Offshore wind power flexible direct current converter station control method and system based on fuzzy control

Abstract

The invention relates to an offshore wind power flexible direct current converter station control method and system based on fuzzy control, and the method comprises the following steps: constructing an MMC mathematical model according to the MMC converter structure data of an offshore wind power flexible direct current converter station; constructing a VSG equivalent model of the MMC converter based on the MMC mathematical model; and adopting a fuzzy controller based on a JAYA algorithm to perform adaptive optimization on VSG parameters in the VSG equivalent model so as to obtain an optimal angular frequency control scheme output by the VSG equivalent model. Compared with the prior art, the method gives consideration to the operation economy, safety and stability of the offshore wind power flexible DC power transmission system, carries out the adaptive optimization of the parameters of the control strategy of the flexible DC converter station, so as to suppress the fluctuation of the active power and frequency of the system under the disturbance of the system, effectively increases the inertia of the system, and improves the reliability of the system. And the active frequency supporting capability of the system is improved.

Description

Technical Field

[0001] This invention relates to the field of control technology for offshore wind power flexible DC converter stations, and in particular to a control method and system for offshore wind power flexible DC converter stations based on fuzzy control. Background Technology

[0002] As offshore wind farms expand into deeper waters and on a larger scale, their distance from shore continues to increase. Flexible DC transmission systems (VSC-HVDC) have become the preferred structure for offshore wind power transmission, and modular multilevel converters (MMCs) have become the mainstream choice due to their low loss, low harmonic content, and high scalability. For large-scale, long-distance power transmission, traditional AC submarine cables suffer from high losses and costs, while flexible DC transmission offers significant advantages such as no commutation failure risk, independent active and reactive power control, and suitability for long-distance, high-capacity transmission, making it more suitable for transmitting offshore wind power. However, this also raises a series of problems. The isolation provided by VSC-HVDC means that offshore wind farms cannot provide inertia support to the grid. With the large-scale integration of offshore wind power into the grid, the proportion of traditional synchronous generators in the grid gradually decreases, leading to a significant drop in grid inertia and consequently affecting system frequency stability.

[0003] In order to address a series of frequency issues brought about by the large-scale integration of offshore wind power into the grid, many countries' grid guidelines have put forward new requirements for the participation of offshore wind power in system frequency regulation, and many scholars have also conducted a lot of related research.

[0004] To enhance the active support capabilities of power electronic grid-connected equipment, Virtual Synchronous Generator (VSG) technology has been proposed. By simulating the rotor motion equations and excitation system characteristics of a synchronous generator through control algorithms, the converter exhibits inertia, damping, and primary frequency and voltage regulation capabilities similar to a synchronous machine. Applying VSG technology to the grid-connected side of offshore wind power flexible DC converter stations can effectively provide the system with necessary virtual inertia and damping, improving system frequency stability and enhancing adaptability to weak grids. In actual operation, offshore wind farm output exhibits strong fluctuations, the grid strength at the connection point may dynamically change, and the system operating point may also deviate. Fixed-parameter VSG controllers struggle to maintain optimal dynamic performance and stability margins under a wide range of variable operating conditions. Improper parameter settings may lead to insufficient system damping causing oscillations, slow response speeds, excessive overshoot, or even instability under certain conditions.

[0005] Numerous studies have addressed the optimization of VSG parameters. Some have proposed a dual-fuzzy improved VSG control strategy to adaptively adjust the VSG's inertia and damping coefficients; others have used changes in the battery's state of charge (SOC) to generate partial active power reference values ​​for the VSG through an energy storage coordinating controller to coordinate adaptive parameters; still others have proposed a fractional-order virtual inertial VSG control strategy. Invention CN117117901A discloses a frequency control method for offshore wind power flexible DC systems, employing an adaptive frequency control strategy for wind turbines across the entire wind speed range. This method optimizes frequency regulation parameters through a unified structural model and combines fuzzy control to adjust the frequency regulation parameters of the offshore wind power flexible DC system in real time. However, for the specific scenario of offshore wind power connected to the grid via VSC-HVDC, the system structure, dynamic characteristics, and operational constraints are significantly different, and research specifically addressing adaptive optimization strategies for VSG parameters in this scenario remains insufficient. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a control method and system for offshore wind power flexible DC converter stations based on fuzzy control, thereby increasing system inertia and improving the system's active frequency support capability.

[0007] The objective of this invention can be achieved through the following technical solutions: A control method for an offshore wind power flexible DC converter station based on fuzzy control includes the following steps: Based on the structural data of the MMC converter in the offshore wind power flexible DC converter station, an MMC mathematical model is constructed. Based on the MMC mathematical model, construct the VSG equivalent model of the MMC converter; A fuzzy controller based on the JAYA algorithm is used to adaptively optimize the VSG parameters in the VSG equivalent model in order to obtain the optimal angular frequency control scheme output by the VSG equivalent model.

[0008] Furthermore, the expression for the MMC mathematical model is: In the formula, The active power output of the MMC converter. The reactive power output of the MMC converter. The AC voltage output by the MMC converter. The voltage on the AC grid side. X is the power angle, and X is the equivalent reactance of the MMC converter.

[0009] Furthermore, the expression for the VSG equivalent model of the MMC converter is as follows: In the formula, J This is a virtual moment of inertia; The rated angular frequency; The output angular frequency of the active control loop in the VSG equivalent model; P m For virtual mechanical power; P e Electromagnetic power; D The damping coefficient; P ref Active power command; k p The active power-frequency droop factor; The output angle of the VSG equivalent model.

[0010] Furthermore, the VSG equivalent model also includes a small-signal model of the VSG active power loop, the expression of which is: In the formula, The step value given by the active power. ; The step value given by the power grid frequency. ζ represents the damping ratio of the second-order oscillating system; This represents the natural angular frequency of the second-order oscillation system. This indicates the overshoot of the second-order oscillating system; T s This indicates the settling time of the second-order oscillation system; This represents the change in VSG output electromagnetic power under the condition that the rated angular frequency of the power grid remains constant. Changes in active power reference value The closed-loop transfer function; The value for VSG is called the power transfer factor; The power coefficient for virtual damping; This refers to the change in the output electromagnetic power of the VSG while the active power reference value remains constant. Changes in active power reference value The closed-loop transfer function; This is the active power-frequency droop factor.

[0011] Furthermore, the expression for the dynamic response analysis model corresponding to the VSG equivalent model is: In the formula, The active power deviation of the system, The output angular frequency of the active control loop in the VSG equivalent model. The rated angular frequency, The active power-frequency droop factor is... The damping coefficient is... For virtual rotational inertia, This is an active power command. Electromagnetic power, This is the rated angular frequency.

[0012] Furthermore, in the dynamic response analysis model of the VSG equivalent model, based on virtual inertia... J and virtual damping D The dynamic response at angular frequency is obtained. J and D The adaptive adjustment rule; based on the power deviation For virtual inertia J and virtual damping D The impact, received The adjustment rules.

[0013] Furthermore, the expression for the fuzzy controller based on the JAYA algorithm is: In the formula, e Represents the difference in angular frequency. e c Represents the rate of change of angular frequency. e and e c For the input of the fuzzy controller; k 1 and k 2 represents the inertia and damping power adjustment coefficient; J 0、 D 0 represents the initial virtual inertia and damping coefficient; , The fuzzy controller outputs a correction value; It is the absolute value of the difference between the output power and the set value; The fuzzy controller controls the input variables. e and e c After fuzzification using fuzzy sets, membership functions are determined by combining Gaussian and generalized bell shapes, and defuzzification is performed using the centroid method, thereby determining the output correction amount of the fuzzy controller. and To address virtual inertia J and virtual damping D To take control; The fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}, where NB is negative large, NM is negative medium, NS is negative small, ZO is zero, PS is positive small, PM is positive medium, and PB is positive large.

[0014] Furthermore, the virtual inertia of the fuzzy controller based on the JAYA algorithm J and virtual damping D The constraints include: In the formula, P max This represents the maximum active power output of the VSG. f c max The maximum value of the cutoff frequency. This is the total equivalent inductive reactance along the connection path between the MMC converter and the power grid.

[0015] Furthermore, the solution process for the fuzzy controller based on the JAYA algorithm includes: S301: Set the upper and lower limits of the optimization search space and perform population initialization; the expression for population initialization is: In the formula, X 0 represents the initial group. X max and X min These are the upper and lower bounds of the optimization search space, respectively, and rand is a uniformly random number in (0, 1); S302: Find the best and worst solutions in the current population; S303: Update the population individuals based on the current optimal and worst solutions. The corresponding update expression is: In the formula, X For design variables, X j,k,i Let j be the j-th variable of the k-th individual in the i-th iteration. X ’ j,k,i for X j,k,i Updated value after iteration r 1,j,i and r 2,j,i Let each represent a random number in (0, 1). X j,best,i and X j,worst,iLet represent the best and worst solutions among all populations during the i-th iteration; S304: Determine whether the fitness function value of the updated population individual is less than the fitness function value of the current optimal solution. If so, replace the original population individual with the updated population individual; otherwise, keep the original population individual. The expression for calculating the fitness function value is: In the formula, F is the fitness function value. This refers to the system frequency deviation. S305: Determine whether the preset termination condition is met. If yes, output the final solution result; otherwise, return to step S302. The termination condition is determined based on the time integral of the absolute error, and the expression for calculating the time integral of the absolute error is as follows: In the formula, This is the time integral value of the absolute error.

[0016] This invention also provides a control system for an offshore wind power flexible DC converter station based on fuzzy control, comprising the steps of operating the control method for an offshore wind power flexible DC converter station based on fuzzy control as described above, including: A module is established to construct an MMC mathematical model based on the structural data of the MMC converter in the offshore wind power flexible DC converter station; The construction module is used to construct the VSG equivalent model of the MMC converter based on the MMC mathematical model; The optimization module is used to adaptively optimize the VSG parameters in the VSG equivalent model using a fuzzy controller based on the JAYA algorithm, so as to obtain the optimal angular frequency control scheme output by the VSG equivalent model.

[0017] Compared with the prior art, the present invention has the following advantages: (1) This invention fully considers that the isolation characteristics of VSC-HVDC make it impossible for offshore wind farms to provide inertial support to the power grid. With the continuous increase in the penetration rate of offshore wind power, the problem of frequency stability caused by the decrease in the equivalent inertia of the system is becoming increasingly prominent. The invention introduces the VSG virtual synchronous machine control architecture on the grid-connected side of the flexible DC converter station to simulate the inertia and damping characteristics of the synchronous generator and provide the necessary frequency and voltage support for the system. Then, the parameters of VSG were further adaptively optimized, and a fuzzy controller based on the JAYA algorithm was designed. The control parameters were optimized and tuned to enable it to adapt to complex operating conditions such as fluctuations in offshore wind power output and changes in AC grid strength, thereby improving the active frequency support capability of the offshore wind power flexible DC system.

[0018] (2) Based on the dynamic response analysis characteristics of the VSG equivalent model, this invention determines the adaptive adjustment rules of virtual inertia and damping parameters, and uses the difference in angular frequency and the rate of change of angular frequency as the input of the fuzzy controller. The control rules of the fuzzy controller are formulated by algorithm synthesis through fuzzy theory, and the constraints of virtual inertia and damping are proposed. It is applicable to the complex working conditions of offshore wind farms. In the solution process, the adaptive dynamic optimization solution is achieved through the JAYA algorithm, which effectively suppresses the active power and frequency fluctuations of the system under disturbance, optimizes the dynamic response capability of the system, improves the overall system stability, and enhances the active frequency support capability of the system. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a control method for an offshore wind power flexible DC converter station based on fuzzy control provided in an embodiment of the present invention. Figure 2 This is a diagram of the MMC topology of the method of the present invention; Figure 3 This is a block diagram of the VSG small-signal model control method of the present invention; Figure 4 This is a block diagram of the fuzzy adaptive optimization control method of the present invention; Figure 5 This is a flowchart of the JAYA algorithm in this invention; Figure 6 This is a voltage waveform diagram from the present invention; Figure 7 This is a comparison diagram of the frequency response waveforms in this invention; Figure 8 This is a comparison diagram of active power waveforms in this invention. Detailed Implementation

[0020] 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, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0023] Example 1 like Figure 1 As shown, a control method for an offshore wind power flexible DC converter station based on fuzzy control includes the following steps: S1: Based on the structural data of the MMC converter in the offshore wind power flexible DC converter station, construct the MMC mathematical model, such as... Figure 2 As shown; As a preferred but non-limiting embodiment, the established MMC model includes its output active power. P e and reactive power Q e Its expression is as follows: In the formula, The active power output of the MMC converter. X represents the reactive power output of the MMC converter, and X is the equivalent reactance of the MMC converter, which is much greater than 1. R Thus ignoring R To simplify calculations.

[0024] S2: Construct the VSG equivalent model of the MMC converter based on the MMC mathematical model; As a preferred but non-limiting embodiment, the established VSG equivalent model of MMC includes active power control of VSG, and its expression is as follows: In the formula, J This is a virtual moment of inertia; The rated angular frequency; The output angular frequency of the active control loop in the VSG equivalent model; P m For virtual mechanical power; P e Electromagnetic power; D The damping coefficient; P ref Active power command; k p The active power-frequency droop factor; The output angle of the VSG equivalent model.

[0025] The established VSG equivalent model of MMC includes the VSG small-signal model, and the control block diagram is as follows: Figure 3 As shown, its expression is as follows: In the formula, The step value given by the active power. ; The step value given by the power grid frequency. ζ is the damping ratio of the second-order oscillating system. It is the natural oscillation angular frequency. For overshoot, T s To adjust the time.

[0026] The VSG equivalent model provides the physical background and parameter meaning for the small-signal model. The parameters J and D of VSG are components of the state matrix of the small-signal model. Small-signal models are mathematical tools for analyzing and optimizing the dynamic performance of VSG controllers. Through small-signal analysis, we can quantitatively study how J and D affect the stability and dynamic response of the system, thus providing theoretical guidance for the tuning and optimization of VSG parameters.

[0027] S3: A fuzzy controller based on the JAYA algorithm is used to adaptively optimize the VSG parameters in the VSG equivalent model to obtain the optimal angular frequency control scheme output by the VSG equivalent model, so that it can adapt to complex operating conditions such as offshore wind power output fluctuations and AC grid intensity changes, and improve the system's active frequency support capability.

[0028] As a preferred but non-limiting embodiment, the dynamic response analysis model corresponding to the VSG equivalent model is expressed as follows: In the formula, let , , .

[0029] As can be seen from the above formula, increasing the virtual inertia... J It can make the rate of change of frequency A smoother surface, thus improving system stability; increased virtual damping D and active sagging adjustment coefficient K p It helps to reduce the amount of change in angular frequency. This reduces overshoot during frequency regulation, thus decreasing the amount of overshoot. This indicates the virtual inertia of the VSG. J and damping parameters D It has a significant impact on the frequency immunity performance.

[0030] Furthermore, the angular frequency shift and its rate of change of the VSG output exhibit different characteristics at different stages, and can be dynamically adjusted. Jand D This achieves a dynamic response to angular frequencies; the angular frequency difference is defined as... e The rate of change of angular frequency is e c Based on the dynamic response analysis of VSG, it can be concluded that J and D The adaptive adjustment rules are shown in Table 1.

[0031] Table 1 Select the difference in angular frequency e and rate of change of angular frequency e c As an input to the fuzzy controller, its expression is as follows: In the formula, e Represents the difference in angular frequency. e c Represents the rate of change of angular frequency, and selects... e and e c As input to the fuzzy controller.

[0032] For the difference in angular frequency e and rate of change of angular frequency e c Power deviation For virtual inertia J and virtual damping D The main impact is reflected in the balance of the system's dynamic response. The adjustment rules are shown in Table 2.

[0033] Table 2 To further explain, power deviation When the value is large, increase the virtual inertia. J This helps mitigate frequency fluctuations, enhances the system's immunity to power disturbances, and prevents instability caused by rapid frequency changes. Increasing virtual damping... D This can accelerate the dissipation of energy from power fluctuations, reduce oscillations, and enhance system stability; power deviation When the virtual inertia J is relatively small, reducing the virtual inertia J can improve the system's response speed to frequency changes, while reducing the virtual damping can improve the system's response speed to frequency changes. D This helps improve system recovery speed and avoids sluggish regulation caused by excessive damping. By appropriately adjusting virtual inertia and damping, the stability and response capability of the system under different power fluctuation conditions can be effectively improved.

[0034] Fuzzy adaptive optimization control block diagram as follows Figure 4As shown, the VSG fuzzy controller model has the following expression: In the formula, k 1 and k 2 represents the inertia and damping power adjustment coefficient; J 0、 D 0 represents the initial virtual inertia and damping coefficient; , The fuzzy controller outputs a correction value; It is the absolute value of the difference between the output power and the set value.

[0035] Input variables in a fuzzy controller e , e c With output variables J , D The fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}, where NB is negative large, NM is negative medium, NS is negative small, ZO is zero, PS is positive small, PM is positive medium, and PB is positive large. Membership functions are determined using a combination of Gaussian and generalized bell shapes, and the centroid method is employed for defuzzification. Based on fuzzy theory, an algorithm is synthesized to develop a fuzzy controller. J , D The control rules were summarized, and the parameters were determined. J , D The control rules are shown in Tables 3 and 4.

[0036] Table 3 Table 4 The constraints for virtual inertia and damping in a fuzzy controller are expressed as follows: In the formula, P max This represents the maximum active power output of the VSG. f c max This represents the maximum value of the cutoff frequency.

[0037] Reference Figure 5 The solution process for the fuzzy controller based on the JAYA algorithm includes: S301: Set the upper and lower limits of the optimization search space and perform population initialization; the expression for population initialization is: In the formula, X 0 represents the initial group. Xmax and X min These are the upper and lower bounds of the optimization search space, respectively, and rand is a uniformly random number in (0, 1); S302: Find the best and worst solutions in the current population; S303: Update the population individuals based on the current optimal and worst solutions. The corresponding update expression is: In the formula, X For design variables, X j,k,i Let j be the j-th variable of the k-th individual in the i-th iteration. X ’ j,k,i for X j,k,i Updated value after iteration r 1,j,i and r 2,j,i Let each represent a random number in (0, 1). X j,best,i and X j,worst,i Let represent the best and worst solutions among all populations during the i-th iteration; This indicates that the current solution is gradually approaching the optimal solution. This means that the current solution is gradually moving away from the worst solution; S304: Determine whether the fitness function value of the updated population individual is less than the fitness function value of the current optimal solution. If so, replace the original population individual with the updated population individual; otherwise, keep the original population individual. Optionally, to suppress system frequency oscillations, frequency error is selected as the performance evaluation index, and the fitness function is as follows: In the formula, F is the fitness function value; S305: Determine whether the preset termination condition is met. If yes, output the final solution result; otherwise, return to step S302. The choice of objective function in the JAYA algorithm is the core of the optimization algorithm, and its rationality directly determines the performance of the optimization result. This study uses the time integral of absolute error (ITAE) as the performance evaluation standard, and uses it to directly guide the search process of the JAYA algorithm. The expression for ITAE is as follows: In the formula, This is the time integral value of the absolute error.

[0038] The effectiveness of the VSG parameter fuzzy adaptive optimization control based on the JAYA algorithm for offshore wind power flexible DC converter stations provided by this invention will be verified through specific implementation data.

[0039] The rotational inertia and damping coefficient of the VSG are adaptively optimized through simulation fuzzy control, enabling the system to respond quickly to external disturbances and maintain stable operation. Simultaneously, the JAYA algorithm is used to optimize and adjust parameters, effectively suppressing active power and frequency fluctuations under disturbances, optimizing the system's dynamic response capability, improving overall system stability, and enhancing the system's active frequency support capability. The data for this embodiment are shown below: In this experiment, a VSG grid-connected simulation model based on MMC was established in Matlab / Simulink. Some parameter settings are shown in Table 5: Table 5 The MMC grid-side voltage waveform is as follows: Figure 6 As shown, the three-phase voltage exhibits a sinusoidal waveform, with the phase difference maintained at a stable 120° and the amplitude stable, demonstrating the low harmonic advantages of the multi-level topology. The VSG control effectively ensures waveform stability.

[0040] Frequency response waveform as follows Figure 7 As shown, when the VSG uses fixed parameter control, although the system frequency can recover to stability after a short adjustment after being disturbed, the frequency fluctuation is relatively more severe than that of adaptive control, and the buffering effect is poor. The lowest point of the system frequency is 49.76Hz. However, when the virtual synchronous machine parameter fuzzy adaptive optimization control strategy based on the JAYA algorithm proposed in this paper is adopted, the lowest point of the system frequency is 49.82Hz. After the system frequency recovers, the frequency oscillation amplitude is significantly smaller than that of conventional VSG control, and the overshoot at the highest point of the frequency is reduced by 45%, resulting in a better frequency modulation effect.

[0041] Active power waveform as follows Figure 8 As shown, when the VSG uses fixed parameter control, the active power can recover to stability after a short period of adjustment after the system is disturbed, but the overshoot is large, with the maximum offset in the initial stage being about 260kW. However, when the virtual synchronous machine parameter fuzzy adaptive optimization control strategy based on the JAYA algorithm proposed in this paper is adopted, the offset of active power is significantly reduced, with the maximum offset in the initial stage being only 195kW. The power overshoot and rate of change are significantly reduced, effectively reducing the impact of power mutation on the system.

[0042] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A control method for an offshore wind power flexible DC converter station based on fuzzy control, characterized in that, Includes the following steps: Based on the structural data of the MMC converter in the offshore wind power flexible DC converter station, an MMC mathematical model is constructed. Based on the MMC mathematical model, construct the VSG equivalent model of the MMC converter; A fuzzy controller based on the JAYA algorithm is used to adaptively optimize the VSG parameters in the VSG equivalent model in order to obtain the optimal angular frequency control scheme output by the VSG equivalent model.

2. The control method for an offshore wind power flexible DC converter station based on fuzzy control according to claim 1, characterized in that, The expression for the MMC mathematical model is: In the formula, The active power output of the MMC converter. The reactive power output of the MMC converter. The AC voltage output by the MMC converter. The voltage on the AC grid side. X is the power angle, and X is the equivalent reactance of the MMC converter.

3. The control method for an offshore wind power flexible DC converter station based on fuzzy control according to claim 2, characterized in that, The expression for the VSG equivalent model of the MMC converter is: In the formula, J This is a virtual moment of inertia; The rated angular frequency; The output angular frequency of the active control loop in the VSG equivalent model; P m For virtual mechanical power; P e Electromagnetic power; D The damping coefficient; P ref Active power command; k p The active power-frequency droop factor; The output angle of the VSG equivalent model.

4. The control method for an offshore wind power flexible DC converter station based on fuzzy control according to claim 3, characterized in that, The VSG equivalent model also includes a small-signal model of the VSG active power loop, the expression of which is: In the formula, The step value given by the active power. ; The step value given by the power grid frequency. ζ represents the damping ratio of the second-order oscillating system; This represents the natural angular frequency of the second-order oscillation system. This indicates the overshoot of the second-order oscillating system; T s This indicates the settling time of the second-order oscillation system; This represents the change in VSG output electromagnetic power under the condition that the rated angular frequency of the power grid remains constant. Changes in active power reference value The closed-loop transfer function; The value for VSG is called the power transfer factor; The power coefficient for virtual damping; This refers to the change in the output electromagnetic power of the VSG while the active power reference value remains constant. Changes in active power reference value The closed-loop transfer function; This is the active power-frequency droop factor.

5. The control method for an offshore wind power flexible DC converter station based on fuzzy control according to claim 4, characterized in that, The expression for the dynamic response analysis model corresponding to the VSG equivalent model is: In the formula, The active power deviation of the system, The output angular frequency of the active control loop in the VSG equivalent model. The rated angular frequency, The active power-frequency droop factor is... The damping coefficient is... For virtual rotational inertia, Active power command, Electromagnetic power, This is the rated angular frequency.

6. The control method for an offshore wind power flexible DC converter station based on fuzzy control according to claim 5, characterized in that, In the dynamic response analysis model of the VSG equivalent model, based on virtual inertia J and virtual damping D The dynamic response at angular frequency is obtained. J and D The adaptive adjustment rule; based on the power deviation For virtual inertia J and virtual damping D The impact, received The adjustment rules.

7. The control method for an offshore wind power flexible DC converter station based on fuzzy control according to claim 5, characterized in that, The expression for the fuzzy controller based on the JAYA algorithm is: In the formula, e Represents the difference in angular frequency. e c Represents the rate of change of angular frequency. e and e c For the input of the fuzzy controller; k 1 and k 2 represents the inertia and damping power adjustment coefficient; J 0、 D 0 represents the initial virtual inertia and damping coefficient; , The fuzzy controller outputs a correction value; It is the absolute value of the difference between the output power and the set value; The fuzzy controller processes input variables. e and e c After fuzzification using fuzzy sets, membership functions are determined by combining Gaussian and generalized bell shapes, and defuzzification is performed using the centroid method, thereby determining the output correction amount of the fuzzy controller. and To address virtual inertia J and virtual damping D To take control; The fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}, where NB is negative large, NM is negative medium, NS is negative small, ZO is zero, PS is positive small, PM is positive medium, and PB is positive large.

8. The control method for an offshore wind power flexible DC converter station based on fuzzy control according to claim 7, characterized in that, The virtual inertia of the fuzzy controller based on the JAYA algorithm J and virtual damping D The constraints include: In the formula, P max This represents the maximum active power output of the VSG. f c max The maximum value of the cutoff frequency. This is the total equivalent inductive reactance along the connection path between the MMC converter and the power grid.

9. A control method for an offshore wind power flexible DC converter station based on fuzzy control according to claim 7, characterized in that, The solution process for the fuzzy controller based on the JAYA algorithm includes: S301: Set the upper and lower limits of the optimization search space and perform population initialization; the expression for population initialization is: In the formula, X 0 represents the initial group. X max and X min These are the upper and lower bounds of the optimization search space, respectively, and rand is a uniformly random number in (0, 1); S302: Find the best and worst solutions in the current population; S303: Update the population individuals based on the current optimal and worst solutions. The corresponding update expression is: In the formula, X For design variables, X j,k,i Let j be the j-th variable of the k-th individual in the i-th iteration. X ’ j,k,i for X j,k,i Updated value after iteration r 1,j,i and r 2,j,i Let each represent a random number in (0, 1). X j,best,i and X j,worst,i Let represent the best and worst solutions among all populations during the i-th iteration; S304: Determine whether the fitness function value of the updated population individual is less than the fitness function value of the current optimal solution. If so, replace the original population individual with the updated population individual; otherwise, keep the original population individual. The expression for calculating the fitness function value is: In the formula, F is the fitness function value. This refers to the system frequency deviation. S305: Determine whether the preset termination condition is met. If yes, output the final solution result; otherwise, return to step S302. The termination condition is determined based on the time integral of the absolute error, and the expression for calculating the time integral of the absolute error is as follows: In the formula, This is the time integral value of the absolute error.

10. A control system for an offshore wind power flexible DC converter station based on fuzzy control, comprising the steps of operating a control method for an offshore wind power flexible DC converter station based on fuzzy control as described in any one of claims 1-9, characterized in that, include: A module is established to construct an MMC mathematical model based on the structural data of the MMC converter in the offshore wind power flexible DC converter station; The construction module is used to construct the VSG equivalent model of the MMC converter based on the MMC mathematical model; The optimization module is used to adaptively optimize the VSG parameters in the VSG equivalent model using a fuzzy controller based on the JAYA algorithm, so as to obtain the optimal angular frequency control scheme output by the VSG equivalent model.

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