum universe of discourse.

[0114] The fuzzy equivalent model of the new energy grid-connected system is determined based on the first fuzzy state matrix, the second fuzzy state matrix, the membership function, and the fuzzy rules.

[0115] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0116] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources, characterized in that, include: Obtain the second-order nonlinear model of the new energy grid-connected system, and extract the sinusoidal quantity from the second-order nonlinear model. Divide by the difference between the phase angle output of the phase control unit and the phase angle of the power grid. Then multiply by the difference between the phase angle output by the phase control unit and the phase angle of the power grid. The phase angle output by the phase control unit is compared with the phase angle difference of the power grid. and integrator output value As the first state variables respectively Second state variable ; According to the first state variable and the second state variable The second-order nonlinear model is rewritten, and the state-space model of the new energy grid-connected system is determined based on the second-order differential equation system, thus obtaining the state-space matrix of the new energy grid-connected system; The nonlinear terms in the state space matrix As a fuzzy object, and based on the fuzzy object, the first state variable is determined. Domain of discourse ,and , For blurred boundaries, The initial value is Any value within the range, specifically including: Determine the first state variable Maximum range of variation ,in, First state variable The static operating phase angle, the expression for the static operating phase angle is: ， In the formula, This is the grid-connected current amplitude. For grid impedance, It is the impedance angle. This refers to the voltage amplitude of the power grid. Due to the new energy grid connection system, The proportion in a cycle is small, therefore Ignore this and obtain the first state variable. Domain of discourse ; According to the first state variable Domain of discourse Determine the fuzzy boundary of the fuzzy object and determine the first fuzzy state matrix. Second fuzzy state matrix In this context, sina / a and 1 are used as the fuzzy boundaries of the fuzzy objects. Among them, the first fuzzy state matrix The expression is: ， Second fuzzy state matrix The expression is: ， In the formula, The proportional-integral coefficient in the phase control unit PI controller. The voltage amplitude of the power grid. For line inductance, This is the grid-connected current amplitude. For grid voltage angular velocity, The integral coefficient in the phase control unit PI controller; According to the first fuzzy state matrix and the second fuzzy state matrix List a system of linear matrix inequalities and determine whether there exists a common positive definite matrix in the solution of the system of linear matrix inequalities. ; If a common positive definite matrix exists Then for the first state variable domain of discourse The scope is expanded to obtain the maximum universe of discourse for the first state variable, and the membership function and fuzzy rules are determined based on the maximum universe of discourse. The fuzzy equivalent model of the new energy grid-connected system is determined based on the first fuzzy state matrix, the second fuzzy state matrix, the membership function, and the fuzzy rules.

2. The nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources according to claim 1, characterized in that, The expression for the second-order nonlinear model is: ， In the formula, The differential of the phase angle output by the phase control unit and the phase angle difference of the power grid. This is the derivative of the integrator output value in the PI controller of the phase control unit. The proportional-integral coefficient in the phase control unit PI controller. The voltage amplitude of the power grid. For line inductance, For grid-connected current, The phase control unit outputs the phase angle difference between the phase angle and the grid phase angle. For grid voltage angular velocity, The integral coefficient in the phase control unit PI controller. The integrator output value in the phase control unit PI controller; Wherein, according to the first state variable and the second state variable The rewritten expression for the second-order nonlinear model is: ， In the formula, The derivative of the first state variable, It is the derivative of the second state variable.

3. The nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources according to claim 1, characterized in that, The expression for the state space matrix is: ， In the formula, The state space matrix, The proportional-integral coefficient in the phase control unit PI controller. The voltage amplitude of the power grid. For line inductance, For grid-connected current, For grid voltage angular velocity, The integral coefficient in the phase control unit PI controller.

4. The nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources according to claim 1, characterized in that, According to the first fuzzy state matrix and the second fuzzy state matrix The expression for the system of linear matrix inequalities is as follows: ， In the formula, It is the transpose symbol. It is a common positive definite matrix.

5. The nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources according to claim 1, characterized in that, The expression for the membership function is: ， ， In the formula, , Let be the membership functions of the first fuzzy subspace and the second fuzzy subspace, respectively. .

6. The nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources according to claim 1, characterized in that, The fuzzy rule is: if Then the state space matrix ;if Then the state space matrix .

7. The nonlinear fuzzy large-signal modeling method for grid-connected stability control of new energy sources according to claim 1, characterized in that, The expression for the fuzzy equivalent model of the new energy grid-connected system is as follows: ， in, , , In the formula, Let be the differential matrix of the state variables. For the state variable matrix, Let be the membership function of the i-th fuzzy subspace. Let be the matrix of the i-th state subspace. The proportional-integral coefficient in the phase control unit PI controller. The voltage amplitude of the power grid. For line inductance, This is the grid-connected current amplitude. For grid voltage angular velocity, The integral coefficient in the phase control unit PI controller.

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