A fuzzy theory-based virtual synchronous adaptive control method for energy storage converter

Abstract

The application discloses a virtual synchronous adaptive control method for an energy storage converter based on fuzzy theory, designs a VSG control system for an energy storage system converter based on fuzzy control theory, is used for simulating synchronous machine excitation regulator characteristics, prime mover speed regulator and synchronous machine rotor motion equation, mainly comprises a reactive power-voltage control module, an active power-frequency control module and a virtual parameter adaptive optimization module. The reactive power-voltage link of the VSG mainly simulates the reactive power-voltage droop characteristics of the synchronous machine, the active power-frequency link of the VSG mainly simulates the active power-frequency droop characteristics of the prime mover speed regulator and the second-order model of the synchronous machine rotor motion equation, and the virtual parameter adaptive optimization link adopts fuzzy control to determine the value of each virtual parameter to adjust the virtual synchronous control parameters of the converter, so as to dynamically adapt the system state, thereby enhancing the sustainable working time and the fluctuation suppression capability of the energy storage system VSG, and improving the dynamic response performance of the VSG.

Description

Technical Field

[0001] This invention relates to the technical field of energy storage converter control methods, specifically to a virtual synchronous adaptive control method for energy storage converters based on fuzzy theory. Background Technology

[0002] With the rapid development of renewable energy power generation, its penetration rate in the power grid has been increasing year by year. Renewable energy sources, such as wind and solar power generation, as well as battery energy storage systems, all need to be connected to the power grid through power electronic converters. However, grid-connected power electronic converters usually adopt grid voltage-oriented current control, which makes these power sources exhibit the characteristics of current sources to the power grid and unable to provide inertia for the power system. Therefore, under traditional control strategies, these power sources will have a negative impact on the stability of the power grid.

[0003] To address this issue, Virtual Synchronous Generator (VSG) control technology, which modifies the control strategy of power electronic converters to simulate the characteristics of synchronous generators, has gained widespread attention. Converters using VSGs can enable wind and solar power and battery energy storage systems to provide the necessary rotational inertia and damping to the grid, thereby improving the stability of renewable energy penetration into the grid and participating in active regulation of the grid. However, the state of the grid and energy storage system has a significant impact on the response characteristics of VSGs, and the variation patterns are quite complex.

[0004] Therefore, it is necessary to invent a virtual synchronous adaptive control method for energy storage converters based on fuzzy theory to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a virtual synchronous adaptive control method for energy storage converters based on fuzzy theory. By simulating the characteristics of the synchronous machine excitation regulator, the prime mover speed governor, and the synchronous machine rotor motion equation, the energy storage system can have voltage regulation and primary frequency regulation performance similar to that of a synchronous generator. On this basis, a virtual parameter adaptive optimization module is added. The virtual parameter adaptive optimization module uses fuzzy control to determine the values ​​of each virtual parameter to adjust the virtual synchronous control parameters of the converter to dynamically adapt to the system state, thereby enhancing the sustainable working time and fluctuation suppression capability of the energy storage system VSG and improving the dynamic response performance of the VSG.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A virtual synchronous adaptive control method for energy storage converters based on fuzzy theory includes:

[0008] The reactive-voltage link of S1 and VSG simulates the reactive-voltage droop characteristics of a synchronous machine.

[0009] The active-frequency link of S2 and VSG simulates the active-frequency droop characteristics of the prime mover speed governor and the second-order model of the synchronous machine rotor motion equation.

[0010] S3. The virtual parameter adaptive optimization step determines the values ​​of each virtual parameter to adjust the converter's virtual synchronization control parameters in order to dynamically adapt to the system state.

[0011] Furthermore, the virtual parameter adaptive step in step S3 employs fuzzy control theory to adaptively adjust the virtual synchronization control parameters of the converter. The specific implementation process is as follows:

[0012] A1. Determine the input and output variables, taking the energy storage system SOC level and the system bus voltage U as the reference values. * System bus angular frequency ω * The state variables are used as input variables, and parameters such as reactive power-voltage droop coefficient n, virtual droop coefficient m, virtual inertia time constant H, and virtual damping coefficient D are used as output variables.

[0013] A2. Determine the quantization factor of the input variable and the scaling factor of the output variable in the virtual parameter adaptive link. The input and output variables of the fuzzy controller are mapped one-to-one with the exact quantities in their respective actual domains and the fuzzy quantities in their respective fuzzy domains through the corresponding quantization factor or scaling factor, thus completing the domain transformation between the exact quantities and the fuzzy quantities.

[0014] A3. Determine the membership functions of the input and output variables of the virtual parameter adaptive loop to obtain the SOC level of the energy storage system and the system bus voltage U. * System bus angular frequency ω * Fuzzy values ​​for input state variables;

[0015] A4. Determine the fuzzy control rules for the virtual parameter adaptive link to obtain the set values ​​of output virtual parameters such as reactive power-voltage droop coefficient n, virtual droop coefficient m, virtual inertia time constant H, and virtual damping coefficient D.

[0016] Furthermore, the calculation of the quantization factor of the input variable and the scaling factor of the output variable in the virtual parameter adaptive step A2 is as follows:

[0017] B1. Let the basic fuzzy universe of discourse for the input variable, the SOC level of the energy storage system, be [0, 1], and the system bus voltage U... * The basic fuzzy domain is System bus angular frequency ω * The basic fuzzy domain is The fuzzy subset universes of discourse for their corresponding fuzzy linguistic variables S, V, and Ω are respectively represented as {0, 1, ..., X}. S {0, 1, ..., X} V{0, 1, ..., X} Ω};

[0018] B2. Let the basic fuzzy universe of discourse for the output variable reactive power-voltage droop coefficient n be [0, n]. max The fundamental fuzzy domain of the virtual adjustment coefficient m is [0, m]. max The fundamental fuzzy universe of discourse for the virtual inertia time constant H is [0, H]. max The fundamental fuzzy domain of the virtual damping coefficient D is [0, D]. max The fuzzy subset universes of discourse for their corresponding fuzzy linguistic variables N, M, H, and D are respectively represented as {0, 1, ..., X}. N {0, 1, ..., X} M {0, 1, ..., X} H {0, 1, ..., X} D};

[0019] B3. The quantization factor of the input variable and the scaling factor of the output variable are determined by the maximum value of the basic fuzzy universe of discourse and the maximum value of the fuzzy subset universe of discourse of the variable:

[0020]

[0021] In the formula, k is the quantization factor of the input variable or the scaling factor of the output variable, X is the maximum value of the basic fuzzy universe of discourse of the variable, and x is the maximum value of the fuzzy subset universe of discourse of the variable.

[0022] B4. Input variable energy storage system SOC level, system bus voltage U * System bus angular frequency ω * The quantization factor is:

[0023]

[0024] B5. The scaling factors for the output variable reactive power-voltage droop coefficient n, virtual droop coefficient m, virtual inertia time constant H, and virtual damping coefficient D are:

[0025]

[0026] Furthermore, the membership functions of the input and output variables, the partitioning of fuzzy subsets of each variable, and the selection of membership functions in the virtual parameter adaptive stage of step A3 are specifically implemented as follows:

[0027] C1. Select 3 or 7 fuzzy variables. The fuzzy sets of the input fuzzy linguistic variables V and Ω are divided into 3 fuzzy subsets: negative (N), zero (Z), and positive (P). The fuzzy sets of the input fuzzy linguistic variable s and the output fuzzy linguistic variables N, M, H, and D are divided into 7 fuzzy subsets: negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB), denoted as:

[0028]

[0029] C2. The normal curve in the bell-shaped membership function, which has high sensitivity, is used as the membership function for each fuzzy set. The normal function is shown in the following formula:

[0030]

[0031] In the formula, a represents the median value of the normal membership function, and b represents the width of the normal membership function.

[0032] Furthermore, the VSG reactive power-voltage droop equation in step S1 is as follows:

[0033]

[0034] In the formula, This represents the per-unit value of the actual reactive power output of the VSG. U is the per-unit value of the commanded reactive power. * denoted as the per-unit value of the system bus voltage, and n is the reactive power-voltage droop coefficient.

[0035] Furthermore, in step S2, the active-frequency element of the VSG is used to simulate the motion equations of the prime mover governor and the synchronous machine rotor. Specifically, it is achieved by simulating the active-frequency droop characteristics of the prime mover governor and the second-order model of the synchronous machine rotor motion equation. The VSG active-frequency droop equation and rotor motion equation used are as follows:

[0036]

[0037] In the formula, This represents the per-unit value of the actual active power output of the VSG. ω is the per-unit value of the active power command. * Here, m is the per-unit value of the system bus angular frequency, and m is the virtual droop coefficient. H is the per-unit value of the active power of the virtual prime mover, H is the virtual inertia time constant, and D is the virtual damping coefficient.

[0038] The beneficial effects of this invention are as follows: By simulating the characteristics of the synchronous machine excitation regulator, the prime mover speed governor, and the synchronous machine rotor motion equation, this invention enables the energy storage system to have voltage regulation and primary frequency regulation performance similar to that of a synchronous generator. On this basis, a virtual parameter adaptive optimization module is added, thereby enhancing the sustainable working time and fluctuation suppression capability of the energy storage system VSG and improving the dynamic response performance of the VSG. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. 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.

[0040] Figure 1 This is a diagram of the VSG control model of the energy storage converter based on fuzzy theory in this invention.

[0041] Figure 2 This is a block diagram of the reactive power-voltage link control of the present invention;

[0042] Figure 3 This is a block diagram of the active frequency control stage of the present invention. Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0044] like Figure 1-3 The illustrated method for virtual synchronous adaptive control of energy storage converters based on fuzzy theory includes:

[0045] S1, the reactive-voltage link of VSG simulates the reactive-voltage droop characteristic of a synchronous machine. The reactive-voltage droop equation of VSG in step S1 is as follows:

[0046]

[0047] In the formula, This represents the per-unit value of the actual reactive power output of the VSG. U is the per-unit value of the commanded reactive power. * is the per-unit value of the system bus voltage, and n is the reactive power-voltage droop coefficient;

[0048] S2, the active-frequency element of the VSG simulates the active-frequency droop characteristics of the prime mover governor and the second-order model of the synchronous machine rotor motion equation. In step S2, the active-frequency element of the VSG is used to simulate the motion equation of the prime mover governor and the synchronous machine rotor. Specifically, it simulates the active-frequency droop characteristics of the prime mover governor and the second-order model of the synchronous machine rotor motion equation. The VSG active-frequency droop equation and rotor motion equation used are as follows:

[0049]

[0050] In the formula, This represents the per-unit value of the actual active power output of the VSG. ω is the per-unit value of the active power command. * Here, m is the per-unit value of the system bus angular frequency, and m is the virtual droop coefficient. H is the per-unit value of the active power of the virtual prime mover, H is the virtual inertia time constant, and D is the virtual damping coefficient.

[0051] S3. The virtual parameter adaptive optimization stage determines the values ​​of each virtual parameter to adjust the virtual synchronization control parameters of the converter in order to dynamically adapt to the system state.

[0052] The virtual parameter adaptive step in step S3 uses fuzzy control theory to adaptively adjust the virtual synchronization control parameters of the converter. The specific implementation process is as follows:

[0053] A1. Determine the input and output variables, taking the energy storage system SOC level and the system bus voltage U as the reference values. * System bus angular frequency ω * The state variables are used as input variables, and parameters such as reactive power-voltage droop coefficient n, virtual droop coefficient m, virtual inertia time constant H, and virtual damping coefficient D are used as output variables.

[0054] A2. Determine the quantization factor of the input variable and the scaling factor of the output variable in the virtual parameter adaptive link. The input and output variables of the fuzzy controller are mapped one-to-one with the exact quantities in their respective actual domains and the fuzzy quantities in their respective fuzzy domains through the corresponding quantization factor or scaling factor, thus completing the domain transformation between the exact quantities and the fuzzy quantities.

[0055] A3. Determine the membership functions of the input and output variables of the virtual parameter adaptive loop to obtain the SOC level of the energy storage system and the system bus voltage U. * System bus angular frequency ω * Fuzzy values ​​for input state variables;

[0056] A4. Determine the fuzzy control rules for the virtual parameter adaptive link to obtain the set values ​​of output virtual parameters such as reactive power-voltage droop coefficient n, virtual droop coefficient m, virtual inertia time constant H, and virtual damping coefficient D.

[0057] The calculation process for the quantization factor of the input variable and the scaling factor of the output variable in the virtual parameter adaptive step A2 is as follows:

[0058] B1. Let the basic fuzzy universe of discourse for the input variable, the SOC level of the energy storage system, be [0, 1], and the system bus voltage U... * The basic fuzzy domain is System bus angular frequency ω * The basic fuzzy domain is The fuzzy subset universes of discourse for their corresponding fuzzy linguistic variables S, V, and Ω are respectively represented as {0, 1, ..., X}. S {0, 1, ..., X} V {0, 1, ..., X} Ω};

[0059] B2. Let the basic fuzzy universe of discourse for the output variable reactive power-voltage droop coefficient n be [0, n]. max The fundamental fuzzy domain of the virtual adjustment coefficient m is [0, m]. max The fundamental fuzzy universe of discourse for the virtual inertia time constant H is [0, H]. max The fundamental fuzzy domain of the virtual damping coefficient D is [0, D]. max The fuzzy subset universes of discourse for their corresponding fuzzy linguistic variables N, M, H, and D are respectively represented as {0, 1, ..., X}. N {0, 1, ..., X} M {0, 1, ..., X} H {0, 1, ..., X} D};

[0060] B3. The quantization factor of the input variable and the scaling factor of the output variable are determined by the maximum value of the basic fuzzy universe of discourse and the maximum value of the fuzzy subset universe of discourse of the variable:

[0061]

[0062] In the formula, k is the quantization factor of the input variable or the scaling factor of the output variable, X is the maximum value of the basic fuzzy universe of discourse of the variable, and x is the maximum value of the fuzzy subset universe of discourse of the variable.

[0063] B4. Input variable energy storage system SOC level, system bus voltage U * System bus angular frequency ω * The quantization factor is:

[0064]

[0065] B5. The scaling factors for the output variable reactive power-voltage droop coefficient n, virtual droop coefficient m, virtual inertia time constant H, and virtual damping coefficient D are:

[0066]

[0067] The virtual parameter adaptive step in step A3 involves the membership functions of the input and output variables, the partitioning of fuzzy subsets of each variable, and the selection of membership functions. The specific implementation process is as follows:

[0068] C1. Select 3 or 7 fuzzy variables. The fuzzy sets of the input fuzzy linguistic variables V and Ω are divided into 3 fuzzy subsets: negative (N), zero (Z), and positive (P). The fuzzy sets of the input fuzzy linguistic variable s and the output fuzzy linguistic variables N, M, H, and D are divided into 7 fuzzy subsets: negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB), denoted as:

[0069]

[0070] C2. The normal curve in the bell-shaped membership function, which has high sensitivity, is used as the membership function for each fuzzy set. The normal function is shown in the following formula:

[0071]

[0072] In the formula, a represents the median value of the normal membership function, and b represents the width of the normal membership function.

[0073] like Figure 1 As shown, the present invention is based on the VSG control model of the energy storage system converter, which mainly consists of a reactive-voltage control module, an active-frequency control module, and a parameter adaptive optimization module.

[0074] like Figure 2 As shown, this invention is based on the implementation of the reactive power-voltage link of the VSG. First, a reactive power reference value is obtained according to the reactive power command and drooping link of the VSG. The difference between this reference value and the actual reactive power output value is then passed through a proportional-integral (PI) link and superimposed on the rated voltage to obtain the modulation voltage. The reactive power-voltage link of the VSG is used to simulate the characteristics of a synchronous machine excitation regulator, specifically by simulating the reactive power-voltage droop characteristics of the synchronous machine. The VSG reactive power-voltage droop equation used is shown below:

[0075]

[0076] In the formula, This represents the per-unit value of the actual reactive power output of the VSG. U is the per-unit value of the commanded reactive power. * denoted as the per-unit value of the system bus voltage, and n is the reactive power-voltage droop coefficient.

[0077] like Figure 3 As shown, the implementation of the active-frequency link in the VSG involves first obtaining an active power reference value based on the VSG's active power command and droop stage. This reference value is then subtracted from the actual active power output value detected by the reference value, and the difference is passed through an inertia damping stage to obtain the speed difference. This difference is then superimposed with the rated speed to obtain the output angular frequency. The output angular frequency changes the modulation phase through an integral stage, thereby altering the actual power output. The VSG's active-frequency link is used to simulate the motion equations of the prime mover governor and the synchronous machine rotor. Specifically, it simulates the active-frequency droop characteristics of the prime mover governor and the second-order model of the synchronous machine rotor motion equations. The VSG active-frequency droop equation and rotor motion equations used are shown below:

[0078]

[0079] In the formula, This represents the per-unit value of the actual active power output of the VSG. ω is the per-unit value of the active power command. * Here, m is the per-unit value of the system bus angular frequency, and m is the virtual droop coefficient. H is the per-unit value of the active power of the virtual prime mover, H is the virtual inertia time constant, and D is the virtual damping coefficient.

[0080] The working process of the parameter adaptive optimization stage of the VSG control model based on the energy storage system converter in this invention is as follows:

[0081] 1. The parameter adaptive optimization stage is used to adjust the virtual synchronous control parameters of the converter to dynamically adapt to the system state. Specifically, it adjusts the SOC level of the energy storage system and the system bus voltage U. * System bus angular frequency ω * Using state variables as input variables, and parameters such as reactive power-voltage droop coefficient n, virtual droop coefficient m, virtual inertia time constant H, and virtual damping coefficient D as output variables, fuzzy theory is used to determine the values ​​of each virtual parameter.

[0082] 2. Determine the quantization factor of the input variable and the scaling factor of the output variable. The input and output variables of the fuzzy controller are mapped one-to-one with the exact quantities of their respective actual domains and the fuzzy quantities of their respective fuzzy domains through the corresponding quantization factor or scaling factor, thus completing the domain transformation between the exact quantities and the fuzzy quantities.

[0083] 3. Let the basic fuzzy universe of discourse for the input variable, the SOC level of the energy storage system, be [0, 1], and the system bus voltage U * The basic fuzzy domain is System bus angular frequency ω * The basic fuzzy domain is The fuzzy subset universes of discourse for their corresponding fuzzy linguistic variables S, V, and Ω are respectively represented as {0, 1, ..., X}. S {0, 1, ..., X} V {0, 1, ..., X} Ω};

[0084] 4. Let the basic fuzzy universe of discourse for the output variable reactive power-voltage droop coefficient n be [0, n]. max The fundamental fuzzy domain of the virtual adjustment coefficient m is [0, m]. max The fundamental fuzzy universe of discourse for the virtual inertia time constant H is [0, H]. max The fundamental fuzzy domain of the virtual damping coefficient D is [0, D]. max The fuzzy subset universes of discourse for their corresponding fuzzy linguistic variables N, M, H, and D are respectively represented as {0, 1, ..., X}. N {0, 1, ..., X} M {0, 1, ..., X} H {0, 1, ..., X} D};

[0085] 5. The quantization factor of the input variable and the scaling factor of the output variable are determined by the maximum value of the basic fuzzy universe of discourse and the maximum value of the fuzzy subset universe of discourse of the variable:

[0086]

[0087] In the formula, k is the quantization factor of the input variable or the scaling factor of the output variable, X is the maximum value of the basic fuzzy universe of discourse of the variable, and x is the maximum value of the fuzzy subset universe of discourse of the variable. That is, the input variables are the SOC level of the energy storage system and the system bus voltage U. * System bus angular frequency ω * The quantization factor is:

[0088]

[0089] The scaling factors for the output variables reactive power-voltage droop coefficient n, virtual droop coefficient m, virtual inertia time constant H, and virtual damping coefficient D are:

[0090]

[0091] 6. Determine the membership functions of the input and output variables, selecting 3 or 7 fuzzy variables. The fuzzy sets of the input fuzzy linguistic variables V and Ω are divided into 3 fuzzy subsets: negative (N), zero (Z), and positive (P). The fuzzy sets of the input fuzzy linguistic variable s and the output fuzzy linguistic variables N, M, H, and D are divided into 7 fuzzy subsets: negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB), denoted as:

[0092]

[0093] 7. The normal curve in the bell-shaped membership function, which has high sensitivity, is used as the membership function for each fuzzy set. The normal function is shown in the following formula:

[0094]

[0095] In the formula, a represents the median value of the normal membership function, and b represents the width of the normal membership function;

[0096] 8. Determine fuzzy control rules

[0097] Fuzzy rule table of reactive power-voltage droop coefficient n

[0098]

[0099]

[0100] Fuzzy rule table of virtual adjustment coefficient m

[0101]

[0102] Fuzzy rule table of virtual inertia time constant H

[0103]

[0104]

[0105] Fuzzy rule table of virtual damping coefficient D

[0106]

Claims

1. A virtual synchronous adaptive control method for energy storage converters based on fuzzy theory, characterized in that, include: The reactive-voltage link of S1 and VSG simulates the reactive-voltage droop characteristics of a synchronous machine. The active-frequency link of S2 and VSG simulates the active-frequency droop characteristics of the prime mover speed governor and the second-order model of the synchronous machine rotor motion equation. S3. The virtual parameter adaptive optimization stage determines the values ​​of each virtual parameter to adjust the virtual synchronization control parameters of the converter in order to dynamically adapt to the system state. The virtual parameter adaptive step in step S3 uses fuzzy control theory to adaptively adjust the virtual synchronization control parameters of the converter. The specific implementation process is as follows: A1. Determine the input and output variables, including the SOC level of the energy storage system and the system bus voltage. System bus angular frequency As an input variable, the reactive power-voltage droop coefficient Virtual adjustment coefficient Virtual inertia time constant and virtual damping coefficient As an output variable; A2. Determine the quantization factor of the input variable and the scaling factor of the output variable in the virtual parameter adaptive link. The input and output variables of the fuzzy controller are mapped one-to-one with the exact quantities in their respective actual domains and the fuzzy quantities in their respective fuzzy domains through the corresponding quantization factor or scaling factor, thus completing the domain transformation between the exact quantities and the fuzzy quantities. A3. Determine the membership functions of the input and output variables of the virtual parameter adaptive loop to obtain the SOC level of the energy storage system and the system bus voltage. System bus angular frequency fuzzy values; A4. Determine the fuzzy control rules for the virtual parameter adaptive loop and obtain the reactive power-voltage droop coefficient. Virtual adjustment coefficient Virtual inertia time constant and virtual damping coefficient The set value.

2. The virtual synchronous adaptive control method for energy storage converters based on fuzzy theory according to claim 1, characterized in that, The calculation process for the quantization factor of the input variable and the scaling factor of the output variable in the virtual parameter adaptive step A2 is as follows: B1. Let the basic fuzzy universe of discourse for the input variable, the SOC level of the energy storage system, be... System bus voltage The basic fuzzy domain is System bus angular frequency The basic fuzzy domain is Their corresponding fuzzy linguistic variables , , The fuzzy subset domains are respectively denoted as , , ; B2. Let the output variable be the reactive power-voltage droop coefficient. The basic fuzzy domain is Virtual adjustment coefficient The basic fuzzy domain is Virtual inertia time constant The basic fuzzy domain is Virtual damping coefficient The basic fuzzy domain is Their corresponding fuzzy linguistic variables , , , The fuzzy subset domains are respectively denoted as , , , ; B3. The quantization factor of the input variable and the scaling factor of the output variable are determined by the maximum value of the basic fuzzy universe of discourse and the maximum value of the fuzzy subset universe of discourse of the variable: （1）； In the formula, This refers to the quantification factor of the input variable or the scaling factor of the output variable. The maximum value of the basic fuzzy universe of discourse for the variable. The maximum value of the universe of discourse for the fuzzy subset of the variable; B4. Input variable energy storage system SOC level and system bus voltage System bus angular frequency The quantization factor is: （2）； B5. Output variable: reactive power-voltage droop coefficient Virtual adjustment coefficient Virtual inertia time constant and virtual damping coefficient The scaling factor is: （3）。 3. The virtual synchronous adaptive control method for energy storage converters based on fuzzy theory according to claim 2, characterized in that, The virtual parameter adaptive step in step A3 involves the membership functions of the input and output variables, the partitioning of fuzzy subsets of each variable, and the selection of membership functions. The specific implementation process is as follows: C1. Select 3 or 7 fuzzy variables, among which, input fuzzy linguistic variables , The fuzzy set is divided into three fuzzy subsets: negative (N), zero (Z), and positive (P); the input fuzzy linguistic variables are... and output fuzzy linguistic variables , , , The fuzzy set is divided into 7 fuzzy subsets, namely negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB), denoted as: （4）； C2. The normal curve in the bell-shaped membership function, which has high sensitivity, is used as the membership function for each fuzzy set. The normal function is shown in the following formula: （5）； In the formula, This represents the intermediate value of the normal membership function. This represents the width of the normal membership function.

4. The virtual synchronous adaptive control method for energy storage converters based on fuzzy theory according to claim 1, characterized in that, The VSG reactive power-voltage droop equation in step S1 is as follows: （6）； In the formula, This represents the per-unit value of the actual reactive power output of the VSG. This is the per-unit value of the commanded reactive power. This is the per-unit value of the system bus voltage. This is the reactive power-voltage droop coefficient.

5. The virtual synchronous adaptive control method for energy storage converters based on fuzzy theory according to claim 1, characterized in that, In step S2, the active-frequency element of the VSG is used to simulate the motion equations of the prime mover governor and the synchronous machine rotor. Specifically, it simulates the active-frequency droop characteristics of the prime mover governor and the second-order model of the synchronous machine rotor motion equation. The VSG active-frequency droop equation and rotor motion equation used are as follows: （7）； In the formula, This represents the per-unit value of the actual active power output of the VSG. This is the per-unit value of the active power command. This is the per-unit value of the system bus angular frequency. This is a virtual adjustment coefficient. This represents the per-unit value of the active power of the virtual prime mover. The virtual inertia time constant. This is the virtual damping coefficient.

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