Fuzzy control algorithm-based droop control parameter optimization method and system

Abstract

The invention discloses a droop control parameter optimization method and system based on a fuzzy control algorithm, and relates to the technical field of power system control, and the method comprises the following steps: monitoring the frequency deviation and voltage deviation of a power system in real time, and obtaining the current state information of a droop control system; introducing a fuzzy control algorithm, and performing fuzzy reasoning on the frequency deviation and the voltage deviation to obtain an adjustment amount of a droop control parameter; applying the adjustment amount to a droop controller, and dynamically adjusting the frequency and voltage response of the system; and the effectiveness of the optimization method is verified by comparing the dynamic response performance of the system before and after optimization. According to the method, the fuzzy logic is introduced, so that the method can show higher adaptive capacity when facing uncertain and non-linear changes, and the system is allowed to dynamically adjust the droop coefficient according to the actual operation condition, thereby better coping with the fluctuation in the power system.

Description

Technical Field

[0001] The invention relates to the technical field of power system control, and in particular to a droop control parameter optimization method and system based on a fuzzy control algorithm. Background Art

[0002] In power systems, droop control is a common frequency and voltage control method for distributed generation systems. Traditional droop control strategies are simple and easy to implement, but their control performance relies on precise parameter settings and cannot cope with complex system dynamics and uncertainties. Therefore, the droop control parameter optimization method based on fuzzy control algorithm has become one of the current research hotspots.

[0003] As an intelligent control method, fuzzy control can achieve better control performance when the system model is not accurately or completely understood. Introducing fuzzy control into droop control parameter optimization can effectively improve the stability and robustness of the system and meet the control requirements of distributed power generation systems under complex working conditions. Summary of the invention

[0004] In view of the existing problems in the droop control parameter optimization and system based on the fuzzy control algorithm, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that the control performance depends on accurate parameter settings and cannot cope with complex system dynamic characteristics and uncertainties.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a droop control parameter optimization method based on a fuzzy control algorithm, which comprises the following steps:

[0008] Monitor the frequency deviation and voltage deviation of the power system in real time and obtain the current status information of the droop control system;

[0009] The fuzzy control algorithm is introduced to perform fuzzy reasoning on the frequency deviation and voltage deviation to obtain the adjustment amount of the droop control parameter;

[0010] Applying the adjustment amount to the droop controller to dynamically adjust the frequency and voltage response of the system;

[0011] The effectiveness of the optimization method is verified by comparing the dynamic response performance of the system before and after optimization.

[0012] As a preferred solution of the droop control parameter optimization method based on fuzzy control algorithm of the present invention, wherein: the fuzzy control algorithm comprises the following steps:

[0013] Define fuzzy variables, including frequency deviation, voltage deviation and droop coefficient adjustment, wherein each fuzzy variable has multiple membership functions;

[0014] Constructing a fuzzy rule base, and defining corresponding droop coefficient adjustment rules based on the combination of the frequency deviation and the voltage deviation;

[0015] The input frequency deviation and voltage deviation are fuzzified, and the fuzzy rules are inferred according to the fuzzy rule base to obtain the fuzzy output.

[0016] As a preferred solution of the droop control parameter optimization method based on fuzzy control algorithm of the present invention, wherein: the fuzzy rule base includes the following fuzzy rules,

[0017] If the frequency deviation is large in negative and the voltage deviation is large in negative, the droop coefficient adjustment amount is large in increase;

[0018] If the frequency deviation is negative medium, and the voltage deviation is negative medium, then the droop coefficient adjustment amount is increasing medium;

[0019] If the frequency deviation is small negative, and the voltage deviation is small negative, then the droop coefficient adjustment amount is small increase;

[0020] If the frequency deviation is zero and the voltage deviation is zero, the droop coefficient adjustment amount is unchanged;

[0021] If the frequency deviation is positive and small, and the voltage deviation is positive and small, then the droop coefficient adjustment amount is reduced and small;

[0022] If the frequency deviation is in the middle and the voltage deviation is in the middle, the droop coefficient adjustment is reduced;

[0023] If the frequency deviation is positive and large, and the voltage deviation is positive and large, the droop coefficient adjustment amount is reduced and large.

[0024] As a preferred solution of the droop control parameter optimization method based on fuzzy control algorithm of the present invention, wherein: the reasoning process of the fuzzy rule includes:

[0025] According to the frequency deviation and voltage deviation collected in real time, the fuzzy logic system is used to match the corresponding fuzzy rules to calculate the fuzzy output value of the droop coefficient adjustment amount;

[0026] Defuzzification is performed on the fuzzy output value to obtain the value of the droop coefficient adjustment amount;

[0027] Among them, the defuzzification process is to calculate the center of gravity of the fuzzy set, and the calculation formula is expressed as:

[0028]

[0029] In the formula, μ i Indicates the membership value corresponding to each rule in the fuzzy output, x i It represents the specific value corresponding to the fuzzy output variable, and i represents the index of the fuzzy rule.

[0030] As a preferred solution of the droop control parameter optimization method based on the fuzzy control algorithm of the present invention, when calculating the droop coefficient adjustment amount, it is composed of several rules, and a weighted average is performed during the calculation to obtain the final result. The calculation formula is expressed as:

[0031]

[0032] Where ΔQ represents the final calculated droop coefficient adjustment, n represents the total number of successfully matched fuzzy rules, and ω i and Δk i They represent the importance of the i-th fuzzy rule and the droop coefficient adjustment it recommends, respectively.

[0033] As a preferred solution of the droop control parameter optimization method based on fuzzy control algorithm of the present invention, wherein: the weighted average includes the following steps:

[0034] Perform fuzzy processing on the input frequency deviation and voltage deviation;

[0035] According to the frequency deviation membership, the fuzzy set of frequency deviation is calculated;

[0036] According to the voltage deviation membership, the fuzzy set of voltage deviation is calculated;

[0037] According to the fuzzy rule base, matching is performed to obtain the output of the corresponding rule, and the adjustment amount of the output droop coefficient is calculated according to the fuzzy reasoning process.

[0038] As a preferred solution of the droop control parameter optimization method based on fuzzy control algorithm described in the present invention, the parameters of the droop controller are adjusted according to the droop coefficient adjustment amount output by the fuzzy controller and applied to the system in real time. The optimization effect is verified by real-time monitoring of the dynamic response of the system frequency and voltage, and the fuzzy rules are further adjusted as needed.

[0039] In a second aspect, an embodiment of the present invention provides a droop control parameter optimization system based on a fuzzy control algorithm, which includes a monitor, a droop controller, a fuzzy controller, and an optimization module;

[0040] The monitor is used to obtain input frequency deviation and voltage deviation;

[0041] The droop controller is used to monitor the current state information of the droop control system in real time and dynamically adjust the droop parameters of the system;

[0042] The fuzzy controller is used to design a fuzzy rule base and perform fuzzy inference based on the fuzzy control rule base to obtain the adjustment amount of the droop control parameter;

[0043] The optimization module is used to dynamically adjust system parameters according to the adjustment amount and apply them to the power grid in real time to improve the dynamic response performance of the system.

[0044] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned droop control parameter optimization method based on the fuzzy control algorithm is implemented.

[0045] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned droop control parameter optimization method based on the fuzzy control algorithm is implemented.

[0046] The beneficial effects of the present invention are as follows: by introducing fuzzy logic, the method can show stronger adaptability in the face of uncertainty and nonlinear changes, and it allows the system to dynamically adjust the droop coefficient according to the actual operating conditions, so as to better cope with fluctuations in the power system. For example, in the case of large deviations in frequency or voltage, the fuzzy controller can respond quickly and stabilize the system by increasing or decreasing the droop coefficient. By real-time monitoring of the state information of the power system and combining it with a carefully designed fuzzy rule base for reasoning, it is ensured that more accurate power distribution can be achieved even in a complex network environment, which not only improves the efficiency of the system, but also reduces potential problems caused by power imbalance. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0048] Figure 1 Flow chart of the droop control parameter optimization method based on fuzzy control algorithm. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0051] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0052] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0053] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0054] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] Example 1

[0056] Reference Figure 1, which is the first embodiment of the present invention, and provides a droop control parameter optimization method based on a fuzzy control algorithm, comprising the following steps:

[0057] S1. Monitor the frequency deviation and voltage deviation of the power system in real time to obtain the current status information of the droop control system.

[0058] For example: using MATLAB / Simulink as a simulation platform.

[0059] The system configuration includes: Assume that we have an island-type microgrid consisting of multiple distributed power sources (such as wind turbines, solar photovoltaic panels) and energy storage devices (such as batteries). Each distributed power source is equipped with an inverter interface to convert the generated power into AC form to access local loads or store it in energy storage devices. In addition, necessary sensors are installed to measure key parameters such as frequency and voltage.

[0060] Connect a high-precision frequency meter to each inverter output, which can continuously record the actual output frequency, compare it with the set target frequency, and calculate the frequency deviation;

[0061] Similarly, voltage sensors are set at each node to detect the instantaneous voltage value on the bus and compare it with the reference voltage to obtain the voltage deviation. The data of these sensors should be able to be transmitted to the central controller in real time or directly input into the Simulink model for processing.

[0062] Once the frequency deviation and voltage deviation data are obtained, they can be transmitted to the main control unit responsible for executing the droop control algorithm through the communication network or internal bus. At this time, the main control unit will fuzzify the two deviations according to the preset fuzzy rule base and determine the appropriate droop coefficient adjustment amount to compensate for the current imbalance.

[0063] S2. Introduce fuzzy control algorithm, perform fuzzy reasoning on frequency deviation and voltage deviation, and obtain the adjustment amount of droop control parameters.

[0064] The fuzzy control algorithm includes the following steps:

[0065] Define fuzzy variables, including frequency deviation, voltage deviation and droop coefficient adjustment, wherein each fuzzy variable has multiple membership functions;

[0066] Constructing a fuzzy rule base, and defining corresponding droop coefficient adjustment rules based on the combination of the frequency deviation and the voltage deviation;

[0067] The input frequency deviation and voltage deviation are fuzzified, and the fuzzy rules are inferred according to the fuzzy rule base to obtain the fuzzy output.

[0068] Frequency deviation: defined as "Negative Big" (NB), "Negative Middle" (NM), "Negative Small" (NS), "Zero" (ZE), "Positive Small" (PS), "Positive Middle" (PM), and "Positive Big" (PB).

[0069] Voltage deviation: defined as "negative big" (NB), "negative middle" (NM), "negative small" (NS), "zero" (ZE), "positive small" (PS), "positive middle" (PM), and "positive big" (PB).

[0070] Droop coefficient adjustment amount: defined as "large decrease" (BD), "medium decrease" (MD), "small decrease" (SD), "unchanged" (ZE), "small increase" (SI), "medium increase" (MI), and "large increase" (BI).

[0071] The fuzzy rule base includes the following fuzzy rules:

[0072] If the frequency deviation is large in negative and the voltage deviation is large in negative, the droop coefficient adjustment amount is large in increase;

[0073] If the frequency deviation is negative medium, and the voltage deviation is negative medium, then the droop coefficient adjustment amount is increasing medium;

[0074] If the frequency deviation is small negative, and the voltage deviation is small negative, then the droop coefficient adjustment amount is small increase;

[0075] If the frequency deviation is zero and the voltage deviation is zero, the droop coefficient adjustment amount is unchanged;

[0076] If the frequency deviation is positive and small, and the voltage deviation is positive and small, then the droop coefficient adjustment amount is reduced and small;

[0077] If the frequency deviation is in the middle and the voltage deviation is in the middle, the droop coefficient adjustment is reduced;

[0078] If the frequency deviation is positive and large, and the voltage deviation is positive and large, the droop coefficient adjustment amount is reduced and large.

[0079] The reasoning process of fuzzy rules includes:

[0080] According to the frequency deviation and voltage deviation collected in real time, the fuzzy logic system is used to match the corresponding fuzzy rules to calculate the fuzzy output value of the droop coefficient adjustment amount;

[0081] Defuzzification is performed on the fuzzy output value to obtain the value of the droop coefficient adjustment amount;

[0082] Among them, the defuzzification process is to calculate the center of gravity of the fuzzy set, and the calculation formula is expressed as:

[0083]

[0084] In the formula, μ i Indicates the membership value corresponding to each rule in the fuzzy output, x i It represents the specific value corresponding to the fuzzy output variable, and i represents the index of the fuzzy rule.

[0085] S3. Apply the adjustment amount to the droop controller to dynamically adjust the frequency and voltage response of the system.

[0086] When calculating the droop coefficient adjustment, it is composed of several rules, and the weighted average is used to obtain the final result. The calculation formula is expressed as:

[0087]

[0088] Where ΔQ represents the final calculated droop coefficient adjustment, n represents the total number of successfully matched fuzzy rules, and ω i and Δk i They represent the importance of the i-th fuzzy rule and the droop coefficient adjustment it recommends, respectively.

[0089] The weighted average includes the following steps:

[0090] Perform fuzzy processing on the input frequency deviation and voltage deviation;

[0091] According to the frequency deviation membership, the fuzzy set of frequency deviation is calculated;

[0092] According to the voltage deviation membership, the fuzzy set of voltage deviation is calculated;

[0093] According to the fuzzy rule base, matching is performed to obtain the output of the corresponding rule, and the adjustment amount of the output droop coefficient is calculated according to the fuzzy reasoning process.

[0094] The center of gravity method is used to calculate the specific droop coefficient adjustment ΔQ, and then ΔQ is applied to the droop controller to adjust the frequency and voltage response of the system.

[0095] The droop control parameter optimization method based on fuzzy control algorithm can effectively dynamically adjust the frequency and voltage response of the system and improve the stability and response speed of the system. This method is particularly suitable for microgrids in distributed power generation systems and can significantly improve the adaptability and robustness of the system.

[0096] S4. Verify the effectiveness of the optimization method by comparing the dynamic response performance of the system before and after optimization.

[0097] According to the droop coefficient adjustment amount output by the fuzzy controller, the parameters of the droop controller are adjusted and applied to the system in real time. By real-time monitoring of the dynamic response of the system frequency and voltage, the optimization effect is verified, and the fuzzy rules are further adjusted as needed.

[0098] In summary, by introducing fuzzy logic, this method can show stronger adaptability in the face of uncertainty and nonlinear changes. It allows the system to dynamically adjust the droop coefficient according to the actual operating conditions, so as to better cope with fluctuations in the power system. For example, in the case of large deviations in frequency or voltage, the fuzzy controller can respond quickly and stabilize the system by increasing or decreasing the droop coefficient. By monitoring the state information of the power system in real time and combining it with a carefully designed fuzzy rule base for reasoning, it ensures that more accurate power distribution can be achieved even in complex network environments, which not only improves the efficiency of the system, but also reduces potential problems caused by power imbalance.

[0099] Example 2

[0100] On the basis of the first embodiment, this embodiment further provides a droop control parameter optimization system based on a fuzzy control algorithm, comprising a monitor, a droop controller, a fuzzy controller, and an optimization module;

[0101] The monitor is used to obtain input frequency deviation and voltage deviation;

[0102] The droop controller is used to monitor the current state information of the droop control system in real time and dynamically adjust the droop parameters of the system;

[0103] The fuzzy controller is used to design a fuzzy rule base and perform fuzzy inference based on the fuzzy control rule base to obtain the adjustment amount of the droop control parameter;

[0104] The optimization module is used to dynamically adjust system parameters according to the adjustment amount and apply them to the power grid in real time to improve the dynamic response performance of the system.

[0105] This embodiment also provides a computer device, which is suitable for the case of a droop control parameter optimization method based on a fuzzy control algorithm, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the droop control parameter optimization method based on the fuzzy control algorithm proposed in the above embodiment.

[0106] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0107] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for optimizing droop control parameters based on a fuzzy control algorithm as proposed in the above embodiment is implemented.

[0108] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A droop control parameter optimization method based on fuzzy control algorithm, characterized in that: The following steps are included: Monitor the frequency deviation and voltage deviation of the power system in real time and obtain the current status information of the droop control system; The fuzzy control algorithm is introduced to perform fuzzy reasoning on the frequency deviation and voltage deviation to obtain the adjustment amount of the droop control parameter; Applying the adjustment amount to the droop controller to dynamically adjust the frequency and voltage response of the system; The effectiveness of the optimization method is verified by comparing the dynamic response performance of the system before and after optimization.

2. The droop control parameter optimization method based on fuzzy control algorithm according to claim 1, characterized in that: The fuzzy control algorithm includes the following steps: Define fuzzy variables, including frequency deviation, voltage deviation and droop coefficient adjustment, wherein each fuzzy variable has multiple membership functions; Constructing a fuzzy rule base, and defining corresponding droop coefficient adjustment rules based on the combination of the frequency deviation and the voltage deviation; The input frequency deviation and voltage deviation are fuzzified, and the fuzzy rules are inferred according to the fuzzy rule base to obtain the fuzzy output.

3. The droop control parameter optimization method based on fuzzy control algorithm according to claim 2, characterized in that: The fuzzy rule base includes the following fuzzy rules: If the frequency deviation is large in negative and the voltage deviation is large in negative, the droop coefficient adjustment amount is large in increase; If the frequency deviation is negative medium, and the voltage deviation is negative medium, then the droop coefficient adjustment amount is increasing medium; If the frequency deviation is small negative, and the voltage deviation is small negative, then the droop coefficient adjustment amount is small increase; If the frequency deviation is zero and the voltage deviation is zero, the droop coefficient adjustment amount is unchanged; If the frequency deviation is positive and small, and the voltage deviation is positive and small, then the droop coefficient adjustment amount is reduced and small; If the frequency deviation is in the middle and the voltage deviation is in the middle, the droop coefficient adjustment is in the decreasing middle; If the frequency deviation is positive and large, and the voltage deviation is positive and large, the droop coefficient adjustment amount is reduced and large.

4. The droop control parameter optimization method based on fuzzy control algorithm according to claim 3, characterized in that: The reasoning process of the fuzzy rules includes: According to the frequency deviation and voltage deviation collected in real time, the fuzzy logic system is used to match the corresponding fuzzy rules to calculate the fuzzy output value of the droop coefficient adjustment amount; Defuzzification is performed on the fuzzy output value to obtain the value of the droop coefficient adjustment amount; Among them, the defuzzification process is to calculate the center of gravity of the fuzzy set, and the calculation formula is expressed as: In the formula, μ i Indicates the membership value corresponding to each rule in the fuzzy output, x i It represents the specific value corresponding to the fuzzy output variable, and i represents the index of the fuzzy rule.

5. The droop control parameter optimization method based on fuzzy control algorithm according to claim 4, characterized in that: When calculating the droop coefficient adjustment, it is composed of several rules, and the weighted average is used to obtain the final result. The calculation formula is expressed as: Where ΔQ represents the final calculated droop coefficient adjustment, n represents the total number of successfully matched fuzzy rules, and ω i and Δk i They represent the importance of the i-th fuzzy rule and the droop coefficient adjustment it recommends, respectively.

6. The droop control parameter optimization method based on fuzzy control algorithm according to claim 5, characterized in that: The weighted average comprises the following steps: Perform fuzzy processing on the input frequency deviation and voltage deviation; According to the frequency deviation membership, the fuzzy set of frequency deviation is calculated; According to the voltage deviation membership, the fuzzy set of voltage deviation is calculated; According to the fuzzy rule base, matching is performed to obtain the output of the corresponding rule, and the adjustment amount of the output droop coefficient is calculated according to the fuzzy reasoning process.

7. The droop control parameter optimization method based on fuzzy control algorithm according to claim 6, characterized in that: According to the droop coefficient adjustment amount output by the fuzzy controller, the parameters of the droop controller are adjusted and applied to the system in real time. By real-time monitoring of the dynamic response of the system frequency and voltage, the optimization effect is verified, and the fuzzy rules are further adjusted as needed.

8. A droop control parameter optimization system based on a fuzzy control algorithm, based on the droop control parameter optimization method based on a fuzzy control algorithm according to any one of claims 1 to 7, characterized in that: Includes monitor, droop controller, fuzzy controller, and optimization module; The monitor is used to obtain input frequency deviation and voltage deviation; The droop controller is used to monitor the current state information of the droop control system in real time and dynamically adjust the droop parameters of the system; The fuzzy controller is used to design a fuzzy rule base and perform fuzzy inference based on the fuzzy control rule base to obtain the adjustment amount of the droop control parameter; The optimization module is used to dynamically adjust system parameters according to the adjustment amount and apply them to the power grid in real time to improve the dynamic response performance of the system.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the droop control parameter optimization method based on the fuzzy control algorithm according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the droop control parameter optimization method based on the fuzzy control algorithm according to any one of claims 1 to 7 are implemented.

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