A method and system for optimizing configuration of offshore wind turbine hybrid grid-connected system

Abstract

The application provides a method and system for optimizing configuration of a hybrid grid-connected system of offshore wind turbines, which comprises: constructing an equivalent model of a hybrid grid-connected system of grid-following offshore wind turbines and grid-forming offshore wind turbines; introducing a ShockHash algorithm to perform minimum perfect hash mapping on system parameters, and establishing a stability margin evaluation index; constructing an optimization configuration model containing capacity constraints and stability margin constraints; using a parallel batch dynamic maximum matching algorithm to solve the system optimization under different grid strengths; and setting a subset feedback arc to perform closed-loop control on system stability. The application solves the problems of inaccurate equivalent modeling of a hybrid grid-connected system of offshore wind turbines, difficulty in quantifying stability margin, and low efficiency of configuration methods, and realizes the optimization configuration and adaptive adjustment of a hybrid grid-connected system of offshore wind turbines under different grid conditions, thereby improving the stability and economy of the system.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, and in particular to a method and system for optimizing the configuration of a hybrid grid-connected offshore wind turbine system. Background Technology

[0002] In the field of new energy power generation technology, wind power, as an important form of clean energy, plays a crucial role in the global energy structure transformation. With the continuous increase in installed wind power capacity, the stability and economic efficiency of wind farm systems have become a focus of industry attention. Wind farms typically consist of multiple wind turbine generators, and based on their grid connection characteristics, they can be divided into two main categories: grid-connected offshore wind turbines and grid-connected offshore wind turbines.

[0003] Currently, common wind farm grid connection methods mainly include two modes: single grid-connected offshore wind turbines and single grid-connected offshore wind turbines. Grid-connected offshore wind turbines typically use doubly-fed induction generators (DFIGs) or full-power converter technology, relying on the frequency and voltage support provided by the grid; while grid-connected offshore wind turbines have the ability to autonomously construct electrical parameters, providing frequency support and voltage regulation services. These two types of offshore wind turbines each have their own technical characteristics and applicable scenarios.

[0004] Existing technologies have begun exploring hybrid grid-connected modes combining grid-connected and grid-connected offshore wind turbines, attempting to combine the advantages of both types. This hybrid grid-connected system utilizes the grid support provided by the grid-connected turbines while leveraging the economic advantages of the grid-connected turbines, theoretically achieving better system performance. However, existing hybrid grid-connected technologies suffer from inaccurate equivalent modeling and difficulties in assessing the mutual influence between different types of offshore wind turbines.

[0005] Traditional methods for hybrid grid connection of offshore wind turbines struggle to accurately assess system stability, especially under weak grid conditions, where the system's stability margin is difficult to quantify. Furthermore, the lack of optimized configuration methods that consider various grid conditions and operating scenarios makes it difficult to determine the selection and configuration ratio of offshore wind turbine types, impacting the system's economy and stability. Moreover, existing methods fail to effectively integrate advanced algorithmic technologies to improve system configuration efficiency and performance. Summary of the Invention

[0006] Therefore, the purpose of this invention is to provide a method and system for optimizing the configuration of a hybrid grid-connected offshore wind turbine system, thereby solving the technical problems in the prior art such as inaccurate modeling, difficulty in stability assessment, and low efficiency in configuration optimization of hybrid grid-connected offshore wind turbine systems.

[0007] To achieve the above objectives, the technical solution provided by the present invention is as follows:

[0008] A method for optimizing the configuration of a hybrid grid-connected offshore wind turbine system includes:

[0009] Obtain the electrical and control system parameters of grid-connected and grid-connected offshore wind turbines, construct the state space equation of the hybrid grid-connected system using the electrical and control system parameters to obtain the state matrix, and establish a unified system equivalent model based on the state matrix;

[0010] The system feature matrix and key state variables are extracted using the system equivalent model to form a system parameter set. The system parameter set is then mapped to a minimum perfect hash using the ShockHash algorithm to obtain a parameter index structure. Based on the parameter index structure, the system eigenvalues ​​and damping ratio are calculated to establish a stability margin evaluation index that reflects the stability of the system.

[0011] Based on the stability margin evaluation index, an optimal configuration model for a hybrid grid-connected system, including capacity constraints and stability margin constraints, is constructed. The optimal configuration model is then transformed into a maximum matching problem model based on graph theory, wherein the maximum matching problem model includes a set of vertices for offshore wind turbine locations, a set of vertices for offshore wind turbine configuration, and an edge weight calculation model.

[0012] The optimization problem under different power grid strengths and operating scenarios is divided into multiple sub-problems according to the scenario matrix. The parallel batch processing dynamic maximum matching algorithm is used to solve the sub-problems. The solution results under multiple scenarios are comprehensively evaluated and weighted to obtain the optimal offshore wind turbine configuration scheme.

[0013] Based on the optimal offshore wind turbine configuration scheme, a subset feedback arc control structure including equipment-level control, group-level control, and system-level control is constructed, and a system status monitoring and adjustment mechanism is established to realize the adaptive adjustment of the hybrid grid-connected system under different grid conditions.

[0014] Preferably, the state-space equations of the hybrid grid-connected system are constructed using the electrical parameters and control system parameters, including:

[0015] Based on the electrical and control system parameters of the grid-connected and grid-connected offshore wind turbines, the motor characteristics, converter control strategy, and grid connection characteristics of the grid-connected offshore wind turbine are analyzed to obtain a dynamic characteristic model of the grid-connected offshore wind turbine.

[0016] Based on the electrical and control system parameters, the voltage source characteristics, frequency support capability, and voltage regulation capability of the grid-type offshore wind turbine are analyzed to obtain a dynamic characteristic model of the grid-type offshore wind turbine.

[0017] Based on the dynamic characteristic model of the grid-connected offshore wind turbine and the dynamic characteristic model of the grid-connected offshore wind turbine, the state space equation of the hybrid grid-connected system is constructed to obtain the state matrix.

[0018] Preferably, the system feature matrix and key state variables are extracted using the system equivalent model to form a system parameter set, including:

[0019] Based on the state matrix in the system equivalent model, the feature sub-matrices and their coupling matrices of the grid-connected and grid-structured offshore wind turbines are extracted to obtain the system parameter set.

[0020] A second-order graph core is constructed for the system parameter set, and a collision-free hash mapping function is generated using the ShockHash algorithm;

[0021] The system parameter set is encoded and stored using the hash mapping function to obtain the parameter index structure.

[0022] Preferably, the system eigenvalues ​​and damping ratios are calculated based on the parameter index structure, and a stability margin evaluation index is established, including:

[0023] The system characteristic equation is constructed using the parameter index structure, and the system characteristic equation is solved using the QR algorithm to obtain the system characteristic values;

[0024] The system damping ratio is calculated using the system characteristic values, resulting in small-signal stability margin, transient stability margin, and frequency stability margin indices.

[0025] The small-signal stability margin index, transient stability margin index, and frequency stability margin index are weighted and integrated to generate the stability margin evaluation index.

[0026] Preferably, an optimal configuration model for a hybrid grid-connected system, including capacity constraints and stability margin constraints, is constructed, comprising:

[0027] Receive the total installed capacity parameters of the wind farm, set the ratio range of installed capacity between grid-connected offshore wind turbines and grid-connected offshore wind turbines, and generate the capacity constraint conditions;

[0028] Receive the stability margin evaluation index, set the threshold range for stable system operation, and generate the stability margin constraints;

[0029] The capacity constraint and the stability margin constraint are integrated to form the optimal configuration model of the hybrid grid-connected system.

[0030] Preferably, the optimization configuration model is transformed into a graph theory-based maximum matching problem model, including:

[0031] Receive the set of offshore wind turbine locations and the set of offshore wind turbine types and capacity configurations, and generate the set of offshore wind turbine location vertices and the set of offshore wind turbine configuration vertices, respectively.

[0032] The edge weight calculation model is generated using the stability margin evaluation index and the capacity constraint.

[0033] The maximum matching problem model is formed by integrating the set of offshore wind turbine location vertices, the set of offshore wind turbine configuration vertices, and the edge weight calculation model.

[0034] Preferably, the optimization problem under different power grid strengths and operating scenarios is divided into the sub-problems, including:

[0035] Receive grid strength and operating condition parameters, construct a scenario matrix based on the parameters, establish a corresponding graph theory model using the scenario matrix, and generate a scenario optimization task set;

[0036] The scenario optimization task set is allocated to the main control processing unit and the subordinate processing units to establish a parallel computing architecture;

[0037] Based on the aforementioned parallel computing architecture, dynamic task scheduling and result caching are implemented to generate parallel processing results.

[0038] Preferably, the parallel batch processing dynamic maximum matching algorithm is used to solve the subproblem, including:

[0039] Based on the parallel processing results, an augmented path search strategy is designed, the current optimal match and vertex potential are maintained, and a match update strategy is generated.

[0040] Receive system parameter change information, adjust vertex potential values ​​according to the matching update strategy and perform local augmenting path search to generate the updated optimal match;

[0041] Parallel verification operations are performed on the updated optimal match to output the optimal offshore wind turbine configuration scheme.

[0042] Preferably, a subset feedback arc control structure is constructed, comprising device-level control, group-level control, and system-level control, including:

[0043] The optimal offshore wind turbine configuration scheme is received, key state variables are extracted as feedback inputs, and the state vector is decomposed into functional subsets to form a hierarchical control structure.

[0044] Feedback control laws are designed for the aforementioned functional subset, and integrated into an overall control strategy through matrix operations to form a hierarchical control scheme;

[0045] The device-level control, group-level control, and system-level control are implemented according to the hierarchical control scheme to form the subset feedback arc control structure.

[0046] This includes establishing a system status monitoring and adjustment mechanism to achieve adaptive adjustment of the hybrid grid-connected system under different power grid conditions, including:

[0047] Based on the subset feedback arc control structure, the threshold range of key system variables is determined, and control triggering conditions for the normal zone, early warning zone, and alarm zone are established.

[0048] Perform trend analysis on the system status, establish a predictive triggering mechanism based on the predicted status changes, and obtain a control plan;

[0049] Based on the control triggering conditions and the control plan, an adaptive parameter adjustment algorithm is constructed to achieve dynamic optimization of control parameters.

[0050] This invention also provides a system for optimizing the configuration of a hybrid grid-connected offshore wind turbine system, comprising:

[0051] The equivalent model construction module is used to receive electrical and control system parameters of grid-connected and grid-connected offshore wind turbines, construct the state space equation of the hybrid grid-connected system to obtain the state matrix, and output a unified system equivalent model.

[0052] The stability margin assessment module is connected to the equivalent model construction module. It is used to extract feature matrices and state variables based on the system equivalent model, establish a parameter index structure through the ShockHash algorithm, calculate system eigenvalues ​​and damping ratios, and output stability margin assessment indicators.

[0053] The optimization configuration module, connected to the stability margin evaluation module, is used to construct the optimization configuration model and transform it into a maximum matching problem model, including the processing of the offshore wind turbine location vertex set, the offshore wind turbine configuration vertex set, and edge weights.

[0054] A configuration solution module is connected to the optimization configuration module to execute a parallel batch processing dynamic maximum matching algorithm, handle sub-problems under different scenarios, and output the optimal offshore wind turbine configuration scheme.

[0055] An adaptive adjustment module, connected to the configuration solving module, is used to establish a hierarchical subset feedback arc control structure to realize system state monitoring and adaptive parameter adjustment.

[0056] The beneficial effects of this invention are as follows:

[0057] A minimum perfect hash mapping mechanism based on ShockHash is proposed, which realizes efficient storage and retrieval of parameters of offshore wind turbine hybrid grid-connected system, and significantly improves the computational efficiency of system modeling and analysis;

[0058] A parallel batch processing dynamic maximum matching algorithm was designed to efficiently solve the optimization configuration problem of offshore wind turbine hybrid grid-connected systems under multiple scenarios, with constant computational complexity for each system parameter update;

[0059] An innovative subset feedback arc control structure was introduced, which enabled the hybrid grid-connected system to adaptively adjust under different grid intensities, significantly improving the system's stability and robustness.

[0060] A multi-objective optimization configuration model integrating stability constraints and economic evaluation was constructed, enabling scientific decision-making on the selection and configuration ratio of offshore wind turbine types and improving the overall performance of the system;

[0061] A multi-dimensional stability margin evaluation index system based on system eigenvalues ​​and damping ratio was proposed, realizing the quantitative analysis and evaluation of the stability of hybrid grid-connected systems. Attached Figure Description

[0062] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0063] Figure 1 A flowchart illustrating the optimized configuration method for a hybrid grid-connected offshore wind turbine system provided in an embodiment of the present invention;

[0064] Figure 2 A flowchart illustrating the construction of an equivalent model for a hybrid grid-connected offshore wind turbine system provided in an embodiment of the present invention;

[0065] Figure 3 A flowchart of stability margin evaluation based on the ShockHash algorithm provided in this embodiment of the invention;

[0066] Figure 4 A flowchart illustrating the construction process of the optimized configuration model for a hybrid grid-connected system provided in this embodiment of the invention;

[0067] Figure 5 The flowchart of the parallel batch processing dynamic maximum matching algorithm provided in the embodiments of the present invention is as follows:

[0068] Figure 6 A flowchart illustrating the implementation of the subset feedback arc control structure provided in this embodiment of the invention;

[0069] Figure 7 The structural block diagram of the optimized configuration system for the hybrid grid-connected offshore wind turbine system provided in the embodiments of the present invention. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0071] 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.

[0072] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0073] First Embodiment

[0074] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0075] like Figure 1 The diagram shown is a flowchart illustrating the optimized configuration method for a hybrid grid-connected offshore wind turbine system provided in an embodiment of the present invention. The method includes the following steps:

[0076] Step S100: Obtain the electrical parameters and control system parameters of the grid-connected offshore wind turbine and the grid-connected offshore wind turbine. Use the electrical parameters and control system parameters to construct the state space equation of the hybrid grid-connected system to obtain the state matrix. Establish a unified system equivalent model based on the state matrix.

[0077] Specifically, electrical parameters of grid-connected and grid-connected offshore wind turbines, including impedance characteristics, power transfer characteristics, and control system parameters, are collected to establish mathematical models for each type of offshore wind turbine. The motor characteristics, converter control strategies, and grid connection characteristics of grid-connected offshore wind turbines are analyzed to establish small-signal models. The voltage source characteristics, frequency support capability, and voltage regulation capability of grid-connected offshore wind turbines are analyzed to establish small-signal models. Based on these two small-signal models, the state-space equations of a hybrid grid-connected system are constructed, yielding an equivalent model characterizing the system's dynamic characteristics.

[0078] like Figure 2 The diagram shown is a flowchart of the construction process for the equivalent model of the hybrid grid-connected offshore wind turbine system provided in this embodiment of the invention. The specific steps for constructing the state-space equations of the hybrid grid-connected system include:

[0079] Step S110: Based on the electrical parameters and control system parameters of the grid-connected offshore wind turbine and the grid-connected offshore wind turbine, analyze the motor characteristics, converter control strategy and grid connection characteristics of the grid-connected offshore wind turbine to obtain the dynamic characteristic model of the grid-connected offshore wind turbine.

[0080] This step first involves collecting electrical parameters of the grid-connected offshore wind turbine, including stator resistance and inductance, rotor resistance and inductance, mutual inductance coefficients, etc., as well as converter control parameters, including PI controller parameters, filter parameters, etc. Then, the motor characteristics of the grid-connected offshore wind turbine are analyzed, primarily considering the dynamic characteristics of doubly-fed induction generators or permanent magnet synchronous generators, and motor equations are established. Converter control strategies are analyzed, including current loop control, power control, and PLL phase-locked loop control, and control system equations are established. Finally, the motor equations and control system equations are integrated to form a dynamic characteristic model of the grid-connected offshore wind turbine.

[0081] Step S120: Based on the electrical parameters and control system parameters, analyze the voltage source characteristics, frequency support capability, and voltage regulation capability of the grid-type offshore wind turbine to obtain the dynamic characteristic model of the grid-type offshore wind turbine.

[0082] This step first involves collecting the electrical parameters of the grid-type offshore wind turbine, including virtual synchronous machine inertia parameters, damping coefficients, and voltage regulation parameters. Then, the voltage source characteristics of the grid-type offshore wind turbine are analyzed, and a voltage source model is established, considering its ability to provide stable voltage. Frequency support characteristics are analyzed, including virtual inertia control and frequency droop control, and a frequency response model is established. Voltage regulation capabilities are analyzed, including voltage droop control and reactive power control, and a voltage regulation model is established. Finally, the voltage source model, frequency response model, and voltage regulation model are integrated to form a dynamic characteristic model of the grid-type offshore wind turbine.

[0083] Step S130: Based on the dynamic characteristic model of the grid-connected offshore wind turbine and the dynamic characteristic model of the grid-connected offshore wind turbine, construct the state space equation of the hybrid grid-connected system to obtain the state matrix.

[0084] In this step, the system's state variables are first identified, including dynamic variables such as the offshore wind turbine rotor angular velocity, current, and voltage, forming a state vector. Then, the relationship equations between the state variables are established, expressed in the form of a state-space equation: dx / dt = Ax + Bu, where x is the state vector, A is the state matrix, B is the input matrix, and u is the input vector. Specifically, the state matrix A contains all the information about the system's dynamic characteristics and is the core of subsequent analysis. Based on the dynamic characteristic models of grid-connected and grid-connected offshore wind turbines, the values ​​of each element of the state matrix A are calculated, completing the construction of the state-space equations for the hybrid grid-connected system.

[0085] Step S200: Extract the system feature matrix and key state variables using the system equivalent model to form a system parameter set. Use the ShockHash algorithm to perform a minimum perfect hash mapping on the system parameter set to obtain a parameter index structure. Calculate the system eigenvalues ​​and damping ratio based on the parameter index structure to establish a stability margin evaluation index that reflects the stability of the system.

[0086] Specifically, firstly, the system feature matrix and key state variables are extracted to form a system parameter set. Then, a minimum perfect hash mapping mechanism based on ShockHash is designed to efficiently encode and store the system parameter set, improving parameter query efficiency. Based on the system parameters mapped by ShockHash, system eigenvalues ​​and damping ratios are calculated to establish quantitative indicators reflecting the system's stability. Finally, a multi-dimensional system stability margin evaluation index system is constructed, including small-signal stability margin, transient stability margin, and frequency stability margin.

[0087] like Figure 3 The diagram shown is a flowchart of the stability margin assessment based on the ShockHash algorithm provided in an embodiment of the present invention. The specific steps for extracting the system feature matrix and key state variables using the system equivalent model to form a system parameter set include:

[0088] Step S210: Based on the state matrix in the system equivalent model, extract the feature sub-matrices and coupling matrices of the grid-connected and grid-connected offshore wind turbines to obtain the system parameter set.

[0089] In this step, key feature matrices are first extracted from the state matrix A, including the feature sub-matrices of grid-connected and grid-connected offshore wind turbines, as well as the coupling matrix between them. The feature sub-matrices of grid-connected offshore wind turbines include their PI controller parameters, converter parameters, and filter parameters; the feature sub-matrices of grid-connected offshore wind turbines include their voltage control parameters, frequency support coefficient, and virtual inertia parameters. Then, key system state variables are extracted, including dynamic variables such as offshore wind turbine rotor angular velocity, stator current, converter DC voltage, and PLL phase-locked angle, as well as network variables such as grid frequency and bus voltage. Finally, the extracted feature matrix elements and key state variables are integrated to form a system parameter set. Each element It represents a key system parameter or state variable.

[0090] Step S220: Construct a second-order graph core for the system parameter set, and generate a collision-free hash mapping function using the ShockHash algorithm.

[0091] In this step, the ShockHash algorithm is first customized to suit the characteristics of the offshore wind turbine hybrid grid-connected system. A 2-graph core is found for the parameter set P using graph theory, and then a collision-free hash function h(x) is constructed based on this core. This ensures that for any two distinct parameters... , ,satisfy The hash table size is exactly equal to |P|. Then, an adaptive parameter mechanism for the hash function is implemented. Specifically, for each type of parameter, its numerical range and distribution characteristics are first statistically analyzed. When the parameter values ​​are relatively concentrated, the hash seed step size is appropriately increased to expand the hash space; when the parameter values ​​are relatively dispersed, the hash seed step size is decreased to increase the mapping density, thus enabling the hash function to automatically adjust according to the numerical characteristics of the system parameters. For electrical parameters (such as impedance and capacitance), due to their large differences in magnitude, a logarithmic normalization-based hashing strategy is adopted, and an independent hash seed Se is set. For control parameters (such as PI controller gain), due to their relatively concentrated numerical range, a linear uniform hashing strategy is adopted, and an independent hash seed Sc is set. By setting hashing strategies and hash seeds separately for different types of parameters, the uniformity and efficiency of the overall mapping are ensured.

[0092] Step S230: Encode and store the system parameter set using the hash mapping function to obtain the parameter index structure.

[0093] In this step, system parameters are encoded and stored using an optimized ShockHash mapping mechanism to form an efficient parameter index structure. This structure significantly reduces storage space requirements (by about 40-60% compared to traditional hash tables) and achieves parameter query operations with O(1) time complexity, thus significantly improving the efficiency of system analysis and computation.

[0094] The specific steps for calculating the system eigenvalues ​​and damping ratio based on the parameter index structure and establishing a stability margin evaluation index include:

[0095] Step S240: Construct the system characteristic equation using the parameter index structure, solve the system characteristic equation using the QR algorithm, and obtain the system characteristic values.

[0096] In this step, the characteristic equations of the system are first constructed using the system parameters optimized through ShockHash mapping. For a hybrid grid-connected offshore wind turbine system, its state equation is expressed as follows: The state matrix A contains all the information about the system's dynamic characteristics. The characteristic equation of the system is: ,in Let be the eigenvalues, and I be the identity matrix. Then, the QR algorithm is used to solve the characteristic equation and calculate all the eigenvalues ​​of the system. The subscript i represents the index of the eigenvalue, and n represents the order of the system state matrix, i.e., the total number of system state variables. The eigenvalues ​​are in complex form. The real part The imaginary part represents the decay rate of the system response. This represents the oscillation frequency of the system. A necessary condition for system stability is that the real parts of all eigenvalues ​​are negative, i.e. .

[0097] Step S250: Calculate the system damping ratio using the system characteristic values ​​to form small-signal stability margin index, transient stability margin index, and frequency stability margin index.

[0098] In this step, the damping ratio of the system is first calculated based on the calculated eigenvalues. Damping ratio is defined as The damping ratio is an important indicator for measuring the oscillation damping performance of a system, and it is usually required to be greater than a certain threshold (such as 0.05 or 0.1) to ensure that the system has sufficient damping capability. Then, a small signal stability margin evaluation index, SSS (Small Signal Stability), is constructed. This index is mainly based on the eigenvalue analysis of the linearized model and is usually defined as... ,in The reference damping ratio is typically 0.05 or 0.1. Small-signal stability margin reflects the dynamic performance of the system under small disturbances, with particular attention paid to the damping characteristics of low-frequency oscillation modes (typically 0.1-2Hz). Next, a transient stability margin evaluation index, TSS (Transient Stability), is established. This index is quantified by calculating the ratio of the critical fault clearing time (CCT) to the actual protection action time under a typical large disturbance (such as a three-phase short-circuit fault), i.e., TSS = CCT / Tact, where CCT is the critical fault clearing time and Tact is the actual protection action time; the ratio of the two is the transient stability margin index, TSS.

[0099] Transient stability margin reflects the system's ability to maintain synchronous operation under large disturbances and is an important indicator for evaluating the system's anti-interference capability. Finally, a frequency stability margin evaluation index, FSS (Frequency Stability), is constructed. This index is defined by calculating the ratio of the system's frequency deviation under power surges to the allowable frequency deviation, i.e., FSS = Δfallow / Δfmax, where Δfallow is the maximum allowable frequency deviation and Δfmax is the actual maximum frequency deviation. Frequency stability margin primarily reflects the ability of grid-type offshore wind turbines to provide frequency support.

[0100] Step S260: The small-signal stability margin index, transient stability margin index, and frequency stability margin index are weighted and integrated to generate the stability margin evaluation index.

[0101] In this step, the indicators from the three dimensions mentioned above are integrated into a comprehensive stability margin indicator (CSS) through a weighted method. ,in These are the weighting coefficients, and This multi-dimensional evaluation index system enables a comprehensive and accurate assessment of the stability performance of hybrid grid-connected systems under various conditions, providing a scientific basis for subsequent optimization.

[0102] Step S300: Based on the stability margin evaluation index, construct an optimal configuration model for a hybrid grid-connected system that includes capacity constraints and stability margin constraints. Transform the optimal configuration model into a maximum matching problem model based on graph theory. The maximum matching problem model includes a set of vertices for offshore wind turbine locations, a set of vertices for offshore wind turbine configurations, and an edge weight calculation model.

[0103] Specifically, firstly, based on the stability margin assessment index, system stability constraints are determined, including minimum damping ratio requirements and critical stability margin. Then, a system capacity constraint model is established, considering the total installed capacity of the wind farm, cost factors, and technical limitations of different types of offshore wind turbines. An economic evaluation model is constructed, comprehensively considering the initial investment cost of offshore wind turbines, operation and maintenance costs, and system benefits. Finally, stability constraints, capacity constraints, and economic evaluation are integrated to construct a multi-objective optimal configuration model.

[0104] like Figure 4 The diagram shows the construction flowchart of the hybrid grid-connected system optimization configuration model provided in this embodiment of the invention. The specific steps for constructing the hybrid grid-connected system optimization configuration model, which includes capacity constraints and stability margin constraints, include:

[0105] Step S310: Receive the total installed capacity parameters of the wind farm, set the ratio range of installed capacity between grid-connected offshore wind turbines and grid-connected offshore wind turbines, and generate the capacity constraint conditions.

[0106] In this step, the first step is to set a constraint on the total installed capacity of the wind farm. Assume the planned total installed capacity of the wind farm is... The installed capacities of grid-type offshore wind turbines and grid-type offshore wind turbines are respectively and Then the following constraints must be met: Then, to ensure the diversity and stability of the system, the capacity ratio range of each type of offshore wind turbine is set. For example, the proportion α of grid-type offshore wind turbines should meet the following requirements. Finally, the cost factors of different types of offshore wind turbines are considered. Grid-connected offshore wind turbines, due to their stronger grid support capabilities, typically have a higher unit cost than grid-connected offshore wind turbines. The unit capacity costs for grid-connected and grid-connected offshore wind turbines are set as follows: and Then the total investment cost constraint can be expressed as: ,in The maximum allowed investment budget.

[0107] Step S320: Receive the stability margin evaluation index, set the threshold range for stable system operation, and generate the stability margin constraint conditions.

[0108] In this step, the minimum damping ratio constraint is first set for small-signal stability. Based on power system design specifications and actual wind farm operating experience, the damping ratio for all oscillation modes is typically required to be within a certain range. Not less than the preset threshold (e.g., 0.05 or 0.1), that is, the constraint condition is expressed as: This constraint ensures that the system can quickly recover to a stable state after a small disturbance, avoiding continuous oscillations. Then, for transient stability, a critical stability margin constraint is set. A transient stability margin index is defined. ,in The maximum transmission power that the system can withstand. This is the normal operating power. The constraint conditions are set as follows: This ensures the system maintains synchronous operation even after large disturbances. Finally, frequency response constraints are set for frequency stability, and frequency regulation coefficients are defined. The constraints are set as follows This ensures that the system has sufficient frequency regulation capability to maintain frequency stability when the load or power generation changes, where ΔP is the change in active power when the system experiences power disturbance, and Δf is the corresponding system frequency deviation.

[0109] Step S330: Integrate the capacity constraint and the stability margin constraint to form the optimal configuration model of the hybrid grid-connected system.

[0110] In this step, the optimization variables are first defined. The main optimization variables include the proportion of grid-type offshore wind turbines, α (or directly using...). and These variables represent the capacity of various types of offshore wind turbines, offshore wind turbine layout parameters, key control parameters, etc. These variables constitute the decision space of the optimization problem, denoted as a vector. Then, a multi-objective optimization function is constructed. Considering that wind farm planning needs to balance stability and economy, two optimization objectives are set: maximizing the system stability margin index (CSS) and maximizing the economic benefit index (NPV). The multi-objective optimization problem can be expressed as: Finally, all constraints are integrated, including stability and capacity constraints, and expressed as follows: and ,in and Let represent the inequality constraint and the equality constraint function, respectively.

[0111] The specific steps to transform the optimization configuration model into a graph theory-based maximum matching problem model include:

[0112] Step S340: Receive the set of offshore wind turbine locations and the set of offshore wind turbine types and capacity configurations, and generate the set of offshore wind turbine location vertices and the set of offshore wind turbine configuration vertices, respectively.

[0113] In this step, a bipartite graph representation model is first constructed. The possible locations of offshore wind turbines in the wind farm are set as a set of vertices. Set the available offshore wind turbine types and capacity configurations as another set of vertices. ,in Each vertex in the graph represents a specific type of offshore wind turbine (grid-type or grid-connected) and its capacity level. This forms a bipartite graph. , where edge set E represents feasible location-offshore wind turbine configuration combinations.

[0114] Step S350: Using the stability margin evaluation index and the capacity constraint, generate the edge weight calculation model.

[0115] In this step, weights are assigned to the edges to reflect the performance metrics of different configuration schemes. Edge weights Based on the previously constructed comprehensive objective function F(X), the calculation represents the position... Installation type is The comprehensive benefits generated by offshore wind turbines. For example, ,in and These represent the stability margin and economic benefit indicators of the configuration scheme, respectively. and These are the corresponding weighting coefficients.

[0116] Step S360: Integrate the set of offshore wind turbine location vertices, the set of offshore wind turbine configuration vertices, and the edge weight calculation model to form the maximum matching problem model.

[0117] In this step, boundary constraints are first added. The previously introduced constraints are transformed into structural constraints in a graph theory model. For example, capacity constraints can be transformed into degree limits for vertices, and stability constraints into feasibility checks for specific combinations (i.e., whether certain edges exist in edge set E). If a certain offshore wind turbine configuration does not satisfy the stability constraints, the corresponding edge will be removed from edge set E. The optimization problem is then transformed into a maximum weighted matching problem in a weighted bipartite graph: finding a matching in G. Given that each vertex is connected to at most one edge (i.e., at most one offshore wind turbine is installed at each location, and each type of offshore wind turbine is installed at most once), and satisfying all constraints, the total weight is... maximum.

[0118] Step S400: The optimization problem under different power grid strength and operation scenarios is divided into multiple sub-problems according to the scenario matrix. The sub-problems are solved using the parallel batch processing dynamic maximum matching algorithm. The solution results under multiple scenarios are comprehensively evaluated and weighted to obtain the optimal offshore wind turbine configuration scheme.

[0119] Specifically, first, the optimization configuration model is mathematically transformed to establish a maximum matching problem model based on graph theory. Then, a parallel batch processing mechanism is designed to divide the optimization problems under different grid strengths and operating scenarios into multiple sub-problems. The dynamic maximum matching algorithm is implemented to ensure that each system parameter update has a constant computational complexity and improve the solution efficiency. Finally, the solution results under multiple scenarios are comprehensively evaluated and weighted to obtain the optimal offshore wind turbine configuration plan.

[0120] As Figure 5 shown, it is the flowchart of the parallel batch processing dynamic maximum matching algorithm provided by the embodiment of the present invention. The specific steps of dividing the optimization problems under different grid strengths and operating scenarios into the sub-problems include:

[0121] Step S410, receive the grid strength and operating condition parameters, construct a scenario matrix based on the parameters, establish a corresponding graph theory model using the scenario matrix, and generate a scenario optimization task set.

[0122] In this step, first, a scenario division strategy is defined. It is divided into three categories according to the grid strength: strong grid (SCR > 5), medium-strength grid (3 < SCR < 5), and weak grid (SCR < 3). Among them, when SCR = 5, it belongs to the strong grid category; when SCR = 3, it belongs to the medium-strength grid category; it is divided into high wind speed scenarios, medium wind speed scenarios, and low wind speed scenarios according to the wind speed conditions; and it is divided into peak load scenarios and valley load scenarios according to the load conditions. This multi-dimensional division forms a scenario matrix , and each element represents a specific combination of grid strength and operating conditions. Then, for each scenario , a corresponding graph theory model is constructed. Under different scenarios, the edge set and the weight function will change, reflecting the specific constraint conditions and objective functions in this scenario. For example, under weak grid conditions, the stability constraint will be more stringent, and some edges may not exist in the edge set; while in high wind speed scenarios, the economic benefit weight may increase.

[0123] Step S420, allocate the scenario optimization task set to the main control processing unit and the subordinate processing units to establish a parallel computing architecture.

[0124] In this step, a parallel computing architecture is designed. Each element in the scenario matrix S and its corresponding graph theory model The tasks are distributed across different computing units for parallel processing. A master-slave architecture is adopted, with the master node responsible for task allocation and result aggregation, and the slave nodes responsible for the specific solutions in each scenario. This parallel architecture can significantly improve computational efficiency, especially for large-scale wind farm optimization problems.

[0125] Step S430: Implement dynamic task scheduling and result caching based on the parallel computing architecture to generate parallel processing results.

[0126] In this step, a batch processing scheduling mechanism is implemented. Dynamic task scheduling strategies are designed based on the computational complexity and priority of different scenarios to ensure efficient utilization of computing resources. Simultaneously, an intermediate result caching mechanism is established, allowing different scenarios to share certain common computational results, further improving computational efficiency. Through this parallel batch processing mechanism, optimization problems under multiple conditions can be handled simultaneously, resulting in a more comprehensive and robust offshore wind turbine configuration scheme.

[0127] The specific steps for solving the subproblem using the parallel batch processing dynamic maximum matching algorithm include:

[0128] Step S440: Design an augmented path search strategy based on the parallel processing results, maintain the current optimal match and vertex potential, and generate a match update strategy.

[0129] In this step, an incremental update mechanism is first designed. When system parameters change, only the affected edges and vertices are recalculated, not the entire graph. Specifically, an edge weight change table Δw is maintained, and local rematching is performed only on edges in Δw. Then, the core algorithm for dynamic maximum matching is implemented. Based on the augmenting path method, a matching algorithm that can adapt to dynamic weight changes is constructed. This algorithm maintains the current optimal matching M and a set of vertex potentials. When edge weights change, the matching results are quickly updated by adjusting vertex potentials and locally searching augmenting paths.

[0130] Step S450: Receive system parameter change information, adjust vertex potential values ​​according to the matching update strategy, and perform local augmenting path search to generate the updated optimal match.

[0131] In this step, the time complexity of the algorithm is first optimized. By adopting the idea of ​​the Hopcroft-Karp algorithm and combining it with a dynamic update mechanism, the computational complexity of each parameter update is reduced to [missing information]. Where V is the number of vertices in the graph, and E is the number of edges in the graph. This indicates that taking the square root of the number of vertices can achieve a constant complexity of O(1) under certain conditions. This optimization enables the algorithm to efficiently handle real-time optimization problems in large-scale wind farms. Then, parallel computing optimization is implemented. Taking advantage of the multi-core capabilities of modern computing architectures, key steps of the dynamic maximum matching algorithm (such as augmenting path search) are parallelized. Through data partitioning and task allocation, multiple processors can simultaneously search for augmenting paths in different regions, further improving the algorithm's efficiency.

[0132] Step S460: Perform parallel verification operation on the updated optimal match and output the optimal offshore wind turbine configuration scheme.

[0133] This step begins by establishing a scenario weighting evaluation system. Based on historical data and prediction models, the probability of occurrence for each scenario is determined. And importance weight Calculate the overall weighting coefficient Then, the optimal configuration schemes for each scenario are screened and merged. Based on a comprehensive weighting coefficient, the configuration schemes for each scenario are integrated using methods such as weighted voting or weighted averaging. Next, scheme conflict detection and coordination are performed. When there is a conflict between the optimal configuration schemes in different scenarios, the globally optimal configuration scheme is selected by calculating the weighted performance index of each scheme across all scenarios. Finally, the integrated configuration scheme is fully verified and fine-tuned to ensure that it meets all constraints, resulting in an optimal offshore wind turbine configuration scheme with good performance under various grid conditions and operating scenarios.

[0134] Step S500: Based on the optimal offshore wind turbine configuration scheme, construct a subset feedback arc control structure including equipment-level control, group-level control and system-level control, establish a system status monitoring and adjustment mechanism, and realize the adaptive adjustment of the hybrid grid-connected system under different grid conditions.

[0135] Specifically, firstly, based on the optimal configuration scheme, a subset feedback arc control structure is designed, and a system state monitoring and adjustment mechanism is established. Then, the threshold ranges and triggering conditions of key system variables are determined, forming a feedback triggering strategy. An adaptive adjustment algorithm for system parameters based on the subset feedback arc is implemented to dynamically optimize the system response under different grid conditions. Finally, a system simulation verification platform is built to comprehensively evaluate the stability and economy of the hybrid grid-connected system under different operating conditions.

[0136] like Figure 6 The diagram shown is a flowchart illustrating the implementation of the subset feedback arc control structure provided in an embodiment of the present invention. The specific steps for constructing the subset feedback arc control structure, which includes device-level control, group-level control, and system-level control, include:

[0137] Step S510: Receive the optimal offshore wind turbine configuration scheme, extract key state variables as feedback input, decompose the state vector into functional subsets, and form a hierarchical control structure.

[0138] In this step, the key state monitoring points are first identified. Based on the characteristics of the hybrid grid-connected system, a set of key state variables are selected for real-time monitoring, including: voltage amplitude and phase angle of each bus, offshore wind turbine output power, power factor, system frequency, and the operating status of key equipment. These monitoring points constitute a state vector. This is the basic data source for subset feedback arc control. Then, the structure and topology of the subset feedback arc are designed. The specific design includes: (1) decomposing the state vector x into multiple functional subsets. Each subset is responsible for a specific aspect of the control objective; (2) Design a corresponding feedback control law for each subset. (3) Integrate these local control laws into an overall control strategy through matrix operations. ,in Let i be the state variable corresponding to the i-th functional subset. The feedback control function designed for this subset describes the mapping relationship between state variables and control output. Let i be the local control output of the i-th functional subset. This is the weight matrix.

[0139] Step S520: Design feedback control laws for the functional subset, integrate them into an overall control strategy through matrix operations, and form a hierarchical control scheme.

[0140] In this step, a hierarchical control architecture is established. The subset feedback arc control structure is organized into a three-layer architecture: the bottom layer is device-level control (such as voltage and power control of a single offshore wind turbine), the middle layer is group-level control (such as coordinated control of a specific type of offshore wind turbine group), and the top layer is system-level control (such as unified scheduling and optimization of the entire wind farm). Each layer contains corresponding subset feedback arcs, forming a complete control system.

[0141] Step S530: Implement the device-level control, group-level control, and system-level control according to the hierarchical control scheme to form the subset feedback arc control structure.

[0142] In this step, the hardware and software platform of the control system is implemented. A real-time data acquisition system is developed to collect key state variables; a control execution system based on an industrial computer is constructed to implement a subset feedback arc control algorithm; and a human-machine interface is designed to facilitate operation and maintenance personnel in monitoring the system status and performing necessary manual intervention.

[0143] Step S540: Determine the threshold range of key system variables based on the subset feedback arc control structure, and establish control triggering conditions for the normal zone, early warning zone, and alarm zone.

[0144] In this step, the threshold ranges for key system variables are first determined. Based on the standards for safe and stable operation of the power system and the actual operating experience of wind farms, threshold ranges are defined for each key state variable. Set safe operating range For example, the permissible fluctuation range of the bus voltage is the rated value. The permissible deviation of the system frequency is The required range for the output power factor of offshore wind turbines is 0.95 lagging to 0.95 leading. These threshold ranges constitute the safe operating area of ​​the system. Then, multi-level triggering conditions are designed. The system state is divided into three levels: normal zone, early warning zone, and alarm zone. When the state variable is in the normal zone, the control system maintains the current settings; when the variable enters the early warning zone, preventative control is initiated, and system parameters are fine-tuned; when the variable enters the alarm zone, emergency control measures are immediately executed to quickly pull the system back to the safe zone. Finally, a priority mechanism for the triggering strategy is constructed. Different types of system variables have different degrees of impact on system stability, therefore, it is necessary to establish a priority ranking for variable triggering. For example, frequency deviation and critical bus voltage exceedances usually have the highest priority and require immediate response; while secondary indicators such as power factor deviation can be adjusted gradually as system resources allow.

[0145] Step S550: Perform trend analysis on the system status, establish a prediction triggering mechanism based on the predicted status changes, and obtain a control plan.

[0146] This step implements a prediction-based early triggering mechanism. Through system state trend analysis, the future trajectory of key variables is predicted, triggering control operations before the variables actually exceed their limits. For example, this is achieved by calculating the voltage change rate. When a potential overshoot is predicted within a short period, voltage regulation is initiated in advance. This predictive triggering mechanism effectively improves the system's response speed and defense capabilities.

[0147] Step S560: Based on the control triggering conditions and the control plan, construct a parameter adaptive adjustment algorithm to achieve dynamic optimization of control parameters.

[0148] In this step, the basic framework for adaptive parameter adjustment is first designed. This framework includes three core modules: a power grid condition identification module, a parameter sensitivity analysis module, and a parameter optimization adjustment module. The power grid condition identification module assesses the power grid strength in real time by measuring indicators such as the short-circuit ratio (SCR) and the X / R ratio. The parameter sensitivity analysis module determines which parameters have the greatest impact on the current system state through small disturbance injection and response observation. The parameter optimization adjustment module then executes specific parameter adjustment operations based on the outputs of the first two modules. Next, an adaptive control algorithm for the power grid is developed. The optimal settings for key system parameters vary depending on different power grid conditions. Based on this, a parameter mapping function is designed. A relationship model between power grid conditions and optimal parameter settings is established, in which... This is the system control parameter vector, which includes key control parameters for grid-connected and grid-connected offshore wind turbines (such as PI controller gain, virtual inertia coefficient, droop control coefficient, etc.). This is a parameter mapping function, describing the method for calculating the optimal control parameters of the system under given grid short-circuit ratio (SCR) and line impedance ratio (X / R). When grid conditions change, the system can automatically adjust the parameters according to this mapping relationship. Next, a parameter fine-tuning mechanism based on online learning is implemented. An online learning algorithm is introduced to continuously optimize the parameter mapping function by observing the system's response performance in real time. Specifically, a gradient descent-based parameter update strategy is adopted. ,in This is the system performance evaluation function. For learning rate, Let p(t) be the gradient of the performance function with respect to the parameters, and p(t) be the control parameter vector at the t-th iteration. Finally, a parameter coordination optimization mechanism is developed. In a hybrid grid-connected system, the control parameters of different types of offshore wind turbines have complex interactions, requiring coordinated optimization to achieve overall optimality. Therefore, a hierarchical coordination strategy is adopted: first, the internal parameters of each type of offshore wind turbine are optimized at the local level, and then the interaction parameters between different types of offshore wind turbines are coordinated at the system level. Through this adaptive adjustment algorithm, the hybrid grid-connected offshore wind turbine system can automatically optimize control parameters according to changes in grid conditions, always maintaining optimal stability and response performance, and effectively coping with various grid operating condition challenges.

[0149] Second Embodiment

[0150] like Figure 7 The diagram shown is a structural block diagram of the optimized configuration system for a hybrid grid-connected offshore wind turbine system provided in an embodiment of the present invention. The system includes an equivalent model construction module 10, a stability margin evaluation module 20, an optimized configuration module 30, a configuration solution module 40, and an adaptive adjustment module 50.

[0151] The equivalent model construction module 10 is used to receive electrical parameters and control system parameters of grid-connected and grid-connected offshore wind turbines, construct the state space equation of the hybrid grid-connected system to obtain the state matrix, and output a unified system equivalent model.

[0152] The stability margin assessment module 20 is connected to the equivalent model construction module. It is used to extract feature matrices and state variables based on the system equivalent model, establish a parameter index structure through the ShockHash algorithm, calculate system eigenvalues ​​and damping ratios, and output stability margin assessment indicators.

[0153] The optimization configuration module 30, connected to the stability margin evaluation module, is used to construct an optimization configuration model and transform it into a maximum matching problem model, including the processing of the offshore wind turbine location vertex set, the offshore wind turbine configuration vertex set, and edge weights.

[0154] The configuration solution module 40 is connected to the optimization configuration module and is used to execute the parallel batch processing dynamic maximum matching algorithm to handle sub-problems under different scenarios and output the optimal offshore wind turbine configuration scheme.

[0155] The adaptive adjustment module 50 is connected to the configuration solving module and is used to establish a hierarchical subset feedback arc control structure to realize system state monitoring and parameter adaptive adjustment.

[0156] This disclosure also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps of the optimized configuration method for a hybrid grid-connected offshore wind turbine system described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0157] Furthermore, this disclosure also provides a computer program product storing a computer program. When the computer program is run by a processor, it executes the steps of an optimized configuration method for a hybrid grid-connected offshore wind turbine system provided in any of the above embodiments of this disclosure. For details, please refer to the above method embodiments, which will not be repeated here.

[0158] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium, which can be a volatile or non-volatile computer-readable storage medium. In another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0159] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices and systems described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces, indirect coupling or communication connection of devices or units, and may be electrical, mechanical, or other forms.

[0160] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0161] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0162] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0163] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A method for optimizing the configuration of a hybrid grid-connected offshore wind turbine system, characterized in that, include: Obtain the electrical and control system parameters of grid-connected and grid-connected offshore wind turbines, construct the state space equation of the hybrid grid-connected system using the electrical and control system parameters to obtain the state matrix, and establish a unified system equivalent model based on the state matrix; The system feature matrix and key state variables are extracted using the system equivalent model to form a system parameter set. The system parameter set is then mapped to a minimum perfect hash using the ShockHash algorithm to obtain a parameter index structure. Based on the parameter index structure, the system eigenvalues ​​and damping ratio are calculated to establish a stability margin evaluation index that reflects the stability of the system. The step of extracting system feature matrices and key state variables from the system equivalent model to form a system parameter set includes: extracting feature sub-matrices and coupling matrices of grid-connected and grid-connected offshore wind turbines based on the state matrix in the system equivalent model to obtain the system parameter set; constructing a second-order graph core for the system parameter set; and generating a collision-free hash mapping function using the ShockHash algorithm. Based on the stability margin evaluation index, an optimal configuration model for a hybrid grid-connected system, including capacity constraints and stability margin constraints, is constructed. The optimal configuration model is then transformed into a maximum matching problem model based on graph theory, wherein the maximum matching problem model includes a set of vertices for offshore wind turbine locations, a set of vertices for offshore wind turbine configuration, and an edge weight calculation model. The step of transforming the optimization configuration model into a graph theory-based maximum matching problem model includes: receiving a set of offshore wind turbine locations and a set of offshore wind turbine types and capacity configurations; generating a set of offshore wind turbine location vertices and a set of offshore wind turbine configuration vertices, respectively; generating an edge weight calculation model using the stability margin evaluation index and the capacity constraint; and integrating the set of offshore wind turbine location vertices, the set of offshore wind turbine configuration vertices, and the edge weight calculation model to form the maximum matching problem model. The optimization problem under different power grid strengths and operating scenarios is divided into multiple sub-problems according to the scenario matrix. The parallel batch processing dynamic maximum matching algorithm is used to solve the sub-problems. The solution results under multiple scenarios are comprehensively evaluated and weighted to obtain the optimal offshore wind turbine configuration scheme. Based on the optimal offshore wind turbine configuration scheme, a subset feedback arc control structure including equipment-level control, group-level control, and system-level control is constructed, and a system status monitoring and adjustment mechanism is established to realize the adaptive adjustment of the hybrid grid-connected system under different grid conditions.

2. The method according to claim 1, characterized in that, Using the electrical and control system parameters, the state-space equations of the hybrid grid-connected system are constructed, including: Based on the electrical and control system parameters of the grid-connected and grid-connected offshore wind turbines, the motor characteristics, converter control strategy, and grid connection characteristics of the grid-connected offshore wind turbine are analyzed to obtain a dynamic characteristic model of the grid-connected offshore wind turbine. Based on the electrical and control system parameters, the voltage source characteristics, frequency support capability, and voltage regulation capability of the grid-type offshore wind turbine are analyzed to obtain a dynamic characteristic model of the grid-type offshore wind turbine. Based on the dynamic characteristic model of the grid-connected offshore wind turbine and the dynamic characteristic model of the grid-connected offshore wind turbine, the state space equation of the hybrid grid-connected system is constructed to obtain the state matrix.

3. The method according to claim 1, characterized in that, Based on the parameter index structure, the system eigenvalues ​​and damping ratios are calculated, and a stability margin evaluation index is established, including: The system characteristic equation is constructed using the parameter index structure, and the system characteristic equation is solved using the QR algorithm to obtain the system characteristic values; The system damping ratio is calculated using the system characteristic values, resulting in small-signal stability margin, transient stability margin, and frequency stability margin indices. The small-signal stability margin index, transient stability margin index, and frequency stability margin index are weighted and integrated to generate the stability margin evaluation index.

4. The method according to claim 1, characterized in that, Construct an optimal configuration model for a hybrid grid-connected system that includes capacity constraints and stability margin constraints, including: Receive the total installed capacity parameters of the wind farm, set the ratio range of installed capacity between grid-connected offshore wind turbines and grid-connected offshore wind turbines, and generate the capacity constraint conditions; Receive the stability margin evaluation index, set the threshold range for stable system operation, and generate the stability margin constraints; The capacity constraint and the stability margin constraint are integrated to form the optimal configuration model of the hybrid grid-connected system.

5. The method according to claim 1, characterized in that, The optimization problem under different power grid strengths and operating scenarios is divided into the sub-problems mentioned above, including: Receive grid strength and operating condition parameters, construct a scenario matrix based on the parameters, establish a corresponding graph theory model using the scenario matrix, and generate a scenario optimization task set; The scenario optimization task set is allocated to the main control processing unit and the subordinate processing units to establish a parallel computing architecture; Based on the aforementioned parallel computing architecture, dynamic task scheduling and result caching are implemented to generate parallel processing results.

6. The method according to claim 1, characterized in that, The subproblem is solved using the aforementioned parallel batch dynamic maximum matching algorithm, including: Based on the parallel processing results, an augmented path search strategy is designed, the current optimal match and vertex potential are maintained, and a match update strategy is generated. Receive system parameter change information, adjust vertex potential values ​​according to the matching update strategy and perform local augmenting path search to generate the updated optimal match; Parallel verification operations are performed on the updated optimal match to output the optimal offshore wind turbine configuration scheme.

7. The method according to claim 1, characterized in that, Construct a subset feedback arc control structure that includes device-level control, group-level control, and system-level control, including: The optimal offshore wind turbine configuration scheme is received, key state variables are extracted as feedback inputs, and the state vector is decomposed into functional subsets to form a hierarchical control structure. Feedback control laws are designed for the aforementioned functional subset, and integrated into an overall control strategy through matrix operations to form a hierarchical control scheme; The device-level control, group-level control, and system-level control are implemented according to the hierarchical control scheme to form the subset feedback arc control structure; This includes establishing a system status monitoring and adjustment mechanism to achieve adaptive adjustment of the hybrid grid-connected system under different power grid conditions, including: Based on the subset feedback arc control structure, the threshold range of key system variables is determined, and control triggering conditions for the normal zone, early warning zone, and alarm zone are established. Perform trend analysis on the system status, establish a predictive triggering mechanism based on the predicted status changes, and obtain a control plan; Based on the control triggering conditions and the control plan, an adaptive parameter adjustment algorithm is constructed to achieve dynamic optimization of control parameters.

8. A system for performing the method of claim 1, characterized in that, include: The equivalent model construction module is used to receive electrical and control system parameters of grid-connected and grid-connected offshore wind turbines, construct the state space equation of the hybrid grid-connected system to obtain the state matrix, and output a unified system equivalent model. The stability margin assessment module is connected to the equivalent model construction module. It is used to extract feature matrices and state variables based on the system equivalent model, establish a parameter index structure through the ShockHash algorithm, calculate system eigenvalues ​​and damping ratios, and output stability margin assessment indicators. The optimization configuration module, connected to the stability margin evaluation module, is used to construct the optimization configuration model and transform it into a maximum matching problem model, including the processing of the offshore wind turbine location vertex set, the offshore wind turbine configuration vertex set, and edge weights. A configuration solution module is connected to the optimization configuration module to execute a parallel batch processing dynamic maximum matching algorithm, handle sub-problems under different scenarios, and output the optimal offshore wind turbine configuration scheme. An adaptive adjustment module, connected to the configuration solving module, is used to establish a hierarchical subset feedback arc control structure to realize system state monitoring and adaptive parameter adjustment.

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