A collaborative optimization method and system for wind turbine generator system power generation performance

Abstract

The present invention provides a collaborative optimization method and system for the power generation performance of a wind turbine, the method comprising: obtaining real-time operation data of the wind turbine collected by PLC, analyzing the mutual influence relationship between the functions to be optimized and the optimization functions of the wind turbine, determining the activation order of each optimization function according to the mutual influence relationship of the optimization functions, always enabling the optimal gain air density adaptation function, performing optimal gain air density compensation for the real-time operation control of the wind turbine, enabling each optimization function in the activation order, and collaboratively optimizing the power generation performance of the wind turbine; wherein each optimization function includes: impeller balance optimization, optimal gain optimization, blade angle optimization, and yaw optimization. The collaborative optimization method fully considers the relationship between different factors affecting the power generation performance of the wind turbine, integrates each optimization function, reasonably adjusts the power generation performance of the wind turbine, effectively improves the power generation of the wind turbine, and at the same time, reduces the optimization time of the wind turbine by orderly connecting each optimization function.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a collaborative optimization method and system for wind turbine generator set power generation performance. Background Art

[0002] The current mainstream wind turbines generally adopt upwind, horizontal axis, three-blade structure, and their working principle is: the impeller absorbs wind energy and converts it into electrical energy. However, in the process of the impeller converting wind energy into mechanical energy, there are many factors that affect energy conversion, for example, the more common ones are: impeller imbalance, Kopt (proportional constant at optimal speed, also called optimal gain or optimal torque gain) execution inaccurately resulting in failure to track the optimal tip speed ratio in the MPPT (Maximum Power Point Tracking) section, blade wind energy absorption efficiency due to differences in manufacturing and theory resulting in optimal blade angle execution deviation, unit yaw deviation, etc.

[0003] Based on this, the industry has proposed many optimization strategies, but the solutions corresponding to these factors are generally aimed at the problem itself. In actual implementation, the mutual influence between the various factors is often ignored, resulting in low accuracy and reliability of a single optimization strategy when it is actually implemented, and it is impossible to achieve the goal of efficiently improving the power generation performance of wind turbines. Summary of the invention

[0004] Therefore, in order to overcome the defects in the prior art and improve the power generation performance of a wind turbine, the present invention provides a method and system for collaboratively optimizing the power generation performance of a wind turbine.

[0005] In a first aspect, the present invention provides a method for collaboratively optimizing power generation performance of a wind turbine generator set, comprising:

[0006] Obtain real-time operation data of wind turbines collected by PLC;

[0007] Analyze the relationship between the functions to be optimized and the optimization functions of the wind turbine, where the optimization functions include: optimization of impeller balance, optimization of optimal gain, optimization of blade angle and optimization of yaw;

[0008] Determine the activation order of each optimization function according to the mutual influence relationship between the optimization functions;

[0009] The optimal gain air density adaptation function is always turned on to perform optimal gain air density compensation for real-time operation control of wind turbines;

[0010] Enable each optimization function in the order in which it is enabled to collaboratively optimize the power generation performance of the wind turbine.

[0011] Optionally, the order of starting each optimization function is: yaw optimization, impeller balance optimization, optimal gain optimization, and blade angle optimization; among which:

[0012] Yaw optimization, optimal gain optimization and blade angle optimization are all combined with the optimal gain air density adaptation function;

[0013] When the optimal gain optimization and the blade angle optimization are turned on, the optimal gain optimization is turned on first, and then the optimal gain optimization and the blade angle optimization are combined for execution.

[0014] Optionally, real-time operating data includes: wind speed, wind direction, rotation speed, cabin acceleration, yaw state, propeller angle, power, wind deviation, yaw state and air density.

[0015] Optionally, after each optimization function is enabled in the enabling sequence and the power generation performance of the wind turbine is collaboratively optimized, the following steps are also included:

[0016] Determine whether the current optimal compensation configuration is equal to the non-compensation configuration;

[0017] If the judgment result is yes, the current optimization compensation configuration is controlled to be equal to the collaborative optimization compensation configuration;

[0018] If the judgment result is no, the current optimal compensation configuration is controlled to be equal to the non-compensation configuration;

[0019] Determine whether the number of statistical evaluation objects in the wind speed range reaches the preset number requirement;

[0020] If the judgment result is yes, the overall effect of collaborative optimization is determined based on the evaluation object.

[0021] Optionally, after determining whether the number of statistical evaluation objects in the wind speed interval reaches a preset number requirement, the method further includes:

[0022] If the judgment result is no, then determine whether the preset switching time has been reached;

[0023] If the judgment result is yes, then return to the step of determining whether the current optimal compensation configuration is equal to the non-compensation configuration;

[0024] If the judgment result is no, the process returns to the step of determining whether the number of statistical evaluation objects in the wind speed interval reaches a preset number requirement.

[0025] Optionally, the overall effect of collaborative optimization is determined according to the evaluation object, including:

[0026] Calculate the evaluation index of wind turbine after collaborative optimization according to the evaluation object, including: the power generation increase ratio, load reduction ratio and impeller speed one-fold frequency reduction ratio corresponding to the collaborative optimization compensation configuration;

[0027] The overall effect of collaborative optimization is determined based on the evaluation indicators.

[0028] Optionally, the evaluation objects include: power, one-fold frequency of impeller speed, blade structure load, tower bottom load, three-fold frequency of impeller speed, and first-order amplitude of tower.

[0029] In a second aspect, the present invention provides a collaborative optimization system for wind turbine power generation performance, comprising:

[0030] A collection unit, used to obtain real-time operation data of wind turbines collected by PLC;

[0031] An analysis unit is used to analyze the relationship between the functions to be optimized and the optimization functions of the wind turbine, and determine the activation order of each optimization function according to the relationship between the optimization functions; wherein each optimization function includes: impeller balance optimization, optimal gain optimization, blade angle optimization and yaw optimization;

[0032] A compensation unit, used to always enable the optimal gain air density adaptation function and perform optimal gain air density compensation for the real-time operation control of the wind turbine;

[0033] The optimization unit is used to activate each optimization function according to the activation sequence to coordinately optimize the power generation performance of the wind turbine.

[0034] In a third aspect, the present invention provides an edge computing device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor performs the steps of the collaborative optimization method for the power generation performance of a wind turbine as described in the first aspect or any optional embodiment of the first aspect.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the collaborative optimization method for the power generation performance of a wind turbine as described in the first aspect or any optional embodiment of the first aspect.

[0036] The technical solution of the present invention has the following advantages:

[0037] The present invention provides a collaborative optimization method and system for the power generation performance of a wind turbine, the method comprising: obtaining real-time operation data of the wind turbine collected by PLC, analyzing the mutual influence relationship between the functions to be optimized and the optimization functions of the wind turbine, determining the activation order of each optimization function according to the mutual influence relationship of the optimization functions, always enabling the optimal gain air density adaptation function, performing optimal gain air density compensation for the real-time operation control of the wind turbine, enabling each optimization function in the activation order, and collaboratively optimizing the power generation performance of the wind turbine; wherein each optimization function includes: impeller balance optimization, optimal gain optimization, blade angle optimization, and yaw optimization. The collaborative optimization method fully considers the relationship between different factors affecting the power generation performance of the wind turbine, integrates each optimization function, reasonably adjusts the power generation performance of the wind turbine, effectively improves the power generation of the wind turbine, and at the same time, reduces the optimization time of the wind turbine by orderly connecting each optimization function. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A flow chart of a method for collaboratively optimizing power generation performance of a wind turbine provided by an embodiment of the present invention;

[0040] Figure 2 A flowchart of a specific example of a method for collaboratively optimizing power generation performance of a wind turbine provided in an embodiment of the present invention;

[0041] Figure 3-Figure 4 They are respectively flowcharts of another specific example of the collaborative optimization method for wind turbine power generation performance provided by an embodiment of the present invention;

[0042] Figure 5 A schematic diagram of the structure of a collaborative optimization system for wind turbine power generation performance provided by an embodiment of the present invention;

[0043] Figure 6 A schematic diagram of an edge computing device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0046] The collaborative optimization method for the power generation performance of a wind turbine provided in the embodiment of the present invention can effectively adjust the power generation performance of the wind turbine, thereby improving the power generation of the wind turbine. The flow chart thereof is as follows: Figure 1 As shown, including:

[0047] Step S1, obtaining real-time operation data of the wind turbine generator set collected by PLC.

[0048] Based on PLC (Programmable Logic Controller), real-time operation data of wind turbines is collected to effectively allocate PLC resources of wind turbines. In addition, the programmable logic controllers currently used in industry are equivalent to or close to the host of a compact computer. Their advantages in scalability and reliability make them widely used in various industrial control fields at present, which can better improve the power generation performance of the unit.

[0049] Specifically, the real-time operating data of the wind turbine may include: wind speed, wind direction, rotation speed, cabin acceleration, yaw state, blade angle, power, wind deviation, yaw state, and air density, etc. The operating data of the wind turbine is not limited to this, and can be added or deleted according to actual application conditions.

[0050] Step S2, analyzing the relationship between the functions to be optimized and the optimized functions of the wind turbine generator system.

[0051] After executing step S1 to obtain the real-time operating data of the wind turbine, it can be determined whether the wind turbine needs to be optimized based on its real-time operating data, and at the same time, the functions of the wind turbine to be optimized are analyzed, that is, which optimization processes need to be performed on the wind turbine. For example, if it is determined that there is no static or dynamic deviation to the wind, there is no need for a yaw optimization process. Not limited to this, the method for analyzing the functions of the wind turbine to be optimized is the same as the prior art and is not listed one by one. Among them, each optimization function may include: impeller balance optimization, optimal gain optimization (hereinafter referred to as Kopt optimization), blade angle optimization, and yaw optimization, etc. Research and development personnel have found that there is a mutual influence relationship between the optimization functions. For example, under different impeller states, the power of the wind turbine is different, the optimal tip speed ratio is different, and therefore, the optimal gain is also different. Therefore, it is necessary to complete the impeller balance optimization first, and then perform the Kopt optimization.

[0052] Step S3, determining the activation order of each optimization function according to the mutual influence relationship of the optimization functions.

[0053] Since the optimization functions have an influencing relationship with each other, the activation order of the optimization functions can be determined according to the relationship, and then step S5 is executed.

[0054] Step S4, always turning on the optimal gain air density adaptation function to perform optimal gain air density compensation on the real-time operation control of the wind turbine generator set.

[0055] In the process of collaborative optimization of wind turbines, the optimal gain air density adaptation (hereinafter referred to as Kopt air density adaptation) function is always turned on to achieve optimal gain air density compensation for real-time operation control of wind turbines. It should be noted that the execution order of step S4 is not limited to Figure 1 The order shown may also be such that step S4 is performed before step S2 or S3, as long as the optimal gain air density adaptation function is always enabled, which is within the protection scope of this embodiment.

[0056] Step S5, start each optimization function in the start sequence to collaboratively optimize the power generation performance of the wind turbine.

[0057] The collaborative optimization method for the power generation performance of wind turbines in this embodiment can effectively improve the power generation of wind turbines and reduce the time of the optimization process by orderly connecting and scheduling various optimization functions and integrating the power generation performance optimization process to achieve collaborative power generation performance optimization.

[0058] In a specific embodiment, in the above embodiment, the process of enabling each optimization function to optimize the wind turbine is as follows:

[0059] (1) After the Kopt air density adaptation function is turned on, Kopt air density compensation is performed continuously. The compensation formula can be expressed as:

[0060] Kopt1=Kopt0*Factor1;

[0061] Among them, Kopt1 represents the optimal gain after air density compensation, and Kopt0 represents the optimal gain according to ρ in the aircraft design stage. 设计 The optimal gain is obtained, Factor1 is the Kopt compensation coefficient based on the design and measured air density, and its calculation formula is: Factor1 = ρ 测量 / ρ 设计 ρ 测量 is the measured air density of the unit, ρ 设计 It is the preset air density, and its specific value can be adjusted according to the application.

[0062] (2) The wind deviation compensation formula after yaw optimization is completed can be expressed as:

[0063] Yawerror1=Yawerror0+△Yawerror;

[0064] Among them, Yawerror1 represents the wind deviation after optimization correction, Yawerror0 represents the wind deviation actually measured by the unit, and △Yawerror is the additional compensation wind deviation.

[0065] (3) After the impeller balance optimization is completed, the three blade angle compensation formula can be expressed as:

[0066] Pitch1Demand1=Pitch1Demand0+△P1;

[0067] Pitch2Demand1=Pitch2Demand0+△P2;

[0068] Pitch3Demand1=Pitch3Demand0+△P3;

[0069] Among them, Pitch1Demand1 represents the given value of the pitch angle of blade 1 after the optimal balance correction of the impeller, Pitch1Demand0 represents the given value of the pitch angle of blade 1 of the wind turbine before compensation, and △P1 is the additional compensation pitch angle of blade 1; similarly, Pitch2Demand1 represents the given value of the pitch angle of blade 2 after the optimal balance correction of the impeller, Pitch2Demand0 represents the given value of the pitch angle of blade 2 of the wind turbine before compensation, and △P2 is the additional compensation pitch angle of blade 2; Pitch3Demand1 represents the given value of the pitch angle of blade 3 after the optimal balance correction of the impeller, Pitch3Demand0 represents the given value of the pitch angle of blade 3 of the wind turbine before compensation, and △P3 is the additional compensation pitch angle of blade 3.

[0070] (4) After the blade angle optimization is completed, the compensation formula for the three blade angles can be expressed as:

[0071] Pitch1Demand2=Pitch1Demand1+△P;

[0072] Pitch2Demand2=Pitch2Demand1+△P;

[0073] Pitch3Demand2=Pitch3Demand1+△P;

[0074] Among them, Pitch1Demand2 represents the given value of the pitch angle of blade 1 after the optimal correction, Pitch1Demand1 represents the given value of the pitch angle of blade 1 after the optimal correction of the impeller balance, △P is the additional compensation pitch angle of the blade, and the additional compensation pitch angles of the three blades of the wind turbine are the same. The optimal correction of blade 2 and blade 3 is similar to that of blade 1, and will not be repeated.

[0075] (5) The compensation formula after Kopt optimization is completed can be expressed as:

[0076] Kopt2=Kopt1*Factor2;

[0077] Among them, Kopt2 represents the optimal gain after Kopt optimization correction, Kopt1 represents the optimal gain after air density compensation, and Factor2 is the Kopt compensation coefficient obtained by Kopt optimization.

[0078] It is worth noting that in each of the above-mentioned optimization processes, based on the real-time operating information of the wind turbine generator set and according to the algorithms of each optimization function, parameters such as the additional compensation blade angle and the additional compensation wind deviation are determined. In a specific embodiment, the specific process of analyzing the mutual influence relationship of the optimization functions in step S2 of the above-mentioned embodiment is as follows.

[0079] The order of starting each optimization function is: yaw optimization, impeller balance optimization, optimal gain optimization, blade angle optimization; among them: yaw optimization, optimal gain optimization and blade angle optimization are combined with the optimal gain air density adaptation function. When starting the optimal gain optimization and the blade angle optimization, start the optimal gain optimization first, and then combine the optimal gain optimization and the blade angle optimization. Based on the mutual influence relationship between the optimization functions, if all optimization functions need to be turned on, the flow chart is as follows Figure 2 As shown, that is, the connection order of each optimization function is: first start yaw optimization + Kopt air density adaptation, then start impeller balance optimization, and finally perform blade angle + Kopt joint optimization + Kopt air density adaptation to achieve coordinated optimization of wind turbines.

[0080] In a specific embodiment, after the above embodiment executes step S5, it also includes verifying the collaborative optimization result, and its flow chart is as follows: Figure 3 As shown, including:

[0081] Step S6, determining whether the current optimal compensation configuration is equal to the non-compensation configuration.

[0082] After the wind turbine is collaboratively optimized, the current optimal compensation configuration can be compared with the non-compensation configuration, and the comparison content may include Kopt compensation coefficient, additional compensation for wind deviation, additional compensation blade angle, additional compensation blade angle, etc., but is not limited thereto. If the judgment result is yes, step S7 is executed, otherwise, step S8 is executed.

[0083] Step S7, controlling the current optimization compensation configuration to be equal to the collaborative optimization compensation configuration.

[0084] Step S8, controlling the current optimal compensation configuration to be equal to the non-compensation configuration.

[0085] Among them, the collaborative optimization compensation configuration and the non-compensation configuration can be set in advance without specific restrictions, as shown in the following table:

[0086] Serial number Optimal compensation configuration Factor1 △Yawerror △P1 △P2 △P3 △P Factor2 1 No compensation configuration 1 0 0 0 0 0 1 2 Collaborative optimization configuration 0.9 5 -0.5 0 0 0.5 1.1

[0087] Therefore, based on the above steps S6-S8, the optimization results of each function can be applied simultaneously and switched at a fixed time, that is, the switching effect of the timing compensation configuration is achieved. After executing step S7 or S8, step S9 is executed.

[0088] Step S9, determining whether the number of statistical evaluation objects in the wind speed interval reaches a preset number requirement.

[0089] In actual application, the wind speed range and the preset number requirements can be determined according to the situation and are not limited. The evaluation object can also be collected through PLC, and may include: power, impeller speed frequency, blade structure load, tower bottom load, impeller speed frequency, tower first-order amplitude, etc. After the number of statistical evaluation objects reaches the preset number requirement, step S10 is executed.

[0090] Step S10, determining the overall effect of collaborative optimization according to the evaluation object.

[0091] Specifically, the process of determining the overall effect of collaborative optimization is: calculating the evaluation index after collaborative optimization of the wind turbine according to the evaluation object, wherein the evaluation index includes: the power generation improvement ratio, load reduction ratio and impeller speed one-fold frequency reduction ratio corresponding to the collaborative optimization compensation configuration; determining the overall effect of collaborative optimization based on the evaluation index. For example, the power generation improvement ratio can be calculated based on the power amplitude, specifically, the power generation improvement ratio = [(power mean under collaborative optimization configuration) - (power mean under non-compensation configuration)] / (power mean under non-compensation configuration), the calculation method of other evaluation indicators is similar to this, and will not be repeated.

[0092] The collaborative optimization method for wind turbine power generation performance of this embodiment can verify the optimization effect of the collaborative optimization method, such as increasing power generation in a specified wind speed range, reducing vibration or load on related components, etc., to increase power generation, and help reduce unit vibration, reduce load on related components, reduce the risk of blade stall, etc. In a specific embodiment, after the above step S9, if the judgment result is no, the following steps are performed.

[0093] Step S11, determining whether a preset switching time has been reached.

[0094] In actual application, the preset switching time can be set according to the application situation, for example, the preset switching time is set to 30 minutes. If the preset switching time is reached, the process returns to the step of determining whether the current optimal compensation configuration is equal to the non-compensation configuration; if the preset switching time is not reached, the process returns to the step of determining whether the number of statistical evaluation objects in the wind speed interval meets the preset number requirement; the overall process can be found in Figure 4 .

[0095] like Figure 5 As shown, based on the same inventive concept as the above-mentioned collaborative optimization method for wind turbine power generation performance, an embodiment of the present invention further provides a collaborative optimization system for wind turbine power generation performance, the system comprising:

[0096] The acquisition unit 1 is used to obtain the real-time operation data of the wind turbine collected by the PLC; the analysis unit 2 is used to analyze the mutual influence relationship between the functions to be optimized and the optimization functions of the wind turbine, and determine the activation order of each optimization function according to the mutual influence relationship of the optimization functions; wherein each optimization function includes: impeller balance optimization, optimal gain optimization, blade angle optimization and yaw optimization; the compensation unit 3 is used to always turn on the optimal gain air density adaptation function, and perform optimal gain air density compensation on the real-time operation data; the optimization unit 4 is used to turn on each optimization function in the activation order, and coordinately optimize the power generation performance of the wind turbine.

[0097] like Figure 6 As shown, based on the same inventive concept as the collaborative optimization method for the power generation performance of a wind turbine set described above, one or more embodiments of the present invention can also provide an edge computing device, including: at least one processor; and a memory that is communicatively connected to the at least one processor, wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the steps of the collaborative optimization method for the power generation performance of a wind turbine set as described in any one of at least one embodiment of the present invention. Among them, the detailed implementation process of the collaborative optimization method for the power generation performance of a wind turbine set has been described in detail in this specification and will not be repeated here. Based on this, the calculation, storage and scheduling of the above-mentioned collaborative optimization process can all be completed on the edge computing device, and the output of the collaborative optimization is still executed by the PLC, thereby reducing the occupation of PLC resources by the collaborative optimization function.

[0098] like Figure 6As shown, based on the same inventive concept as the above-mentioned collaborative optimization method for wind turbine power generation performance, one or more embodiments of the present invention can also provide a computer-readable storage medium, on which a computer program is stored, for non-instantaneously storing computer executable instructions, which, when executed by a processor, implement the collaborative optimization method for wind turbine power generation performance provided by at least one embodiment of the present invention. The detailed implementation process of the collaborative optimization method for wind turbine power generation performance has been described in detail in this specification, and will not be repeated here.

[0099] The logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable storage medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, apparatus, or device and execute instructions), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, a "computer-readable storage medium" may be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device. More specific examples of computer-readable storage media (a non-exhaustive list) include the following: an electrical connection with one or more wirings (electronic devices), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM, Erasable Programmable Read-Only Memory, or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM, Compact Disc Read-Only Memory). In addition, the computer-readable storage medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0100] It should be understood that the various parts of the present disclosure can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of 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, Programmable Gate Array), a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0101] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A collaborative optimization method for wind turbine power generation performance, characterized in that: include: Obtain real-time operation data of wind turbines collected by PLC; Analyze the relationship between the functions to be optimized and the optimization functions of the wind turbine, where the optimization functions include: optimization of impeller balance, optimization of optimal gain, optimization of blade angle and optimization of yaw; The activation order of each optimization function is determined according to the mutual influence relationship of the optimization functions, and the activation order of each optimization function is: yaw optimization, impeller balance optimization, optimal gain optimization, and blade angle optimization; wherein: the yaw optimization, the optimal gain optimization, and the blade angle optimization are all combined with the optimal gain air density adaptation function; when the optimal gain optimization and the blade angle optimization are activated, the optimal gain optimization is activated first, and then the optimal gain optimization and the blade angle optimization are combined for execution; The optimal gain air density adaptation function is always turned on to perform optimal gain air density compensation for the real-time operation control of the wind turbine set; The optimization functions are activated in the activation sequence to collaboratively optimize the power generation performance of the wind turbine.

2. The collaborative optimization method for wind turbine power generation performance according to claim 1, characterized in that: The real-time operating data includes: wind speed, wind direction, rotation speed, cabin acceleration, propeller angle, power, wind deviation, yaw state and air density.

3. The collaborative optimization method for wind turbine power generation performance according to claim 1 or 2, characterized in that: After the optimization functions are activated in the activation sequence to collaboratively optimize the power generation performance of the wind turbine, the method further includes: Determine whether the current optimal compensation configuration is equal to the non-compensation configuration; If the judgment result is yes, the current optimization compensation configuration is controlled to be equal to the collaborative optimization compensation configuration; If the judgment result is no, the current optimal compensation configuration is controlled to be equal to the non-compensation configuration; Determine whether the number of evaluation objects in the wind speed range reaches the preset number requirement; If the judgment result is yes, the overall effect of collaborative optimization is determined according to the evaluation object.

4. The method for collaborative optimization of wind turbine power generation performance according to claim 3, characterized in that: After determining whether the number of evaluation objects in the wind speed interval reaches the preset number requirement, the method further includes: If the judgment result is no, then determine whether the preset switching time has been reached; If the judgment result is yes, then return to the step of determining whether the current optimal compensation configuration is equal to the non-compensation configuration; If the judgment result is no, the process returns to the step of judging whether the number of evaluation objects in the wind speed interval reaches the preset number requirement.

5. The method for collaborative optimization of wind turbine power generation performance according to claim 3, characterized in that: Determining the overall effect of collaborative optimization according to the evaluation object includes: Calculate the evaluation index after the collaborative optimization of the wind turbine according to the evaluation object, the evaluation index includes: the power generation improvement ratio, load reduction ratio and impeller speed one-fold frequency reduction ratio corresponding to the collaborative optimization compensation configuration; The overall effect of collaborative optimization is determined based on the evaluation index.

6. The method for collaborative optimization of wind turbine power generation performance according to claim 5, characterized in that: The evaluation objects include: power, first-order frequency of impeller speed, blade structure load, tower bottom load, third-order frequency of impeller speed, and first-order amplitude of tower.

7. A collaborative optimization system for wind turbine power generation performance, based on the collaborative optimization method for wind turbine power generation performance according to claim 1, characterized in that: include: A collection unit, used to obtain real-time operation data of the wind turbine collected by the PLC; An analysis unit is used to analyze the functions to be optimized and the mutual influence relationship between the optimization functions of the wind turbine generator set, and determine the activation order of each optimization function according to the mutual influence relationship between the optimization functions; The optimization functions include: impeller balance optimization, optimal gain optimization, blade angle optimization and yaw optimization; A compensation unit, used for always turning on the optimal gain air density adaptation function to perform optimal gain air density compensation on the real-time operation control of the wind turbine set; The optimization unit is used to activate each optimization function according to the activation sequence to coordinately optimize the power generation performance of the wind turbine.

8. An edge computing device, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor performs the steps of the collaborative optimization method for the power generation performance of a wind turbine as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the collaborative optimization method for the power generation performance of a wind turbine set as described in any one of claims 1 to 6 are implemented.

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