Offshore wind power plant active power output distribution method and device considering fan fatigue load
By defining the fatigue coefficient in offshore wind farms and using particle swarm optimization algorithms to distribute active output, the problem of difficulty in obtaining data in the prior art is solved, and the rapid and effective fatigue load management and cost reduction of offshore wind farms are achieved.
Patent Information
- Application Number
- CN202411508287.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-25
- Publication Date
- 2025-03-11
AI Technical Summary
The existing fatigue load evaluation method requires the acquisition of parameter data by adding sensors, making it difficult to achieve short-term rapid application in actual projects, resulting in increased operation and maintenance costs of offshore wind farms and insufficient practicality.
The fatigue coefficient is defined based on the output power of offshore wind farm units, and the active power of each wind turbine is output through the particle swarm optimization algorithm to achieve active output distribution and balance the fatigue coefficient of each wind farm unit.
It reduces the cost of active output allocation of offshore wind farms, improves the feasibility of actual engineering applications, solves the problem of sensor data acquisition, and realizes fast and effective fatigue load management.
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Figure CN119602400A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of offshore wind power technology, and in particular to a method and device for allocating active output of an offshore wind farm taking into account wind turbine fatigue loads. Background Art
[0002] With the rapid development of offshore wind power technology, the fatigue load problem faced by wind turbines has become increasingly prominent. In order to ensure the stable operation of wind farms, it is necessary to effectively manage the fatigue load of wind turbines and reduce the operation and maintenance costs. In related technologies, fatigue load assessment can be carried out using the SN curve method, rain flow counting method and loss equivalent load method.
[0003] However, in the related technologies, the existing fatigue load assessment methods all require the addition of sensors to obtain the necessary parameter data for assessment, which makes it difficult to achieve short-term rapid application in actual engineering projects, resulting in increased operation and maintenance costs for offshore wind farms and insufficient practicality, which needs to be urgently addressed. Summary of the invention
[0004] The present application provides an offshore wind farm active output distribution method and device taking into account wind turbine fatigue load, in order to solve the problems in the related art that the existing fatigue load assessment methods all require the addition of sensors to obtain the necessary parameter data for assessment, which is difficult to achieve short-term rapid application in actual engineering, resulting in increased operation and maintenance costs of offshore wind farms and insufficient practicality.
[0005] The first aspect of the present application provides a method for allocating active output of an offshore wind farm taking into account fatigue loads of wind turbines, comprising the following steps: constructing a fatigue load model of the target offshore wind farm based on the power generation fatigue load and air fatigue load of each wind turbine group in the target offshore wind farm; confirming the objective function of the target offshore wind farm based on the fatigue load model, and calculating the fitness value of each wind turbine group based on the objective function; based on all fitness values and the output power constraint of the target offshore wind farm, updating the optimal position of the target offshore wind farm and each wind turbine group by a preset particle swarm optimization algorithm until a preset iteration condition is reached, outputting the active power of each wind turbine group, and allocating the active output of the target offshore wind farm according to the active power of each wind turbine group.
[0006] Optionally, in one embodiment of the present application, the power generation fatigue load and the air fatigue load are:
[0007]
[0008] in, is the power generation fatigue load, is the air fatigue load, P(τ) is the output power of the wind turbine at time τ, Ieff (τ) is the effective turbulence intensity at time τ, P max is the rated power of the wind turbine, T life is the life of the wind turbine, N rep is the maintenance compensation coefficient of the wind turbine, N dis is the interference coefficient, I a is the turbulence coefficient of the wind farm, I w is the wake turbulence intensity, S is the area swept by the fan impeller, N t is the inference coefficient of the wind turbine.
[0009] Optionally, in one embodiment of the present application, the objective function is:
[0010]
[0011] Where N is the number of wind turbine groups, is the average fatigue coefficient, is the wind turbine 0 The fatigue factor at the moment, is the power generation fatigue load of the wind turbine at time t, is the air fatigue load of the wind turbine at time t.
[0012] Optionally, in one embodiment of the present application, the output power constraint condition of the target offshore wind farm is:
[0013]
[0014] in, is the allocated power of the i-th wind turbine group, P coor is the target power command of the wind farm, and ξ is the upper limit of the allowable power error.
[0015] The second aspect of the present application provides an offshore wind farm active output distribution device taking into account wind turbine fatigue loads, including: a construction module, used to construct a fatigue load model of the target offshore wind farm based on the power generation fatigue load and air fatigue load of each wind turbine group in the target offshore wind farm; a calculation module, used to confirm the objective function of the target offshore wind farm based on the fatigue load model, and calculate the fitness value of each wind turbine group based on the objective function; an allocation module, used to update the optimal position of the target offshore wind farm and each wind turbine group based on all fitness values and the output power constraint conditions of the target offshore wind farm by a preset particle swarm optimization algorithm until a preset iteration condition is reached, output the active power of each wind turbine group, and distribute the active output of the target offshore wind farm according to the active power of each wind turbine group.
[0016] Optionally, in one embodiment of the present application, the power generation fatigue load and the air fatigue load are:
[0017]
[0018] in, is the power generation fatigue load, is the air fatigue load, P(τ) is the output power of the wind turbine at time τ, I eff (τ) is the effective turbulence intensity at time τ, P max is the rated power of the wind turbine, T life is the life of the wind turbine, N rep is the maintenance compensation coefficient of the wind turbine, N dis is the interference coefficient, I a is the turbulence coefficient of the wind farm, I w is the wake turbulence intensity, S is the area swept by the fan impeller, N t is the inference coefficient of the wind turbine.
[0019] Optionally, in one embodiment of the present application, the objective function is:
[0020]
[0021] Where N is the number of wind turbine groups, is the average fatigue coefficient, is the wind turbine 0 The fatigue factor at the moment, is the power generation fatigue load of the wind turbine at time t, is the air fatigue load of the wind turbine at time t.
[0022] Optionally, in one embodiment of the present application, the output power constraint condition of the target offshore wind farm is:
[0023]
[0024] in, is the allocated power of the i-th wind turbine group, P coor is the target power command of the wind farm, and ξ is the upper limit of the allowable power error.
[0025] A third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for allocating active power of an offshore wind farm taking into account fatigue loads of wind turbines as described in the above embodiments.
[0026] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for allocating active power of an offshore wind farm taking into account fatigue loads of wind turbines.
[0027] A fifth aspect of the present application provides a computer program, which, when executed, implements the above-mentioned method for allocating active power of an offshore wind farm taking into account fatigue loads of wind turbines.
[0028] The embodiment of the present application defines the fatigue coefficient based on the output power of the offshore wind farm unit, confirms the objective function for the purpose of balancing the fatigue coefficient of each wind farm unit, and uses the particle swarm optimization algorithm to output the active power of each wind turbine unit, thereby reducing the cost of active output distribution of offshore wind farms and having higher feasibility in actual engineering applications. Thus, the problem that the existing fatigue load assessment methods in the related art all need to add sensors to obtain the necessary parameter data for assessment, which is difficult to achieve short-term rapid application in actual engineering, resulting in increased operation and maintenance costs of offshore wind farms and insufficient practicality is solved.
[0029] 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
[0030] 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:
[0031] Figure 1 A flowchart of an offshore wind farm active output distribution method considering wind turbine fatigue loads provided according to an embodiment of the present application;
[0032] Figure 2 A schematic diagram of the algorithm flow of particle swarm optimization according to an embodiment of the present application;
[0033] Figure 3 It is a structural schematic diagram of an offshore wind farm active power distribution device considering wind turbine fatigue load according to an embodiment of the present application;
[0034] Figure 4 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION
[0035] 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.
[0036] The following describes the active output distribution method and device of an offshore wind farm considering the fatigue load of a wind turbine according to an embodiment of the present application with reference to the accompanying drawings. In view of the related technologies mentioned in the above background technology, the existing fatigue load evaluation methods all need to add sensors to obtain the necessary parameter data for evaluation, which is difficult to achieve short-term rapid application in actual engineering, resulting in increased operation and maintenance costs of offshore wind farms and insufficient practicality. The present application provides an offshore wind farm active output distribution method considering the fatigue load of wind turbines. In this method, a fatigue coefficient is defined based on the output power of the offshore wind farm unit, and an objective function is confirmed for the purpose of balancing the fatigue coefficients of each unit in the wind farm. The particle swarm optimization algorithm is used to output the active power of each wind turbine unit, thereby reducing the cost of active output distribution of the offshore wind farm, and the feasibility in actual engineering applications is higher. Thus, the problem that the existing fatigue load evaluation methods in the related technologies all need to add sensors to obtain the necessary parameter data for evaluation, which is difficult to achieve short-term rapid application in actual engineering, resulting in increased operation and maintenance costs of offshore wind farms and insufficient practicality is solved.
[0037] Specifically, Figure 1 A schematic flow chart of a method for allocating active power of an offshore wind farm taking into account wind turbine fatigue loads provided in an embodiment of the present application.
[0038] like Figure 1 As shown, the offshore wind farm active output distribution method considering wind turbine fatigue loads includes the following steps:
[0039] In step S101, a fatigue load model of the target offshore wind farm is constructed based on the power generation fatigue load and air fatigue load of each wind turbine in the target offshore wind farm.
[0040] It can be understood that in the embodiments of the present application, the fatigue loads of the wind turbine are mainly divided into two types, namely the fatigue loads generated by the power generation process and the fatigue loads generated by air turbulence. When the unit is in the shutdown state, it only bears the air turbulence fatigue load, while in the power generation state, it will bear both the power generation fatigue load and the air turbulence fatigue load.
[0041] Optionally, in one embodiment of the present application, the power generation fatigue load and the air fatigue load are:
[0042]
[0043] in, is the power generation fatigue load, is the air fatigue load, P(τ) is the output power of the wind turbine at time τ, I eff (τ) is the effective turbulence intensity at time τ, P maxis the rated power of the wind turbine, T life is the life of the wind turbine, N rep is the maintenance compensation coefficient of the wind turbine, N dis is the interference coefficient, I a is the turbulence coefficient of the wind farm, I w is the wake turbulence intensity, S is the area swept by the fan impeller, N t is the inference coefficient of the wind turbine.
[0044] In the actual implementation process, the interference coefficient is generally taken as a fixed value when the geographical location of the wind farm is determined. Based on the above expression, according to the Palmgren-Milner linear cumulative fatigue theory, the total fatigue load of the unit can be regarded as the sum of these two parts of fatigue load.
[0045] In step S102, the objective function of the target offshore wind farm is confirmed based on the fatigue load model, and the fitness value of each wind turbine is calculated based on the objective function.
[0046] It is understandable that in the embodiment of the present application, the fatigue coefficient of each unit in the balanced wind farm can be taken as a target, and the target function of the target offshore wind farm can be confirmed, so as to calculate using the target function to obtain the fitness value of each wind turbine unit.
[0047] Optionally, in one embodiment of the present application, the objective function is:
[0048]
[0049] Where N is the number of wind turbine groups, is the average fatigue coefficient, is the wind turbine 0 The fatigue factor at the moment, is the power generation fatigue load of the wind turbine at time t, is the air fatigue load of the wind turbine at time t.
[0050] In the actual implementation process, is the wind farm output power constraint of the wind turbine at time t.
[0051] In step S103, based on all fitness values and the output power constraints of the target offshore wind farm, the preset particle swarm optimization algorithm updates the optimal position of the target offshore wind farm and each wind turbine until the preset iteration condition is reached, outputs the active power of each wind turbine, and distributes the active output of the target offshore wind farm according to the active power of each wind turbine.
[0052] It should be noted that the preset particle swarm optimization algorithm and the preset iteration conditions can be set by those skilled in the art according to actual conditions and are not specifically limited here.
[0053] It is understandable that in the embodiments of the present application, after determining the objective function and constraints, the particle swarm optimization (PSO) algorithm can be used to seek the optimal solution for active power distribution. After finding the optimal solution according to the PSO algorithm, that is, the active power of each wind turbine, it will be sent to each wind turbine through the wind farm energy management platform to realize active power distribution, thereby maximizing the fatigue coefficient balance of the wind turbine, achieving effective management of fatigue loads, and reducing the operation and maintenance costs of offshore wind farms. The preset iteration condition can be that the number of iterations reaches the maximum value or the output allocation result meets the accuracy requirements.
[0054] Among them, in the preset particle swarm optimization algorithm, the speed and position are updated according to the following formula:
[0055]
[0056] In the formula, is the velocity of the ith particle in dimension d at iteration number k, c 1 ,c 2 is the acceleration coefficient, r 1 ,r 2 is a random number uniformly distributed in (0,1), is the local optimal position of the ith particle in dimension d, is the position of the ith particle in dimension d at iteration number k, is the global optimal position of the particle in dimension d.
[0057] Optionally, in one embodiment of the present application, the output power constraint condition of the target offshore wind farm is:
[0058]
[0059] in, is the allocated power of the i-th wind turbine group, P coor is the target power command of the wind farm, and ξ is the upper limit of the allowable power error.
[0060] In the actual implementation process, the output power constraints of the target offshore wind farm are set with the goal of ensuring that the wind farm can operate safely and stably under the premise of meeting the target power instructions.
[0061] like Figure 2 As shown, the working content of the embodiment of the present application is described in detail below with a specific embodiment.
[0062] Among them, for the PSO main function, after the active power distribution starts, the number of particles is set according to the wind turbine group, the position and speed are initialized, the number of iterations k=1, and then the fitness value of each particle is calculated to update the global optimal position and the local optimal position according to the calculated fitness value, and the number of iterations k is detected to reach the maximum value or the output result meets the accuracy requirement. If not, the position and speed of each particle are updated, k=k+1 is set, and if it is satisfied, the global optimal position is output and the process ends. Among them, the fitness value of each particle can be calculated through the fitness function, and the objective function is formed by using the wake model, turbulence intensity model, wind turbine model and wind turbine fatigue load assessment model to calculate the fitness value.
[0063] According to the offshore wind farm active output distribution method considering the fatigue load of the wind turbine proposed in the embodiment of the present application, the fatigue coefficient is defined based on the output power of the offshore wind farm unit, the objective function is confirmed with the purpose of balancing the fatigue coefficient of each wind farm unit, and the active power of each wind turbine unit is output using the particle swarm optimization algorithm, thereby reducing the cost of active output distribution of offshore wind farms and having higher feasibility in actual engineering applications. Thus, the problem that the existing fatigue load assessment methods in the related art all need to add sensors to obtain the necessary parameter data for assessment, which is difficult to achieve short-term rapid application in actual engineering, resulting in increased operation and maintenance costs of offshore wind farms and insufficient practicality is solved.
[0064] Next, an offshore wind farm active power distribution device considering wind turbine fatigue loads proposed in an embodiment of the present application will be described with reference to the accompanying drawings.
[0065] Figure 3 It is a structural schematic diagram of an offshore wind farm active power distribution device taking into account wind turbine fatigue load according to an embodiment of the present application.
[0066] like Figure 3 As shown, the offshore wind farm active output distribution device 10 considering wind turbine fatigue loads includes: a construction module 100 , a calculation module 200 and a distribution module 300 .
[0067] The construction module 100 is used to construct a fatigue load model of the target offshore wind farm based on the power generation fatigue load and air fatigue load of each wind turbine in the target offshore wind farm.
[0068] The calculation module 200 is used to confirm the objective function of the target offshore wind farm based on the fatigue load model, and calculate the fitness value of each wind turbine generator set based on the objective function.
[0069] The allocation module 300 is used to update the optimal position of the target offshore wind farm and each wind turbine group based on all fitness values and the output power constraint conditions of the target offshore wind farm by a preset particle swarm optimization algorithm until the preset iteration conditions are met, output the active power of each wind turbine group, and allocate the active output of the target offshore wind farm according to the active power of each wind turbine group.
[0070] Optionally, in one embodiment of the present application, the power generation fatigue load and the air fatigue load are:
[0071]
[0072] in, is the power generation fatigue load, is the air fatigue load, P(τ) is the output power of the wind turbine at time τ, I eff (τ) is the effective turbulence intensity at time τ, P max is the rated power of the wind turbine, T life is the life of the wind turbine, N rep is the maintenance compensation coefficient of the wind turbine, N dis is the interference coefficient, I a is the turbulence coefficient of the wind farm, I w is the wake turbulence intensity, S is the area swept by the fan impeller, N t is the inference coefficient of the wind turbine.
[0073] Optionally, in one embodiment of the present application, the objective function is:
[0074]
[0075] Where N is the number of wind turbine groups, is the average fatigue coefficient, is the wind turbine 0 The fatigue factor at the moment, is the power generation fatigue load of the wind turbine at time t, is the air fatigue load of the wind turbine at time t.
[0076] Optionally, in one embodiment of the present application, the output power constraint condition of the target offshore wind farm is:
[0077]
[0078] in, is the allocated power of the i-th wind turbine group, P coor is the target power command of the wind farm, and ξ is the upper limit of the allowable power error.
[0079] It should be noted that the above explanation of the embodiment of the offshore wind farm active power distribution method considering wind turbine fatigue load is also applicable to the offshore wind farm active power distribution device considering wind turbine fatigue load of this embodiment, which will not be repeated here.
[0080] According to the offshore wind farm active output distribution device that considers the fatigue load of the wind turbine proposed in the embodiment of the present application, the fatigue coefficient is defined based on the output power of the offshore wind farm unit, the objective function is confirmed with the purpose of balancing the fatigue coefficient of each wind farm unit, and the particle swarm optimization algorithm is used to output the active power of each wind turbine unit, thereby reducing the cost of active output distribution of offshore wind farms and having higher feasibility in actual engineering applications. Thus, the problem that the existing fatigue load assessment methods in the related art all need to add sensors to obtain the necessary parameter data for assessment, which is difficult to achieve short-term rapid application in actual engineering, resulting in increased operation and maintenance costs of offshore wind farms and insufficient practicality is solved.
[0081] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:
[0082] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .
[0083] When the processor 402 executes the program, the offshore wind farm active output distribution method considering the fatigue load of the wind turbine provided in the above embodiment is implemented.
[0084] Furthermore, the electronic device further comprises:
[0085] The communication interface 403 is used for communication between the memory 401 and the processor 402 .
[0086] The memory 401 is used to store computer programs that can be executed on the processor 402 .
[0087] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
[0088] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.
[0089] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.
[0090] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.
[0091] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above method for allocating active power of an offshore wind farm considering fatigue load of wind turbines is implemented.
[0092] This embodiment also provides a computer program, which, when executed, implements the above-mentioned method for allocating active power of an offshore wind farm taking into account fatigue loads of wind turbines.
[0093] 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 N 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.
[0094] 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 technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.
[0095] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.
[0096] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.
[0097] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0098] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.
[0099] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.
[0100] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. 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 limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.
Claims
1. A method for distributing active power of an offshore wind farm considering fatigue load of wind turbines, characterized in that: The following steps are involved: Based on the power generation fatigue load and air fatigue load of each wind turbine in the target offshore wind farm, construct a fatigue load model of the target offshore wind farm; confirming the objective function of the target offshore wind farm based on the fatigue load model, and calculating the fitness value of each wind turbine generator set based on the objective function; Based on all fitness values and the output power constraint conditions of the target offshore wind farm, a preset particle swarm optimization algorithm is used to update the optimal position of the target offshore wind farm and each wind turbine group until a preset iteration condition is reached, the active power of each wind turbine group is output, and the active output of the target offshore wind farm is distributed according to the active power of each wind turbine group.
2. The method according to claim 1, characterized in that: The power generation fatigue load and the air fatigue load are: in, is the power generation fatigue load, is the air fatigue load, P(τ) is the output power of the wind turbine at time τ, I eff (τ) is the effective turbulence intensity at time τ, P max is the rated power of the wind turbine, T life is the life of the wind turbine, N rep is the maintenance compensation coefficient of the wind turbine, N dis is the interference coefficient, I a is the turbulence coefficient of the wind farm, I w is the wake turbulence intensity, S is the area swept by the fan impeller, N t is the inference coefficient of the wind turbine.
3. The method according to claim 1, characterized in that The objective function is: Where N is the number of wind turbine groups, is the average fatigue coefficient, is the fatigue coefficient of the wind turbine at time t0, is the power generation fatigue load of the wind turbine at time t, is the air fatigue load of the wind turbine at time t.
4. The method according to claim 1, characterized in that: The output power constraint of the target offshore wind farm is: in, is the allocated power of the i-th wind turbine group, P coor is the target power command of the wind farm, and ξ is the upper limit of the allowable power error.
5. An offshore wind farm active power distribution device taking into account wind turbine fatigue load, characterized in that: include: A construction module, configured to construct a fatigue load model of the target offshore wind farm based on the power generation fatigue load and the air fatigue load of each wind turbine in the target offshore wind farm; A calculation module, configured to confirm an objective function of the target offshore wind farm based on the fatigue load model, and calculate a fitness value of each wind turbine generator set based on the objective function; The allocation module is used to update the optimal position of the target offshore wind farm and each wind turbine group by a preset particle swarm optimization algorithm based on all fitness values and the output power constraint conditions of the target offshore wind farm until a preset iteration condition is reached, output the active power of each wind turbine group, and allocate the active output of the target offshore wind farm according to the active power of each wind turbine group.
6. The device according to claim 5, characterized in that The power generation fatigue load and the air fatigue load are: in, is the power generation fatigue load, is the air fatigue load, P(τ) is the output power of the wind turbine at time τ, I eff (τ) is the effective turbulence intensity at time τ, P max is the rated power of the wind turbine, T life is the life of the wind turbine, N rep is the maintenance compensation coefficient of the wind turbine, N dis is the interference coefficient, I a is the turbulence coefficient of the wind farm, I w is the wake turbulence intensity, S is the area swept by the fan impeller, N t is the inference coefficient of the wind turbine.
7. The device according to claim 5, characterized in that The objective function is: Where N is the number of wind turbine groups, is the average fatigue coefficient, is the fatigue coefficient of the wind turbine at time t0, is the power generation fatigue load of the wind turbine at time t, is the air fatigue load of the wind turbine at time t.
8. The device according to claim 5, characterized in that The output power constraint of the target offshore wind farm is: in, is the allocated power of the i-th wind turbine group, P coor is the target power command of the wind farm, and ξ is the upper limit of the allowable power error.
9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for allocating active power of an offshore wind farm taking into account fatigue loads of wind turbines as described in any one of claims 1 to 4.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the offshore wind farm active output distribution method considering wind turbine fatigue load as described in any one of claims 1 to 4.