Wind power plant active power output control method and device based on wake effect

Abstract

The invention provides a wind power plant active power output control method and device based on a wake flow effect, and relates to the technical field of offshore power generation. Flow field dynamic prediction modeling based on a wake flow model is constructed based on impeller surface wind speed and wind direction information of a wind turbine generator in an offshore wind power plant; constructing a network topology structure of an offshore wind power plant on the basis of flow field dynamic prediction modeling, and clustering wind turbine generators; designing an offshore wind power plant control framework based on the clustered wind turbine generator clusters; if the control framework is a distributed control framework, active power output of the wind turbine generator cluster is controlled based on a distributed scheduling strategy; and if the control framework is a distributed control framework, active power output of the wind turbine generator cluster is controlled based on a distributed scheduling strategy. According to the application, the wind turbine generator sets can flexibly adjust respective power output according to the flow field prediction information and the network topology structure so as to realize overall optimization control of the wind power plant.

Description

Technical Field

[0001] The present application relates to the technical field of offshore power generation, and in particular to a method and device for controlling active power output of a wind farm based on wake effect. Background Art

[0002] As wind power technology continues to mature, the wind power market has also begun to shift from land to sea. Offshore wind resources are abundant, and they have the advantages of not occupying land resources, low noise and visual pollution, and close to the load center, and have broad development prospects. However, due to the particularity of offshore environmental conditions and engineering and technical challenges, the operation and control of offshore wind farms have become more complicated. One of the key issues is the wake effect, that is, when the wind rotor rotates, the airflow it generates will affect the performance and power generation efficiency of subsequent wind rotors. Therefore, offshore wind farms need to carry out comprehensive optimization control of the entire field to maximize the power generation efficiency and reliability of the entire wind farm. Therefore, the comprehensive optimization control of the entire field of offshore wind farms is an issue that needs to be solved urgently. Summary of the invention

[0003] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0004] The first aspect of the present application proposes a method for controlling the active power output of a wind farm based on the wake effect, comprising: constructing a flow field dynamic prediction modeling based on a wake model based on the wind speed information and wind direction information of the impeller surface of the wind turbines in the offshore wind farm; on the basis of the flow field dynamic prediction modeling, constructing a network topology structure of the offshore wind farm, and clustering the wind turbines based on the network topology structure; designing an offshore wind farm control framework based on the clustered wind turbine clusters, the offshore wind farm control framework being a decentralized control framework or a distributed control framework; if the offshore wind farm control framework is a decentralized control framework, then controlling the active power output of the wind turbine cluster in the offshore wind farm based on a decentralized scheduling strategy; if the offshore wind farm control framework is a distributed control framework, then controlling the active power output of the wind turbine cluster in the offshore wind farm based on a distributed scheduling strategy.

[0005] According to one embodiment of the present application, based on the impeller surface wind speed information and wind direction information of the wind turbine in the offshore wind farm, a flow field dynamic prediction modeling based on the wake model is constructed, including: collecting and analyzing the impeller surface wind speed information and wind direction information, and introducing the wake model to construct a training data set; using a deep learning technology based on physical information to construct a deep neural network, and realizing the flow field dynamic prediction modeling by predicting the wind speed and wind direction of the entire impeller surface of the wind turbine, wherein deep learning is used to combine the wake prediction model equations describing the atmospheric flow into the deep neural network; and the robustness of the flow field dynamic prediction modeling is verified.

[0006] According to an embodiment of the present application, wind turbines are clustered based on network topology, including: considering the network topology characteristics of offshore wind farms, and using a method combining K-means++ and spectral clustering to cluster wind turbines.

[0007] According to one embodiment of the present application, if the offshore wind farm control framework is a distributed control framework, the active power output of the wind turbine cluster in the offshore wind farm is controlled based on a distributed scheduling strategy, including: if the offshore wind farm control framework is a distributed control framework, the operating parameters of the wind turbines in each wind turbine cluster are adjusted according to the operating status and requirements of the offshore wind farm and the coupling relationship between the wind turbines in each wind turbine cluster, so that the overall active power of the wind farm is maximized or a specific target is achieved.

[0008] According to one embodiment of the present application, if the offshore wind farm control framework is a distributed control framework, the active power output of the wind turbine cluster in the offshore wind farm is controlled based on a distributed scheduling strategy, including: if the offshore wind farm control framework is a distributed control framework, the optimal power output of the wind turbine in each wind turbine cluster is calculated, and communicated with neighboring clusters; by exchanging information and coordinating decisions, each wind turbine cluster is controlled to jointly iteratively solve sub-problems until a global optimal solution is reached.

[0009] According to one embodiment of the present application, by exchanging information and coordinating decisions, each wind turbine cluster is controlled to jointly iterate and solve sub-problems until a global optimal solution is reached, including: using a method that combines an alternating direction multiplier method and a meta-heuristic algorithm to equivalently decompose the objective function of the original problem into multiple solvable sub-problems, and controlling each wind turbine cluster to jointly iterate and solve the sub-problems until a global optimal solution is reached.

[0010] The second aspect of the present application proposes a control device for active power output of a wind farm based on the wake effect, including: a modeling module, which is used to construct a flow field dynamic prediction modeling based on a wake model based on the wind speed information and wind direction information of the impeller surface of the wind turbine in the offshore wind farm; a clustering module, which is used to construct a network topology structure of the offshore wind farm based on the dynamic prediction modeling of the flow field, and cluster the wind turbines based on the network topology structure; a design module, which is used to design an offshore wind farm control framework based on the clustered wind turbine cluster, and the offshore wind farm control framework is a decentralized control framework or a distributed control framework; a first control module, which is used to control the active power output of the wind turbine cluster in the offshore wind farm based on a decentralized scheduling strategy if the offshore wind farm control framework is a decentralized control framework; a second control module, which is used to control the active power output of the wind turbine cluster in the offshore wind farm based on a distributed scheduling strategy if the offshore wind farm control framework is a distributed control framework.

[0011] According to one embodiment of the present application, the modeling module is also used to: collect and analyze the wind speed information and wind direction information on the impeller surface, and introduce the wake model to construct a training data set; use deep learning technology based on physical information to construct a deep neural network, and realize flow field dynamic prediction modeling by predicting the wind speed and wind direction on the entire impeller surface of the wind turbine, wherein deep learning is used to combine the wake prediction model equations describing the atmospheric flow into the deep neural network; and verify the robustness of the flow field dynamic prediction modeling.

[0012] According to an embodiment of the present application, the clustering module is further used to: consider the network topology characteristics of the offshore wind farm and adopt a method combining K-means++ and spectral clustering to realize wind turbine component clustering.

[0013] According to one embodiment of the present application, the first control module is also used for: if the offshore wind farm control framework is a distributed control framework, then according to the operating status and requirements of the offshore wind farm and the coupling relationship between the wind turbines in each wind turbine cluster, adjusting the operating parameters of the wind turbines in each wind turbine cluster, so as to maximize the overall active power of the wind farm or achieve a specific target.

[0014] According to one embodiment of the present application, the second control module is also used for: if the offshore wind farm control framework is a distributed control framework, then calculating the optimal power output of the wind turbines in each wind turbine cluster and communicating with neighboring clusters; by exchanging information and coordinating decisions, controlling each wind turbine cluster to jointly iterate and solve sub-problems until a global optimal solution is reached.

[0015] According to one embodiment of the present application, the second control module is also used to: use a method that combines the alternating direction multiplier method and the meta-heuristic algorithm to equivalently decompose the objective function of the original problem into multiple solvable sub-problems, and control each wind turbine cluster to jointly iteratively solve the sub-problems until the global optimal solution is reached.

[0016] The third aspect embodiment of the present application proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the control method for active power output of a wind farm based on wake effect as described in the first aspect embodiment of the present application.

[0017] The fourth aspect embodiment of the present application proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the control method of active power output of a wind farm based on wake effect as described in the first aspect embodiment of the present application.

[0018] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for controlling the active power output of a wind farm based on the wake effect as described in the first aspect of the present application.

[0019] The present application achieves at least the following beneficial effects: The present application achieves parameterized representation of the coupling relationship between wind turbines by modeling the dynamic prediction of the flow field based on the wake model, constructing the network topology of the offshore wind farm, and clustering the wind turbines based on the structure, so as to better understand and analyze the interaction between the units in the wind farm, and provide a feasible control unit. The present application selects a suitable control framework (decentralized or distributed) according to the specific situation to achieve global optimization control of the offshore wind farm. Under the decentralized framework, the wind turbines in each cluster are controlled by decentralized scheduling strategies; while under the distributed framework, the distributed scheduling strategies are used to coordinate the power output between different clusters to achieve the global optimal solution, which enables the wind turbines to flexibly adjust their respective power outputs according to the flow field prediction information and the network topology to achieve the overall optimization control of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0021] Figure 1 It is a schematic diagram of an exemplary implementation of a method for controlling active power output of a wind farm based on wake effect shown in the present application.

[0022] Figure 2 This is a schematic diagram of the process of constructing a deep neural network using deep learning technology based on physical information as shown in this application.

[0023] Figure 3 This is a schematic diagram of a wind farm network construction process for wake effects under different conditions shown in this application.

[0024] Figure 4 This is a schematic diagram of an offshore wind farm optimization scheduling implementation process shown in this application.

[0025] Figure 5 It is a schematic diagram of a control device for active power output of a wind farm based on wake effect shown in the present application.

[0026] Figure 6 It is a schematic diagram of an electronic device shown in one embodiment of the present application. DETAILED DESCRIPTION

[0027] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0028] Figure 1 is a schematic diagram of an exemplary implementation of a method for controlling active power output of a wind farm based on wake effect shown in the present application, such as Figure 1 As shown, the control method of active power output of a wind farm based on wake effect comprises the following steps:

[0029] S101, based on the wind speed information and wind direction information of the wind turbine blades in the offshore wind farm, a flow field dynamic prediction model based on a wake model is constructed.

[0030] Figure 2 This is a schematic diagram of a deep neural network process based on deep learning technology based on physical information shown in this application. Figure 2 As shown, the wind speed and direction information on the impeller surface is collected and analyzed, and the wake model is introduced to construct a training data set; a deep neural network is constructed using deep learning technology based on physical information, and the dynamic prediction modeling of the flow field is realized by predicting the wind speed and wind direction on the entire impeller surface of the wind turbine. Deep learning is used to combine the wake prediction model equations describing the atmospheric flow into the deep neural network; the robustness of the dynamic prediction modeling of the flow field is verified through the control-oriented FLORIS wind farm simulation.

[0031] Specifically, based on the physical information framework of the wake model, a neural network consisting of three deep neural networks (DNN1, DNN2 and DNN3) is constructed. Among them, DNN1 takes dimensionless space-time coordinates as input and dimensionless stream function and pressure as output. It is used to approximate the mapping relationship between continuous space-time coordinates and corresponding quantities. Given any time and any position, the deep neural network is trained to return and output; DNN2 is based on DNN1, and the dimensionless downwind speed and crosswind speed are obtained by using automatic differentiation to differentiate the output of DNN1. In order to realize the training of the neural network with the existing data of the impeller surface, the observation process of mapping the output to the wind speed sensor measurement is embedded in DNN2; DNN3 is the combination of neural network and physical information. It incorporates the reduced-order wake model on the basis of DNN2, and its output is the residual term. After deep learning, the neural network can realize the wake prediction of the wind speed of the entire impeller surface in combination with the wake model mechanism, and comprehensively use the wind speed and wind direction data of the impeller surface to predict the flow field.

[0032] S102, based on the flow field dynamic prediction modeling, constructing a network topology structure of the offshore wind farm, and clustering the wind turbines based on the network topology structure.

[0033] This application aims to build a full-field control optimization framework for wind farms. Based on the wake distribution of wind farms under different wind speed and wind direction conditions, the mathematical description of the coupling relationship between wind farm units is studied to achieve parameterization of the coupling relationship; based on graph theory, the directed network of wind farms based on wake interaction relationships is studied, and a wind farm topological model based on adjacency matrix and graph spectrum transformation is established; clustering methods and intelligent algorithms suitable for large offshore wind farm network structures are studied to achieve clustering of wind farm unit control and further simplify the wind farm topological network.

[0034] Figure 3 This is a schematic diagram of a wind farm network construction process for wake effects under different conditions shown in this application, such as Figure 3 As shown, under different wind conditions and wind turbine states, the flow correlation between wind turbines will change due to the influence of relative position and wind turbine action, thereby changing the wake superposition state between units and affecting the wake distribution characteristics. In order to parameterize the coupling relationship between wind farm units, a topological network is used to represent the wind farm units and their associations, where the nodes in the network represent wind turbines and the edges of the network represent the interaction relationships between wind turbines. Under certain wind conditions and control conditions, the wind farm network is divided into multiple subsets with smaller wake coupling degrees according to the adjacency matrix representing the coupling relationship between wind turbines. Subsets can be determined by many different methods. This project comprehensively considers the structural characteristics of offshore wind farm networks, and adopts a method combining K-means++ and spectral clustering to achieve network clustering, using multiple small, distributed methods to solve the full-field optimization problem of large, centralized wind farms.

[0035] S103, designing an offshore wind farm control framework based on the clustered wind turbine cluster, where the offshore wind farm control framework is a decentralized control framework or a distributed control framework.

[0036] In the present application, corresponding to an offshore wind farm with simple or complex associations, two network structures, a decentralized control framework and a distributed control framework, can be designed based on the clustered wind turbine clusters.

[0037] S104: If the offshore wind farm control framework is a distributed control framework, the active power output of the wind turbine cluster in the offshore wind farm is controlled based on a distributed scheduling strategy.

[0038] Figure 4 This is a schematic diagram of an offshore wind farm optimization scheduling implementation process shown in this application, such as Figure 4As shown in the figure, if the offshore wind farm control framework is a decentralized control framework, compared with the original centralized structure, the number of wind turbines in each cluster can be drastically reduced after clustering, and there is no shared wind turbine between clusters. Therefore, the optimization solution process will be greatly simplified and is very suitable for online optimization. Mathematical algorithms such as Sequential Quadratic Programming (SQP) can be used to achieve the solution.

[0039] In specific implementation, the operating parameters of the wind turbines in each wind turbine cluster are adjusted according to the operating status and requirements of the offshore wind farm and the coupling relationship between the wind turbines in each wind turbine cluster, so as to maximize the overall active power of the wind farm or reach a specific target.

[0040] S105: If the offshore wind farm control framework is a distributed control framework, the active power output of the wind turbine cluster in the offshore wind farm is controlled based on a distributed scheduling strategy.

[0041] If the offshore wind farm control framework is a distributed control framework, compared with the decentralized communication structure, the distributed control framework has coupling between clusters due to the existence of shared nodes. Therefore, each cluster cannot be directly optimized and solved. For this type of distributed optimization problem, the optimal power output of the wind turbines in each wind turbine cluster is calculated, and it communicates with neighboring clusters; by exchanging information and coordinating decisions, each wind turbine cluster is controlled to jointly iterate and solve sub-problems until the global optimal solution is reached.

[0042] In specific implementation, the distributed algorithm (Alternating Direction Method of Multipliers, ADMM) and the meta-heuristic algorithm (Evolutionary Optimization, EO) can be combined. The ADMM algorithm decomposes the objective function of the original problem into multiple solvable sub-problems equivalently and solves them through alternating iterations. The EO algorithm is a heuristic global optimization method that can be used to coordinate the solutions of sub-problems to obtain the global optimal solution of the original problem.

[0043] This application achieves parameterized representation of the coupling relationship between wind turbines by modeling the dynamic prediction of the flow field based on the wake model, constructing the network topology of the offshore wind farm, and clustering the wind turbines based on the structure. This can better understand and analyze the interaction between the units in the wind farm, and provide a feasible control unit. This application selects a suitable control framework (decentralized or distributed) according to the specific situation to achieve global optimization control of the offshore wind farm. Under the decentralized framework, the wind turbines in each cluster are controlled by a decentralized scheduling strategy; under the distributed framework, the distributed scheduling strategy is used to coordinate the power output between different clusters to achieve a global optimal solution, which enables the wind turbines to flexibly adjust their respective power outputs according to the flow field prediction information and the network topology to achieve overall optimization control of the wind farm.

[0044] Figure 5 is a schematic diagram of a control device for active power output of a wind farm based on wake effect shown in the present application, such as Figure 5 As shown, the control device 500 for active power output of a wind farm based on wake effect includes a modeling module 501, a clustering module 502, a design module 503, a first control module 504 and a second control module 505, wherein:

[0045] The modeling module 501 is used to construct a flow field dynamic prediction modeling based on a wake model based on the wind speed information and wind direction information of the impeller surface of the wind turbine in the offshore wind farm.

[0046] The clustering module 502 is used to construct a network topology structure of the offshore wind farm based on the flow field dynamic prediction modeling, and cluster the wind turbines based on the network topology structure.

[0047] The design module 503 is used to design an offshore wind farm control framework based on the clustered wind turbine clusters. The offshore wind farm control framework is a decentralized control framework or a distributed control framework.

[0048] The first control module 504 is configured to control the active power output of the wind turbine cluster in the offshore wind farm based on a decentralized scheduling strategy if the offshore wind farm control framework is a decentralized control framework.

[0049] The second control module 505 is configured to control the active power output of the wind turbine cluster in the offshore wind farm based on a distributed scheduling strategy if the offshore wind farm control framework is a distributed control framework.

[0050] Furthermore, the modeling module 501 is also used to: collect and analyze the wind speed information and wind direction information on the impeller surface, and introduce the wake model to construct a training data set; use deep learning technology based on physical information to construct a deep neural network, and realize flow field dynamic prediction modeling by predicting the wind speed and wind direction on the entire impeller surface of the wind turbine, wherein deep learning is used to combine the wake prediction model equations describing the atmospheric flow into the deep neural network; and verify the robustness of the flow field dynamic prediction modeling.

[0051] Furthermore, the clustering module 502 is also used to: consider the network topology characteristics of the offshore wind farm and adopt a method combining K-means++ and spectral clustering to realize wind turbine component clustering.

[0052] Furthermore, the first control module 504 is also used to: if the offshore wind farm control framework is a distributed control framework, adjust the operating parameters of the wind turbines in each wind turbine cluster according to the operating status and requirements of the offshore wind farm and the coupling relationship between the wind turbines in each wind turbine cluster, so as to maximize the overall active power of the wind farm or achieve a specific target.

[0053] Furthermore, the second control module 505 is also used for: if the offshore wind farm control framework is a distributed control framework, then calculating the optimal power output of the wind turbines in each wind turbine cluster and communicating with neighboring clusters; by exchanging information and coordinating decisions, controlling each wind turbine cluster to jointly iteratively solve sub-problems until a global optimal solution is reached.

[0054] Furthermore, the second control module 505 is also used to: use a method combining the alternating direction multiplier method and the meta-heuristic algorithm to equivalently decompose the objective function of the original problem into multiple solvable sub-problems, and control each wind turbine cluster to jointly iteratively solve the sub-problems until a global optimal solution is reached.

[0055] In order to implement the above embodiment, the present application embodiment further proposes an electronic device 600, such as Figure 6 As shown, the electronic device 600 includes: a processor 601 and a memory 602 that is communicatively connected to the processor, the memory 602 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 601 to implement the control method for active power output of a wind farm based on wake effect as shown in the above embodiment.

[0056] In order to implement the above embodiment, the embodiment of the present application also proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the control method of active power output of a wind farm based on wake effect as shown in the above embodiment.

[0057] In order to implement the above embodiments, the embodiments of the present application further provide a computer program product, including a computer program, which, when executed by a processor, implements the control method for active power output of a wind farm based on wake effect as shown in the above embodiments.

[0058] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0059] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0060] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0061] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for controlling active power output of a wind farm based on wake effect, characterized in that: include: Based on the wind speed and direction information of the wind turbine blades in the offshore wind farm, a dynamic prediction model of the flow field based on the wake model is constructed; On the basis of the flow field dynamic prediction modeling, a network topology structure of the offshore wind farm is constructed, and wind turbines are clustered based on the network topology structure; Designing an offshore wind farm control framework based on the clustered wind turbine cluster, wherein the offshore wind farm control framework is a decentralized control framework or a distributed control framework; If the offshore wind farm control framework is a distributed control framework, the active power output of the wind turbine cluster in the offshore wind farm is controlled based on a distributed scheduling strategy; If the offshore wind farm control framework is a distributed control framework, the active power output of the wind turbine cluster in the offshore wind farm is controlled based on a distributed scheduling strategy.

2. The method according to claim 1, characterized in that The method of constructing a flow field dynamic prediction model based on a wake model based on the wind speed information and wind direction information of the wind turbines in the offshore wind farm includes: Collect and analyze the wind speed and direction information on the impeller surface, introduce the wake model, and construct a training data set; A deep neural network is constructed using deep learning technology based on physical information. The wind speed and wind direction of the entire impeller surface of the wind turbine are predicted to achieve dynamic prediction modeling of the flow field. Deep learning is used to combine the wake prediction model equation describing the atmospheric flow into the deep neural network. Robustness verification of flow field dynamic prediction modeling.

3. The method according to claim 2, characterized in that The clustering of wind turbines based on the network topology structure includes: Considering the network topology characteristics of offshore wind farms, a method combining K-means++ and spectral clustering is used to realize wind turbine component clustering.

4. The method according to claim 3, characterized in that If the offshore wind farm control framework is a distributed control framework, the active power output of the wind turbine cluster in the offshore wind farm is controlled based on a distributed scheduling strategy, including: If the offshore wind farm control framework is a distributed control framework, the operating parameters of the wind turbines in each wind turbine cluster are adjusted according to the operating status and requirements of the offshore wind farm and the coupling relationship between the wind turbines in each wind turbine cluster, so that the overall active power of the wind farm is maximized or reaches a specific target.

5. The method according to claim 4, characterized in that If the offshore wind farm control framework is a distributed control framework, the active power output of the wind turbine cluster in the offshore wind farm is controlled based on a distributed scheduling strategy, including: If the offshore wind farm control framework is a distributed control framework, then the optimal power output of the wind turbines in each wind turbine cluster is calculated and communicated with neighboring clusters; By exchanging information and coordinating decisions, each wind turbine cluster is controlled to iteratively solve sub-problems together until the global optimal solution is reached.

6. The method according to claim 5, characterized in that The above-mentioned method controls each wind turbine cluster to jointly iteratively solve sub-problems by exchanging information and coordinating decisions until a global optimal solution is reached, including: By combining the alternating direction multiplier method with the meta-heuristic algorithm, the objective function of the original problem is equivalently decomposed into multiple solvable sub-problems, and each wind turbine cluster is controlled to iteratively solve the sub-problems together until the global optimal solution is reached.

7. A control device for active power output of a wind farm based on wake effect, characterized in that: include: A modeling module is used to construct a flow field dynamic prediction model based on a wake model based on the wind speed information and wind direction information of the wind turbine blades in the offshore wind farm; A clustering module, used to construct a network topology structure of the offshore wind farm based on the flow field dynamic prediction modeling, and cluster the wind turbines based on the network topology structure; A design module, used for designing an offshore wind farm control framework based on the clustered wind turbine cluster, wherein the offshore wind farm control framework is a decentralized control framework or a distributed control framework; A first control module, configured to control the active power output of the wind turbine cluster in the offshore wind farm based on a decentralized scheduling strategy if the offshore wind farm control framework is a decentralized control framework; The second control module is used to control the active power output of the wind turbine cluster in the offshore wind farm based on a distributed scheduling strategy if the offshore wind farm control framework is a distributed control framework.

8. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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