Wind power plant multi-unit collaborative output improvement method based on lead aircraft-wing aircraft mode

Abstract

The invention relates to the technical field of new energy, and discloses a wind power plant multi-unit collaborative output improvement method based on a lead aircraft-wing aircraft mode, and the method comprises the steps: obtaining first collection data, second collection data, and / or prediction data; based on the first acquisition data, the second acquisition data and / or the prediction data, positioning a wake flow influence area and a wind direction sensitive area of the wind power plant; with output maximization of the wind power plant as a target, target strategies are specifically formulated based on the wake flow influence area and the wind direction sensitive area obtained through positioning, and the target strategies comprise a wake flow avoidance strategy and a power distribution strategy; based on the formulated target strategy, control instructions for the lead aircraft unit and the wing aircraft unit are generated; and executing the control instruction for the lead aircraft unit, and sending the control instruction for the wing aircraft unit to the corresponding wing aircraft unit. The method can achieve the optimal power generation performance of the whole wind power plant.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, specifically to a method for improving the coordinated output of multiple wind turbines in a wind farm based on a lead-wingman mode. Background Technology

[0002] With the rapid development of the wind power industry, wind farms are expanding in scale and the number of wind turbines within them is increasing daily. In traditional wind farm control models, each wind turbine often employs an independent control strategy or relies on a central control system for unified scheduling. Under independent control, each turbine operates based solely on local information collected by its own sensors, failing to fully consider factors such as wake effects between turbines and spatial differences in wind speed and direction, resulting in low wind energy resource utilization efficiency. While a central control system can provide comprehensive planning, it suffers from high communication latency and poor system reliability. A failure in the central controller severely impacts the operation of the entire wind farm. Furthermore, processing real-time data from a large number of turbines places a heavy computational burden, making it difficult to make quick, accurate, and effective control decisions. Moreover, existing control methods struggle to achieve optimal power generation performance across the entire wind farm while ensuring equipment reliability and extending equipment lifespan. The pursuit of high power generation may lead to some turbines operating under prolonged high loads and stresses, accelerating equipment aging, increasing the risk of failure, and raising maintenance costs. Therefore, a new control algorithm is urgently needed to achieve efficient collaborative control of multiple wind turbines in a wind farm and to balance the relationship between power generation performance and equipment reliability. Summary of the Invention

[0003] In view of this, the present invention provides a method for improving the coordinated output of multiple wind turbine units in a wind farm, so as to solve the problem that multiple wind turbine units in a wind farm cannot be efficiently coordinated and controlled.

[0004] In a first aspect, the present invention provides a method for enhancing the coordinated output of multiple wind turbine units in a wind farm based on a lead-winger mode, wherein the multiple wind turbine units in the wind farm include a lead turbine unit and a wingman turbine unit, and the method is applied to the lead turbine unit, the method comprising: Acquire first collected data, second collected data, and / or predicted data, wherein the first collected data is data collected by the sensors of the lead aircraft crew itself, the second collected data is data collected by the sensors of the wingman crew, and the predicted data is data predicted based on the first collected data and / or the second collected data; Based on the first collected data, the second collected data, and / or the predicted data, the wake influence area and wind direction sensitive area of ​​the wind farm are located. With the goal of maximizing the output of the wind farm, a targeted strategy is formulated based on the wake influence area and the wind direction sensitive area obtained by location. The target strategy includes a wake avoidance strategy and a power allocation strategy. Based on the established target strategy, control commands are generated for the lead aircraft crew and the wingman crew. Execute control commands for the lead aircraft crew and send control commands for the wingman crew to the corresponding wingman crew.

[0005] Secondly, the present invention provides a wind farm multi-unit collaborative output enhancement device based on a lead-winger mode, wherein the wind farm includes lead turbine units and winger units, the device is applied to the lead turbine unit, and the device includes: The data acquisition module is used to acquire first collected data, second collected data, and / or predicted data. The first collected data is data collected by the sensors of the lead aircraft crew itself, the second collected data is data collected by the sensors of the wingman crew, and the predicted data is data predicted based on the first collected data and / or the second collected data. The positioning module is used to locate the wake influence area and wind direction sensitive area of ​​the wind farm based on the first collected data, the second collected data, and / or the predicted data. The strategy formulation module is used to formulate a target strategy based on the wake influence area and the wind direction sensitive area obtained by positioning, with the goal of maximizing the output of the wind farm. The target strategy includes a wake avoidance strategy and a power allocation strategy. A control command generation module is used to generate control commands for the lead aircraft crew and the wingman crew based on the established target strategy. The execution module is used to execute control commands for the lead aircraft group and send control commands for the wingman group to the corresponding wingman group.

[0006] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for improving the coordinated output of multiple wind turbines in a wind farm based on the lead-wing mode described in the first aspect or any corresponding embodiment.

[0007] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the method for improving the coordinated output of multiple wind turbines in a wind farm based on a lead-wing mode, as described in the first aspect or any corresponding embodiment.

[0008] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause the computer to execute the method for improving the coordinated output of multiple wind turbines in a wind farm based on the lead-wing mode in the first aspect or any corresponding embodiment described above.

[0009] This invention provides a distributed control algorithm for multiple wind turbines in a wind farm. By constructing a distributed control architecture of lead turbine and wingmen, and utilizing various sensors and communication devices configured in each turbine, it achieves efficient data transmission and collaborative control. Under the premise of ensuring optimal utilization and reliability of equipment, it achieves optimal power generation performance of the entire wind farm, reduces power generation costs, and improves the overall economic benefits and operational stability of the wind farm. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of the present invention, the drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a method for improving the coordinated output of multiple wind turbines in a wind farm based on a lead-wingman mode, according to an embodiment of the present invention. Figure 2 This is a structural block diagram of a wind farm multi-unit collaborative output enhancement device based on the lead-wingman mode according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] According to an embodiment of the present invention, a method for improving the coordinated output of multiple wind turbine units in a wind farm is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of executable computer instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0014] This embodiment provides a method for enhancing the collaborative output of multiple wind turbine units in a wind farm based on a lead-winger mode. The multiple wind turbine units in the wind farm include a lead turbine unit (hereinafter referred to as the lead turbine) and a wingman unit (hereinafter referred to as the wingman). The method is applied to the lead turbine unit. Figure 1 This is a flowchart of a method for improving the coordinated output of multiple wind turbine units in a wind farm according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Obtain first collected data, second collected data, and / or predicted data. The first collected data is data collected by the sensors of the lead aircraft crew itself, the second collected data is data collected by the sensors of the wingman crew, and the predicted data is data predicted based on the first collected data and / or the second collected data. Specifically, the first and second collected data include real-time wind speed, wind direction, power output, rotational speed, blade pitch angle, vibration, etc.

[0015] The predicted data can be obtained by using a pre-trained prediction model based on the collected data (first collected data and / or second collected data). When making predictions, it can be combined with short-term weather forecasts for the area where the wind farm is located.

[0016] Step S102: Based on the first collected data, the second collected data, and / or the predicted data, locate the wake influence area and wind direction sensitive area of ​​the wind farm; Specifically, the wake effect zone is the area where wind speed loss is significant (e.g., greater than 10% or 15%). The wind direction sensitive zone is the area where even a small change in wind direction causes a large fluctuation in wind turbine power.

[0017] Step S103: With the goal of maximizing the output of the wind farm, a targeted strategy is formulated based on the wake influence area and the wind direction sensitive area obtained by location. The target strategy includes a wake avoidance strategy and a power allocation strategy. Wake avoidance strategies include, for example, controlling upstream wind turbines to yaw at a small angle to disperse the wake and allow downstream turbines to generate more power. Another example is adjusting the blade pitch angle of turbines located in the wake's influence zone to reduce load, i.e., changing the force on the turbines and weakening the pulsating load caused by the wake. Regarding power allocation strategies, these could include allocating lower target output power to turbines in wind-sensitive areas, which are more susceptible to crosswinds and turbulence; or increasing the rotational speed of units in high-wind-speed areas (i.e., the prevailing wind zone) to maximize power output, i.e., allocating higher target output power. Furthermore, in this embodiment, when strong winds are predicted, a load reduction command can be issued in advance to protect equipment safety.

[0018] Specifically, when formulating a power allocation strategy, it is necessary to quantify the power output potential of each wind turbine based on its wind speed-power curve and environmental correction factors, and then specify the power allocation strategy within the power output potential range. The wind speed-power curve of the wind turbine can be provided by the manufacturer or obtained by fitting measured data. The environmental correction factors include at least one of the following: air density, turbulence intensity, wind shear, temperature, and altitude.

[0019] For units in wind-sensitive areas, it is necessary to refresh the flow field frequently and redistribute power quickly.

[0020] In addition, security constraints are required when formulating target strategies.

[0021] Step S104: Generate control commands for the lead aircraft crew and the wingman crew based on the established target strategy; Step S105: Execute control commands for the lead aircraft group and send control commands for the wingman group to the corresponding wingman group.

[0022] In this embodiment, the multiple wind turbines in the wind farm can be divided into one lead turbine and multiple wing turbines. The lead turbine acts as the core decision-maker for coordinated control, while the wing turbines act as execution units, cooperating with the lead turbine to adjust its operating status. The selection of either type needs to be comprehensively determined based on the wind farm topology, wind resource characteristics, turbine performance, and coordination requirements. Each turbine is equipped with various sensors, including wind speed and direction sensors, vibration sensors, and power sensors, to collect real-time turbine operating status data and environmental data. It is also equipped with communication equipment to support data transmission, command interaction, and communication with external monitoring systems between turbines.

[0023] The wind farm multi-unit collaborative output enhancement method provided in this embodiment aims to provide a distributed control algorithm for multiple wind turbine units within a wind farm. By constructing a distributed control architecture of lead turbine and wingman turbines, and utilizing various sensors and communication devices configured in each unit, it achieves efficient data transmission and collaborative control. Under the premise of ensuring optimal equipment utilization and reliability, it achieves optimal power generation performance of the entire wind farm, reduces power generation costs, and improves the overall economic benefits and operational stability of the wind farm.

[0024] In some optional implementations, step S102, namely locating the wake influence area and wind direction sensitive area of ​​the wind farm based on the first collected data, the second collected data, and / or the predicted data, includes: Step S1021: Based on the first collected data, the second collected data, and / or the predicted data, decompose the flow field features.

[0025] The first collected data, the second collected data, and / or the predicted data here mainly include hourly wind speed, hourly wind direction, and raw turbulence monitoring values.

[0026] Specifically, flow field characteristics can be analyzed from dimensions such as wind speed gradient, turbulence intensity, and wind direction frequency. Wind speed gradient refers to the rate of change of wind speed in the horizontal direction. Turbulence intensity can be calculated based on hourly wind speed data, by statistically analyzing the average wind speed and standard deviation within a specified time window (e.g., 10 minutes), using the following formula: Turbulence Intensity (TI) = Standard Deviation of Wind Speed ​​ / Average Wind Speed. Wind direction frequency can be obtained by statistically analyzing the frequency of occurrence in each wind direction interval (e.g., 16 directions, with each interval being 22.5°), reflecting the dominant wind direction and wind direction distribution characteristics.

[0027] By analyzing the wind speed characteristics, we can obtain information such as point-to-point / machine-to-machine average wind speed, wind speed spatial distribution (high and low wind speed zones), wind speed attenuation magnitude (upstream to downstream), and wind speed recovery distance.

[0028] From the decomposition of wind direction characteristics, we can obtain information such as: prevailing wind direction for each aircraft / the entire region, wind direction deflection, rate of change of wind direction, wind direction non-uniformity, and wind direction distortion. From the decomposition of turbulence characteristics, we can obtain information such as turbulence intensity, spatial distribution of turbulence, and the growth law of turbulence with the wake.

[0029] Step S1022: Based on the flow field characteristics obtained from the decomposition, draw a wind speed vector distribution map of the entire wind farm area.

[0030] Specifically, the entire wind farm can be divided into uniform grids, and then the wind speed and direction of each grid point can be obtained through spatial mapping, interpolation and other methods. Finally, the wake model can be introduced for correction, and the wind speed vector distribution map of the entire wind farm can be obtained.

[0031] Step S1023: Based on the wind speed vector distribution map, analyze the wind speed and direction distribution patterns within the wind farm, and locate the wake influence area and wind direction sensitive area.

[0032] The wake influence zone can be determined using preset criteria. For example, it can be identified as a wake influence zone if any one of the following conditions is met: 1. Areas where the wind speed decreases by ≥10%~20% relative to the free-flow wind speed; 2. A strip / fan-shaped area located downstream of the upwind fan and extending along the prevailing wind direction; 3. Multiple air blasts overlap, resulting in areas where wind speed decreases continuously and significantly; 4. Regions where turbulence intensity is significantly increased.

[0033] Wind-sensitive areas can also be identified using preset criteria. For example, an area can be designated as a wind-sensitive area if it meets any one of the following conditions: 1. At the same time, wind direction varies greatly at different locations (large spatial gradient of wind direction); 2. The wind direction frequently shifts, oscillates, and becomes unstable; 3. Due to topography / mountains / aircraft bypass, wind direction distortion, separation, and backflow occur; 4. The arrows in the vector graphics are inconsistent and have obvious deviations in some areas.

[0034] In some optional embodiments, the wind farm's turbines include at least one of the following sensors: Wind speed and direction sensor; Vibration sensor; Power sensor; Blade pitch angle sensor.

[0035] In some optional implementations, the method for enhancing the collaborative output of multiple wind turbine units in a wind farm based on the lead-wingman mode also includes: The wingman receives feedback data from the lead aircraft crew. After receiving the control command sent by the lead aircraft crew, the wingman crew executes the corresponding operation according to the command, adjusts its own operating parameters, and monitors the crew's operating status in real time. The execution results and current operating data are used as feedback data and fed back to the lead aircraft crew through the communication equipment.

[0036] In some optional implementations, the method for enhancing the collaborative output of multiple wind turbine units in a wind farm based on the lead-wingman mode also includes: The execution effect of the control command is evaluated based on the feedback data from the wingman unit, the degree of consistency between the actual adjustment values ​​of the key actuators of the wind turbine and the command target values ​​obtained by the monitoring platform in the main control room, and the trend of wind conditions and output changes within a preset time (e.g., 15-30 minutes) after the execution of the control command. If the execution effect deviates from the expected goal and does not meet the preset deviation threshold, the target strategy is adjusted for dynamic optimization control.

[0037] In this embodiment, after receiving control commands from the lead unit, the wingman unit executes the corresponding operations and adjusts its own operating parameters. Simultaneously, it monitors the unit's operating status in real time and feeds back the execution results and current operating data to the lead unit via communication equipment. The feedback data includes information such as the actual adjusted blade pitch angle, rotational speed, power output, and changes in equipment status. Based on the feedback data, the lead unit evaluates the effectiveness of the control commands by assessing the consistency between the actual adjustment values ​​of the key turbine actuators obtained from the main control room monitoring platform and the command target values, and by observing the trends in wind conditions and output changes after 15-30 minutes of adjustment. If a significant deviation is found between the execution results and the expected target, the lead unit promptly adjusts the control strategy and commands, performing dynamic adjustment control.

[0038] Some optional implementations also include: Based on the monitoring data and evaluation results from the wind farm's main control room monitoring platform, a plan for the unit's operating load and maintenance is developed.

[0039] Specifically, the monitoring data from the main control room monitoring platform includes changes in wind conditions within the wind farm, changes in the operating status of each turbine, and changes in equipment health status. For example, when sudden changes in wind speed and direction, fault warning signals from turbines, or deterioration in equipment performance are detected, the data is re-analyzed, the control strategy is adjusted, and new control commands are generated to achieve dynamic and coordinated adjustment and control of the wind turbines.

[0040] In this embodiment, the main turbine unit can not only formulate a global control strategy to achieve optimal power generation performance, optimal equipment utilization, and ensure reliability across the entire wind farm, but also rationally arrange the operating load and maintenance plan of the unit based on the monitoring data and evaluation results of the wind farm's main control room monitoring platform, thereby avoiding excessive operation of some units and ensuring equipment reliability.

[0041] In summary, the embodiments of the present invention can achieve the following: 1. Improve power generation performance: By considering the wake effect and wind speed and direction distribution within the wind farm, and optimizing the coordinated operation between units, wake losses can be effectively reduced and the efficiency of wind energy resource utilization can be improved. Compared with traditional control methods, the power generation of the entire wind farm can be increased.

[0042] 2. Ensure equipment reliability: Optimize operating load and maintenance plans based on equipment health status assessment to avoid over-operation of units, reduce equipment failure risk, extend equipment service life, and reduce equipment maintenance frequency and costs.

[0043] 3. Enhanced System Flexibility and Stability: Adopting a distributed control architecture, even if some units or communication links fail, other units can continue operating under the coordination of the main unit, ensuring the wind farm's basic power generation capacity and significantly improving system stability. Simultaneously, this architecture can quickly adapt to changes in wind conditions and unit operating status, exhibiting greater flexibility and adaptability.

[0044] 4. Reduce operating costs: By optimizing power generation performance and ensuring equipment reliability, economic losses caused by insufficient power generation and equipment failure are reduced, thereby lowering the operation and maintenance costs and power generation costs of the wind farm and improving the overall economic benefits of the wind farm.

[0045] This embodiment also provides a wind farm multi-unit collaborative output enhancement device based on a lead-wingman mode. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0046] This embodiment provides a wind farm multi-unit collaborative output enhancement device based on a lead-winger mode. The wind farm's multiple units include lead turbine units and winger units. The device is applied to the lead turbine unit, such as... Figure 2 As shown, the device includes: The data acquisition module 201 is used to acquire first collected data, second collected data, and / or predicted data. The first collected data is data collected by the sensors of the lead aircraft crew itself, the second collected data is data collected by the sensors of the wingman crew, and the predicted data is data predicted based on the first collected data and / or the second collected data. The positioning module 202 is used to locate the wake influence area and wind direction sensitive area of ​​the wind farm based on the first collected data, the second collected data, and / or the predicted data. The strategy formulation module 203 is used to formulate a target strategy based on the wake influence area and the wind direction sensitive area obtained by positioning, with the goal of maximizing the output of the wind farm. The target strategy includes a wake avoidance strategy and a power allocation strategy. The control command generation module 204 is used to generate control commands for the lead aircraft crew and the wingman crew based on the established target strategy. The execution module 205 is used to execute control commands for the lead aircraft group and send control commands for the wingman group to the corresponding wingman group.

[0047] In some optional embodiments, the positioning module 202 includes: The disassembly unit is used to disassemble the flow field features based on the first collected data, the second collected data, and / or the predicted data; The drawing unit is used to draw the wind speed vector distribution map of the entire wind farm based on the flow field characteristics obtained from the decomposition. The positioning unit is used to analyze the wind speed and direction distribution patterns within the wind farm based on the wind speed vector distribution map, and to locate the wake influence area and wind direction sensitive area.

[0048] In some optional implementations, the disassembly unit is specifically used to disassemble the flow field features from multiple dimensions, including: wind speed gradient dimension, turbulence intensity dimension, and wind direction frequency dimension.

[0049] The wind turbines in the dismantling unit include at least one of the following sensors: Wind speed and direction sensor; Vibration sensor; Power sensor; Blade pitch angle sensor.

[0050] In some optional implementations, the wind farm multi-unit collaborative output enhancement device based on the lead-wingman mode also includes: The feedback data receiving module is used to receive feedback data from the wingman crew. After receiving the control command sent by the lead aircraft crew, the wingman crew performs the corresponding operation according to the command, adjusts its own operating parameters, and monitors the crew's operating status in real time. The execution results and current operating data are used as the feedback data and fed back to the lead aircraft crew through the communication equipment.

[0051] In some optional implementations, the wind farm multi-unit collaborative output enhancement device based on the lead-wingman mode also includes: The evaluation module is used to evaluate the execution effect of the control command based on the feedback data from the wingman unit, the degree of consistency between the actual adjustment value of the key actuator of the wind turbine and the command target value obtained by the monitoring platform in the main control room, and the trend of wind conditions and output changes within a preset time after the execution of the control command. The dynamic adjustment module is used to adjust the target strategy and perform dynamic optimization control if the execution effect deviates from the expected target and does not meet the preset deviation threshold.

[0052] In some optional implementations, the wind farm multi-unit collaborative output enhancement device based on the lead-wingman mode also includes: The planning module is used to formulate the unit's operating load and maintenance plan based on the monitoring data and evaluation results from the wind farm's main control room monitoring platform.

[0053] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0054] In this embodiment, the wind farm multi-unit collaborative output enhancement device based on the lead-wing mode is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0055] This invention also provides a computer device having the above-described features. Figure 2 The device shown is a wind farm multi-unit collaborative output enhancement device based on the lead-wingman mode.

[0056] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention. This computer device can be a computer device deployed in a long-haul aircraft unit, such as... Figure 3 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Figure 3 Take a processor 10 as an example.

[0057] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0058] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0059] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0060] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0061] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0062] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0063] The computer device also includes a communication interface for communicating with other devices or communication networks.

[0064] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0065] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0066] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for improving the coordinated output of multiple wind turbines in a wind farm based on a lead-wingman mode, characterized in that, The wind farm comprises multiple turbine units, including a lead turbine unit and wingman turbine units. The method is applied to the lead turbine unit, and the method includes: Acquire first collected data, second collected data, and / or predicted data, wherein the first collected data is data collected by the sensors of the lead aircraft crew itself, the second collected data is data collected by the sensors of the wingman crew, and the predicted data is data predicted based on the first collected data and / or the second collected data; Based on the first collected data, the second collected data, and / or the predicted data, the wake influence area and wind direction sensitive area of ​​the wind farm are located. With the goal of maximizing the output of the wind farm, a targeted strategy is formulated based on the wake influence area and the wind direction sensitive area obtained by location. The target strategy includes a wake avoidance strategy and a power allocation strategy. Based on the established target strategy, control commands are generated for the lead aircraft crew and the wingman crew. Execute control commands for the lead aircraft crew and send control commands for the wingman crew to the corresponding wingman crew.

2. The method according to claim 1, characterized in that, The step of locating the wake influence zone and wind direction sensitive zone of the wind farm based on the first collected data, the second collected data, and / or the predicted data includes: Based on the first collected data, the second collected data, and / or the predicted data, the flow field characteristics are decomposed; Based on the flow field characteristics obtained from the decomposition, a wind speed vector distribution map of the entire wind farm is drawn; Based on the wind speed vector distribution map, the wind speed and direction distribution patterns within the wind farm are analyzed to locate the wake influence area and the wind direction sensitive area.

3. The method according to claim 2, characterized in that, The disassembly flow field characteristics include: The flow field characteristics are analyzed from multiple dimensions, including: wind speed gradient dimension, turbulence intensity dimension, and wind direction frequency dimension.

4. The method according to claim 1, characterized in that, The wind farm's turbines include at least one of the following sensors: Wind speed and direction sensor; Vibration sensor; Power sensor; Blade pitch angle sensor.

5. The method according to claim 1, characterized in that, Also includes: The wingman receives feedback data from the lead aircraft crew. After receiving the control command sent by the lead aircraft crew, the wingman crew executes the corresponding operation according to the command, adjusts its own operating parameters, and monitors the crew's operating status in real time. The execution results and current operating data are used as feedback data and fed back to the lead aircraft crew through the communication equipment.

6. The method according to claim 5, characterized in that, Also includes: The execution effect of the control command is evaluated based on the feedback data from the wingman unit, the degree of consistency between the actual adjustment values ​​of the key actuators of the wind turbine and the command target values ​​obtained by the monitoring platform in the main control room, and the trend of wind conditions and output changes within a preset time after the execution of the control command. If the execution effect deviates from the expected goal and does not meet the preset deviation threshold, the target strategy is adjusted for dynamic optimization control.

7. The method according to claim 1, characterized in that, Also includes: Based on the monitoring data and evaluation results from the wind farm's main control room monitoring platform, a plan for the unit's operating load and maintenance is developed.

8. A wind farm multi-unit collaborative output enhancement device based on a lead-wingman mode, characterized in that, The wind farm comprises multiple turbine units, including a lead turbine unit and wingman turbine units. The device is applied to the lead turbine unit and includes: The data acquisition module is used to acquire first collected data, second collected data, and / or predicted data. The first collected data is data collected by the sensors of the lead aircraft crew itself, the second collected data is data collected by the sensors of the wingman crew, and the predicted data is data predicted based on the first collected data and / or the second collected data. The positioning module is used to locate the wake influence area and wind direction sensitive area of ​​the wind farm based on the first collected data, the second collected data, and / or the predicted data. The strategy formulation module is used to formulate a target strategy based on the wake influence area and the wind direction sensitive area obtained by positioning, with the goal of maximizing the output of the wind farm. The target strategy includes a wake avoidance strategy and a power allocation strategy. A control command generation module is used to generate control commands for the lead aircraft crew and the wingman crew based on the established target strategy. The execution module is used to execute control commands for the lead aircraft group and send control commands for the wingman group to the corresponding wingman group.

9. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for improving the coordinated output of multiple wind turbines in a wind farm based on a lead-wing mode, as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the method for improving the coordinated output of multiple wind turbines in a wind farm based on the lead-wing mode, as described in any one of claims 1 to 7.

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