Wind power plant active yaw optimization method and system considering wind turbine fatigue load

Abstract

The invention discloses a wind power plant active yaw optimization method and system considering a fatigue load of a wind turbine, and relates to the technical field of wind turbine power generation, and the method comprises the steps: obtaining a wake flow model for calculating the equivalent inflow wind speed of a wind wheel surface of the wind turbine; determining a wind wheel surface equivalent inflow wind speed-yaw angle-thrust coefficient two-dimensional lookup table and a wind wheel surface equivalent inflow wind speed-yaw angle-power two-dimensional lookup table of each wind turbine in the wind power plant; constructing an equivalent fatigue load proxy model of the wind turbine based on a blade aerodynamic load and blade gravity coupling effect; and constructing an optimized objective function by taking power maximization and equivalent fatigue load minimization as objectives, solving the optimized objective function through an optimization algorithm according to the actually measured wind turbine-wind measurement data, the wake flow model, each two-dimensional query table and the equivalent fatigue load proxy model, and determining a target yaw angle combination. On the basis of the scheme, the fatigue load caused by the wake effect is reduced while the power improvement is guaranteed, and the active yaw optimization reliability is improved on the whole.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine power generation, and in particular to a method and system for optimizing active yaw of a wind farm taking into account fatigue load of a wind turbine. Background Art

[0002] In large-scale wind farms, the wind turbines of wind turbine groups are usually distributed in a concentrated manner. When the wind flows through the upstream wind turbine, the energy is absorbed, causing the upstream wind turbine rotor to rotate, resulting in the upstream wind turbine's wake to have wind speed loss, increased turbulence intensity, and dynamic meandering. The downstream wind turbines in the wake area have reduced output power due to the reduced wind speed. This loss of power output due to the wake effect may be as high as 40% or more. At the same time, the additional turbulence, dynamic meandering and wind shear generated by the wake will increase the fatigue load of the downstream wind turbine, thereby shortening the operating life of the entire wind farm.

[0003] In order to increase the production capacity of wind farms without significantly increasing the fatigue load of wind turbines, existing technologies mainly reduce the wake effect by optimizing the yaw angle under active yaw control to perform multi-objective collaborative optimization of wind farm production capacity and fatigue life. However, the fatigue load of wind turbines is mostly directly converted from the blade load calculation results of simulation software or calculated using empirical formulas. It only considers the impact of aerodynamic loads on blades and is difficult to comprehensively evaluate the fatigue load of wind turbines, resulting in low reliability of active yaw optimization. Summary of the Invention

[0004] The present invention provides a method and system for active yaw optimization of wind farms that takes into account the fatigue load of wind turbines. This solves the technical problem that in the prior art, when reducing the wake effect by optimizing the yaw angle under active yaw control, only the influence of the aerodynamic load on the blades is usually considered when calculating the fatigue load of the wind turbine, making it difficult to comprehensively evaluate the fatigue load of the wind turbine, resulting in low reliability of active yaw optimization.

[0005] A first aspect of the present invention provides a method for optimizing active yaw of a wind farm taking into account fatigue loads of wind turbines, comprising:

[0006] Obtain the wake model for calculating the equivalent inflow wind speed on the rotor surface of the wind turbine;

[0007] Determine a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient and a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power for each wind turbine in the wind farm;

[0008] Based on the coupling effect of blade aerodynamic load and blade gravity, an equivalent fatigue load proxy model of wind turbine is constructed;

[0009] An optimization objective function is constructed with the goals of maximizing power and minimizing equivalent fatigue load. The optimization objective function is solved by an optimization algorithm based on the measured wind turbine-wind measurement data of the wind farm, the wake model, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of each wind wheel surface, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of each wind wheel surface, and the equivalent fatigue load proxy model to determine the target yaw angle combination of the wind farm.

[0010] Furthermore, the determination of the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient and the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of the rotor surface of each wind turbine in the wind farm includes:

[0011] Performing numerical simulations on each wind turbine in the wind farm using the equivalent inflow wind speed and yaw angle of the rotor surface as input operating condition variables to determine the simulated total thrust and simulated power of each wind turbine under different input operating condition variables;

[0012] Determine the thrust coefficient corresponding to each of the simulated total thrusts respectively, and construct a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of the rotor surface of each wind turbine using the associated input operating condition variables and the thrust coefficient;

[0013] Based on the associated input operating condition variables and simulated power, a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of the wind turbine rotor surface is constructed.

[0014] Furthermore, the equivalent fatigue load proxy model of the wind turbine is constructed based on the coupling effect of the blade aerodynamic load and the blade gravity, including:

[0015] The aerodynamic load analysis of wind turbine blades is conducted based on blade element momentum theory, and the aerodynamic blade bending moment model of wind turbine is constructed;

[0016] Analyze the gravity force of wind turbine blades and build a gravity blade bending moment model for wind turbines;

[0017] Taking the maximum bending moment difference as the target, the aerodynamic blade bending moment model and the gravity blade bending moment model are integrated to construct an equivalent fatigue load proxy model of the wind turbine.

[0018] Furthermore, the aerodynamic blade bending moment model includes:

[0019] ;

[0020] ;

[0021] Where, is the bending moment in the fan impeller rotation plane caused by the blade tangential force, is the gas density, is the actual velocity of air on the blade element, is the airfoil chord length of the blade element, is the blade element radius, is the leaf element thickness, is the airfoil lift coefficient, is the angle between the actual velocity of the air on the blade element and the rotation plane, is the airfoil drag coefficient, The bending moment outside the rotating plane of the fan impeller caused by the axial thrust of the blades;

[0022] The gravity blade bending moment model includes:

[0023] ;

[0024] ;

[0025] Where, is the out-of-plane bending moment caused by the blade weight, is the blade mass, is the gravitational constant, is the radial position of the blade's center of gravity, is the elevation angle, is the in-plane bending moment of the blade caused by the blade weight, is the azimuth;

[0026] The equivalent fatigue load proxy model includes:

[0027] ;

[0028] ;

[0029] in, , ;

[0030] Where, is the total bending moment on the blade root outside the rotation plane, is the total bending moment on the blade root in the rotation plane, is the maximum out-of-plane bending moment difference, The azimuth is The total bending moment outside the rotation plane at the blade root is The azimuth is The total bending moment outside the rotation plane at the blade root is is the maximum bending moment difference in the plane, The azimuth is The total bending moment at the blade root in the rotation plane is The azimuth is The total bending moment at the blade root in the rotation plane.

[0031] Furthermore, the optimization objective function is constructed with the goal of maximizing power and minimizing equivalent fatigue load. The optimization objective function is solved by an optimization algorithm based on the measured wind turbine-wind measurement data of the wind farm, the wake model, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of each wind rotor surface, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of each wind rotor surface, and the equivalent fatigue load proxy model to determine the target yaw angle combination of the wind farm, including:

[0032] Collecting measured wind turbine-wind measurement data of the wind farm, and determining the downwind coordinates of each wind turbine in the wind farm based on the measured wind turbine-wind measurement data and numbering the downwind coordinates;

[0033] For any optimized yaw angle, the optimized equivalent inflow wind speed of each wind turbine is determined by the wake model based on the measured wind turbine-wind measurement data and the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of each wind turbine;

[0034] According to the optimized equivalent inflow wind speed and optimized yaw angle of each wind turbine, the optimized power of each wind turbine is determined by a corresponding two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of the wind turbine;

[0035] The equivalent fatigue load proxy model is used to determine the optimized equivalent fatigue load of each wind turbine at the optimized yaw angle based on the equivalent inflow wind speed of each optimized wind rotor surface;

[0036] An optimization objective function is constructed with the goals of maximizing power and minimizing equivalent fatigue load. The optimization objective function is optimized and solved according to the optimized power and optimized equivalent fatigue load at any optimized yaw angle through an optimization algorithm, and the target yaw angle combination of the wind farm is output.

[0037] Furthermore, the optimization algorithm is a sequential quadratic programming optimization algorithm.

[0038] A second aspect of the present invention provides a wind farm active yaw optimization system that takes into account wind turbine fatigue loads, comprising:

[0039] The wake modeling module is used to obtain the wake model for calculating the equivalent inflow wind speed on the wind turbine rotor surface;

[0040] A query table construction module is used to determine a two-dimensional query table of equivalent inflow wind speed-yaw angle-thrust coefficient and a two-dimensional query table of equivalent inflow wind speed-yaw angle-power for each wind turbine in the wind farm;

[0041] Load modeling module, used to build an equivalent fatigue load proxy model of the wind turbine based on the coupling effect of blade aerodynamic load and blade gravity;

[0042] An optimization solution module is used to construct an optimization objective function with the goals of maximizing power and minimizing equivalent fatigue load. The optimization objective function is solved by an optimization algorithm based on the measured wind turbine-wind measurement data of the wind farm, the wake model, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of each wind wheel surface, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of each wind wheel surface, and the equivalent fatigue load proxy model to determine the target yaw angle combination of the wind farm.

[0043] A third aspect of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the active yaw optimization method for a wind farm considering fatigue loads of wind turbines as described in any one of the above items.

[0044] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method for optimizing active yaw of a wind farm considering fatigue load of a wind turbine as described in any one of the above items is implemented.

[0045] A fifth aspect of the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the active yaw optimization method for a wind farm considering fatigue loads of wind turbines as described in any one of the above items.

[0046] It can be seen from the above technical solutions that the present invention has the following advantages:

[0047] The above-mentioned scheme of the present invention provides a wind farm active yaw optimization method considering the fatigue load of the wind turbine, including: obtaining a wake model for calculating the equivalent inflow wind speed of the wind turbine's rotor surface; determining a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient and a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of the wind turbine's rotor surface for each wind turbine in the wind farm; constructing an equivalent fatigue load proxy model of the wind turbine based on the coupling effect of the blade aerodynamic load and the blade gravity; constructing an optimization objective function with the goals of maximizing power and minimizing equivalent fatigue load, solving the optimization objective function through an optimization algorithm based on the measured wind turbine-wind measurement data of the wind farm, the wake model, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of each wind turbine surface, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of each wind turbine surface and the equivalent fatigue load proxy model, and determining the target yaw angle combination of the wind farm. Based on the above scheme, the use of a high-precision wake model improves the accuracy of predicting the equivalent inflow wind speed on the wind rotor surface. The equivalent fatigue load proxy model is designed based on the blade aerodynamic load and blade gravity optimization, which can more comprehensively evaluate the fatigue load of the wind turbine. Multi-objective collaborative optimization is carried out by comprehensively maximizing power and minimizing equivalent fatigue load, aiming to ensure power improvement while significantly reducing the fatigue load caused by the wake effect, thereby improving the overall reliability of active yaw optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A flowchart of the steps of a wind farm active yaw optimization method considering wind turbine fatigue loads provided by an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of the aerodynamic forces on a blade element provided by an embodiment of the present invention;

[0051] Figure 3 A schematic diagram of the gravity force on a blade provided in an embodiment of the present invention;

[0052] Figure 4 A schematic diagram showing the effect of wake flow on load distribution of wind turbine blades provided by an embodiment of the present invention;

[0053] Figure 5 A schematic diagram of a multi-objective optimization framework provided by an embodiment of the present invention;

[0054] Figure 6A schematic diagram of a wind turbine layout provided by an embodiment of the present invention;

[0055] Figure 7 A two-dimensional query representation of a wind turbine provided by an embodiment of the present invention;

[0056] Figure 8 A schematic diagram showing a comparison of average results of all-wind direction optimization of total power improvement rate under different weight coefficient values ​​provided by an embodiment of the present invention;

[0057] Figure 9 A schematic diagram comparing the average results of full-wind direction optimization of the amplitude difference between the shimmy bending moment and the flapping bending moment under different weight coefficient values ​​provided by an embodiment of the present invention;

[0058] Figure 10 A flowchart of the steps of a wind farm active yaw optimization method considering wind turbine fatigue loads provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] An embodiment of the present invention provides a method and system for active yaw optimization of a wind farm that takes into account the fatigue load of the wind turbine. The method is used to solve the technical problem that when the existing technology reduces the wake effect by optimizing the yaw angle under active yaw control, only the influence of the aerodynamic load on the blades is usually considered when calculating the fatigue load of the wind turbine, making it difficult to comprehensively evaluate the fatigue load of the wind turbine, resulting in low reliability of active yaw optimization.

[0060] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0061] See also Figure 1 , Figure 1 A flowchart of the steps of a wind farm active yaw optimization method considering wind turbine fatigue loads provided by an embodiment of the present invention.

[0062] The present invention provides a wind farm active yaw optimization method considering wind turbine fatigue load, comprising:

[0063] Step 101: Obtain a wake model for calculating the equivalent inflow wind speed at the rotor surface of a wind turbine.

[0064] It should be noted that, in one implementation, for a given model of wind turbine, due to its rotor surface area , power generation efficiency , and power factor curves It is known that the wind turbine power can be determined by simply obtaining the effective inflow wind speed of the wind turbine (the equivalent inflow wind speed at the rotor surface). However, due to the existence of wake, in order to accurately reflect the total power of the wind farm, it is necessary to accurately predict the impact of the wind turbine wake on the plane speed of each wind turbine rotor. To this end, this embodiment considers using a wake model to calculate the equivalent inflow wind speed of the wind turbine rotor surface.

[0065] In a specific implementation of this embodiment, the wake model is the Ishihara-Qian wake model.

[0066] It should be noted that traditional wake models such as Jensen have the characteristics of simple models but limited accuracy. In order to more accurately simulate the wake effect of wind farms, it is necessary to adopt a higher-precision wake model. The Ishihara-Qian wake model is a Gaussian-based analytical single wake model proposed by Ishihara and Qian. It can more accurately and efficiently reflect the effect of wind turbine wake on the overall power generation of the wind farm. In this embodiment, it is called the Ishihara-Qian wake model. The Ishihara-Qian model is a three-dimensional wake model that provides wake width, velocity loss, additional turbulence, and wake deflection caused by active yaw. In the Ishihara-Qian model, all parameters are automatically determined as functions of the ambient turbulence intensity and thrust coefficient. Moreover, the wake model provides a double Gaussian distribution of turbulence intensity in the wake area. In specific implementation:

[0067] 1) For a given Wind farm wind turbine layout with base wind turbines ,When calculating the power and wake model in each wind direction, it is necessary to transform its coordinates so that the future flow direction is aligned with the direction of the wind farm. It should be noted that the wind turbine number ( ) increases in sequence along the x direction in the coordinate system in any wind direction. is the number of wind turbines in the wind farm; for any wind turbine The equivalent inflow wind speed of the wind wheel surface can be calculated by the following formula:

[0068] (1)

[0069] Where, Number the wind turbines , is the equivalent inflow wind speed on the rotor surface, for The equivalent inflow wind speed of the wind turbine rotor is: is the wind wheel surface area, for The downwind coordinate of the hub center of wind turbine No. is the cross-wind coordinate in the coordinate system, is the vertical coordinate in the coordinate system, for The wind speed distribution on the rotor surface of wind turbine No. is the integral over the rotor plane;

[0070] 2) The Ishihara-Qian wake model also incorporates the effect of turbulence on the wake wind speed loss. Therefore, it is necessary to introduce the effective inflow turbulence of the wind turbine surface to each wind turbine. , the specific calculation method is as follows:

[0071] (2)

[0072] Where, Number the wind turbines , is the effective inflow turbulence intensity on the rotor surface, for The effective inflow turbulence of the wind turbine rotor surface is for The distribution of the standard deviation of the fluctuating wind speed on the rotor surface of wind turbine No. is the integral over the rotor plane, is the wind wheel surface area;

[0073] 3) For any wind turbine in the wind farm , the wind speed loss in its wake will lead to a decrease in the power generation of the downstream wind turbine. At the same time, the additional turbulence generated in the wake will affect the wake recovery speed, so they need to be calculated separately:

[0074] (3)

[0075] (4)

[0076] (5)

[0077] (6)

[0078] (7)

[0079] (8)

[0080] Where, relative to The coordinates of the hub center of wind turbine No. is the vertical coordinate in the coordinate system, for The wind speed loss in the wake of wind turbine No. for The equivalent inflow wind speed of the wind turbine rotor is: From any point in the yz plane to The spanwise distance of wind turbine No. from the center of the wake, is the characteristic width of the wake, is the wind wheel diameter, for The offset value of the wake center caused by the yaw of wind turbine No. is the height of the wind turbine hub center, for Additional turbulence in the wake of wind turbine No. for The distribution of the standard deviation of the fluctuating wind speed caused by the additional turbulence of wind turbine No. for The equivalent inflow wind speed of the wind turbine rotor is: The inflow wind shear and The additional turbulence correction term caused by the interaction of the wake shear of the wind turbine No. for The effective inflow turbulence of the wind turbine rotor surface is is the yaw angle, is the yaw angle The effective thrust coefficient under The end point of the linear trail deflection The characteristic wake width at is the thrust coefficient; 、 、 、 、 、 、 、 、 、 、 and are all Ishihara-Qian wake model parameters, Number the wind turbines , see Table 1 for details:

[0081] Table 1 Parameters of the Ishihara-Qian wake model

[0082]

[0083] When using formulas (3) to (8) to calculate the wake loss and additional turbulence of a single wind turbine, the equivalent inflow wind speed and turbulence calculated by formulas (1) and (2) need to be used as input. Since the inflow of the downstream wind turbine will be affected by the wake of the upstream wind turbine, the calculation needs to be carried out in the order of the wind turbine's downwind coordinates, from upstream to downstream. For the downstream wind turbine affected by the wake of multiple upstream wind turbines, its inflow needs to consider the superposition effect of multiple wakes. The specific calculation steps are as follows:

[0084] 4) For wind farms The wind speed distribution on the rotor surface of wind turbine No. 1 is considered by linear superposition method. The specific calculation method for the wake loss caused by a typhoon turbine is as follows:

[0085] (9)

[0086] Where, Number the wind turbines , for The downwind coordinate of the hub center of wind turbine No. is the cross-wind coordinate in the coordinate system, is the vertical coordinate in the coordinate system, for The wind speed distribution on the rotor surface of wind turbine No. for The free stream wind speed of wind turbine No. Number the upstream wind turbines , for Wind turbines are currently The wind speed loss caused on the rotor surface of wind turbine No. 1 can be calculated according to formula (3);

[0087] In addition, the superposition of additional turbulence intensity must be considered. According to the research of Qian and Ishihara, directly superimposing the additional turbulence intensity of two wind turbines using the linear square sum principle cannot reproduce the turbulence superposition results in the actual flow field. Therefore, in addition to calculating the upstream In addition to the additional turbulence caused by the wind turbine, a correction term needs to be introduced to take into account the turbulence caused by the upstream wind turbine. The wind turbine closest to The turbulence superposition of fan No. 1 can be calculated by the following formula:

[0088] (10)

[0089] in:

[0090] (11)

[0091] (12)

[0092] (13)

[0093] (14)

[0094] (15)

[0095] (16)

[0096] Where, Number the wind turbines , Number the upstream wind turbines , for The downwind coordinate of the hub center of wind turbine No. is the cross-wind coordinate in the coordinate system, is the vertical coordinate in the coordinate system, for The distribution of the standard deviation of the fluctuating wind speed on the rotor surface of wind turbine No. is the standard deviation of the inflow pulsating wind speed, For upstream Wind turbine No. The standard deviation of the additional turbulent fluctuating wind speed caused by the location of wind turbine No. To cooperate with upstream The wind turbine closest to The standard deviation correction term of the turbulent pulsation wind speed caused by wind turbine No. for The standard deviation of the additional turbulent fluctuating wind speed at the blade tip generated by wind turbine No. From any point in the yz plane to The spanwise distance of the wake center of wind turbine No. is the wind wheel diameter, for The coordinates of the hub center of wind turbine No. is the wake complete overlap geometry judgment condition 1, is the wake complete overlap geometry judgment condition 2, is the fan wake width, for The width of the wind turbine wake is is the height of the wind turbine hub center, For The number of the upstream wind turbine closest to wind turbine No. , for Wind turbine and The secant length of the wake intersection area of ​​wind turbine No. is the wake partial overlap geometric judgment condition 1, is the wake overlap geometric judgment condition 2, for The coordinates of the hub center of wind turbine No. for The width of the wind turbine wake is for Wind turbine No. The width of the fan wake at for Wind turbine No. The width of the fan wake at is a symbolic function, 、 and All are Ishihara-Qian wake model parameters.

[0097] Step 102: Determine a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient and a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power for each wind turbine in the wind farm.

[0098] Step 102 includes the following sub-steps:

[0099] The equivalent inflow wind speed and yaw angle of the rotor surface are used as input operating variables to perform numerical simulation on each wind turbine in the wind farm, and the simulated total thrust and simulated power of each wind turbine under different input operating variables are determined.

[0100] Determine the thrust coefficient corresponding to each simulated total thrust, and use the associated input operating condition variables and thrust coefficients to construct a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient for each wind turbine;

[0101] Based on the associated input operating condition variables and simulated power, a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of the wind turbine rotor surface is constructed.

[0102] The two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of the wind rotor surface (thrust coefficient curve) refers to a two-dimensional lookup table that establishes a mapping relationship between the equivalent inflow wind speed, yaw angle and thrust coefficient of the wind rotor surface.

[0103] The two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power on the wind rotor surface refers to a two-dimensional lookup table that establishes a mapping relationship between the equivalent inflow wind speed, yaw angle and power on the wind rotor surface.

[0104] The simulated total thrust refers to the total thrust calculated when the power is numerically simulated.

[0105] Simulated power refers to the power calculated when performing numerical simulation on a wind turbine.

[0106] It should be noted that in this embodiment, in order to avoid complex real-time calculations, the simulated total thrust and simulated power are obtained through numerical simulation calculations under different wind rotor surface equivalent inflow wind speeds and yaw angle conditions. After the corresponding thrust coefficient is obtained according to the simulated total thrust, a two-dimensional look-up table with a mapping relationship is established based on the thrust coefficient and simulated power under different simulated conditions. The definition of the thrust coefficient is as follows:

[0107] (17)

[0108] Where, is the thrust coefficient, is the total thrust, is the wind wheel area, is the equivalent inflow wind speed on the rotor surface, is the air density.

[0109] Step 103: Based on the coupling effect between the blade aerodynamic load and the blade gravity, an equivalent fatigue load proxy model of the wind turbine is constructed.

[0110] It should be noted that this embodiment comprehensively considers the coupling between blade aerodynamic load and blade gravity to design the equivalent fatigue load proxy model of the wind turbine.

[0111] In a specific implementation of this embodiment, an aerodynamic blade bending moment model is established based on blade element momentum theory, taking into account the bending moment generated by aerodynamic loads on the blades. Furthermore, the influence of gravity is taken into account, and the bending moment experienced by the blades is divided into a shimmying bending moment experienced within the rotation plane and a flapping bending moment experienced outside the rotation plane. The maximum difference between the in-plane and out-of-plane bending moments is used as the equivalent fatigue load. In specific implementation, step 103 includes the following sub-steps:

[0112] S1. Analyze the aerodynamic load of wind turbine blades based on blade element momentum theory and construct an aerodynamic blade bending moment model of wind turbine.

[0113] It should be noted that this embodiment uses the classic blade element momentum theory to calculate the aerodynamic load on the blade, and uses a simplified method to convert the equivalent fatigue load of the blade in the wake region into the load amplitude difference during the rotation process. This method effectively avoids the problem of large computational complexity in fatigue load analysis of the traditional time-domain rain flow counting method, and significantly improves the analysis efficiency. The blade element momentum theory is the theoretical basis of current actuation methods. Its basic idea is to discretize the blades of the wind turbine along the span direction into several micro-segments, namely blade elements. Assuming that the flow on each blade element does not interfere with each other, it can be regarded as a two-dimensional airfoil. In this way, by integrating the force and torque on each blade element along the span direction, the force and torque on the entire wind rotor plane can be obtained.

[0114] On wind turbine blades, Figure 2 As shown in (a), the blade element radius is , the thickness of the leaf element is The axial momentum change rate and angular momentum change rate of the gas in the ring swept by the blade element are recorded as the aerodynamic lift and aerodynamic drag of the blade element, and the lift and drag generated by each blade element can be decomposed into two components in the direction of the fan rotation plane and the direction perpendicular to the rotation plane, such as Figure 2 (b)

[0115] exist Figure 2 (b), is the tangential induced velocity, which can also be written as , is the air velocity caused by the rotation of the blades, is the axial induced velocity, which can also be written as According to the velocity triangle, the actual velocity of air relative to the blade element is:

[0116] (18)

[0117] Where, is the actual velocity of air relative to the blade element (relative total velocity), is the inflow wind speed, is the axial induction factor, is the blade element radius, is the wind wheel speed, is the tangential induction factor;

[0118] The angle between the relative resultant velocity and the rotating plane is ,but

[0119] (19)

[0120] Angle of attack of the airflow for, is the pitch angle, then:

[0121] (20)

[0122] According to the assumption of blade element theory, the lift and drag on any blade element are not affected by other blade elements, so:

[0123] (twenty one)

[0124] Where, is the lifting resistance, is the gas density, is the actual velocity of air on the blade element, is the airfoil chord length of the blade element, is the leaf element thickness, is (the angle of attack is ) airfoil lift coefficient when ;

[0125] Therefore, the lift and drag generated by the air on the blade element are decomposed into different components along the span direction and radial direction respectively, and the total axial thrust and total tangential force generated by the air on the blade element can be obtained as follows:

[0126] (twenty two)

[0127] (twenty three)

[0128] Where, is the total axial thrust, is the number of blades of the wind turbine (for general horizontal axis wind turbines, it is usually 3), is (the angle of attack is The airfoil drag coefficient is is the total tangential force;

[0129] Therefore, the total torque exerted by the air on the blade is for:

[0130] (twenty four)

[0131] The blade element momentum theory assumes that the force acting on the blade element is only related to the change in the momentum of the gas in the ring swept by the blade element, and there is no interaction between the airflows of adjacent rings. Therefore, the axial induction factor There is no change along the blade span;

[0132] According to Newton's third law, the axial thrust on the blade element is equal, so:

[0133] (25)

[0134] Similarly, according to the torque:

[0135] (26)

[0136] Simplifying formula (25) we have:

[0137] (27)

[0138] Similarly, simplifying formula (26) yields:

[0139] (28)

[0140] Define the axial force coefficient of the wind turbine and tangential force coefficient , then formula (28) and formula (29) can be expressed as:

[0141] (29)

[0142] (30)

[0143] In the formula, chord length solidity , defined as the total blade chord length at a given radius divided by the circumference at that radius;

[0144] According to equations (29) and (30), the axial induction factor of the wind turbine can be solved by iteration Tangential induction factor , and then complete the solution of other physical quantities represented by the induction factor;

[0145] According to (22) and (23), the blade element force can be calculated, where the inflow velocity of each blade element is The equivalent inflow wind speed on the rotor surface can be calculated by the wake model in step 101. , and then the bending moment at the blade root is obtained by integrating and summing each blade element along the span direction, thereby obtaining the aerodynamic blade bending moment model:

[0146] (31)

[0147] (32)

[0148] Where, is the bending moment in the fan impeller rotation plane caused by the blade tangential force, The bending moment outside the rotating plane of the fan impeller caused by the axial thrust of the blades.

[0149] S2. Analyze the gravity force of wind turbine blades and construct a gravity blade bending moment model of wind turbine.

[0150] It should be noted that in addition to the bending moment caused by the aerodynamic load on the blade, the influence of gravity also needs to be considered; compared with the aerodynamic load, the direction of gravity is always vertically downward, such as Figure 3 As shown, the elevation angle It refers to the angle between the fan impeller rotation plane and the earth's horizontal plane, which is generally small (no more than 5°). It refers to the angle of rotation clockwise from the vertical upward direction;

[0151] exist Figure 3 Medium gravity It can be decomposed into the gravity component in the rotation plane and the component of gravity perpendicular to the plane of rotation :

[0152] (33)

[0153] (34)

[0154] like Figure 3 As shown in (a), the gravity component in the rotation plane can be decomposed into radial forces in the rotation plane. and the force perpendicular to the radial direction ,in It does not contribute to the root bending moment of the blade, but the force perpendicular to the radial direction for:

[0155] (35)

[0156] The out-of-plane bending moment and in-plane bending moment generated by gravity on the blade, that is, the gravity blade bending moment model is expressed as:

[0157] (36)

[0158] (37)

[0159] Where, is the out-of-plane bending moment caused by the blade weight, is the blade mass, is the gravitational constant, is the radial position of the blade's center of gravity, is the in-plane bending moment of the blade caused by the blade's weight.

[0160] S3. Taking the maximum bending moment difference as the target, the equivalent fatigue load proxy model of the wind turbine is constructed by integrating the aerodynamic blade bending moment model and the gravity blade bending moment model.

[0161] It should be noted that the total bending moment on the blade root outside and inside the rotation plane can be expressed as:

[0162] (38)

[0163] (39)

[0164] Where, is the total bending moment on the blade root outside the rotation plane (flapping bending moment on the blade root), is the total bending moment on the blade root in the rotation plane (blade root shimmying bending moment);

[0165] Finally, consider velocity distributions other than uniform effective velocity distributions , in order to be able to detect the effect of partial wake overlap on the loads, taking into account the hub height (height from the base of the tower to the center of the hub) The maximum difference in the in-plane bending moment is calculated by the transverse velocity distribution at ) and the maximum difference between the out-of-plane bending moment ( ), and use this as the equivalent fatigue load; because the maximum load difference caused by partial wake overlap is expected to be and Therefore, the fatigue load of the blade under the influence of partially overlapping wake is simplified to the root bending moment difference at 90° and 270° azimuth angles, as shown in Figure 4 As shown, the maximum out-of-plane and in-plane bending moment differences are calculated as follows:

[0166] (40)

[0167] (41)

[0168] Where, is the maximum bending moment difference outside the blade plane, The azimuth is The total bending moment outside the rotation plane at the blade root is The azimuth is The total bending moment outside the rotation plane at the blade root is is the maximum bending moment difference in the blade plane, The azimuth is The total bending moment at the blade root in the rotation plane is The azimuth is The total bending moment at the blade root in the rotation plane.

[0169] Step 104: Construct an optimization objective function with the goal of maximizing power and minimizing equivalent fatigue load. Use an optimization algorithm to solve the optimization objective function based on the wind farm's measured wind turbine-wind measurement data, the wake model, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient for each wind rotor surface, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power for each wind rotor surface, and the equivalent fatigue load proxy model to determine the target yaw angle combination for the wind farm.

[0170] In a specific implementation of this embodiment, step 104 includes the following sub-steps:

[0171] Collecting measured wind turbine-wind measurement data of the wind farm, determining the downwind coordinates of each wind turbine in the wind farm based on the measured wind turbine-wind measurement data and numbering the downwind coordinates;

[0172] For any optimized yaw angle, the optimized equivalent inflow wind speed of each wind turbine is determined by the wake model based on the measured wind turbine-wind measurement data and the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of each wind turbine surface;

[0173] According to the equivalent inflow wind speed and optimized yaw angle of each optimized wind rotor surface, the optimized power of each wind turbine is determined through the corresponding two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of the wind rotor surface;

[0174] The equivalent fatigue load proxy model is used to determine the optimized equivalent fatigue load of each wind turbine at the optimized yaw angle based on the equivalent inflow wind speed of each optimized wind rotor surface.

[0175] An optimization objective function is constructed with the goals of maximizing power and minimizing equivalent fatigue load. The optimization objective function is optimized and solved according to the optimized power and optimized equivalent fatigue load at any optimized yaw angle through the optimization algorithm, and the target yaw angle combination of the wind farm is output.

[0176] Measured wind turbine-measured wind data, including measured wind turbine data and measured wind data; measured wind turbine data refers to data characterizing the properties of the wind turbine, including but not limited to the machine site coordinates (such as the layout coordinates of the wind turbine, etc.), model parameters (such as power curve, thrust coefficient curve, rotor diameter, height of the wind turbine hub center and number of blades, etc.); measured wind data refers to data characterizing the incoming wind conditions, including but not limited to average wind speed, turbulence and wind direction, etc.

[0177] Optimizing the yaw angle, optimizing the equivalent inflow wind speed on the rotor surface, optimizing the power, and optimizing the equivalent fatigue load refer to the yaw angle, equivalent inflow wind speed on the rotor surface, power, and equivalent fatigue load determined during the optimization solution process; the target yaw angle combination includes a set of target yaw angles, and the target yaw angle refers to the yaw angle output after the optimization solution is completed.

[0178] The optimization objective function refers to the function for optimizing the yaw angle.

[0179] It should be noted that if Figure 5 As shown in the figure, this embodiment constructs a multi-objective collaborative optimization framework for wind farms with power and equivalent fatigue load as the objectives, constructs an optimization objective function with power maximization and equivalent fatigue load minimization as the objectives, collects the measured wind turbine-wind measurement data of the wind farm, and extracts the wind direction from the measured wind turbine-wind measurement data for any yaw angle. After that, the initial coordinates of the wind turbine are converted to the main wind direction, and the wind turbines are numbered according to the wind direction. The wake model is used to predict the first From the upstream No. 1 to No. The wind speed loss and additional turbulence caused by wind turbine No. are superimposed to calculate the The optimized equivalent inflow wind speed of the wind rotor surface at wind turbine No. 1 is obtained, and the corresponding optimized power is matched through the two-dimensional lookup table of equivalent inflow wind speed of the wind rotor surface-yaw angle-power until the optimized power of all wind turbines in the wind farm is determined. During this period, the thrust coefficient corresponding to the upstream wind turbine can be matched through the two-dimensional lookup table of equivalent inflow wind speed of the wind rotor surface-yaw angle-thrust coefficient to calculate the wind speed loss and additional turbulence. In addition, since the equivalent inflow wind speed of the wind rotor surface is related to the aerodynamic load, the optimized equivalent fatigue load of each wind turbine under the optimized yaw angle is determined based on the optimized equivalent inflow wind speed of the wind rotor surface through the equivalent fatigue load proxy model. Finally, the optimization processor uses the optimization algorithm to solve the optimization objective function, thereby outputting the target yaw angle combination for active yaw control.

[0180] It is understandable that the total power of a wind farm is related to the total power generation of the wind farm, so maximizing the wind turbine power can maximize the production capacity of the wind farm.

[0181] It should be noted that if the equivalent inflow wind speed and yaw angle of the wind wheel surface as input operating condition variables do not exist in the two-dimensional query table, interpolation calculation can be considered. The specific interpolation calculation process can be referred to the existing technology and will not be repeated here.

[0182] In a more specific implementation of this embodiment, the optimization algorithm is a sequential quadratic programming optimization algorithm.

[0183] In a more specific implementation of this embodiment, the optimization objective function includes:

[0184] (42)

[0185] in, ;

[0186] Where, To optimize the objective function, is the weight coefficient, Number the wind turbines , is the number of wind turbines in the wind farm, is the yaw angle, for The dimensionless power of wind turbine No. for The dimensionless out-of-plane bending moment difference of the blades of wind turbine No. for The dimensionless in-plane bending moment difference of the blade of wind turbine No. For a single The maximum power of wind turbine No. for The power of wind turbine No. for The maximum bending moment difference outside the blade plane of wind turbine No. For a single The maximum value of the out-of-plane bending moment difference of wind turbine No. for The maximum bending moment difference in the blade plane of wind turbine No. For a single The maximum value of the in-plane bending moment difference of wind turbine No. is the maximum allowable yaw angle; 、 and It is the reference value of the specific dimensionless processing, the total power .

[0187] It should be noted that the optimization processor uses an optimization algorithm to find a set of target yaw angles , so that the total power (total power generation) is maximized and the blade fatigue load is minimized. In order to achieve this goal, the maximum allowable yaw angle is defined as a constraint condition. In specific implementation, it can be set ; At the same time, the optimization objective function introduces the weight coefficient Reasonable control of the power weight in the multi-objective optimization objective function. This more clear weight distribution between power and fatigue load can better take into account the optimization of power generation and fatigue load, and comprehensively consider the balance between the two to ensure that the fatigue load caused by the wake effect is greatly reduced while the power is increased, thereby achieving the improvement of wind farm production capacity without significantly increasing the fatigue load of the wind turbine. In specific implementation, by adjusting the weight coefficient Different optimization objectives are obtained, among which Corresponding to the single-objective optimization of load, that is, not considering power output, but only optimizing the fatigue load of the wind turbine, It corresponds to the single-objective optimization of power, that is, the fatigue load of the wind turbine is not considered, and only the maximum power output of the wind turbine is considered.

[0188] This embodiment also provides a specific implementation case to illustrate the optimization effect of the optimization objective function:

[0189] The target wind farm consists of Figure 6 The 2×3 Choshi 2.4MW wind turbines shown are designed for wind direction , to find the target yaw setting; and for each wind direction, using the total power ( ), average out-of-plane bending moment ( ) and the mean in-plane bending moment ( ), the hub height wind speed is 8 m / s, the turbulence is 6%, and the wind turbine parameters are shown in Table 2. The two-dimensional lookup table of thrust coefficient and power of the wind turbine model are shown in Figure 7 (a) and Figure 7 (b) shows:

[0190] Table 2 Choshi 2.4MW wind turbine parameters

[0191]

[0192] Regarding the computational domain of the wind farm, the x-direction is from -5 D to 15 D (where D is the rotor diameter), the y-direction is from -5 D to 5 D, the computational grid resolution is 0.1 D, and the z-direction is from 0 to 4 H (where H is the hub height), the computational grid resolution is 0.2 D;

[0193] The results of all wind directions are summed and averaged to obtain the final power and fatigue load optimization results. Figure 8 and Figure 9 A comparison of several optimization strategies with different load and power weights is listed in Figure 8 It can be seen that when the weight coefficient When the power of the wind farm is lower than 0.4, the power of the wind farm is significantly reduced, so it is better to set the weight coefficient Set to greater than or equal to 0.4, from Figure 9 It can be seen that the yaw optimization method using active control can significantly reduce the flapping bending moment on the root of the wind turbine blade. , and the swing bending moment on the blade root is The optimization contribution of the fan is very small, because the wind turbine has a very small elevation angle, which causes the gravity to account for the majority of the rotational vibration. The contribution of active wake yaw control is very large. There is little optimization, on the contrary, gravity has The influence of is very small, and the bending moment caused by aerodynamic force accounts for the vast majority, so Can be significantly optimized.

[0194] In an embodiment of the present invention, a high-precision wake model is used to improve the accuracy of predicting the equivalent inflow wind speed on the wind wheel surface. An equivalent fatigue load proxy model is designed based on the blade aerodynamic load and blade gravity optimization, which can more comprehensively evaluate the fatigue load of the wind turbine. Multi-objective collaborative optimization is performed by comprehensively maximizing power and minimizing equivalent fatigue load, aiming to ensure power improvement while significantly reducing the fatigue load caused by the wake effect, thereby improving the overall reliability of active yaw optimization.

[0195] See also Figure 10 , Figure 10 A structural block diagram of a wind farm active yaw optimization system considering wind turbine fatigue loads provided by an embodiment of the present invention.

[0196] This embodiment provides a wind farm active yaw optimization system that considers wind turbine fatigue loads, including:

[0197] The wake modeling module 1001 is used to obtain a wake model for calculating the equivalent inflow wind speed of the wind turbine rotor surface;

[0198] A lookup table construction module 1002 is used to determine a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient and a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power for each wind turbine in the wind farm;

[0199] The load modeling module 1003 is used to construct an equivalent fatigue load proxy model of the wind turbine based on the coupling effect of blade aerodynamic load and blade gravity;

[0200] The optimization solution module 1004 is used to construct an optimization objective function with the goals of maximizing power and minimizing equivalent fatigue load. The optimization objective function is solved through an optimization algorithm based on the measured wind turbine-wind measurement data of the wind farm, the wake model, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient for each wind rotor surface, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power for each wind rotor surface, and the equivalent fatigue load proxy model to determine the target yaw angle combination of the wind farm.

[0201] Furthermore, the query table construction module 1002 is specifically configured to:

[0202] The equivalent inflow wind speed and yaw angle of the rotor surface are used as input operating variables to perform numerical simulation on each wind turbine in the wind farm, and the simulated total thrust and simulated power of each wind turbine under different input operating variables are determined.

[0203] Determine the thrust coefficient corresponding to each simulated total thrust, and use the associated input operating condition variables and thrust coefficients to construct a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient for each wind turbine;

[0204] Based on the associated input operating condition variables and simulated power, a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of the wind turbine rotor surface is constructed.

[0205] Furthermore, the load modeling module 1003 is specifically configured to:

[0206] The aerodynamic load analysis of wind turbine blades is conducted based on blade element momentum theory, and the aerodynamic blade bending moment model of wind turbine is constructed;

[0207] Analyze the gravity force of wind turbine blades and build a gravity blade bending moment model for wind turbines;

[0208] Taking the maximum bending moment difference as the target, the equivalent fatigue load proxy model of the wind turbine is constructed by integrating the aerodynamic blade bending moment model and the gravity blade bending moment model.

[0209] Furthermore, the aerodynamic blade bending moment model includes:

[0210] ;

[0211] ;

[0212] Where, is the bending moment in the fan impeller rotation plane caused by the blade tangential force, is the gas density, is the actual velocity of air on the blade element, is the airfoil chord length of the blade element, is the blade element radius, is the leaf element thickness, is the airfoil lift coefficient, is the angle between the actual velocity of the air on the blade element and the rotation plane, is the airfoil drag coefficient, The bending moment outside the rotating plane of the fan impeller caused by the axial thrust of the blades;

[0213] Gravity blade bending moment model, including:

[0214] ;

[0215] ;

[0216] Where, is the out-of-plane bending moment caused by the blade weight, is the blade mass, is the gravitational constant, is the radial position of the blade's center of gravity, is the elevation angle, is the in-plane bending moment of the blade caused by the blade weight, is the azimuth;

[0217] Equivalent fatigue load proxy models, including:

[0218] ;

[0219] ;

[0220] in, , ;

[0221] Where, is the total bending moment on the blade root outside the rotation plane, is the total bending moment on the blade root in the rotation plane, is the maximum out-of-plane bending moment difference, The azimuth is The total bending moment outside the rotation plane at the blade root is The azimuth is The total bending moment outside the rotation plane at the blade root is is the maximum bending moment difference in the plane, The azimuth is The total bending moment at the blade root in the rotation plane is The azimuth is The total bending moment at the blade root in the rotation plane.

[0222] Furthermore, the optimization solution module 1004 is specifically used to:

[0223] Collecting measured wind turbine-wind measurement data of the wind farm, determining the downwind coordinates of each wind turbine in the wind farm based on the measured wind turbine-wind measurement data and numbering the downwind coordinates;

[0224] For any optimized yaw angle, the optimized equivalent inflow wind speed of each wind turbine is determined by the wake model based on the measured wind turbine-wind measurement data and the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of each wind turbine surface;

[0225] According to the equivalent inflow wind speed and optimized yaw angle of each optimized wind rotor surface, the optimized power of each wind turbine is determined through the corresponding two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of the wind rotor surface;

[0226] The equivalent fatigue load proxy model is used to determine the optimized equivalent fatigue load of each wind turbine at the optimized yaw angle based on the equivalent inflow wind speed of each optimized wind rotor surface.

[0227] An optimization objective function is constructed with the goals of maximizing power and minimizing equivalent fatigue load. The optimization objective function is optimized and solved according to the optimized power and optimized equivalent fatigue load at any optimized yaw angle through the optimization algorithm, and the target yaw angle combination of the wind farm is output.

[0228] Furthermore, the optimization algorithm is a sequential quadratic programming optimization algorithm.

[0229] An embodiment of the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the active yaw optimization method for a wind farm considering the fatigue load of a wind turbine as described in any of the above embodiments.

[0230] An embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the active yaw optimization method for a wind farm considering the fatigue load of a wind turbine as in any of the above embodiments are implemented.

[0231] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the wind farm active yaw optimization method considering wind turbine fatigue load as described in any of the above embodiments.

[0232] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0233] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0234] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0235] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0236] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0237] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wind farm active yaw optimization method considering wind turbine fatigue load, characterized in that: include: Obtain the wake model for calculating the equivalent inflow wind speed on the rotor surface of the wind turbine; Determine a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient and a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power for each wind turbine in the wind farm; Based on the coupling effect of blade aerodynamic load and blade gravity, an equivalent fatigue load proxy model of wind turbine is constructed; An optimization objective function is constructed with the goals of maximizing power and minimizing equivalent fatigue load. The optimization objective function is solved by an optimization algorithm based on the measured wind turbine-wind measurement data of the wind farm, the wake model, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of each wind wheel surface, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of each wind wheel surface, and the equivalent fatigue load proxy model to determine the target yaw angle combination of the wind farm.

2. The wind farm active yaw optimization method considering wind turbine fatigue load according to claim 1, characterized in that: The two-dimensional lookup table for determining the equivalent inflow wind speed-yaw angle-thrust coefficient of the rotor surface and the two-dimensional lookup table for the equivalent inflow wind speed-yaw angle-power of the rotor surface of each wind turbine in the wind farm includes: Performing numerical simulations on each wind turbine in the wind farm using the equivalent inflow wind speed and yaw angle of the rotor surface as input operating variables to determine the simulated total thrust and simulated power of each wind turbine under different input operating variables; Determine the thrust coefficient corresponding to each of the simulated total thrusts respectively, and construct a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of the rotor surface of each wind turbine using the associated input operating condition variables and the thrust coefficient; Based on the associated input operating condition variables and simulated power, a two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of the wind turbine rotor surface is constructed.

3. The wind farm active yaw optimization method considering wind turbine fatigue load according to claim 1, characterized in that: The equivalent fatigue load proxy model of the wind turbine is constructed based on the coupling effect of blade aerodynamic load and blade gravity, including: The aerodynamic load analysis of wind turbine blades is conducted based on blade element momentum theory, and the aerodynamic blade bending moment model of wind turbine is constructed; Analyze the gravity force of wind turbine blades and build a gravity blade bending moment model for wind turbines; Taking the maximum bending moment difference as the target, the aerodynamic blade bending moment model and the gravity blade bending moment model are integrated to construct an equivalent fatigue load proxy model of the wind turbine.

4. The wind farm active yaw optimization method considering wind turbine fatigue load according to claim 3, characterized in that: The aerodynamic blade bending moment model comprises: ； ； Where, is the bending moment in the fan impeller rotation plane caused by the blade tangential force, is the gas density, is the actual velocity of air on the blade element, is the airfoil chord length of the blade element, is the blade element radius, is the leaf element thickness, is the airfoil lift coefficient, is the angle between the actual velocity of the air on the blade element and the rotation plane, is the airfoil drag coefficient, The bending moment outside the rotating plane of the fan impeller caused by the axial thrust of the blades; The gravity blade bending moment model includes: ； ； Where, is the out-of-plane bending moment caused by the blade weight, is the blade mass, is the gravitational constant, is the radial position of the blade's center of gravity, is the elevation angle, is the in-plane bending moment of the blade caused by the blade weight, is the azimuth; The equivalent fatigue load proxy model includes: ； ； in, , ; Where, is the total bending moment on the blade root outside the rotation plane, is the total bending moment on the blade root in the rotation plane, is the maximum out-of-plane bending moment difference, The azimuth is The total bending moment outside the rotation plane at the blade root is The azimuth is The total bending moment outside the rotation plane at the blade root is is the maximum bending moment difference in the plane, The azimuth is The total bending moment at the blade root in the rotation plane is The azimuth is The total bending moment at the blade root in the rotation plane.

5. The wind farm active yaw optimization method considering wind turbine fatigue load according to claim 1, characterized in that: The optimization objective function is constructed with the goal of maximizing power and minimizing equivalent fatigue load, and the optimization objective function is solved by an optimization algorithm based on the measured wind turbine-wind measurement data of the wind farm, the wake model, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of each wind rotor surface, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of each wind rotor surface, and the equivalent fatigue load proxy model to determine the target yaw angle combination of the wind farm, including: Collecting measured wind turbine-wind measurement data of the wind farm, and determining the downwind coordinates of each wind turbine in the wind farm based on the measured wind turbine-wind measurement data and numbering the downwind coordinates; For any optimized yaw angle, the optimized equivalent inflow wind speed of each wind turbine is determined by the wake model based on the measured wind turbine-wind measurement data and the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of each wind turbine; According to the optimized equivalent inflow wind speed and optimized yaw angle of each wind turbine, the optimized power of each wind turbine is determined by a corresponding two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of the wind turbine; The equivalent fatigue load proxy model is used to determine the optimized equivalent fatigue load of each wind turbine at the optimized yaw angle based on the equivalent inflow wind speed of each optimized wind rotor surface; An optimization objective function is constructed with the goals of maximizing power and minimizing equivalent fatigue load. The optimization objective function is optimized and solved according to the optimized power and optimized equivalent fatigue load at any optimized yaw angle through an optimization algorithm, and the target yaw angle combination of the wind farm is output.

6. The wind farm active yaw optimization method considering wind turbine fatigue load according to claim 1, characterized in that: The optimization algorithm is a sequential quadratic programming optimization algorithm.

7. An active yaw optimization system for a wind farm considering fatigue loads of wind turbines, characterized in that: include: The wake modeling module is used to obtain the wake model for calculating the equivalent inflow wind speed on the wind turbine rotor surface; A query table construction module is used to determine a two-dimensional query table of equivalent inflow wind speed-yaw angle-thrust coefficient and a two-dimensional query table of equivalent inflow wind speed-yaw angle-power for each wind turbine in the wind farm; Load modeling module, used to build an equivalent fatigue load proxy model of the wind turbine based on the coupling effect of blade aerodynamic load and blade gravity; An optimization solution module is used to construct an optimization objective function with the goals of maximizing power and minimizing equivalent fatigue load. The optimization objective function is solved by an optimization algorithm based on the measured wind turbine-wind measurement data of the wind farm, the wake model, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-thrust coefficient of each wind wheel surface, the two-dimensional lookup table of equivalent inflow wind speed-yaw angle-power of each wind wheel surface, and the equivalent fatigue load proxy model to determine the target yaw angle combination of the wind farm.

8. A computer device, characterized in that: The invention comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the active yaw optimization method for a wind farm considering fatigue load of a wind turbine as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method for active yaw optimization of a wind farm considering fatigue load of wind turbines according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method for active yaw optimization of a wind farm considering fatigue load of wind turbines according to any one of claims 1 to 6 are implemented.

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