Multi-wind turbine coordinated yaw optimization method for offshore wind farm

Abstract

Provided in the present invention is a multi-wind turbine coordinated yaw optimization method for an offshore wind farm. The method comprises: on the basis of a given wind speed, a given wind direction and given wind turbine parameters, establishing a single-wind turbine double-Gaussian yaw wake model and a multi-wind turbine yaw wake adaptive combined superposition model, and setting the overall operating conditions of a wind farm; on the basis of the overall operating conditions, setting relevant parameters of a particle swarm optimization algorithm, and by means of the particle swarm optimization algorithm with a penalty factor inserted, executing an iterative loop on a wind turbine yaw scheme; and outputting the wind turbine yaw scheme that meets a convergence condition, and using same as a multi-wind turbine coordinated yaw optimization scheme. In the present invention, a yaw multi-wind turbine wake adaptive combined superposition model is proposed on the basis of a yawed wind turbine double-Gaussian wake model with better accuracy and stronger robustness; global coordinated optimization is performed on wind turbine yaw by using a particle swarm optimization algorithm; and by means of introducing the penalty factor, non-compliant yaw strategies are identified and eliminated, thereby ensuring the smoothness of wind turbine operation and improving the power generation efficiency.

Description

A method for optimizing the coordinated yaw of multiple wind turbines in an offshore wind farm Technical Field

[0001] This invention relates to the field of wind power generation technology, and in particular to a method for optimizing the coordinated yaw of multiple wind turbines in an offshore wind farm. Background Technology

[0002] During actual wind turbine operation, the wake field is affected by various complex meteorological conditions, among which yaw has the most significant impact. As an indispensable part of the wind power generation system, wind turbine yaw ensures efficient and safe operation and effective utilization of wind energy resources. In offshore wind farms, yaw control is crucial for improving power generation efficiency and ensuring smooth turbine operation.

[0003] Current wind turbine yaw optimization methods are mostly based on analytical wake models, employing wake superposition models to address situations where a single turbine in a large wind farm is located within the mixed wake of multiple upstream turbines. The most well-known and widely used analytical wake model is the Jensen model, which has been improved by numerous scholars to enhance its applicability and accuracy. Ishihara et al. proposed a Gaussian wake model considering thrust coefficient and environmental turbulence intensity, adding correction terms to make it applicable to both near-wake and far-wake regions. This model also considers variations in additional turbulence intensity in the wake region, leading to the development of a double-Gaussian model suitable for turbulence intensity calculation. To enable the use of analytical wake models for yaw optimization, researchers have improved the Gaussian wake model, allowing it to calculate the turbine wake offset distance, wake region wind speed loss, and additional turbulence intensity under yaw conditions. Commonly used wake superposition models include geometric sum models, linear superposition models, energy conservation models, and square sum models; however, a consensus on the accuracy of each model remains unresolved.

[0004] In traditional single-environment wind turbine yaw optimization, only single-wind-condition optimization under different incoming wind directions and speeds is often considered. Directly applying this to offshore wind farms with variable wind conditions may lead to excessively frequent turbine yaw, thereby exacerbating the fatigue load on the turbine and seriously affecting the service life of the turbine structure. For example, when the wind speed increases from 5 m / s to 6 m / s, the yaw angle of a certain wind turbine may suddenly change from -30° to +30°; similarly, when the wind direction changes from north (N) to north-northwest (NNW), a similar sudden change in yaw angle may occur.

[0005] To overcome this limitation, this invention proposes a wind turbine coordinated yaw optimization method in complex offshore wind farm environments. This method aims to ensure that the yaw of the wind turbine changes smoothly with the incoming wind speed and direction.

[0006] It should be noted that the information disclosed in the above background section is only used to enhance the understanding of the background of the present invention and does not constitute any limitation on the present invention. Summary of the Invention

[0007] In view of the shortcomings of the prior art, the present invention provides a method for multi-turbine collaborative yaw optimization in offshore wind farms. Based on a more accurate and robust yaw turbine dual Gaussian wake model, an adaptive combination and superposition model of yaw turbine wakes is proposed. Particle swarm optimization is used to perform global collaborative optimization of turbine yaw. By introducing a penalty factor to identify and eliminate yaw strategies that do not meet the requirements, the smoothness of turbine operation and the power generation efficiency are ensured.

[0008] This invention provides a method for optimizing the coordinated yaw of multiple wind turbines in an offshore wind farm, comprising:

[0009] Based on the given wind speed, wind direction and wind turbine parameters, establish a single-unit double Gaussian yaw wake model and a multi-unit yaw wake adaptive combination superposition model, and set it as the overall operating condition of the wind farm.

[0010] The relevant parameters of the particle swarm optimization algorithm are set according to the overall operating conditions, and the yaw scheme of the wind turbine is iterated and looped by the particle swarm optimization algorithm with the insertion of penalty factors.

[0011] The wind turbine yaw scheme that meets the convergence condition is output as a multi-wind turbine collaborative yaw optimization scheme.

[0012] In one embodiment of the present invention, the wind turbine parameters further include a smoothness index that limits the range of fluctuation in the wind turbine yaw angle.

[0013] In one embodiment of the present invention, the step of iteratively iterating the wind turbine yaw scheme using a particle swarm optimization algorithm with an inserted penalty factor includes:

[0014] The initial yaw angle scheme of multiple wind turbines in the wind farm is generated by the particle swarm optimization algorithm, and the total power generation of the wind farm is calculated.

[0015] Calculate the penalty factor based on the multi-turbine yaw angle scheme of the wind farm;

[0016] The total power generation and penalty factor of multiple wind farm yaw angle schemes with multiple wind turbines are calculated through iterative loops.

[0017] Multiply the total power generation of the wind farm by the penalty factor to obtain the optimal power generation of the wind farm;

[0018] The optimal power generation of a wind farm is used to determine the corresponding yaw angle scheme for multiple wind turbines.

[0019] In one embodiment of the present invention, the step of calculating the penalty factor based on the yaw angle scheme of a multi-wind turbine wind farm includes:

[0020] The yaw angle of each wind turbine is determined based on the yaw angle scheme of multiple wind turbines in a wind farm.

[0021] Determine whether the yaw angle of each wind turbine exceeds the fluctuation range of the wind turbine yaw angle;

[0022] If no wind turbine yaw angle exceeds the limit, output the base coefficient of the penalty factor;

[0023] If a wind turbine's yaw angle exceeds the range, calculate the proportion of each wind turbine whose yaw angle exceeds the range of yaw angle fluctuation, as well as the proportion of the number of wind turbines in the wind farm that exceed the range of yaw angle fluctuation.

[0024] In one embodiment of the present invention, when the yaw angle of a wind turbine exceeds the limit, the parameter of the output penalty factor is smaller than its base coefficient. The larger the proportion of each wind turbine whose yaw angle exceeds the fluctuation range of the wind turbine yaw angle, the smaller the parameter of the output penalty factor. The larger the proportion of wind turbines in the wind farm that exceed the fluctuation range of the wind turbine yaw angle, the smaller the parameter of the output penalty factor.

[0025] In one embodiment of the present invention, the convergence condition includes maximizing the total power generation of the wind farm within the allowable range of wind turbine yaw angle fluctuations.

[0026] In one embodiment of the present invention, the yaw single-aircraft dual-Gaussian wake model is as follows: k * =0.11C t 1.07 I 0.2 (5) ε=0.23C t 0.25 I 0.17 (6) a = 0.93C t -0.75 I 0.17 (7) b = 0.42C t 0.6 I 0.2 (8) c = 0.15C t -0.25 I -0.7 (9)

[0027] In the formula, U ∞Indicates free flow; ΔU represents the loss velocity at the downstream (x,y,z) position of the wind turbine with the turbine as the origin; D represents the turbine blade diameter; θ represents the turbine yaw angle; I represents the turbulence intensity; Ct represents the turbine thrust coefficient; α k and α m Take values ​​of 0.7 and 0.65 respectively.

[0028] In one embodiment of the present invention, the adaptive combined superposition model of yaw multi-aircraft wake turbulence is: V = ωV SS +(1-ω)V GS (10)

[0029] In the formula, V represents the wake velocity; V SS V represents the wake velocity obtained based on the sum of squares superposition model; GS x represents the wake wind speed obtained based on the geometric superposition model; x1 and x2 represent the x / D values ​​corresponding to a weight coefficient of 1 in the sum of squares superposition model and the geometric superposition model, respectively.

[0030] In one embodiment of the present invention, the execution steps of the particle swarm optimization algorithm include:

[0031] Based on the set parameters, the particle swarm is initialized, and the wind turbine yaw scheme corresponding to each particle in the particle swarm is generated.

[0032] The total power generation of the wind farm corresponding to the particle swarm is calculated by inserting an inertia factor, and this is used as the fitness of the particle swarm.

[0033] By comparing the current fitness of the particle swarm with the historical best, we update the wind turbine yaw scheme that optimizes the global total power generation of the wind farm and the wind turbine yaw scheme that optimizes the local total power generation of the wind farm.

[0034] Determine whether the wind turbine yaw scheme has met the set convergence conditions;

[0035] If not satisfied, the fitness of the particle swarm is calculated by inserting an inertia factor.

[0036] If satisfied, the optimal yaw scheme for the total global power generation of the wind farm is the multi-wind turbine collaborative yaw optimization scheme.

[0037] In one embodiment of the present invention, the step of calculating the total power generation of the wind farm corresponding to the particle swarm by inserting an inertia factor, and using it as the fitness of the particle swarm, includes providing an inertia factor that varies with time to calculate the velocity of the particle swarm and update the position of the particle swarm; wherein the inertia factor, the velocity of the particle swarm, and the position of the particle swarm are: v i =w p v i+c1r d (pb i -x i )+c2r d (gb-x i (12)x i =x i +v i (13)

[0038] In the formula: wp—inertia factor; w1—initial inertia factor; w2—final inertia factor; Niter—total number of iterations; j—current iteration step; vi—velocity of the i-th particle, m / s; c1—individual learning factor; c2—social learning factor; pbi—local optimal position of the particle, m; gb—global optimal position of the particle, m; xi—particle position, m; rd—random number between 0 and 1.

[0039] In one embodiment of the present invention, after the step of calculating the total power generation of the wind farm corresponding to the particle swarm by inserting an inertia factor, the method further includes multiplying the total power generation of the wind farm by a penalty factor to obtain the optimized power generation of the wind farm based on the allowable fluctuation range of the wind turbine yaw angle, and using it as the fitness of the particles.

[0040] In one embodiment of the present invention, by setting the base coefficient of the penalty factor, the wind turbine yaw angle scheme corresponding to the total power generation of the wind farm is screened. In the multi-wind turbine collaborative yaw optimization scheme, the proportion of the total power generation factor of the wind farm is reduced, and the proportion of the wind turbine yaw angle fluctuation range factor is increased.

[0041] The beneficial effects of this invention are as follows: By setting a smoothness index, the yaw fluctuation range of the wind turbine under continuous changes in wind direction and speed is effectively limited, thereby ensuring the smoothness and stability of wind turbine operation. Simultaneously, based on a more accurate and robust yaw turbine dual-Gaussian wake model, an adaptive combined superposition model of yaw multi-turbine wakes is proposed. A particle swarm optimization algorithm is used for global collaborative optimization of wind turbine yaw, fully considering the mutual influence between wind turbines in the wind farm, significantly improving the overall power generation efficiency of the wind farm. Furthermore, by introducing a penalty factor mechanism, non-compliant yaw strategies are identified and eliminated, enhancing the robustness and reliability of the system. This not only optimizes the operation and management of wind farms but also provides strong technical support for achieving sustainable development and efficient operation of the offshore wind power industry.

[0042] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0044] Figure 1 is a flowchart of the multi-wind turbine cooperative yaw optimization method for offshore wind farms according to the present invention;

[0045] Figure 2 shows the power curve and thrust coefficient curve of the wind turbine in one embodiment of the present invention;

[0046] Figure 3 is a schematic diagram showing the positions of multiple fans in one embodiment of the present invention;

[0047] [Corrected according to Rule 91 20.08.2025] Figures 4A-4Y are schematic diagrams of the yaw angle range of multiple wind turbines after single-turbine yaw optimization in one embodiment of the present invention;

[0048] [Corrected according to Rule 91 20.08.2025] Figures 5A-5Y are schematic diagrams of the yaw angle range of multiple wind turbines after adopting the multi-wind turbine collaborative yaw optimization method in one embodiment of the present invention. Detailed Implementation

[0049] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features can be combined with each other. It should also be understood that the terminology used in the embodiments of the present invention is for describing specific implementation schemes and not for limiting the scope of protection of the present invention.

[0050] [Revised according to Article 91, August 2025] Please refer to Figures 1 to 5Y. It should be understood that the structures, proportions, sizes, etc., depicted in the accompanying drawings are merely for illustrative purposes to aid those skilled in the art and are not intended to limit the scope of the invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, without affecting the effectiveness or purpose of the invention, should still fall within the scope of the technical content disclosed in this invention. Furthermore, the terms used in this specification regarding position, quantity, etc., are merely for clarity and not intended to limit the scope of the invention. Changes or adjustments to these relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention's implementation.

[0051] Offshore wind farms refer to power generation sites located in ocean areas that utilize abundant offshore wind energy resources by centrally arranging multiple wind turbine generators to convert wind energy into electrical energy. Turbine yaw refers to a key operation in a wind power generation system, specifically the process of rotating the nacelle (including the rotor and other components) around the vertical centerline of the tower to adjust the rotor's orientation. Its purpose is to ensure the rotor is always aligned with the wind direction, maximizing wind energy capture and guaranteeing efficient, stable, and safe operation of the turbine. Wake effects, in the context of wind power generation, refer to the airflow region formed downstream of the turbine after the rotor has extracted energy from the airflow. This airflow, due to the obstruction and turbulence of the turbine blades, has a lower velocity compared to the upstream wind flow, and is accompanied by increased turbulence intensity and changes in wind direction and speed distribution. The presence of a wake not only affects the incoming wind conditions of downstream turbines, reducing the wind energy they can capture, but may also cause downstream turbines to bear more complex and variable loads, interfering with the coordinated operation and power generation efficiency of the entire wind farm.

[0052] In traditional single-environment wind turbine yaw optimization, only single-wind-condition optimization under different incoming wind directions and speeds is often considered. Directly applying this to offshore wind farms with variable wind conditions may lead to excessively frequent turbine yaw, thereby exacerbating the fatigue load on the turbine and seriously affecting the service life of the turbine structure. For example, when the wind speed increases from 5 m / s to 6 m / s, the yaw angle of a certain wind turbine may suddenly change from -30° to +30°; similarly, when the wind direction changes from north (N) to north-northwest (NNW), a similar sudden change in yaw angle may occur.

[0053] Please refer to Figure 1. This invention provides a method for optimizing the coordinated yaw of multiple wind turbines in an offshore wind farm, comprising:

[0054] Based on the given wind speed, wind direction and wind turbine parameters, establish a single-unit double Gaussian yaw wake model and a multi-unit yaw wake adaptive combination superposition model, and set it as the overall operating condition of the wind farm.

[0055] The relevant parameters of the particle swarm optimization algorithm are set according to the overall operating conditions, and the yaw scheme of the wind turbine is iterated and looped by the particle swarm optimization algorithm with the insertion of penalty factors.

[0056] The wind turbine yaw scheme that meets the convergence condition is output as a multi-wind turbine collaborative yaw optimization scheme.

[0057] Specifically, in this embodiment of the invention, wind speed, wind direction, and turbine parameters can be given based on meteorological measurements and known turbine data from the actual conditions of the wind farm. The single-unit dual-Gaussian yaw wake model can be modeled based on the given parameters and, combined with relevant wind tunnel experimental data and simulation results, constructs relevant formulas to more accurately describe the wake characteristics during turbine yaw. The multi-unit yaw wake adaptive combination superposition model combines the characteristics of geometric superposition models and square sum superposition models, allowing its weight coefficients to vary with spatial location to achieve accurate calculation of wake wind speed and ensure a smooth flow field transition. This establishes the overall operating conditions of the wind farm. By proposing a novel wind turbine yaw wake model and a multi-unit yaw wake adaptive combination superposition model, a foundation is provided for multi-turbine yaw optimization.

[0058] Furthermore, based on the set overall operating conditions, the relevant parameters of the Particle Swarm Optimization (PSO) algorithm are configured, such as the number of particles, iteration steps, inertia factor, individual learning factor, social learning factor, yaw angle limit range, and allowable fluctuation range. During the optimization process, the total power generation of each particle is calculated, and a penalty factor is introduced (for example, the penalty factor could be whether the wind turbine is in its optimal power generation condition; see Figure 2, which shows the relationship between the power curve and thrust coefficient curve of a wind turbine and the average wind speed; the actual wind speed can be changed by adjusting the wind turbine's yaw angle). This process performs secondary processing and filtering on the effectiveness data corresponding to the wind turbine yaw scheme. The total power generation data corresponding to the wind turbine yaw scheme can be multiplied by the penalty factor parameters to obtain corrected data considering the penalty factor's conditions. This allows for the acquisition of effective data under the penalty factor's conditions. In other words, the equipment operating cost corresponding to the penalty factor is compared with the power generation benefit of the selected wind turbine yaw angle for comparison and reference, leading to the comprehensive selection of a more optimized wind turbine yaw scheme.

[0059] More specifically, the particle swarm optimization algorithm is used to evaluate the merits of each particle (each particle corresponds to a set of wind turbine yaw angle schemes), and the particle velocity and position are iteratively updated by the particle swarm optimization algorithm (velocity corresponds to the adjustment direction and step size of the yaw angle, and position corresponds to the yaw angle scheme of each wind turbine in the wind farm) in order to find the optimal wind turbine yaw scheme.

[0060] Furthermore, please refer to Figure 2, which shows the power curve and thrust coefficient curve of a certain wind turbine model. The power curve represents the output power of the wind turbine at different wind speeds, with wind speed on the horizontal axis and power on the vertical axis. The thrust coefficient curve reflects the change in the thrust coefficient of the wind turbine at different wind speeds, with wind speed on the horizontal axis and thrust coefficient on the vertical axis. Through the power curve, we can understand the power generation capacity of the wind turbine at various wind speeds and determine its performance under different operating conditions. The thrust coefficient curve helps to analyze the wind turbine's wind capture and utilization efficiency, as well as the thrust experienced by the turbine blades at different wind speeds. This is of great significance for the layout and operation control of wind turbines in wind farms, especially when performing yaw optimization. It is necessary to consider the performance characteristics of the wind turbine at different wind speeds to ensure that the optimization strategy can effectively improve power generation efficiency and ensure the safe and stable operation of the wind turbine.

[0061] Thus, when the particle swarm optimization algorithm reaches its convergence condition (e.g., the maximum power generation achievable under non-ideal operating conditions represented by the penalty factor), the current wind turbine yaw scheme can be output as a multi-turbine collaborative yaw optimization scheme. This ensures that the yaw angle of the wind turbine changes smoothly with wind speed and direction, avoiding the wind turbine operating under non-ideal conditions represented by the penalty factor (e.g., mechanical wear and performance degradation caused by frequent large yaws to match wind speed), thereby maximizing power generation, extending the service life of the wind turbine structure, and improving the power generation efficiency and operational stability of the entire wind farm.

[0062] In one embodiment, the turbine parameters also include a smoothness index that limits the range of fluctuations in the turbine's yaw angle.

[0063] Specifically, a smoothness index is set to limit the fluctuation range of the wind turbine's yaw angle. During wind farm operation, the wind turbine needs to adjust its yaw angle according to changes in wind direction and speed to maintain optimal wind energy capture efficiency. However, if the yaw angle changes too drastically, it will not only increase the fatigue load on the wind turbine and shorten its structural lifespan, but may also negatively affect the stable operation of the entire wind farm. Therefore, by setting an allowable yaw angle fluctuation range, it is ensured that the wind turbine can smoothly transition when adjusting its yaw angle. Specifically, when the wind direction or wind speed changes, the wind turbine's yaw control system refers to the smoothness index to calculate a reasonable yaw angle adjustment range, avoiding abrupt changes in the yaw angle. For example, when the wind speed increases from 5 m / s to 6 m / s or the wind direction changes from north (N) to north-northwest (NNW), the smoothness index limits the wind turbine's yaw angle from -30° to +30°, thereby reducing the risk of mechanical wear and performance degradation.

[0064] Similarly, the smoothness index can be incorporated into the particle swarm optimization algorithm as a constraint. This ensures that during the iteration process, the particle swarm optimization algorithm considers not only the total power generation of the wind farm but also the yaw angle variation of each wind turbine, meeting the requirements of the smoothness index. In this way, the optimized wind turbine yaw scheme can extend the service life of the turbines while maintaining power generation efficiency, thereby improving the overall operational stability and long-term economic benefits of the wind farm.

[0065] In one embodiment, the step of iteratively iterating the wind turbine yaw scheme using a particle swarm optimization algorithm with an inserted penalty factor includes:

[0066] The initial yaw angle scheme of multiple wind turbines in the wind farm is generated by the particle swarm optimization algorithm, and the total power generation of the wind farm is calculated.

[0067] Calculate the penalty factor based on the multi-turbine yaw angle scheme of the wind farm;

[0068] The total power generation and penalty factor of multiple wind farm yaw angle schemes with multiple wind turbines are calculated through iterative loops.

[0069] Multiply the total power generation of the wind farm by the penalty factor to obtain the optimal power generation of the wind farm;

[0070] The optimal power generation of a wind farm is used to determine the corresponding yaw angle scheme for multiple wind turbines.

[0071] Specifically, in this embodiment of the invention, a set of random yaw angle schemes for multiple wind turbines in a wind farm is generated using a particle swarm optimization algorithm, where each particle represents a possible combination of wind turbine yaw angles. Based on given wind speed, wind direction, and turbine parameters, the established single-turbine double Gaussian wake model and the adaptive combination and superposition model of yaw multi-turbine wakes are used to simulate the wake characteristics of each wind turbine and their mutual influence under different yaw angles. By calculating the power generation of each wind turbine at a specific yaw angle and comprehensively considering the impact of the wake on the power generation efficiency of downstream wind turbines, the total power generation of the wind farm is obtained.

[0072] Furthermore, based on the generated multi-turbine yaw angle scheme for the wind farm, a corresponding penalty factor is calculated. Introducing this penalty factor ensures that changes in the yaw angle of the turbines meet the requirements of the smoothness index, preventing excessive wear and performance degradation of the turbine structure due to drastic fluctuations in yaw angle. Specifically, for each turbine, the number or extent to which its yaw angle exceeds the allowable fluctuation range under different operating conditions is counted. These data are then aggregated to calculate the penalty factor for the entire wind farm.

[0073] Similarly, the particle swarm optimization algorithm iteratively updates the position and velocity of particles, which correspond to the angle parameters of each wind turbine yaw scheme and the change in the yaw angle of the updated scheme, thereby generating multiple sets of different wind farm multi-turbine yaw angle schemes. In each iteration, the total power generation of the wind farm corresponding to each scheme and the penalty factor are recalculated. This process is repeated to gradually explore the optimal yaw angle scheme.

[0074] More specifically, the total power generation of the wind farm obtained in each iteration is multiplied by the corresponding penalty factor to obtain the optimized power generation considering the yaw angle limitation of the wind turbine. The optimized power generation not only reflects the power generation efficiency of the wind farm, but also comprehensively considers the smoothness of the yaw angle of the wind turbine, ensuring the feasibility and reliability of the optimization scheme in actual operation.

[0075] In this way, the optimal yaw angle scheme for multiple wind turbines in the wind farm is continuously tracked and updated during the iteration process. When the particle swarm optimization algorithm reaches the convergence condition, such as the number of iterations reaching a preset value or the change in optimized power generation stabilizing, that is, under the principle of maximizing optimized power generation, the wind turbine yaw angle scheme at this time is output. This can effectively limit the fluctuation range of wind turbine yaw angle while ensuring the power generation efficiency of the wind farm, extend the service life of the wind turbines, and improve the operational stability and economic benefits of the entire wind farm.

[0076] It should be noted that the particle swarm optimization algorithm is used to collaboratively optimize the yaw angle of multiple wind turbines in a wind farm. This takes into account the mutual influence between wind turbines in the entire wind farm. During the iteration process, the power generation of the wind farm is expressed as the total power generation after considering the wake multiplied by a penalty factor, making the iteration results more in line with actual operating requirements. Through collaborative optimization, the wind turbine yaw strategy that maximizes the power generation of the wind farm is found, thereby improving the power generation efficiency of the entire wind farm.

[0077] By introducing a penalty factor as the optimization objective in the iterative process, this mechanism enables the system to automatically eliminate unacceptable yaw strategies during optimization, thereby enhancing the system's robustness and reliability. In practical applications, even in the face of extreme weather conditions or wind turbine failures, the system can ensure the overall operational efficiency and stability of the wind farm through collaborative optimization and the penalty factor mechanism.

[0078] In one embodiment, the step of calculating the penalty factor based on the multi-turbine yaw angle scheme of a wind farm includes:

[0079] The yaw angle of each wind turbine is determined based on the yaw angle scheme of multiple wind turbines in a wind farm.

[0080] Determine whether the yaw angle of each wind turbine exceeds the fluctuation range of the wind turbine yaw angle;

[0081] If no wind turbine yaw angle exceeds the limit, output the base coefficient of the penalty factor;

[0082] If a wind turbine's yaw angle exceeds the range, calculate the proportion of each wind turbine whose yaw angle exceeds the range of yaw angle fluctuation, as well as the proportion of the number of wind turbines in the wind farm that exceed the range of yaw angle fluctuation.

[0083] Specifically, in this embodiment of the invention, firstly, based on the multi-turbine yaw angle scheme of the wind farm, the yaw angle of each wind turbine under specific operating conditions is determined, affecting the subsequent calculation of the penalty factor. Next, the yaw angle of each wind turbine is judged one by one to see if it exceeds the yaw angle fluctuation range. If the yaw angle exceeds the allowable range, it may lead to an increase in the fatigue load on the wind turbine, affecting its service life and operational stability. If no wind turbine's yaw angle exceeds the allowable range, the basic coefficient of the penalty factor is directly output. The basic coefficient can usually be set to 1, indicating that no additional penalty is applied to the total power generation if the fluctuation range is not exceeded; the total power generation at this time is the optimized power generation. If a wind turbine's yaw angle exceeds the allowable range, it is necessary to further calculate the proportion of each wind turbine's yaw angle exceeding the fluctuation range, as well as the proportion of the number of wind turbines in the wind farm exceeding the yaw angle fluctuation range.

[0084] More specifically, the penalty factor is calculated as follows: For a single wind turbine, the degree to which its yaw angle exceeds the allowable fluctuation range is calculated, i.e., the ratio of the exceeding angle to the maximum allowable fluctuation range. For example, if the allowable yaw angle fluctuation range for a wind turbine is ±30°, and the actual yaw angle is +35°, then the exceeding ratio is (35°-30°) / 30° = 5° / 30° ≈ 0.167. For multiple wind turbines in a wind farm: the number of wind turbines exceeding the yaw angle fluctuation range in the entire wind farm is counted, and then the proportion of these wind turbines to the total number of wind turbines in the entire wind farm is calculated. For example, if there are 25 wind turbines in a wind farm, and 5 of them exceed the yaw angle fluctuation range, then the proportion is 5 / 25 = 0.2. Then, the exceeding ratio of a single wind turbine and the proportion of the total number of wind turbines are combined to calculate the final penalty factor. The formula for calculating the penalty factor can be a simple product or a more complex function, depending on the design of the optimization algorithm. For example, the penalty factor could be the difference between the weighted average of the excess capacity of a single wind turbine and the ratio of the total number of wind turbines, and the base coefficient. Through the above steps, a reasonable penalty factor can be obtained, which can be used to evaluate and select different yaw angle schemes in the particle swarm optimization algorithm, ensuring that the final optimized scheme is both efficient and reliable.

[0085] In one embodiment, when a wind turbine's yaw angle exceeds the limit, the parameter of the output penalty factor is smaller than its base coefficient. The larger the proportion of each wind turbine's yaw angle exceeding the fluctuation range of the wind turbine's yaw angle, the smaller the parameter of the output penalty factor. The larger the proportion of wind turbines in the wind farm that exceed the fluctuation range of the wind turbine's yaw angle, the smaller the parameter of the output penalty factor.

[0086] Specifically, in this embodiment of the invention, the base coefficient of the penalty factor in the particle swarm optimization algorithm is typically set to 1, representing that under ideal conditions, all wind turbines' yaw angles do not exceed the allowable fluctuation range. In this case, the total power generation of the wind farm is not penalized and is directly used as the optimized power generation to evaluate the merits of the current yaw angle scheme. When the yaw angle of a wind turbine exceeds the allowable fluctuation range, the larger the excess ratio, the greater the impact on the penalty factor. Specifically, the excess ratio refers to the proportion of the difference between the actual yaw angle of the wind turbine and the maximum allowable fluctuation range angle to the maximum allowable fluctuation range angle. The larger the excess ratio, the more drastic the yaw angle adjustment of the wind turbine, which may lead to a higher risk of mechanical wear and performance degradation. Therefore, a larger penalty needs to be applied to the total power generation to reduce the likelihood of this scheme being selected during the optimization process. Besides the excess ratio of a single wind turbine, the proportion of wind turbines exceeding the allowable yaw angle in the wind farm is also a key factor affecting the penalty factor. The excess wind turbine proportion refers to the proportion of the number of wind turbines in the wind farm whose yaw angle exceeds the allowable range to the total number of wind turbines in the entire wind farm. The larger the proportion of turbines exceeding the allowable number, the more turbines in the wind farm will have yaw angles that do not meet smoothness requirements, resulting in a greater impact on overall operational stability and turbine lifespan. Therefore, when calculating the penalty factor, it is necessary to comprehensively consider the proportion of turbines exceeding the allowable number and impose a more severe penalty on the total power generation to ensure that the optimized scheme can meet the overall smoothness requirements of the wind farm.

[0087] More specifically, the calculation of the penalty factor is a comprehensive evaluation process that combines the excess yaw rate of a single wind turbine with the proportion of wind turbines in the wind farm. The specific calculation formula can be designed as: Penalty Factor = 1 / (1 + α × Excess Rate of a Single Wind Turbine + β × Proportion of Wind Turbines in the Wind Farm). Here, α and β are weighting coefficients used to adjust the influence of the excess yaw rate of a single wind turbine and the proportion of wind turbines in the wind farm on the penalty factor. The penalty factor calculated in this way can comprehensively reflect the smoothness of the multi-turbine yaw angle scheme in a wind farm. The smaller the penalty factor, the more wind turbines in the scheme exceed the allowable yaw angle, and the lower the optimized power generation, thus being filtered out in the particle swarm optimization algorithm; conversely, the closer the penalty factor is to the base coefficient 1, the more the scheme meets the smoothness requirements, the higher the optimized power generation, and the more likely it is to be selected as the final optimal scheme.

[0088] Thus, by inserting a penalty factor into the convergence condition of the particle swarm optimization algorithm, the algorithm can fully consider the smoothness of the wind turbine yaw angle while searching for the maximum power generation, avoid wind turbine damage caused by frequent large yaws, extend the service life of the wind turbine, and improve the operating efficiency and economic benefits of the wind farm.

[0089] In one embodiment, the convergence condition includes maximizing the total power generation of the wind farm within the allowable range of wind turbine yaw angle fluctuations.

[0090] Specifically, in this embodiment of the invention, the convergence condition set by the particle swarm optimization algorithm during the iterative optimization process is crucial for finding a wind turbine yaw scheme that satisfies the smoothness requirement and maximizes power generation. Specifically, the convergence condition includes maximizing the total power generation of the wind farm within the allowable range of wind turbine yaw angle fluctuations. This means that the optimization process not only pursues increased power generation but also ensures that changes in the wind turbine yaw angle meet the requirements of the smoothness index, avoiding wind turbine damage caused by frequent and large yaws.

[0091] During the iteration process, the particle swarm optimization algorithm continuously updates the position and velocity of the particles, with each particle representing a possible yaw angle scheme for the wind turbines. For each scheme, the total power generation of the wind farm is calculated using an established single-turbine dual-Gaussian wake model and a multi-turbine adaptive combined superposition model of yaw wakes. Simultaneously, a penalty factor is calculated based on whether the yaw angle of each wind turbine exceeds the allowable fluctuation range. The introduction of the penalty factor transforms the optimization objective into maximizing the optimized power generation of the wind farm while meeting smoothness requirements; that is, the product of the total power generation and the penalty factor.

[0092] When the iteration process finds that the optimized power generation of a certain yaw angle scheme reaches its maximum value, and this value is no longer exceeded in subsequent iterations, or the change in optimized power generation tends to stabilize with fluctuations less than a set threshold, the algorithm is considered to have reached the convergence condition. At this point, the corresponding yaw angle scheme is the optimal solution that satisfies the convergence condition, which can maximize the total power generation of the wind farm while ensuring smooth changes in the wind turbine yaw angle.

[0093] It should be noted that when determining whether the convergence condition is met, the overall operating status of the wind farm also needs to be considered. For example, the stability of the optimized power generation in several consecutive iterations should be statistically analyzed, and whether the yaw angle of the wind turbine remains within the allowable fluctuation range. Only when these conditions are met can the feasibility and effectiveness of the found optimal yaw angle scheme in actual wind farm operation be ensured, thereby achieving efficient and stable operation of the wind farm.

[0094] Thus, the proposed novel yaw turbine wake model, which combines accuracy and stability, provides effective support for the coordinated yaw optimization of wind turbines in wind farms. The proposed adaptive combination and superposition model of yaw multi-turbine wake fully leverages the advantages of each superposition model. Based on this, the proposed coordinated yaw optimization method for multi-turbine offshore wind farms optimizes turbine yaw by setting a smoothness index and using a particle swarm optimization algorithm to improve the power generation efficiency and fatigue load reduction of the entire wind farm.

[0095] In one embodiment, the yaw single-aircraft dual-Gaussian wake model is as follows: k * =0.11C t 1.07 I 0.2 (18) ε=0.23C t 0.25 I 0.17 (19) a = 0.93C t -0.75 I 0.17 (20)b=0.42C t 0.6 I 0.2 (21) c = 0.15C t -0.25 I -0.7 (twenty two)

[0096] In the formula, U ∞ Indicates free flow; ΔU represents the loss velocity at the downstream (x,y,z) position of the wind turbine with the turbine as the origin; D represents the turbine blade diameter; θ represents the turbine yaw angle; I represents the turbulence intensity; Ct represents the turbine thrust coefficient; α k and α m Take values ​​of 0.7 and 0.65 respectively.

[0097] Specifically, in this embodiment of the invention, based on numerical simulation and wind tunnel test results of the wake of a single yaw turbine, existing wake models of yaw turbines, including the Jensen model, Gaussian model, and BPA model, are compared and analyzed to explore the shortcomings of existing wake models in depth, and a more accurate and robust double Gaussian wake model of a yaw turbine is constructed. Formulas (1) to (9) constitute the double Gaussian wake model of a single yaw turbine, which is used to describe the wind speed loss distribution in the wake region of the turbine under yaw conditions. Among them, the symbols in the formulas, such as the free flow wind speed, the downstream position of the turbine, the turbine blade diameter, the yaw angle, the turbulence intensity, and the turbine thrust coefficient, are all key parameters affecting the wake wind speed loss. Through these formulas, the wake wind speed loss caused by turbine yaw at different locations can be calculated, providing a basis for subsequent multi-turbine wake superposition models and collaborative optimization methods.

[0098] In one embodiment, the adaptive combined superposition model of yaw multi-aircraft wake turbulence is: V = ωV SS +(1-ω)V GS (twenty three)

[0099] In the formula, V represents the wake velocity; V SS V represents the wake velocity obtained based on the sum of squares superposition model; GS The value represents the wake velocity obtained based on the geometric superposition model; x1 and x2 represent the x / D values ​​corresponding to a weight coefficient of 1 in the sum of squares superposition model and the geometric superposition model, respectively. For example, x1 and x2 can be set to 6 and 10 respectively to achieve optimal utilization of the two wake superposition models while ensuring a smooth transition of the flow field.

[0100] Specifically, in this embodiment of the invention, an adaptive combined superposition model is constructed for the sum-of-squares superposition model and the geometric superposition model, and its weight coefficients vary with spatial position. Formulas (10) and (11) are the calculation formulas of the yaw multi-aircraft wake adaptive combined superposition model, which are used to comprehensively consider the advantages of the sum-of-squares superposition model and the geometric superposition model, and adaptively combine the weight coefficients of the two models according to different spatial positions to achieve a more accurate calculation of wake wind speed.

[0101] In one embodiment, the execution steps of the particle swarm optimization algorithm include:

[0102] Based on the set parameters, the particle swarm is initialized, and the wind turbine yaw scheme corresponding to each particle in the particle swarm is generated.

[0103] The total power generation of the wind farm corresponding to the particle swarm is calculated by inserting an inertia factor, and this is used as the fitness of the particle swarm.

[0104] By comparing the current fitness of the particle swarm with the historical best, we update the wind turbine yaw scheme that optimizes the global total power generation of the wind farm and the wind turbine yaw scheme that optimizes the local total power generation of the wind farm.

[0105] Determine whether the wind turbine yaw scheme has met the set convergence conditions;

[0106] If not satisfied, the fitness of the particle swarm is calculated by inserting an inertia factor.

[0107] If satisfied, the optimal yaw scheme for the total global power generation of the wind farm is the multi-wind turbine collaborative yaw optimization scheme.

[0108] Specifically, in this embodiment of the invention, the relevant parameters of the particle swarm optimization algorithm may include, for example, the number of particles, the number of iterations, the inertia factor, the individual learning factor, the social learning factor, and the yaw angle limit range. By performing an initialization operation on the particle swarm, a wind turbine yaw scheme corresponding to each particle in the swarm is generated. Each particle represents a possible combination of wind turbine yaw angles. During initialization, the position and velocity of the particles are randomly generated within a specified range to ensure particle diversity and provide a good starting point for subsequent optimization searches. The inertia factor is used to balance the global and local aspects of the particle search. Initially, the inertia factor is relatively large, allowing particles to conduct a broad global search and avoiding premature entrapment in local optima. As iterations progress, the inertia factor gradually decreases, and particles tend to conduct finer searches within local regions, improving search accuracy. When calculating fitness, the impact of the wind turbine yaw angle on the wake and the overall power generation efficiency of the wind farm must be comprehensively considered. Each particle records its own best position (individual optimum), while the swarm also records the best position among all particles (global optimum).

[0109] Furthermore, in each iteration, the fitness of the current particle is compared with the fitness of the individual optimum and the global optimum to determine whether to update these optimal positions. If the current particle's fitness is better, the corresponding optimal position is updated to guide the particle closer to a better solution. Convergence conditions typically include reaching the maximum number of iterations, the fitness value stabilizing within a certain range, and maximizing the total power generation within the range of wind turbine yaw angle fluctuations. By checking whether these conditions are met, the algorithm determines whether to stop iterating. If not, the fitness of the particle swarm is calculated by inserting an inertia factor, and the above steps are repeated until the convergence conditions are met. This approach maximizes the total power generation of the wind farm while ensuring smooth changes in the wind turbine yaw angle, improving the operating efficiency and economic benefits of the wind farm, extending the service life of the wind turbine, and ensuring the stable operation of the wind farm.

[0110] Thus, an initial particle swarm is randomly generated based on set parameters, with each particle representing a set of wind turbine yaw angle schemes. The total power generation P of the wind farm corresponding to each particle is calculated, and a penalty factor is calculated based on the proportion of units whose yaw angles exceed the allowable fluctuation range. The total power generation P is multiplied by the penalty factor to obtain the corrected fitness P', which serves as the optimization objective function. The particle swarm velocity is adjusted by using a time-varying inertia factor to update the particle positions, ensuring that the yaw angles are within the preset limits. After updating, the particle fitness P' is recalculated, and compared with the current particle swarm, historical local optima, and global optima, updating the individual optimal positions and the global optimal positions. Finally, the algorithm is checked to see if it meets the convergence conditions (such as reaching the maximum number of iterations or the fitness change threshold). If it does not converge, the iteration optimization is repeated; if it converges, the loop is terminated, and the yaw angle scheme corresponding to the global optimal solution is output.

[0111] In one embodiment, the step of calculating the total power generation of the wind farm corresponding to the particle swarm by inserting an inertia factor, and using it as the fitness of the particle swarm, includes providing a time-varying inertia factor to calculate the velocity of the particle swarm and update the position of the particle swarm; wherein the inertia factor, the velocity of the particle swarm, and the position of the particle swarm are: v i =w p v i +c1r d (pb i -x i )+c2r d (gb-x i (25)x i =x i +v i (26)

[0112] In the formula: wp—inertia factor; w1—initial inertia factor; w2—final inertia factor; Niter—total number of iterations; j—current iteration step; vi—velocity of the i-th particle, m / s; c1—individual learning factor; c2—social learning factor; pbi—local optimal position of the particle, m; gb—global optimal position of the particle, m; xi—particle position, m; rd—random number between 0 and 1.

[0113] Specifically, in this embodiment of the invention, formulas (12) to (14) are key formulas used in the particle swarm optimization algorithm to calculate particle velocity and update position. Formula (12) gives the calculation method of the inertia factor wp, which changes with time. The initial inertia factor w1, the final inertia factor w2, the total number of iterations Niter, and the current iteration step j together determine the magnitude of the inertia factor, thereby affecting the motion trend of the particle in the search space. Formulas (13) and (14) calculate the particle velocity and update the particle position, respectively, involving parameters such as individual learning factor c1, social learning factor c2, particle local optimal position pbi, particle global optimal position gb, and random number rd. These parameters work together to enable the particle to explore new solution spaces and gradually converge to a better solution during the search process, which is used to optimize the yaw angle of wind turbines in wind farms to achieve the goals of maximizing power generation efficiency and smoothing yaw angle changes. During the iteration process, the inertia factor of the particle swarm optimization algorithm changes with time to balance the ability of global search and local search, thereby improving optimization efficiency and accuracy.

[0114] In one embodiment, after calculating the total power generation of the wind farm corresponding to the particle swarm by inserting an inertia factor, the method further includes multiplying the total power generation of the wind farm by a penalty factor to obtain the optimized power generation of the wind farm based on the allowable fluctuation range of the wind turbine yaw angle, and using it as the fitness of the particles.

[0115] Specifically, in this embodiment of the invention, the penalty factor is introduced to ensure that the change in the wind turbine yaw angle meets the requirements of the smoothness index, avoiding damage to the wind turbine caused by frequent and large yaws. After calculating the total power generation of the wind farm, the smoothness of the wind turbine yaw angle needs to be further considered. By multiplying the total power generation by the penalty factor, the optimized power generation is obtained. This optimized power generation not only reflects the power generation efficiency of the wind farm, but also comprehensively considers the fluctuation range of the wind turbine yaw angle, ensuring the feasibility and reliability of the optimization scheme in actual operation.

[0116] Thus, the optimized power generation with a penalty factor calculated is used as the particle's fitness to evaluate its performance. In particle swarm optimization, particles with higher fitness values ​​are more likely to be selected as the new individual or global optimum. This approach allows for the search for maximum power generation while simultaneously considering the smoothness of the wind turbine's yaw angle, avoiding damage caused by frequent large yaws, extending turbine lifespan, and improving the wind farm's operational efficiency and economic benefits.

[0117] In one embodiment, by setting a base coefficient for the penalty factor, the wind turbine yaw angle scheme corresponding to the total power generation of the wind farm is screened. In the multi-wind turbine collaborative yaw optimization scheme, the proportion of the total power generation factor of the wind farm is reduced, and the proportion of the wind turbine yaw angle fluctuation range factor is increased.

[0118] Specifically, in this embodiment of the invention, the base coefficient of the penalty factor is typically set to 1, representing the ideal situation, i.e., the penalty factor value when the yaw angles of all wind turbines do not exceed the allowable fluctuation range. In this case, the total power generation of the wind farm is not penalized and is directly used as the optimized power generation to evaluate the merits of the current yaw angle scheme. By setting the base coefficient of the penalty factor, the optimization objective can be adjusted in the multi-wind turbine coordinated yaw optimization scheme.

[0119] More specifically, reducing the weight of the total power generation factor in the wind farm means that the optimization process no longer simply pursues maximum power generation, but focuses more on the smoothness of the turbine yaw angle. Similarly, for wind turbines with different models and characteristics, the values ​​of the foundation coefficients can be adjusted accordingly, for example, adjusting the parameter values ​​of the foundation coefficients based on the acceptable fluctuation range of the turbine yaw angle. At the same time, increasing the weight of the turbine yaw angle fluctuation range factor ensures that the optimized scheme can reduce the mechanical wear of the turbines in actual operation, extend the service life of the turbines, and improve the operational stability and economic benefits of the wind farm.

[0120] [Corrected according to Rule 91, August 2025] Please refer to Figures 3 to 5Y. In one embodiment, a certain wind turbine model has an impeller diameter of 240.55m, a hub height of 143.8m, a rated power of 12MW, a cut-in wind speed of 3m / s, and a cut-out wind speed of 25m / s. The wind farm has 25 wind turbines arranged as shown in Figure 3. The spacing between the turbines is relatively long along the east-west direction and relatively short along the north-south direction. Therefore, four operating wind directions are defined, and for each wind direction, five wind speeds are taken: 5, 10, 15, 20, and 25 m / s, to account for the smooth fluctuation of the turbine yaw angle with wind speed. Furthermore, coordinated yaw optimization is performed for all wind directions. The optimization results for a single wind turbine are shown in Figures 4A-4Y. This optimization result limits the maximum yaw fluctuation range to 30°, and the wind speed spacing is set to 5m / s, resulting in a relatively large fluctuation in the obtained yaw result.

[0121] Please refer to Figure 3. Each point marked on the coordinate system represents the location of a wind turbine, arranged according to their actual layout within the wind farm. This visually presents the wind farm's layout structure and helps in understanding the relative positions and spacing between the turbines. When performing multi-turbine yaw collaborative optimization, the positional relationship between turbines affects the wake effect, varying with the distance and relative position between them. By clearly defining the turbine positions, a more accurate wake model can be established, allowing for analysis of the mutual influences between turbines. This, in turn, enables the development of reasonable collaborative optimization strategies, improving the overall power generation efficiency and operational stability of the wind farm.

[0122] [Corrected according to Rule 91, August 2025] Please refer to Figures 4A-4Y, which show the yaw results of a single wind turbine. The yaw angle varies under different wind speeds and directions. The maximum yaw fluctuation range is 30°, and the wind speed interval is taken as 5 m / s, resulting in significant fluctuations in the yaw results. This reflects the yaw effect of a single wind turbine under different operating conditions. Although it can improve the power generation efficiency of the wind turbine to some extent, the large fluctuations in yaw angle, due to the lack of consideration for the mutual influence between multiple wind turbines and the continuity of wind speed and direction changes, may exacerbate the fatigue load on the wind turbine and affect its service life.

[0123] [Corrected according to Rule 91, August 2025] Please refer to Figures 5A-5Y, which show the results of the multi-turbine yaw collaborative optimization method. The yaw angle changes of multiple turbines under different wind speeds and directions are shown. The effect of the collaborative yaw optimization is significant; the yaw angle changes smoothly with wind speed and direction, with minimal fluctuations. This intuitively demonstrates the practical application effect of the collaborative optimization method, proving that it can effectively solve the problems existing in single-turbine yaw optimization. By considering the mutual influence between turbines in the entire wind farm, as well as the continuity of wind speed and direction changes, smooth adjustment of turbine yaw angles is achieved. This not only reduces the fatigue load on the turbines and extends the service life of the turbine structure, but also has important guiding significance for the actual operation and management of offshore wind farms. It can help wind farm operators formulate more reasonable control strategies and achieve a dual improvement in economic benefits and equipment reliability.

[0124] [Corrected according to Rule 91, August 2025] Thus, by employing the multi-turbine yaw collaborative optimization method in this embodiment of the invention to optimize the overall yaw of wind turbines across all wind directions and speeds, the execution results of the optimized wind turbine yaw angle scheme are shown in Figures 5A-5Y. Overall, the effect of wind turbine collaborative yaw optimization is significant. The wind turbine yaw angle changes smoothly with wind speed and direction, with minimal fluctuations, which can reduce the fatigue load borne by the wind turbine to a certain extent.

[0125] In summary, the multi-turbine coordinated yaw optimization method for offshore wind farms provided by this invention not only focuses on the yaw control of individual turbines but also considers the coordinated optimization of the entire wind farm. By monitoring and analyzing the operating status and environmental conditions of each turbine in the wind farm in real time, it enables refined management of the wind farm, improves its operational efficiency and economic benefits, reduces maintenance costs, and provides a strong guarantee for the long-term stable operation of the wind farm. Furthermore, compared with existing methods, it has significant advantages in turbine operation smoothness, wind farm power generation efficiency, system robustness and reliability, and wind farm operation management. These advantages will contribute to promoting the sustainable development and progress of the offshore wind power industry.

[0126] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for optimizing the coordinated yaw of multiple wind turbines in an offshore wind farm, characterized in that, include: Based on the given wind speed, wind direction and wind turbine parameters, establish a single-unit double Gaussian yaw wake model and a multi-unit yaw wake adaptive combination superposition model, and set it as the overall operating condition of the wind farm. The relevant parameters of the particle swarm optimization algorithm are set according to the overall operating conditions, and the yaw scheme of the wind turbine is iterated and looped by the particle swarm optimization algorithm with the insertion of penalty factors. The wind turbine yaw scheme that meets the convergence condition is output as a multi-wind turbine collaborative yaw optimization scheme.

2. The multi-wind turbine coordinated yaw optimization method according to claim 1, characterized in that, The wind turbine parameters also include a smoothness index that limits the range of fluctuation in the wind turbine's yaw angle.

3. The multi-wind turbine cooperative yaw optimization method according to claim 1, characterized in that, The steps of iteratively iterating the wind turbine yaw scheme using a particle swarm optimization algorithm with an inserted penalty factor include: The initial yaw angle scheme of the wind farm multi-turbine is generated by the particle swarm optimization algorithm, and the total power generation of the wind farm is calculated. Calculate the penalty factor based on the multi-turbine yaw angle scheme of the wind farm; The total power generation and penalty factor of multiple wind farm yaw angle schemes with multiple wind turbines are calculated through iterative loops. Multiply the total power generation of the wind farm by the penalty factor to obtain the optimal power generation of the wind farm; The optimal power generation of a wind farm is used to determine the corresponding yaw angle scheme for multiple wind turbines.

4. The multi-wind turbine cooperative yaw optimization method according to claim 3, characterized in that, The steps for calculating the penalty factor based on the multi-turbine yaw angle scheme of a wind farm include: The yaw angle of each wind turbine is determined based on the yaw angle scheme of multiple wind turbines in a wind farm. Determine whether the yaw angle of each wind turbine exceeds the fluctuation range of the wind turbine yaw angle; If no wind turbine yaw angle exceeds the limit, output the base coefficient of the penalty factor; If a wind turbine's yaw angle exceeds the range, calculate the proportion of each wind turbine whose yaw angle exceeds the range of yaw angle fluctuation, as well as the proportion of the number of wind turbines in the wind farm that exceed the range of yaw angle fluctuation.

5. The multi-wind turbine cooperative yaw optimization method according to claim 4, characterized in that, When a wind turbine's yaw angle exceeds the limit, the output penalty factor parameter is smaller than its base coefficient. The larger the proportion of wind turbines whose yaw angle exceeds the fluctuation range, the smaller the output penalty factor parameter. The larger the proportion of wind turbines in the wind farm whose yaw angle exceeds the fluctuation range, the smaller the output penalty factor parameter.

6. The multi-wind turbine cooperative yaw optimization method according to claim 1, characterized in that, The convergence condition includes maximizing the total power generation of the wind farm within the allowable range of wind turbine yaw angle fluctuations.

7. The multi-wind turbine cooperative yaw optimization method according to claim 1, characterized in that, The yaw single-aircraft dual-Gaussian wake model is as follows: k * =0.11C t 1.07 I 0.2 (5) ε=0.23C t 0.25 I 0.17 (6) a=0.93C t -0.75 I 0.17 (7) b=0.42C t 0.6 I 0.2 (8) c=0.15C t -0.25 I -0.7 (9) In the formula, U ∞ Indicates free flow; ΔU represents the loss velocity at the downstream (x,y,z) position of the wind turbine with the turbine as the origin; D represents the turbine blade diameter; θ represents the turbine yaw angle; I represents the turbulence intensity; Ct represents the turbine thrust coefficient; α k and α m Take values ​​of 0.7 and 0.65 respectively.

8. The multi-wind turbine cooperative yaw optimization method according to claim 1, characterized in that, The adaptive combined superposition model of yaw multi-aircraft wake turbulence is as follows: V=ωV SS +(1-ω)V GS (10) In the formula, V represents the wake velocity; V SS V represents the wake velocity obtained based on the sum of squares superposition model; GS x represents the wake wind speed obtained based on the geometric superposition model; x1 and x2 represent the x / D values ​​corresponding to a weight coefficient of 1 in the sum of squares superposition model and the geometric superposition model, respectively.

9. The multi-wind turbine cooperative yaw optimization method according to claim 1, characterized in that, The execution steps of the particle swarm optimization algorithm include: Based on the set parameters, the particle swarm is initialized, and the wind turbine yaw scheme corresponding to each particle in the particle swarm is generated. The total power generation of the wind farm corresponding to the particle swarm is calculated by inserting an inertia factor, and this is used as the fitness of the particle swarm. By comparing the current fitness of the particle swarm with the historical best, we update the wind turbine yaw scheme that optimizes the global total power generation of the wind farm and the wind turbine yaw scheme that optimizes the local total power generation of the wind farm. Determine whether the wind turbine yaw scheme has met the set convergence conditions; If not satisfied, the fitness of the particle swarm is calculated by inserting an inertia factor. If satisfied, the optimal yaw scheme for the total global power generation of the wind farm is the multi-wind turbine collaborative yaw optimization scheme.

10. The multi-wind turbine cooperative yaw optimization method according to claim 9, characterized in that, The step of calculating the total power generation of the wind farm corresponding to the particle swarm by inserting an inertia factor, and using this as the fitness of the particle swarm, includes providing a time-varying inertia factor to calculate the velocity of the particle swarm and updating the position of the particle swarm; wherein the inertia factor, the velocity of the particle swarm, and the position of the particle swarm are: v i =w p v i +c1r d (pb i -x i )+c2r d (gb-x i ) (12) x i =x i +v i (13) In the formula: wp—inertia factor; w1—initial inertia factor; w2—final inertia factor; Niter—total number of iterations; j—current iteration step; vi—velocity of the i-th particle, m / s; c1—individual learning factor; c2—social learning factor; pbi—local optimal position of the particle, m; gb—global optimal position of the particle, m; xi—particle position, m; rd—random number between 0 and 1.

11. The multi-wind turbine cooperative yaw optimization method according to claim 9, characterized in that, After calculating the total power generation of the wind farm corresponding to the particle swarm by inserting an inertia factor, the method further includes multiplying the total power generation of the wind farm by a penalty factor to obtain the optimized power generation of the wind farm based on the allowable fluctuation range of the wind turbine yaw angle, and using it as the fitness of the particles.

12. The multi-wind turbine cooperative yaw optimization method according to claim 5, characterized in that, By setting a base coefficient for the penalty factor, the wind turbine yaw angle scheme corresponding to the total power generation of the wind farm is screened. In the multi-wind turbine collaborative yaw optimization scheme, the proportion of the total power generation factor of the wind farm is reduced, and the proportion of the wind turbine yaw angle fluctuation range factor is increased.

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