Clustering method and device for directed network structure of large offshore wind plant

Abstract

The invention provides a clustering method and device for a directed network structure of a large offshore wind power plant, and relates to the field of wind power plant control, and the method comprises the steps: obtaining wind power plant wake flow distribution characteristics under different wind speed and wind direction conditions, and obtaining a coupling relation between wind power plant units based on the wind power plant wake flow distribution characteristics; parameterizing the coupling relationship, and representing the wind power plant units and the association relationship thereof by using a topological network; obtaining an adjacent matrix of a coupling relationship according to the topological network, and dividing the wind power plant network into a plurality of subsets with wake flow coupling degrees meeting a preset threshold value through a clustering method; and obtaining the offshore wind power plants with simple association or complex association corresponding to the subsets of which the wake flow coupling degrees meet a preset threshold, and obtaining a distributed network structure and a distributed network structure of the directed network structure of the wind power plants. According to the invention, reconstruction and simplification of the wind power plant network structure are realized through the intelligent clustering method, so that a foundation is laid for comprehensive optimization control of the whole wind power plant.

Description

Technical Field

[0001] The present application relates to the field of wind farm control, and in particular to a clustering method and device for a directed network structure of a large offshore wind farm. Background Art

[0002] At present, offshore wind power has developed rapidly with its advantages of high wind speed, low turbulence, and low wind shear, and has maintained a steady upward trend, becoming a key direction of new energy planning. The wind speed and direction of offshore wind farms are relatively stable, the wake effect is prolonged, and the coupling relationship between wind turbines is highly complex. The traditional control method is the greedy control of a single wind turbine, resulting in a small power generated by downstream wind turbines, which seriously affects the power generation efficiency of wind farms.

[0003] In order to improve the power generation efficiency of wind farms, wake control is adopted for wind farms to reduce the wake effect between wind turbines. Traditional wind farm operation control adopts a centralized control architecture, relying on a centralized controller to communicate and optimize all wind turbines. The centralized control method requires high communication capabilities and is prone to fall into local optimality, resulting in low optimization effect. It is suitable for small wind farms. In recent years, offshore wind farms are gradually becoming larger, and wind farm wind turbines are turning to large-size variable-speed types. In large offshore wind farms, as the number of wind turbines increases, the control variables of the wind farm also increase in a nearly exponential form. Based on this, distributed control architectures and optimization algorithms have received widespread attention from researchers, but related methods are still in the exploratory stage, and the impact of the control process on network changes is not considered, resulting in insufficient optimization efficiency and optimization performance. Summary of the invention

[0004] To address the above problems, a clustering method and device for the directed network structure of a large offshore wind farm was proposed. By introducing graph theory to study the topological network relationship of wind farm units, the wind farm network was reconstructed and simplified through an intelligent clustering method, and a distributed large-scale offshore wind farm optimization scheduling framework was established, thereby laying the foundation for the comprehensive optimization control of the entire wind farm.

[0005] In a first aspect, the present application proposes a clustering method for a directed network structure of a large offshore wind farm, comprising:

[0006] Obtaining the distribution characteristics of the wind farm wake under different wind speed and wind direction conditions, and obtaining the coupling relationship between the wind farm units based on the wind farm wake distribution characteristics;

[0007] Parameterizing the coupling relationship, and using a topological network to represent the wind farm units and their association relationships;

[0008] Obtaining an adjacency matrix of the coupling relationship according to the topological network, and dividing the wind farm network into a plurality of subsets whose wake coupling degrees meet a preset threshold through a clustering method;

[0009] The offshore wind farms with simple or complex associations corresponding to the subsets whose wake coupling degrees meet the preset threshold are obtained, and the decentralized network structure and the distributed network structure of the directed network structure of the wind farm are obtained.

[0010] Optionally, the parameterizing the coupling relationship and using a topological network to represent the wind farm units and their association relationships includes:

[0011] Using the nodes of the topological network to represent the wind farm units;

[0012] The edges of the topological network are used to represent the interaction relationship between the wind farm units.

[0013] Optionally, before using a topological network to represent the wind farm units and their association relationships, the method further includes:

[0014] Determine real-time wind conditions and control conditions.

[0015] Optionally, the clustering method is a combination of K-means++ and spectral clustering.

[0016] The second aspect of the present application proposes a clustering device with a directed network structure for a large offshore wind farm, comprising:

[0017] An acquisition module is used to acquire the distribution characteristics of the wind farm wake under different wind speed and wind direction conditions, and obtain the coupling relationship between the wind farm units based on the wind farm wake distribution characteristics;

[0018] A topology module, used to parameterize the coupling relationship and use a topology network to represent the wind farm units and their association relationships;

[0019] A partitioning module, used to obtain an adjacency matrix of the coupling relationship according to the topological network, and divide the wind farm network into a plurality of subsets whose wake coupling degrees meet a preset threshold value by a clustering method;

[0020] The response module is used to obtain the offshore wind farms with simple or complex associations corresponding to the subsets whose wake coupling degrees meet the preset threshold, and obtain the decentralized network structure and the distributed network structure of the wind farm directed network structure.

[0021] A third aspect of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in any one of the first aspects above is implemented.

[0022] In a fourth aspect, the present application proposes a non-temporary computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method described in any one of the first aspects above is implemented.

[0023] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0024] By introducing graph theory to study the topological network relationship of wind farm units, the wind farm network is reconstructed and simplified through intelligent clustering methods, and a distributed large-scale offshore wind farm optimization scheduling framework is established, laying the foundation for comprehensive optimization control of the entire wind farm.

[0025] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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:

[0027] Figure 1 It is a flow chart of a clustering method of a directed network structure of a large offshore wind farm according to an embodiment of the present application;

[0028] Figure 2 is a structural diagram of a topological network according to an embodiment of the present application;

[0029] Figure 3 It is a block diagram of a clustering device of a directed network structure of a large offshore wind farm according to an embodiment of the present application;

[0030] Figure 4 It is a block diagram of an electronic device. DETAILED DESCRIPTION

[0031] The 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.

[0032] Figure 1 It is a flow chart of a clustering method of a directed network structure of a large offshore wind farm according to an embodiment of the present application, including:

[0033] Step 101, obtaining the distribution characteristics of the wind farm wake under different wind speed and wind direction conditions, and obtaining the coupling relationship between the wind farm units based on the wind farm wake distribution characteristics.

[0034] It should be noted that 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 movement, thereby changing the wake superposition state between units and affecting the wake distribution characteristics.

[0035] In the embodiment of the present application, the distribution characteristics of the wind farm wake under different wind speed and wind direction conditions are obtained, and the coupling relationship between the wind farm units is obtained.

[0036] Step 102: parameterize the coupling relationship and use a topological network to represent the wind farm units and their association relationships.

[0037] It should be noted that before using a topological network to represent wind farm units and their relationships, the real-time wind conditions and control conditions need to be determined.

[0038] In the embodiment of the present application, the association relationship between the wind turbines can be represented by a parameterized manner. Specifically, the nodes of the topological network are used to represent the wind farm units, and the edges of the topological network are used to represent the interaction relationship between the wind farm units.

[0039] In a possible embodiment, Figure 2 As shown, the structure of the topological network containing three nodes is as follows Figure 2 As shown, Figure 2 The middle nodes represent wind turbine 1, wind turbine 2 and wind turbine 3 respectively. The relationship between wind turbine 1, wind turbine 2 and wind turbine 3 is determined by the parameter ω. 1，2 ,ω 1，3 With ω 2，3 express.

[0040] Step 103: obtaining an adjacency matrix of the coupling relationship according to the topological network, and dividing the wind farm network into a plurality of subsets whose wake coupling degrees meet a preset threshold by a clustering method.

[0041] For the topological network mentioned in the embodiment of step 102, the adjacency matrix obtained according to the network is:

[0042]

[0043] In the embodiment of the present application, network clustering is achieved by combining K-means++ with spectral clustering to divide the wind farm network into a plurality of subsets whose wake coupling degrees meet a preset threshold.

[0044] It should be noted that K-means++ is an improved K-means algorithm, which mainly targets the problem of initial centroid selection. The advantage of K-means++ is that it can select better initial centroids, thereby improving the convergence speed of the algorithm and reducing the risk of falling into a local optimal solution. Spectral clustering is a clustering method based on graph theory, which divides a weighted undirected graph into two or more optimal subgraphs, so that the subgraphs are as similar as possible and the distances between subgraphs are as far as possible.

[0045] Step 104, obtaining offshore wind farms with simple or complex associations corresponding to subsets of multiple wake coupling degrees that meet a preset threshold, and obtaining a decentralized network structure and a distributed network structure of a directed network structure of the wind farm;

[0046] In the embodiments of the present application, a large, centralized wind farm overall optimization problem is solved in a multiple, small, distributed manner, thereby corresponding to an offshore wind farm with simple or complex associations, to a decentralized network structure and a distributed network structure with a directed network structure of a wind farm.

[0047] Figure 3 A block diagram 10 of a clustering device of a large offshore wind farm directed network structure according to an embodiment of the present application includes:

[0048] An acquisition module 100 is used to acquire the distribution characteristics of the wind farm wake under different wind speed and wind direction conditions, and obtain the coupling relationship between the wind farm units based on the wind farm wake distribution characteristics;

[0049] A topology module 200 is used to parameterize the coupling relationship and use a topology network to represent wind farm units and their association relationships;

[0050] A division module 300 is used to obtain an adjacency matrix of coupling relationships according to the topological network, and divide the wind farm network into a plurality of subsets whose wake coupling degrees meet a preset threshold value by a clustering method;

[0051] The response module 400 is used to obtain offshore wind farms with simple or complex associations corresponding to subsets of multiple wake coupling degrees that meet a preset threshold, and obtain a decentralized network structure and a distributed network structure of the wind farm directed network structure.

[0052] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0053] Figure 4A schematic block diagram of an example electronic device 700 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0054] like Figure 4 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0055] A number of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0056] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as a voice command response method. For example, in some embodiments, the method proposed in the present application may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the method proposed in the present application described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the method proposed in the present application in any other appropriate manner (e.g., by means of firmware).

[0057] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0058] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0059] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0060] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0061] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0062] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0063] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document is not limited here.

[0064] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A clustering method for a directed network structure of a large offshore wind farm, characterized in that: include: Obtaining the distribution characteristics of the wind farm wake under different wind speed and wind direction conditions, and obtaining the coupling relationship between the wind farm units based on the wind farm wake distribution characteristics; Parameterizing the coupling relationship, and using a topological network to represent the wind farm units and their association relationships; Obtaining an adjacency matrix of the coupling relationship according to the topological network, and dividing the wind farm network into a plurality of subsets whose wake coupling degrees meet a preset threshold through a clustering method; The offshore wind farms with simple or complex associations corresponding to the subsets whose wake coupling degrees meet the preset threshold are obtained, and the decentralized network structure and the distributed network structure of the directed network structure of the wind farm are obtained.

2. The method according to claim 1, characterized in that The parameterizing of the coupling relationship and using a topological network to represent the wind farm units and their association relationships include: Using the nodes of the topological network to represent the wind farm units; The edges of the topological network are used to represent the interaction relationship between the wind farm units.

3. The method according to claim 1, characterized in that: Before using a topological network to represent the wind farm units and their association relationships, the method further includes: Determine real-time wind conditions and control conditions.

4. The method according to claim 1, characterized in that The clustering method is a combination of K-means++ and spectral clustering.

5. A clustering device for a directed network structure of a large offshore wind farm, characterized in that: include: An acquisition module is used to acquire the distribution characteristics of the wind farm wake under different wind speed and wind direction conditions, and obtain the coupling relationship between the wind farm units based on the wind farm wake distribution characteristics; A topology module, used to parameterize the coupling relationship and use a topology network to represent the wind farm units and their association relationships; A partitioning module, used to obtain an adjacency matrix of the coupling relationship according to the topological network, and divide the wind farm network into a plurality of subsets whose wake coupling degrees meet a preset threshold value by a clustering method; The response module is used to obtain the offshore wind farms with simple or complex associations corresponding to the subsets whose wake coupling degrees meet the preset threshold, and obtain the decentralized network structure and the distributed network structure of the wind farm directed network structure.

6. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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