A large disturbance stability analysis method and system for offshore wind turbine electric frequency conversion grid connection

Abstract

The present application relates to motor variable frequency stable control technical field, especially offshore wind motor electric variable frequency grid-connected large interference stability analysis method and system, method includes: the nonlinear dynamic model of motor and generator set variable frequency system is established;With the steady state operating point of nonlinear dynamic model as the coordinate origin, the operation characteristic space is divided into fuzzy subspace;In each fuzzy subspace, the model is locally linearized, and the TS fuzzy model set is constructed;Based on the TS fuzzy model set, the corresponding quadratic Lyapunov function is constructed, and the maximum attraction domain boundary is solved and projected to generate a two-dimensional stable operation region map;According to the maximum state variable boundary in the map, the output power command of the offshore wind power system controller is adjusted to maintain system stability.Through the present application, the problem that the prior art cannot maintain system stability and equipment damage when the rotating unit variable frequency system is subjected to high-power disturbance is effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor variable frequency stability control, and particularly relates to a large disturbance stability analysis method and system for offshore wind motor variable frequency grid connection. BACKGROUND

[0002] In the technical field of motor variable frequency stability control of offshore wind power, large rotating units realize low-frequency power transmission through a variable frequency device, which is a core power conversion link. This technology realizes dynamic balance of energy transmission based on variable frequency regulator adjustment of motor speed, and the core challenge lies in that when an offshore wind farm is subjected to typhoon surge and other large power sudden disturbance, the nonlinear dynamic characteristics of the system are sharply enhanced.

[0003] The prior art mainly adopts a linear approximation model to predict a stability boundary or relies on an experience threshold to set a protection parameter, and although it can cope with normal working conditions, it inherently ignores the strong nonlinearity of the mechanical-electrical coupling effect of the rotating system and the time-space correlation characteristics in disturbance, leading to a disastrous misjudgment of the critical instability boundary; the existing control model cannot accurately quantify the dynamic stability limit of the system to withstand instantaneous surge impact, resulting in frequent triggering of false protection or delayed protection of the variable frequency device under real marine extreme working conditions, which directly causes the collapse of the offshore wind power system, such as collapse of the full direct current chain and overvoltage burnout of power devices, forming a major safety hazard of large-scale equipment damage. The root cause lies in that the existing analysis method cannot depict the energy interaction details of high-frequency disturbance and mechanical inertia of the unit, and lacks the mathematical closure description capability of the stability domain accurate boundary of the multivariable coupling random disturbance.

[0004] The information disclosed in this BACKGROUND section is only intended to enhance the understanding of the general background of the present disclosure and is not intended to be a recognition or any form of suggestion that this information constitutes prior art. SUMMARY

[0005] The present application provides a large disturbance stability analysis method and system for offshore wind motor variable frequency grid connection, which can effectively solve the problems in the background art.

[0006] In order to achieve the above purpose, the technical solution adopted by the present application is:

[0007] A large disturbance stability analysis method for offshore wind motor variable frequency grid connection, the method comprising:

[0008] A 26-dimensional nonlinear dynamic model of the motor and generator set variable frequency system is established, and the nonlinear dynamic model at least includes motor d-axis current and q-axis current, generator sub-transient electromotive force and power angle state variables;

[0009] The steady-state operating point of the nonlinear dynamic model is taken as the coordinate origin for coordinate translation, the inverter-side d-axis current, the converter DC bus voltage, the motor stator winding d-axis current, and the generator stator winding d-axis current are selected as the division parameters, and the operating characteristic space is divided into 16 fuzzy subspaces;

[0010] The nonlinear dynamic model is locally linearized in each fuzzy subspace, and a TS fuzzy model set is constructed, the TS fuzzy model set being associated with each local linear model through a weighting function;

[0011] Based on the TS fuzzy model set, a corresponding quadratic Lyapunov function is constructed, the range of nonlinear term disturbance is expanded at each round, the maximum disturbance boundary is searched based on an iterative solution, and the maximum attractor domain boundary of the system is solved;

[0012] The maximum attractor domain boundary is projected onto the electromechanical power plane to generate a two-dimensional stable operation region map;

[0013] According to the maximum state variable boundary in the two-dimensional stable operation region map, the output power instruction of the offshore wind power system controller is adjusted to maintain system stability.

[0014] Further, the nonlinear dynamic model is configured to establish a continuous energy conversion dynamic path from the motor absorbing low-frequency electric energy to generate mechanical torque, the generator converting the mechanical torque into power frequency electric energy, and the DC link of the converter buffering power fluctuations on both sides.

[0015] Further, the operating characteristic space is divided into 16 fuzzy subspaces, including:

[0016] According to the inverter-side d-axis current obtained after the coordinate translation, the positive and negative dimensions in the operating characteristic space are divided into a first type of division reference region;

[0017] The positive and negative dimensions of the converter DC bus voltage in the operating characteristic space are associated to generate a second type of division reference region;

[0018] The positive and negative dimensions of the motor stator winding d-axis current in the operating characteristic space are combined to form a third type of division reference region;

[0019] The positive and negative dimensions of the generator stator winding d-axis current in the operating characteristic space are linked to construct a fourth type of division reference region;

[0020] The positive and negative subspaces of the first type of division reference region, the positive and negative subspaces of the second type of division reference region, the positive and negative subspaces of the third type of division reference region, and the positive and negative subspaces of the fourth type of division reference region are combined by Cartesian product.

[0021] Based on the traversal mapping of all logical branches in the Cartesian product combination, 16 fuzzy subspaces are defined in the operating feature space;

[0022] According to the fuzzy subspaces, a complete region partition is performed on the operating feature space after the coordinate translation.

[0023] Further, a TS fuzzy model set is constructed, including:

[0024] In each of the fuzzy subspaces formed by the partition of the operating feature space, the extreme values of the partition parameters are brought into a nonlinear dynamic model to generate a linear state space equation matched with the corresponding fuzzy subspace;

[0025] The linear state space equation is bound with the corresponding fuzzy subspace to construct a linear model set corresponding to all the fuzzy subspaces;

[0026] For each of the local linear models in the linear model set, a rule activation weight function is designed according to the variation degree of the system dynamic parameters in the corresponding fuzzy subspace;

[0027] Based on the rule activation weight function, the output vectors of the linear model set are dynamically aggregated to generate a continuous TS fuzzy model set covering the entire operating feature space.

[0028] Further, a quadratic Lyapunov function satisfying the linear matrix inequality (LMI) constraint is solved simultaneously, including:

[0029] For all the local linear models in the TS fuzzy model set, a matrix inequality constraint set reflecting the energy state of the system is constructed according to the Lyapunov theory, and the matrix inequality constraint set includes a positive definite condition and a state derivative decay limit condition to ensure the asymptotic stability of the system;

[0030] In combination with the relevance of the rule activation weight function and the local linear models, a dynamic coupling relationship between the matrix inequality constraint sets is constructed, which embodies the transition coordination condition of the system dynamic behavior under different fuzzy subspaces;

[0031] Based on the continuity of the dynamic coupling relationship in the entire operating feature space, the matrix inequality constraint sets corresponding to all the local linear models in the TS fuzzy model set are solved simultaneously to obtain a unified quadratic form coefficient matrix;

[0032] According to the unified quadratic form coefficient matrix, an analytical expression form of the quadratic Lyapunov function satisfying all the linear matrix inequality (LMI) constraints is synthesized;

[0033] iteratively expanding the disturbance range parameter of the nonlinear dynamic model by a preset step size and restarting the simultaneous solution process of the matrix inequality constraint set and the quadratic form coefficient matrix in each round until the iteration process is terminated when a feasible solution cannot be obtained in the current round;

[0034] determining the boundary defined by the disturbance range parameter corresponding to the last successfully solved round as the maximum attractor domain boundary of the nonlinear dynamic model.

[0035] Further, a two-dimensional stable operation region map is generated, including:

[0036] obtaining the closed hypersurface geometry parameter of the maximum attractor domain boundary defined by the quadratic Lyapunov function in the 26-dimensional state space;

[0037] dimensionally reducing the closed hypersurface geometry parameter along the dominant direction of electromechanical coupling characteristics and mapping it to the orthogonal coordinate axes composed of the inverter side d-axis current and the converter DC voltage coordinate axes and the motor stator winding d-axis current and the generator stator winding d-axis current coordinate axes;

[0038] performing a convex envelope operation on the mapped boundary point set to form an envelope boundary curve, and the area surrounded by the envelope boundary curve represents the operation range of the system that remains transiently stable under power disturbance;

[0039] Taking the envelope boundary curve as the critical surface, a rectangular stable operation region map including the active power upper limit value and the reactive power upper limit value is generated, and the horizontal axis of the rectangular stable operation region map is the active power axis and the vertical axis is the reactive power axis.

[0040] Further, adjusting the output power instruction, including:

[0041] Real-time monitoring of the actual output active power and reactive power of the offshore wind power system in the current electromechanical power plane real-time operation point coordinates;

[0042] Comparing the real-time operation point coordinates with the boundary position relationship of the two-dimensional stable operation region map, when it is detected that the real-time operation point approaches the maximum state variable boundary of the two-dimensional stable operation region map, triggering the power limiting protection action;

[0043] Calculating the intersection of the tangent trajectory of the real-time operation point beyond the maximum state variable boundary direction and the two-dimensional stable operation region map boundary as the target safety power point;

[0044] Converting the active power value and the reactive power value corresponding to the target safety power point into a power instruction format executable by the offshore wind power system controller;

[0045] The output power instruction is dynamically revised according to the instruction interface of the offshore wind power system controller, so that the power operating point of the offshore wind power system is constrained within the boundary range of the two-dimensional stable operating area graph.

[0046] Further, the output power instruction is dynamically rate-optimized, including:

[0047] The power slope change rate of the real-time operating point in the electromechanical power plane is monitored, and the power slope change rate is calculated based on the derivative data of the speed deviation after the coordinate translation in time series;

[0048] When the power limiting protection action is triggered, the power instruction adjustment rate parameter is matched and selected from the adjustment strategy library pre-stored in the offshore wind power system controller according to the absolute value size of the power slope change rate.

[0049] A large disturbance stability analysis system for offshore wind power electromechanical variable frequency grid connection, the system comprises:

[0050] A model construction module establishes a 26-dimensional nonlinear dynamic model of the motor and generator set variable frequency system;

[0051] A space division module performs coordinate translation with the steady-state operating point of the nonlinear dynamic model as the coordinate origin, and divides the operating characteristic space into 16 fuzzy subspaces;

[0052] A local linearization module locally linearizes the nonlinear dynamic model in each fuzzy subspace to construct a TS fuzzy model set, and the TS fuzzy model set is associated with each local linear model through a weighting function;

[0053] A constraint solving module, based on the TS fuzzy model set, constructs a corresponding quadratic Lyapunov function, expands the disturbance range of the nonlinear term in each round, and searches for the maximum disturbance boundary based on iterative solving to solve the maximum attractor domain boundary of the system;

[0054] An image generation module projects the maximum attractor domain boundary to the electromechanical power plane to generate a two-dimensional stable operating area graph;

[0055] An instruction stability module adjusts the output power instruction of the offshore wind power system controller of the offshore wind power system according to the maximum state variable boundary in the two-dimensional stable operating area graph to maintain system stability.

[0056] Further, the image generation module comprises:

[0057] A parameter acquisition unit acquires the geometric appearance parameters of the closed hypersurface formed by the maximum attractor domain boundary defined by the quadratic Lyapunov function in the 26-dimensional state space;

[0058] The dimension reduction mapping unit reduces the closed hypersurface geometry parameter along a main direction of electromechanical coupling characteristics and maps the closed hypersurface geometry parameter to orthogonal coordinate axes composed of the inverter side d-axis current and the converter DC voltage coordinate axes and the motor stator winding d-axis current and the generator stator winding d-axis current coordinate axes;

[0059] The envelope curve unit performs a convex envelope operation on the mapped boundary point set to form an envelope boundary curve, and a region surrounded by the envelope boundary curve represents an operation range of the system that maintains transient stability under power disturbance;

[0060] The image generation unit generates a rectangular stable operation region image including an active power upper limit value and a reactive power upper limit value, with the envelope boundary curve as a critical surface, and the horizontal axis of the rectangular stable operation region image is an active power axis and the vertical axis is a reactive power axis.

[0061] Through the technical scheme of the present application, the following technical effects can be achieved:

[0062] By establishing a 26-dimensional nonlinear dynamic model of the motor and generator set variable frequency system, combining TS fuzzy modeling of 16 fuzzy subspaces and LMI solving driven by Lyapunov theory, the problem that the prior art cannot accurately quantify the stable operation boundary when the rotating unit variable frequency system is subjected to large power disturbance such as typhoon surge, resulting in system instability and equipment damage, is solved.

[0063] The above description is only a summary of the technical scheme of the present application, in order to more clearly understand the technical means of the present application, which can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to more clearly illustrate the technical scheme in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0065] Figure 1 It is a flowchart of a large disturbance stability analysis method for offshore wind power machine electrical variable frequency grid connection;

[0066] Figure 2 It is a topology diagram of offshore wind farm based on motor and generator set variable frequency;

[0067] Figure 3 It is a flowchart of dividing fuzzy subspaces;

[0068] Figure 4 a flowchart for constructing the TS fuzzy model set;

[0069] Figure 5 a flowchart for establishing the quadratic Lyapunov function. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this description, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise. The term "and / or" includes any and all combinations of one or more of the associated listed items.

[0072] Embodiment one;

[0073] As shown in Figure 1 and Figure 2 The present application provides a large disturbance stability analysis method for offshore wind power motor-generator frequency conversion grid connection, the method comprising:

[0074] S10: establishing a 26-dimensional nonlinear dynamic model of the motor and generator set frequency conversion system, the nonlinear dynamic model at least including motor d-axis current and q-axis current, generator sub-transient electromotive force and power angle state variables;

[0075] S20: performing coordinate translation with the steady-state operating point of the nonlinear dynamic model as the coordinate origin, selecting inverter side d-axis current, converter DC bus voltage, motor stator winding d-axis current and generator stator winding d-axis current as the division parameters, and dividing the operation characteristic space into 16 fuzzy subspaces;

[0076] S30: locally linearizing the nonlinear dynamic model in each fuzzy subspace, constructing a TS fuzzy model set, and associating each local linear model through a weighting function;

[0077] S40: based on the TS fuzzy model set, constructing a corresponding quadratic Lyapunov function, expanding the disturbance range of the nonlinear term at each round, searching for the maximum disturbance boundary based on the iterative solution mode, and solving the maximum attractor domain boundary of the system;

[0078] S50: projecting the maximum attractor domain boundary to the electromechanical power plane to generate a two-dimensional stable operation region diagram;

[0079] S60: Adjust the offshore wind power system controller output power command to maintain system stability according to the maximum state variable boundary in the two-dimensional stable operating region map.

[0080] Specifically, firstly, a complete 26-dimensional nonlinear dynamic model is established according to the physical parameters and manufacturer data of the motor and generator set and its frequency conversion device, the model takes the motor d-axis and q-axis currents, the generator sub-transient electromotive force, the power angle and the like as the core states, and describes the coupling evolution of the frequency conversion device under disturbance by the electromagnetic induction relationship, the dynamic of the stator circuit, the mechanical swing equation of the rotor and shaft system, the dynamic of the excitation system and the averaged switching behavior of the converter; in the model establishment process, the coupling terms of electromagnetism and mechanics are optimized and reserved, and the key time constants and coupling coefficients are calibrated through parameter identification and small signal test to ensure the model accuracy, secondly, the steady-state working point of the system under the typical grid-connected operation condition is selected as the coordinate translation reference, the coordinate translation and normalization processing are realized by subtracting the steady-state value of all state variables, so as to realize the numerical stability of subsequent local linearization and fuzzy division; on this basis, the inverter side d-axis current, the converter DC bus voltage, the motor stator winding d-axis current and the generator stator winding d-axis current are selected as the division reference, and the 16 Cartesian combined sub-regions are obtained through the binary division according to the positive and negative signs; in order to ensure the smooth transition between the sub-regions and take into account the discrimination ability, the overlapping membership functions are optimized to define the positive and negative sub-regions of each dimension, and then the 16 fuzzy sub-spaces are formed, then the local linear state space description of the original nonlinear model is obtained at the local working point of each fuzzy sub-space, the local linearization can obtain the local state matrix and input matrix through analytical derivation or numerical difference; the local linear description and the corresponding membership function are bound, the continuous TS fuzzy model set covering the entire operating characteristic space is obtained through the dynamic combination according to the normalized weight through the weighting rule, based on the TS model set, the solving process of the unified quadratic Lyapunov energy function is constructed: the positive definite energy function candidate is set at each sub-model, and the corresponding matrix inequality constraint is derived according to the Lyapunov stability criterion, the coupling constraint between the sub-models is established by using the membership weight to form a complete family of linear matrix inequalities, and the semi-positive definite programming solver is used to solve the LMI constraints and obtain the quadratic coefficient matrix of the energy function derivative which is negative in each sub-space; in order to determine the actual bearing boundary of the system under nonlinear disturbance, the strategy of iteratively expanding the disturbance amplitude is adopted in this embodiment, taking the initial small amplitude nonlinear term disturbance as the starting point, the disturbance amplitude is increased by a predetermined step in each round, the entire LMI constraint is solved again in each round until the solution becomes infeasible or the change of the obtained attraction domain boundary is less than a threshold, for example, the boundary change is less than 1%, and the iteration is ended, the allowable maximum disturbance boundary is recorded and the maximum attraction domain boundary of the system is obtained.For the obtained high-dimensional boundary, each high-dimensional state vector is preferably mapped to the corresponding motor output active and reactive power by densely sampling the Lyapunov function level set and boundary samples on the high-dimensional state space, and the boundary point set is mapped in the dimension reduction along the electromechanical coupling dominant direction or directly selecting the two coordinate components of the above power; the convex hull operation is performed on the mapped two-dimensional point set to obtain the envelope boundary curve, and the critical values of active and reactive power are extracted based on the intersection of the curve with the horizontal and vertical coordinates on the electromechanical power plane, and a rectangular stable operation area diagram parallel to the coordinate axis is generated, and the four sides of the rectangle are taken as the upper limit value under the required conservative margin to facilitate the realization of the controller interface. Finally, the maximum state variable boundary in the two-dimensional stable operation area diagram is converted into a power instruction strategy executable by the offshore wind power system controller: in the grid-connected operation, the current active or reactive point and the position relationship of the stable region are monitored by the monitoring layer at a specified sampling frequency, when the operating point approaches or exceeds the safety boundary, the monitoring layer calculates the target safety power point and sends the active and reactive amplitude limiting and rate constraints to the converter controller through the instruction format adaptation with the converter, and the converter smoothly tracks according to the closed-loop control logic after receiving the new instruction and avoids mechanical impact or electrical transient oscillation through rate shaping.

[0081] Through the technical scheme of the present application, a 26-dimensional nonlinear dynamic model of the motor and generator set variable frequency system is established, combined with TS fuzzy modeling divided into 16 fuzzy subspaces and LMI solving driven by Lyapunov theory, the problem that the prior art cannot accurately quantify the stable operation boundary when the rotating unit variable frequency system is subjected to high-power disturbance such as typhoon surge, resulting in system instability and equipment damage is solved.

[0082] Further, the nonlinear dynamic model is configured to establish a continuous energy conversion dynamic path from the low-frequency electric energy absorbed by the motor to generate mechanical torque, the mechanical torque converted by the generator into power frequency electric energy, and the power fluctuation buffer on both sides of the direct current link of the converter.

[0083] As a preferred embodiment of the above, firstly, the electric motor, as the initial stage of energy conversion, absorbs low-frequency electrical energy through its stator windings, drives the rotor, and generates mechanical torque. This process needs to consider the dynamic response of the excitation current within the motor and the influence of the surrounding magnetic flux to ensure that the motor maintains high efficiency and stable output. The state variable model of the motor details the current, electromagnetic torque, and related dynamic response characteristics. Next, the generator, as the intermediate stage of energy conversion, converts the absorbed mechanical torque into power frequency electrical energy. The dynamic response of the generator is described based on the changes in speed, torque, and transient magnetic field components to ensure that it can quickly respond to changing mechanical inputs at its high-efficiency operating point. The state model of the generator is located on the d-axis. The q-axis electromotive force is fully described, enabling the system to maintain stable power output around the clock. In particular, the adjustment of rotor damping and excitation voltage ensures the adaptability of transient electromotive force and stable operation of the system throughout the process. In the continuous energy conversion path, the converter is responsible for ensuring stable bidirectional power flow. The DC link of the converter is set to realize instantaneous energy buffering and smooth out fluctuations that may occur during power transmission between the motor and the generator. Specifically, through real-time monitoring of DC bus voltage and current, the converter is equipped with multi-level filtering and active control strategies to ensure that the entire conversion path can flexibly adjust its operating state under changing load conditions to support the stable output of the entire offshore wind power system.

[0084] Furthermore, such as Figure 3 As shown, the running feature space is divided into 16 fuzzy subspaces, including:

[0085] Based on the positive and negative dimensions of the inverter-side d-axis current in the operating characteristic space obtained after coordinate translation, the regions are divided into the first category of reference regions.

[0086] The DC bus voltage of the associated converter generates a second type of reference region based on the positive and negative dimensions of the operating characteristic space;

[0087] A third type of reference region is formed by combining the positive and negative dimensions of the d-axis current of the motor stator winding in the operating characteristic space;

[0088] The positive and negative dimensions of the d-axis current of the stator winding of the linkage generator in the operating characteristic space are used to construct a fourth type of reference region.

[0089] The positive and negative subspaces of the first, second, third, and fourth categories of the datum region are combined by Cartesian product.

[0090] Based on the traversal mapping of all logical branches in the Cartesian product combination, 16 fuzzy subspaces are defined in the running feature space;

[0091] The complete region partition is performed on the operating characteristic space after coordinate translation according to the fuzzy subspace.

[0092] As a preferred embodiment of the above, first, the inverter-side d-axis current obtained from the coordinate translation step is subdivided, taking its positive and negative dimensions as the first type of partition reference region, and this region is set to facilitate the identification of the power transmission state of the inverter under different current directions, helping the system to distinguish the dynamic influence caused by the corresponding current, and at the same time, this partition basis provides strong support for the performance optimization of the frequency conversion system in current vector control; second, a second type of partition reference region related to the DC bus voltage of the converter is defined, and it is also distinguished according to its positive and negative dimensions, because the DC bus voltage reflects the instantaneous energy storage capability of energy transmission, and the change of this voltage directly affects the stable operation of the inverter, therefore, through this region partition, the effect of voltage variation on the overall dynamic response of the system can be better managed; the third type of partition reference region is based on the positive and negative dimensions of the d-axis current of the motor stator winding, and the current information on the motor side is related to the change of the energy input of the motor, and this type of partition can help to predict and adjust the operating characteristics of the motor under different loads and control states, ensuring the stable coupling of the motor in the system network; the fourth type of partition involves the positive and negative dimensions of the d-axis current of the generator stator winding, and this consideration of dimensions provides feedback information of the generator in frequency conversion control, which can significantly improve the ability of the generator to dynamically adapt to changes in grid conditions, and through the division of the d-axis current of the generator stator, the synchronization of the power generation process and the stability of the power output are improved; then, in each of the above reference regions, the corresponding positive subspace and negative subspace are sequentially combined and arranged according to the Cartesian product principle, and finally 16 unique fuzzy subspaces are formed, and in these spaces, each subspace reflects the specialized dynamic response of the system under a specific state, ensuring that the control strategy has comprehensive and detailed adaptability under the comprehensive consideration of multiple state variables; through logical traversal and complete definition of each fuzzy subspace generated by the Cartesian product combination, full coverage partition of the characteristic space is realized, and this partition not only ensures the reasonable processing of each operating state, but also makes the expression of the entire dynamic characteristics possible, supporting the subsequent model analysis and the execution of the control algorithm.

[0093] Further, as shown in Figure 4 , a TS fuzzy model set is constructed, including:

[0094] In each fuzzy subspace formed by the partition of the operating characteristic space, the extreme values of the partition parameters are brought into the nonlinear dynamic model to generate a linear state space equation matched with the corresponding fuzzy subspace;

[0095] The linear state space equation is bound with the corresponding fuzzy subspace to construct a linear model set corresponding to all fuzzy subspaces.

[0096] For each local linear model in the set of linear models, design rules to activate the weight function based on the degree of change of the system dynamic parameters in the corresponding fuzzy subspace;

[0097] The output vectors of the linear model set are dynamically aggregated based on the rule-based activation weight function to generate a continuous TS fuzzy model set covering the entire running feature space.

[0098] As a preferred embodiment of the above, after the running feature space is divided into multiple fuzzy subspaces, this implementation selects the local operating point of the nonlinear dynamic model in each subspace for detailed mathematical processing, transforming the complex nonlinear dynamic behavior into local linear state-space equations. The core of this process lies in utilizing the local dynamic characteristics of the system to decompose the nonlinear problem into an easily tractable linear problem, ensuring that the dynamic change characteristics in each subspace can be accurately described. After completing the construction of the linear state-space equations, this implementation binds them to the corresponding fuzzy subspaces to form a set of linear models adapted to the characteristics of each subspace. This step is performed by identifying the specific dynamic patterns of each subspace, enabling the linear model to directly respond to state changes within the subspace, thereby enhancing the sensitivity and accuracy of the overall model. Next, this implementation designs rules to activate weight functions based on the degree of change of the system dynamic parameters in each fuzzy subspace. These functions are used to dynamically evaluate the operation of the state parameters in each subspace. The system dynamically adjusts the current state of each subspace and activates the corresponding linear model. This weighting function, through a weighted dynamic adaptive mechanism, allows the current state of the subspace to be reflected in a wider range of model responses in real time, improving the control system's instantaneous adjustment capability and response speed. Finally, the implementation method activates the weighting function based on the rules designed above, dynamically aggregates the linear model set, and generates a continuous TS fuzzy model set by integrating some model output vectors. This fuzzy model set covers the entire operating feature space and can effectively fill the dynamic response gaps between subspaces, ensuring that the system's stability can be maintained and optimized even under maximum disturbance. In a specific example, when a wind farm is affected by a sudden strong wind, the TS fuzzy model set can quickly determine the state characteristics of each fuzzy subspace and automatically adjust the control strategy in a timely manner through its dynamic weight adjustment mechanism. For example, when the power angle deviation or current deviation changes, the model set can optimize the power command, thereby ensuring the continuous and stable operation of the wind turbine.

[0099] Furthermore, such as Figure 5 As shown, the simultaneous solution of the quadratic Lyapunov function satisfying the linear matrix inequality (LMI) constraint includes:

[0100] For all local linear models in the TS fuzzy model set, a set of matrix inequality constraints reflecting energy state of the system is constructed according to Lyapunov theory, and the set of matrix inequality constraints includes positive definite condition and state derivative attenuation limit condition for ensuring asymptotic stability of the system;

[0101] A dynamic coupling relationship between the set of matrix inequality constraints is constructed in combination with relevance of the rule activation weight function and the local linear model, and the dynamic coupling relationship embodies transition coordination condition of dynamic behavior of the system under different fuzzy subspaces;

[0102] Based on continuity of the dynamic coupling relationship in all operating characteristic spaces, the set of matrix inequality constraints corresponding to all local linear models in the TS fuzzy model set is solved simultaneously to obtain a unified quadratic form coefficient matrix;

[0103] An analytical expression form of a quadratic Lyapunov function satisfying all linear matrix inequality LMI constraints is synthesized according to the unified quadratic form coefficient matrix;

[0104] The disturbance range parameter of the nonlinear dynamic model is iteratively expanded by a preset step size round by round, and the simultaneous solving process of the set of matrix inequality constraints and the quadratic form coefficient matrix is restarted, and the iteration process is terminated when a feasible solution cannot be obtained in the current round;

[0105] A boundary defined by the disturbance range parameter corresponding to the last successfully solved round is determined as a boundary of the largest attraction domain of the nonlinear dynamic model.

[0106] As a preferred embodiment of the above embodiment, firstly, in the TS fuzzy model set, for each local linear model, a set of matrix inequality constraints is designed, which focuses on reflecting the energy state of the system, ensuring the asymptotic stability under dynamic disturbance. Specifically, the positive definite condition guarantees the convergence characteristics of the energy of the system at steady state, and the state derivative decay limit condition controls the stable convergence speed of the system at transient state; after constructing the constraint set, the present embodiment combines the rule activation weight function to establish the dynamic coupling relationship between each local linear model. This relationship is defined by analyzing the state parameter changes in each fuzzy subspace, which embodies the transition coordination conditions between different subspaces of the system. The dynamic coupling relationship makes the transition between different states smoother, ensuring that the system dynamic behavior in different fuzzy subspaces can be coordinated; based on the above dynamic coupling relationship, the present embodiment analyzes and solves the overall continuity of the operating characteristic space, and by simultaneously solving the matrix inequality constraint set corresponding to all local linear models in the TS fuzzy model set, a unified quadratic form coefficient matrix is obtained. This unified matrix is the result of integrating each local model, providing a global perspective stability verification scheme; finally, relying on the unified quadratic form coefficient matrix, the present embodiment synthesizes the analytical expression form of the quadratic Lyapunov function that satisfies all LMI constraints. This Lyapunov function not only can comprehensively describe the energy changes of the system under various dynamic states, but also can provide specific stability evaluation criteria. In actual application process, for example, when the wind farm faces the power angle deviation caused by severe weather changes, this function can effectively judge the stability state of the system and guide the timely adjustment of the control strategy; the disturbance range parameters of the nonlinear dynamic model are expanded by preset step-by-step iteration, starting from the initial condition, a set of small amplitude nonlinear disturbance parameters are set, which are used to simulate the peak load changes that may be encountered in real operation. When starting the iteration process, the disturbance increment is preferably selected as 1% to 5% of the nominal value, ensuring that the disturbance range expansion in each round can accurately capture the dynamic characteristics under the system stability analysis requirements. The strength of the nonlinear term is gradually increased according to the current state and parameters of the system in each round, thereby expanding the disturbance range and testing the robustness and adaptability of the model; after adjusting the disturbance range in each iteration, the simultaneous solution process of the matrix inequality constraint set and the quadratic form coefficient matrix is restarted immediately. Through the reloading of each round, it is ensured that the entire analysis process can timely reflect the stability of the system under different disturbance levels. This operation makes the quadratic form coefficient matrix of the Lyapunov function and the state constraints of the system always in a dynamic updating state, ensuring the best solution under each disturbance range. The solving process uses optimization algorithms such as semi-definite programming solver to solve the linear matrix inequality and capture the new boundary after disturbance;Once the current iteration fails to obtain a feasible solution, i.e. the matrix inequality solver returns an infeasible status, the iteration process is terminated, at this time the system has exceeded the ability of stability convergence, which means the disturbance impact has exceeded the system's automatic adjustment and energy absorption capacity; finally, the boundary defined by the last successfully solved iteration of the disturbance range parameter is determined as the boundary of the largest attractor domain of the nonlinear dynamic model, this boundary provides the state change that the system can withstand in the largest possible range, as a guide standard for subsequent control and actual operation, to ensure that the motor and generator set can still maintain stable operation when facing large disturbances.

[0107] Further, a two-dimensional stable operation region map is generated, including:

[0108] Obtaining the closed hypersurface geometry parameters of the maximum attractor domain boundary defined by the quadratic Lyapunov function in the 26-dimensional state space;

[0109] Dimensionality reduction of the closed hypersurface geometry parameters along the dominant direction of the electromechanical coupling characteristics, and mapping to the orthogonal coordinate axes composed of the inverter side d-axis current and the converter DC voltage coordinate axes, and the motor stator winding d-axis current and the generator stator winding d-axis current coordinate axes;

[0110] Convex envelope operation is performed on the mapped boundary point set to form an envelope boundary curve, and the area surrounded by the envelope boundary curve represents the operation range of the system to maintain transient stability under power disturbance;

[0111] Taking the envelope boundary curve as the critical surface, a rectangular stable operation region map including the active power upper limit value and the reactive power upper limit value is generated, and the horizontal axis of the rectangular stable operation region map is the active power axis and the vertical axis is the reactive power axis.

[0112] As a preferred embodiment of the above embodiment, first, by obtaining the maximum attractor boundary defined by the secondary Lyapunov function, the embodiment determines the closed hypersurface geometry parameters in the 26-dimensional state space from a global perspective, which comprehensively describes the stability boundary of the system in the dynamic state. Through this global geometry, the energy state change of the system in different states can be identified, providing a basis for subsequent dimension reduction mapping; the embodiment uses dimension reduction technology to reduce the parameters of the above closed hypersurface along the dominant direction of the electromechanical coupling characteristics, and map them onto the orthogonal coordinate axes composed of the inverter side d-axis current and the converter DC voltage coordinate axes, and the motor stator winding d-axis current and the generator stator winding d-axis current coordinate axes. This dimension reduction processing, around the output characteristics of the motor, enables the high-dimensional complex data to be effectively simplified into an easily recognizable form in a two-dimensional coordinate space, ensuring that the main features of the dynamic behavior can be intuitively expressed in a two-dimensional space. By performing a convex envelope operation on the boundary point set obtained after dimension reduction, an envelope boundary curve is formed, and the area surrounded by the curve directly represents the operating range of the system that maintains transient stability under power disturbance. This step ensures the accuracy of the stability analysis, enabling the safe operating interval of the system to be quickly determined under different dynamic conditions; after the formation of the boundary curve, a rectangular stable operating area graph including the active power upper limit value and the reactive power upper limit value is generated, with the horizontal axis as the active power axis and the vertical axis as the reactive power axis, which intuitively reflects the safe operating range under different power conditions. For example, in actual operation, when the power demand fluctuates dramatically due to weather changes, the stable operating area graph can quickly identify the critical power range and provide guidance for system control. Through this visual means, the operator can quickly adjust the operating parameters of the electromechanical equipment to ensure system stability.

[0113] Further, adjusting the output power instruction comprises:

[0114] Monitoring the real-time operating point coordinates of the actual active power and reactive power of the offshore wind power system in the current electromechanical power plane in real time;

[0115] Comparing the real-time operating point coordinates with the boundary position relationship of the two-dimensional stable operating area graph, when it is detected that the real-time operating point approaches the maximum state variable boundary of the two-dimensional stable operating area graph, triggering the power limiting protection action;

[0116] Calculating the intersection of the tangent trajectory of the real-time operating point beyond the maximum state variable boundary direction and the boundary of the two-dimensional stable operating area graph as the target safe power point;

[0117] Converting the active power value and reactive power value corresponding to the target safe power point into a power instruction format executable by the offshore wind power system controller;

[0118] According to the dynamic correction of the output power instruction interface of the offshore wind power system controller, the power operating point of the offshore wind power system is constrained within the boundary range of the two-dimensional stable operating area diagram.

[0119] As a preferred embodiment of the above embodiment, first, the embodiment obtains the actual active power and reactive power output by monitoring the output power of the offshore wind power system in real time, and maps them to the current electromechanical power plane. The identification of the real-time operating point coordinates provides accurate data basis for dynamic regulation, ensuring that the system can be adjusted in time under rapidly changing power demand. When the real-time operating point gradually approaches the boundary of the two-dimensional stable operating area diagram, especially when it touches the maximum state variable boundary, the embodiment immediately triggers the power limiting protection action. This function ensures that the system does not exceed the safe power range by setting limits, effectively preventing potential instability caused by excessive power. Through the protection mechanism, once the boundary approach is detected, the output power is promptly limited within the safe range. Next, the embodiment calculates the tangent trajectory of the current operating point beyond the power boundary, and the intersection of the two-dimensional stable operating area diagram boundary is taken as the target safe power point. The target safe power point is the new reference point for the system during power adjustment, and the power offset is minimized to quickly restore stable operation. Once the target safe power point is determined, its corresponding active power value and reactive power value will be converted into a power instruction format executable by the offshore wind power system controller. The conversion process ensures accurate data docking, laying the foundation for subsequent instruction execution. Finally, according to the instruction interface of the offshore wind power system controller, the output power instruction is dynamically corrected to align with the target safe power point. Through this dynamic correction, the power operating point of the offshore wind power system is strictly constrained within the boundary range of the two-dimensional stable operating area diagram, ensuring that the system can operate stably under any load condition. For example, in actual application, when a power surge occurs, the embodiment can quickly monitor and adjust the power transmission state of the power grid, ensuring that the system can continue to maintain its operating stability when facing unpredictable natural disturbances such as strong winds.

[0120] Further, the dynamic rate optimization of the output power instruction includes:

[0121] Monitoring the power slope change rate of the real-time operating point in the electromechanical power plane, which is calculated based on the derivative data of the speed deviation after coordinate translation in time series;

[0122] When the power limiting protection action is triggered, the power instruction adjustment rate parameter is selected from the adjustment strategy library pre-stored in the offshore wind power system controller according to the absolute value of the power slope change rate.

[0123] As a preferred embodiment of the above-mentioned embodiment, firstly, the present embodiment monitors the power slope rate of the real-time operating point in the electromechanical power plane, which is calculated based on the derivative data of the speed deviation in the time series. By continuously monitoring this key indicator, the system can control the instantaneous changes of the power state in real time and provide accurate basis for subsequent adjustment. When the power limiting protection action is triggered, the system selects the appropriate power instruction adjustment rate parameter from the adjustment strategy library pre-stored in the offshore wind power system controller according to the absolute value of the power slope rate. The content in the adjustment strategy library defines the corresponding linear adjustment rate curve according to different power slope intervals. This selection process ensures the specific adaptability of the adjustment strategy to the current state of the system and avoids the efficiency loss caused by blind adjustment. The selected adjustment rate parameter is used to dynamically correct the output power instruction in the closed-loop control logic of the offshore wind power system controller. In this stage, not only the immediate accuracy of the output instruction is emphasized, but also the electrical and mechanical shocks caused by step changes need to be suppressed. Therefore, the dynamic adjustment strategy adjusts the power in a step-by-step manner, effectively balancing the mechanical shock and electrical transient oscillation risk between the motor and the generator set. For example, assuming that the output power demand increases dramatically due to sudden weather changes during operation, the real-time monitored power slope is large. In this case, the present embodiment will automatically select a more moderate adjustment rate curve to ensure smooth transition of power changes, making the adjustment process more stable and reducing the potential impact on equipment.

[0124] Embodiment two;

[0125] Based on the same inventive concept as the aforementioned embodiment of the large disturbance stability analysis method for offshore wind power electromechanical variable frequency grid connection, the present application also provides a large disturbance stability analysis system for offshore wind power electromechanical variable frequency grid connection. The system comprises:

[0126] A model construction module for establishing a 26-dimensional nonlinear dynamic model of the motor and generator set variable frequency system;

[0127] A space division module for performing coordinate translation with the steady-state operating point of the nonlinear dynamic model as the coordinate origin, dividing the operating characteristic space into 16 fuzzy subspaces;

[0128] A local linearization module for locally linearizing the nonlinear dynamic model in each fuzzy subspace to construct a TS fuzzy model set, and associating each local linear model through a weighting function;

[0129] A constraint solving module for constructing a corresponding quadratic Lyapunov function based on the TS fuzzy model set, expanding the disturbance range of the nonlinear term in each round, searching for the maximum disturbance boundary based on iterative solving, and solving the maximum attractor domain boundary of the system;

[0130] The image generation module projects the maximum attraction domain boundary to the electromechanical power plane to generate a two-dimensional stable operation area graph;

[0131] The instruction stabilization module adjusts the output power instruction of the offshore wind power system controller according to the maximum state variable boundary in the two-dimensional stable operation area graph to maintain system stability.

[0132] The above adjustment system in the application can effectively realize a large disturbance stability analysis method for offshore wind turbine electromechanical frequency conversion grid connection, and the technical effects are as described in the above embodiments, which will not be repeated here.

[0133] Further, the image generation module comprises:

[0134] The parameter acquisition unit acquires the closed hypersurface geometry appearance parameter of the maximum attraction domain boundary defined by the quadratic Lyapunov function in the 26-dimensional state space;

[0135] The dimension reduction mapping unit reduces the dimension of the closed hypersurface geometry appearance parameter along the main direction of electromechanical coupling characteristics and maps it to the orthogonal coordinate axis composed of the inverter side d-axis current and the converter DC voltage coordinate axis and the motor stator winding d-axis current and the generator stator winding d-axis current coordinate axis;

[0136] The envelope curve unit performs a convex envelope operation on the mapped boundary point set to form an envelope boundary curve, and the area surrounded by the envelope boundary curve represents the operation range of the system maintaining transient stability under power disturbance;

[0137] The image generation unit generates a rectangular stable operation area graph comprising the active power upper limit value and the reactive power upper limit value, with the active power axis as the horizontal axis and the reactive power axis as the vertical axis, taking the envelope boundary curve as the critical surface.

[0138] Similarly, the above optimization scheme of the system can also correspondingly realize the optimization effect of the method in Embodiment 1, which will not be repeated here.

[0139] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the present specification and drawings are merely illustrative of the exemplary embodiments of the present application defined in the appended claims, and any and all modifications, variations, combinations or equivalents that are within the scope of the present application are intended to be encompassed by the present application. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for analyzing the stability of offshore wind turbines connected to the grid under large disturbances, characterized in that, The method includes: A 26-dimensional nonlinear dynamic model of the frequency conversion system of motor and generator set is established. The nonlinear dynamic model includes at least the d-axis current and q-axis current of the motor, the subtransient electromotive force of the generator and the power angle state variable. Using the steady-state operating point of the nonlinear dynamic model as the origin of the coordinate system, coordinate translation is performed. The inverter side d-axis current, converter DC bus voltage, motor stator winding d-axis current, and generator stator winding d-axis current are selected as partitioning parameters to divide the operating feature space into 16 fuzzy subspaces. The nonlinear dynamic model is locally linearized in each of the fuzzy subspaces to construct a TS fuzzy model set, which is associated with each local linear model through a weighting function. Based on the TS fuzzy model set, a corresponding quadratic Lyapunov function is constructed. The range of nonlinear term perturbation is expanded in each round. The maximum perturbation boundary is searched based on the iterative solution method to solve for the maximum attraction domain boundary of the system. The boundary of the maximum attraction domain is projected onto the electromechanical power plane to generate a two-dimensional stable operating region map; Based on the maximum state variable boundary in the two-dimensional stable operation area diagram, adjust the output power command of the offshore wind power system controller to maintain system stability; The running feature space is divided into 16 fuzzy subspaces, including: Based on the positive and negative dimensions of the inverter-side d-axis current obtained after coordinate translation, the first type of classification reference region is divided into the operating characteristic space. The DC bus voltage of the converter is correlated with the positive and negative dimensions of the operating characteristic space to generate a second type of dividing reference region; The third type of dividing reference region is formed by combining the positive and negative dimensions of the d-axis current of the motor stator winding in the operating characteristic space; The fourth type of dividing reference region is constructed by linking the d-axis current of the generator stator winding with the positive and negative dimensions of the operating characteristic space; The positive and negative subspaces of the first type of partitioning reference region, the positive and negative subspaces of the second type of partitioning reference region, the positive and negative subspaces of the third type of partitioning reference region, and the positive and negative subspaces of the fourth type of partitioning reference region are combined by Cartesian product. Based on the traversal mapping of all logical branches in the Cartesian product combination, 16 fuzzy subspaces are defined in the running feature space; Completeness region segmentation is performed on the coordinate-translated running feature space based on the fuzzy subspace; Generate a two-dimensional stable operating region map, including: Obtain the geometric parameters of the closed hypersurface formed by the boundary of the maximum attraction domain defined by the quadratic Lyapunov function in the 26-dimensional state space; The geometric parameters of the closed hypersurface are reduced in dimension along the direction dominated by electromechanical coupling characteristics and mapped to an orthogonal coordinate axis composed of the d-axis current of the inverter side and the DC voltage coordinate axis of the converter, as well as the d-axis current of the motor stator winding and the d-axis current coordinate axis of the generator stator winding. A convex envelope operation is performed on the mapped boundary point set to form an envelope boundary curve. The region enclosed by the envelope boundary curve represents the operating range of the system that maintains transient stability under power disturbance. Using the envelope boundary curve as the critical surface, a rectangular stable operating region map including the upper limit of active power and the upper limit of reactive power is generated. The horizontal axis of the rectangular stable operating region map is the active power axis, and the vertical axis is the reactive power axis.

2. The method for analyzing the large-disturbance stability of offshore wind turbine frequency converter grid connection according to claim 1, characterized in that, The nonlinear dynamic model is configured to establish a continuous energy conversion dynamic path from the absorption of low-frequency electrical energy by the motor to generate mechanical torque, the conversion of the mechanical torque into power frequency electrical energy by the generator, and the buffering of power fluctuations on both sides of the DC link of the converter.

3. The method for analyzing the large-disturbance stability of offshore wind turbine frequency converter grid connection according to claim 1, characterized in that, Constructing the TS fuzzy model set includes: Within each of the fuzzy subspaces formed by dividing the operating feature space, the extreme values ​​of the division parameters are substituted into the nonlinear dynamic model to generate a linear state-space equation that matches the corresponding fuzzy subspace. The linear state-space equations are bound to the corresponding fuzzy subspaces to construct a set of linear models corresponding to all the fuzzy subspaces; For each of the local linear models in the set of linear models, a rule is designed to activate the weight function based on the degree of change of the system dynamic parameters in the corresponding fuzzy subspace; Based on the rules, the output vectors of the linear model set are dynamically aggregated using the activation weight function to generate a continuous TS fuzzy model set covering the entire operating feature space.

4. The method for analyzing the stability of offshore wind turbines connected to the grid under large disturbances according to claim 3, characterized in that, Solving for the quadratic Lyapunov function that satisfies the linear matrix inequality (LMI) constraint includes: For all the local linear models in the TS fuzzy model set, a set of matrix inequality constraints reflecting the energy state of the system is constructed based on Lyapunov theory. The set of matrix inequality constraints includes positive definite conditions that guarantee the asymptotic stability of the system and state derivative decay constraints. By combining the correlation between the rule activation weight function and the local linear model, a dynamic coupling relationship is constructed between the matrix inequality constraint sets. The dynamic coupling relationship reflects the transition coordination conditions of the system's dynamic behavior under different fuzzy subspaces. Based on the continuity of the dynamic coupling relationship in all the operating feature spaces, the matrix inequality constraint set corresponding to all the local linear models in the TS fuzzy model set is solved simultaneously to obtain a unified quadratic coefficient matrix. Based on the unified quadratic coefficient matrix, an analytical expression of the quadratic Lyapunov function that satisfies all linear matrix inequalities (LMI) constraints is synthesized. The perturbation range parameters of the nonlinear dynamic model are expanded iteratively in rounds according to a preset step size, and the simultaneous solution process of the matrix inequality constraint set and the quadratic coefficient matrix is ​​restarted until a feasible solution cannot be obtained in the current round, at which point the iteration process is terminated. The boundary defined by the disturbance range parameter corresponding to the last successfully solved round is determined as the boundary of the maximum attraction domain of the nonlinear dynamic model.

5. The method for analyzing the stability of offshore wind turbines connected to the grid under large disturbances according to claim 1, characterized in that, The output power adjustment command includes: Real-time monitoring of the actual output active and reactive power of the offshore wind power system at the real-time operating point coordinates in the current electromechanical power plane; By comparing the real-time operating point coordinates with the boundary position relationship of the two-dimensional stable operating area map, when it is detected that the real-time operating point is approaching the boundary of the maximum state variable of the two-dimensional stable operating area map, a power limiting protection action is triggered. The intersection of the tangential trajectory of the real-time running point in the direction exceeding the boundary of the maximum state variable and the boundary of the two-dimensional stable running area map is calculated as the target safe power point; The active power value and reactive power value corresponding to the target safe power point are converted into a power command format that can be executed by the offshore wind power system controller; The output power command is dynamically modified according to the command interface of the offshore wind power system controller, and the power operating point of the offshore wind power system is constrained within the boundary of the two-dimensional stable operating area map.

6. The method for analyzing the large-disturbance stability of offshore wind turbine frequency converter grid connection according to claim 5, characterized in that, Dynamic rate optimization of the output power command includes: The rate of change of the power slope at the real-time operating point in the electromechanical power plane is monitored, and the rate of change of the power slope is calculated based on the derivative data of the rotational speed deviation after coordinate translation on the time series. When the power limiting protection action is triggered, the power command adjustment rate parameter is selected from the adjustment strategy library pre-stored in the offshore wind power system controller according to the absolute value of the power slope change rate.

7. A large-disturbance stability analysis system for offshore wind turbine frequency converter grid connection, characterized in that, The system employs the large-disturbance stability analysis method for offshore wind turbine frequency conversion grid connection as described in claim 1, wherein the system comprises: The model building module establishes a 26-dimensional nonlinear dynamic model of the frequency conversion system of the motor and generator set. The spatial partitioning module uses the steady-state operating point of the nonlinear dynamic model as the origin of the coordinate system to perform coordinate translation, dividing the running feature space into 16 fuzzy subspaces. The local linear module performs local linearization on the nonlinear dynamic model in each fuzzy subspace, constructs the TS fuzzy model set, and the TS fuzzy model set is associated with each local linear model through a weighting function. The constraint solving module, based on the TS fuzzy model set, constructs the corresponding quadratic Lyapunov function, expands the range of nonlinear term perturbation in each round, and searches for the maximum perturbation boundary based on iterative solution to solve the maximum attraction domain boundary of the system; The image generation module projects the boundary of the maximum attraction domain onto the electromechanical power plane to generate a two-dimensional stable operating region map; The command stabilization module adjusts the output power command of the offshore wind power system controller to maintain system stability based on the maximum state variable boundary in the two-dimensional stable operating area diagram.

8. The large-disturbance stability analysis system for offshore wind turbine frequency converter grid connection according to claim 7, characterized in that, The image generation module includes: The parameter acquisition unit acquires the geometric parameters of the closed hypersurface formed by the boundary of the maximum attraction domain defined by the quadratic Lyapunov function in the 26-dimensional state space. The dimension reduction mapping unit reduces the geometric parameters of the closed hypersurface along the dominant direction of electromechanical coupling characteristics and maps them to an orthogonal coordinate axis composed of the inverter side d-axis current and converter DC voltage coordinate axis, as well as the motor stator winding d-axis current and generator stator winding d-axis current coordinate axis. The envelope curve unit performs convex envelope operations on the mapped set of boundary points to form an envelope boundary curve. The region enclosed by the envelope boundary curve represents the operating range of the system that maintains transient stability under power disturbances. The image generation unit uses the envelope boundary curve as the critical surface to generate a rectangular stable operating region map that includes the upper limit of active power and the upper limit of reactive power. The horizontal axis of the rectangular stable operating region map is the active power axis, and the vertical axis is the reactive power axis.

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