ADMM distributed yaw control method and system fusing three-dimensional analysis wake flow model and dynamic grouping, computer and storage medium

Abstract

The invention discloses an ADMM distributed yaw control method and system fusing a three-dimensional analysis wake flow model and dynamic grouping, a computer and a storage medium. The method comprises the following steps: extracting a wind power plant layout and wind turbine generator parameters; constructing wind speed and wind direction vectors by taking the wind speed as a module value and the wind direction as an angle, calculating a coordinate component of the wind speed and wind direction vector of each wind generator set in a Cartesian coordinate system, and calculating the wind speed and wind direction of main inflow of the wind power plant; a wake flow weight coefficient is calculated based on the wake flow superposition area and the distance between the wind turbine generators, and a wake flow weight coefficient matrix is constructed; based on the three-dimensional analysis wake flow model and the wake flow superposition model, the equivalent wind speed of each wind turbine generator is calculated, and then the power of the wind turbine generator is calculated; grouping all the wind turbine generators in the wind power plant in combination with the weight coefficient matrix; and on the basis of an ADMM optimization method, the yaw angle of the wind turbine generator in each group is optimized by taking maximum power as a target. The optimization efficiency is improved while the power generation benefit of the wind power plant is improved.

Description

Technical Field

[0001] This invention pertains to wind farm yaw optimization control, specifically involving an ADMM distributed yaw control method, system, computer, and storage medium that integrates a three-dimensional analytical wake model and dynamic grouping. Background Technology

[0002] As wind farms and wind turbines grow larger, the wake effects between turbines within the farm become more pronounced. To reduce wake effects and improve power output, it's necessary to deflect the wake of upstream turbines to minimize their impact on downstream turbines. Large wind farms require real-time yaw control, rendering traditional centralized control methods unsuitable. To improve power generation efficiency while reducing yaw angle optimization time, large wind farms need to be grouped for distributed yaw control. Summary of the Invention

[0003] The purpose of this invention is to provide an ADMM distributed yaw control method, system, computer, and storage medium that integrates a three-dimensional analytical wake model and dynamic grouping.

[0004] The technical solution to achieve the purpose of this invention is: an ADMM distributed yaw control method that integrates a three-dimensional analytical wake model and dynamic grouping, comprising the following steps:

[0005] Step 1, Data Acquisition and Parameter Extraction: Use the wind farm SCADA system to collect wind speed, wind direction, power and yaw position data for each wind turbine, and extract the layout information of the wind farm in the Cartesian coordinate system, as well as the rotor diameter, hub height and standard power curve of each wind turbine.

[0006] Step 2, Determination of Main Inflow Wind Speed ​​and Direction and Coordinate System Rotation: Establish a Cartesian coordinate system with due north as 0°. Construct wind speed and direction vectors for each wind turbine with wind speed as the modulus and wind direction as the angle, and calculate its coordinate components in the Cartesian coordinate system. Superimpose the coordinate components of all wind turbines and arithmetically average them to calculate the wind speed and direction of the main inflow at the sampling time. Rotate the Cartesian coordinate system with the main inflow wind direction as the positive x-axis to obtain the coordinates of each wind turbine in the new coordinate system, and determine the upstream and downstream order of the wind turbines based on the x-coordinate values ​​from smallest to largest.

[0007] Step 3, Construction of wake influence weight matrix: Based on the wake deflection model and wake diameter, calculate the influence area of ​​the wake of the upstream wind turbine at the rotor plane of the downstream wind turbine in the order of upstream and downstream; combine the straight distance between the upstream and downstream wind turbines to calculate the weight coefficients and construct the weight coefficient matrix of the entire wind farm.

[0008] Step 4, Equivalent wind speed calculation: Based on the upstream and downstream sequence, calculate the wake loss velocity caused by the upstream wind turbine based on the three-dimensional asymmetric double Gaussian analytical wake model; calculate the equivalent wind speed at the rotor plane of the downstream wind turbine based on the influence area and wake superposition model.

[0009] Step 5, Dynamic Grouping and Shared Node Identification: Define each wind turbine as a node and construct a directed graph using the weight coefficient matrix as the edge feature; group all nodes based on a dynamically adjusted group detection algorithm; for nodes whose edge features exceed a preset threshold and do not belong to the same group, copy them to each other's group and mark these nodes as shared nodes;

[0010] Step 6, Distributed Yaw Optimization Solution: Based on the Alternating Direction Multiplier Method (ADMM), with the goal of maximizing the total power of the wind farm, yaw angle optimization is performed independently for each group. A penalty term is introduced into the optimization model to constrain the yaw angle of shared nodes to remain consistent across different groups, and the yaw angle optimization range of all wind turbines is limited to a certain range. The optimal yaw angle for each wind turbine is obtained through iterative solution.

[0011] An ADMM distributed yaw control system integrating a three-dimensional analytical wake model and dynamic grouping includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the ADMM distributed yaw control method integrating a three-dimensional analytical wake model and dynamic grouping.

[0012] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the ADMM distributed yaw control method that integrates a three-dimensional analytical wake model and dynamic grouping.

[0013] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the ADMM distributed yaw control method that integrates a 3D analytical wake model and dynamic grouping.

[0014] The significant advantages of this invention compared to the prior art are as follows:

[0015] 1) Based on the influence area of ​​the wake of the upstream wind turbine at the rotor plane of the downstream wind turbine and the straight distance between them, calculate the weight coefficient between the two wind turbines, construct the weight coefficient matrix of the entire wind farm, and then realize the dynamic grouping of the entire wind farm based on the proposed group detection algorithm.

[0016] 2) The equivalent wind speed of wind turbines is calculated based on the three-dimensional analytical wake model 3DADG. Considering the effects of wake deflection and wake superposition, a power prediction model is built. The model takes into account the vertical difference between wind turbines and can predict the power of wind farms with height differences between wind turbines.

[0017] 3) Based on the dynamic grouping results of the wind farm, combined with the Alternating Directional Multiplier Method (ADMM), considering the constraints between different groups, the distributed optimization of the entire wind farm is realized, which greatly reduces the time spent on the optimization process while ensuring the maximum improvement of wind farm power. Attached Figure Description

[0018] Figure 1 A flowchart of the ADMM distributed yaw control method that integrates a three-dimensional analytical wake model and dynamic grouping.

[0019] Figure 2 This is a layout diagram of wind turbine units in a wind farm according to an embodiment of the present invention.

[0020] Figure 3 This is a diagram showing the grouping results of wind turbine units within a wind farm according to an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0022] like Figure 1 As shown, an ADMM distributed yaw control method integrating a three-dimensional analytical wake model and dynamic grouping is presented, with the following specific steps:

[0023] Step 1: Use the wind farm's SCADA system to collect wind speed, wind direction, power, and yaw position data for each wind turbine, and extract the wind farm's layout information in a Cartesian coordinate system, as well as the rotor diameter, hub height, and standard power curves for each wind turbine. The specific method is as follows:

[0024] The SCADA system was used to collect average data of the wind turbine every 10 minutes, including sampling time, wind speed, wind direction, and yaw position.

[0025] A Cartesian coordinate system is established with a specific wind turbine as the origin, the east direction as the positive x-axis, and the north direction as the positive y-axis. The coordinate position of each wind turbine in the Cartesian coordinate system is then extracted.

[0026] Based on the model of the wind turbine, extract the rotor diameter, hub height, rated power, and corresponding standard power curve for that model.

[0027] Step 2: Establish a Cartesian coordinate system with due north as 0°. Construct wind speed and wind direction vectors for each wind turbine, using wind speed as the modulus and wind direction as the angle. Calculate its coordinate components in this Cartesian coordinate system. Superimpose the coordinate components of all wind turbines and take an arithmetic mean to calculate the wind speed and direction of the main inflow at the sampling time. Rotate the Cartesian coordinate system with the main inflow wind direction as the positive x-axis to obtain the coordinates of each wind turbine in the new coordinate system. Determine the upstream and downstream order of the wind turbines based on the x-coordinate values ​​from smallest to largest. The specific method is as follows:

[0028] Establish a Cartesian coordinate system with true north as 0°. Construct the wind speed and wind direction vectors for each wind turbine, using wind speed as the modulus and wind direction as the angle. Calculate its coordinate components in this Cartesian coordinate system. The specific formula is as follows:

[0029] ;

[0030] Where i is the wind turbine index. Let i be the wind speed of wind turbine unit i. The wind direction is for wind turbine i. Let be the coordinate components of the wind speed and direction vector of wind turbine i in the Cartesian coordinate system;

[0031] By superimposing the coordinate components of all wind turbines and taking the arithmetic mean, the wind speed and direction of the main inflow at the sampling time are calculated. The specific formula is as follows:

[0032] ;

[0033] ;

[0034] ;

[0035] ;

[0036] in, These are the coordinate components of the wind speed and direction vector of the main inflow into the wind farm. It refers to the number of wind turbines in the wind farm. It is the wind speed of the main inflow into the wind farm. The wind direction of the main inflow to the wind farm before the angle was changed. It is the direction of the main inflow wind to the wind farm after the angle is changed;

[0037] Rotate the Cartesian coordinate system with the main inflow wind direction as the positive x-axis to obtain the coordinates of each wind turbine in the new coordinate system. The specific formula is as follows:

[0038] ,

[0039] in, and These are the initial x and y coordinates of wind turbine i, respectively. and These are the x and y coordinates of wind turbine i in the new rectangular coordinate system;

[0040] The upstream and downstream sequence of wind turbine units is determined based on the x-coordinate values ​​in the new coordinate system, from smallest to largest.

[0041] Step 3: Based on the wake deflection model and wake diameter, calculate the influence area of ​​the upstream wind turbine's wake on the rotor plane of the downstream wind turbine in the order of upstream and downstream. Combined with the straight-line distance between the upstream and downstream wind turbines, calculate the weighting coefficients and construct the weighting coefficient matrix for the entire wind farm. The specific method is as follows:

[0042] Based on the wake deflection model and wake diameter, the influence area of ​​the wake of the upstream wind turbine j on the rotor plane of the downstream wind turbine i is calculated sequentially according to the upstream and downstream order of the wind turbines. The specific formula is as follows:

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] ;

[0048] Where j represents the upstream wind turbine and i represents the downstream wind turbine. and These are the x-coordinates of wind turbine units j and i in the new Cartesian coordinate system, respectively. and These are the y-coordinates of wind turbine units j and i in the new Cartesian coordinate system. It is the difference in the x-coordinates of wind turbine units j and i. It is the difference between the y-coordinates of wind turbine units j and i. It is the wake expansion coefficient of wind turbine j. It is the thrust coefficient of wind turbine j. It is the rotor diameter of wind turbine j. It is the wake diameter of wind turbine j at wind turbine i. It is the yaw angle of wind turbine j. It is the initial wake deflection angle of wind turbine j. These are the undetermined parameters for the wake deflection model of wind turbine j. It is the distance from the wake deflection centerline of wind turbine j;

[0049] Based on the positional relationship between the wake of upstream wind turbine j and downstream wind turbine i, the influence area of ​​the wake of upstream wind turbine j at the rotor plane of downstream wind turbine i is calculated in three cases:

[0050] (1) If ,but ;

[0051] (2) If or ,but ;

[0052] (3) If or or ,but The calculation formula is as follows:

[0053] ; ;

[0054] ;

[0055] ;

[0056] ;

[0057] in, It is the rotor diameter of wind turbine i. and These are the hub heights of wind turbine units j and i, respectively. It is the height difference at the hub of wind turbine units j and i. It is the wake radius of wind turbine j at wind turbine i. It is the rotor radius of wind turbine i. It is the projection of the rotor radius of wind turbine i onto the wind direction perpendicular to the main inflow of the wind farm. It is the straight-line distance between the hubs of wind turbine units j and i. It is the area of ​​influence of the wake of the upstream wind turbine j at the rotor plane of the downstream wind turbine i;

[0058] Based on the straight-line distance between upstream and downstream wind turbines, weighting coefficients are calculated, and a weighting coefficient matrix for the entire wind farm is constructed. The specific formula is as follows:

[0059] ;

[0060] ;

[0061] ;

[0062] in, It is the projected area of ​​wind turbine i in the wind direction perpendicular to the main inflow of the wind farm. It is the yaw angle of wind turbine i. It is the weighting coefficient of wind turbine j with respect to i. It is the weighting coefficient matrix of the wind farm.

[0063] Step 4: Based on the upstream and downstream sequence, calculate the wake loss velocity caused by the upstream wind turbine using a three-dimensional asymmetric double Gaussian analytical wake model; calculate the equivalent wind speed at the rotor plane of the downstream wind turbine using an influence area and wake superposition model. The specific method is as follows:

[0064] Based on the upstream and downstream sequence of the wind turbines, the wake loss velocity at the downstream wind turbine i is calculated using a three-dimensional asymmetric double Gaussian analytical wake model. The specific formula is as follows:

[0065] ;

[0066] Where x is the axial distance from the current wind turbine, y is the lateral distance from the current wind turbine, and z is the longitudinal distance from the current wind turbine. This is the current inflow wind speed of the wind turbine. It is the wake velocity deficit normalization coefficient related to the axial distance x and the lateral distance y. and The standard deviations of the Gaussian distribution correspond to the right side (blade downwards) and left side (blade upwards) of the wake cross-section plane, respectively. It is the standard deviation of the Gaussian distribution in the vertical direction. It is the vertical gap between the minimum wake velocity and the center of the wake;

[0067] Based on the superimposed area matrix and the wake superposition model, the equivalent wind speed at the i-rotor plane of the downstream wind turbine is calculated using the following formula:

[0068] ;

[0069] ;

[0070] in, It is the wind speed of the main inflow into the wind farm. This refers to the number of upstream wind turbines that have a wake effect on wind turbine i. It is the wake loss velocity of wind turbine i under the influence of the wake of wind turbine j. It is the wind speed of wind turbine j. It is the ratio of the area of ​​influence of the wake of upstream wind turbine j at the rotor plane of downstream wind turbine i to the projected area of ​​the rotor plane of wind turbine i in the wind direction perpendicular to the main inflow of the wind farm. It is the equivalent wind speed at the wind turbine rotor plane.

[0071] Step 5: Define each wind turbine as a node and construct a directed graph using the weight coefficient matrix as edge features; group all nodes based on a dynamically adjusted group detection algorithm; for nodes whose edge features exceed a preset threshold and do not belong to the same group, copy them to each other's groups and mark these nodes as shared nodes. The specific method is as follows:

[0072] Each wind turbine is defined as a node, and the weights in the weighting matrix W are used to define the nodes. Construct a directed graph G and its transpose graph based on the edge features from node j to node i. ;

[0073] Initialize each node as an independent group; traverse the edge features of each node in the directed graph G with other nodes, and calculate the edge feature threshold of the current node i. The fit of the node to which it belongs If the maximum fit exceeds the fit threshold If the current node is moved to the group with the highest fit, the specific formula for calculating the fit is:

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] in, Let be the edge characteristics between node j (wind turbine j) and node i (wind turbine i) in the directed graph G. The edge characteristics in the directed graph G with node i (wind turbine i) are... It represents the goodness of fit with the community of nodes whose edge features exceed the edge feature threshold of the current node i. The value is the sum of the edge features of the current node i within the community. It is the threshold of the edge features. It is the fit threshold.

[0079] After the initial partitioning is completed, based on the directed transpose graph Iterate through the edge features of each node, and for each edge whose feature is greater than the edge feature threshold,... For nodes that are not in the same group, add the current node to the group that the current node belongs to, and mark the current node as a shared node.

[0080] Step 6: Based on the Alternating Direction Multiplier Method (ADMM), with the goal of maximizing the total power of the wind farm, yaw angle optimization is performed independently for each group. A penalty term is introduced into the optimization model to constrain the yaw angle of shared nodes to remain consistent across different groups, and the yaw angle optimization range of all wind turbines is limited to a certain range. The optimal yaw angle for each wind turbine is obtained through iterative solution. The specific method is as follows:

[0081] The output power of each wind turbine in yaw mode is defined by the following formula:

[0082] ;

[0083] An optimization function is constructed with the objective of maximizing the total power of the wind farm. The specific formula is as follows:

[0084] ;

[0085] in, It is air density. It is the planar area of ​​the wind turbine i. It is the equivalent wind speed at the rotor plane of wind turbine i. This is the power coefficient of wind turbine unit i. It is the yaw angle of wind turbine i. These are all the groups into which wind turbine units are classified. It is one of the groups. Group 'output power' Group The optimization function, and These are the yaw angle and equivalent wind speed of the wind turbines within the group;

[0086] The ADMM optimization algorithm is used to perform distributed optimization for each group. A penalty factor is introduced to constrain the yaw angle of shared nodes in different groups, and the range of the yaw angle is limited to ±30°. The specific formula is as follows:

[0087] ;

[0088] ;

[0089] in, It is a penalty factor constraint for shared nodes. It is a group that shares nodes with the current group. It is one of the groups that have shared nodes. It is a shared node between the two groups. It is one of the shared nodes. It refers to the yaw angle of wind turbines within a specific group. It is the yaw angle of the shared node. yes A copy in the global variable;

[0090] The optimization problem described above can be defined in the form of an augmented Lagrange function, with the specific formula as follows:

[0091] ;

[0092] in, It is a penalty parameter. It is a dual variable. It is a global variable;

[0093] The ADMM algorithm is solved iteratively by updating the Lagrange function variable in each group sequentially. The specific formula is as follows:

[0094] ;

[0095] ;

[0096] ;

[0097] in, and These are the global and dual variables from the previous iteration, respectively.

[0098] This invention also proposes an ADMM distributed yaw control system that integrates a three-dimensional analytical wake model and dynamic grouping, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the ADMM distributed yaw control method that integrates a three-dimensional analytical wake model and dynamic grouping.

[0099] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the ADMM distributed yaw control method that integrates a three-dimensional analytical wake model and dynamic grouping.

[0100] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the ADMM distributed yaw control method that integrates a 3D analytical wake model and dynamic grouping.

[0101] Example

[0102] To verify the effectiveness of the present invention, yaw control was performed on a wind farm consisting of 58 wind turbines. In this embodiment, the location distribution of the wind turbines within the wind farm is shown in the diagram below. Figure 2 As shown.

[0103] Step 1) Use the wind farm SCADA system to collect wind speed, wind direction, power and yaw position data for each wind turbine, and extract the layout information of the wind farm in the plane rectangular coordinate system, and extract the rotor diameter, hub height, rated power and standard power curves of the wind turbines.

[0104] Step 2): Establish a Cartesian coordinate system with true north as 0°. Construct wind speed and direction vectors for each wind turbine, using wind speed as the modulus and wind direction as the angle. Calculate the coordinate components of each wind turbine's wind speed and direction vector in the Cartesian coordinate system. For example, wind turbine No. 1 has a wind speed of 6.72 m / s and a wind direction of 74.91°. Using wind speed as the modulus and wind direction as the direction, the coordinate components of wind turbine No. 1 in the Cartesian system with true north as 0° are (6.49, 1.75) (rounded to two decimal places). Superimpose the coordinate components of the wind speed and direction vectors of all wind turbines and arithmetically average them to obtain the coordinate components of the wind speed and direction vector of the main inflow of the wind farm at that sampling time: (2.46, ...). -0.28), and the wind speed of the main inflow to the wind farm is 6.73 m / s and the wind direction is 276.48°; rotate the plane rectangular coordinate system with the wind direction of the main inflow to the wind farm as the new x-axis to obtain the x-coordinate of each wind turbine in the new plane rectangular coordinate system. The order of the wind turbines from smallest to largest is used as the upstream and downstream order of the wind turbines. Calculate the straight-line distance and azimuth angle between the wind turbines. The upstream and downstream order of the wind turbines obtained after the coordinate system rotation is [58, 57, 35, 56, 34, 18, 55, 33, 17, 54, 16, 32, 53, 15, 52, 31, 14, 51, 30, 13, 50, 12, 49, 29, 11, 48, 47, 10, 28, 9, 27, 46, 8, 45, 26, 44, 7, 25, 6,43, 24, 42, 5, 41, 23, 4, 40, 22, 3, 39, 21, 2, 20, 1, 38, 37, 19, 36];

[0105] Step 3) Following the calculation order from upstream to downstream, based on the wake deflection model and wake diameter, calculate the influence area of ​​the wake of the upstream wind turbine at the rotor plane of the downstream wind turbine. Combined with the straight-line distance between the upstream and downstream wind turbines, calculate the weight coefficient between them. With the incoming wind at 276.48° downward, wind turbine No. 2 is affected by the wake of wind turbine No. 1. The weight coefficient between wind turbine No. 1 and wind turbine No. 2 is calculated to be 0.45. Based on this, construct the weight coefficient matrix of the entire wind farm.

[0106] Step 4) Based on the upstream and downstream sequence of the wind turbine, and using the three-dimensional analytical wake model, calculate the wake loss velocity of the current wind turbine. Based on the superimposed area matrix and the wake superimposed model, calculate the equivalent wind speed at the rotor plane of the current wind turbine. The calculated equivalent wind speed of wind turbine No. 1 is 4.13 m / s.

[0107] Step 5), based on the dynamically adjusted group detection algorithm and combined with the weight coefficient matrix, all wind turbines are grouped, and the wind turbines assigned to multiple groups are extracted as shared nodes. The resulting grouping of the wind farms is: {0:[18, 17, 16, 15, 14], 1: [14, 13, 12, 11, 10, 9], 2: [9, 8, 7, 6, 5, 4, 3, 2, 1], 3: [35, 34, 33, 32], 4: [31, 30, 29], 5: [28, 27, 26, 25], 6: [24, 23, 22, 21, 20, 19], 7: [58, 57, 56, 55, 54, 53], 8:} The set is: [52, 51, 50, 49, 48, 47], 9:[46, 45, 44, 43, 42, 41, 40, 39], 10: [40, 39, 38, 37, 36]}, with shared nodes [9, 14, 39, 40]. The details are as follows: Figure 3 As shown;

[0108] Step 6) Based on the ADMM optimization algorithm, a penalty term for the yaw angle of the wind turbines at the shared nodes is added. With the goal of maximizing power, distributed yaw optimization is performed on the wind turbines in each group, yielding the yaw optimization results for each wind turbine. The required yaw angles for each wind turbine are as follows: {1: 0.0, 10: 0.0, 11: 13.81, 12: 18.98, 13: 12.33, 14: 0.0, 15: 14.93, 16: 18.7, 17: 22.89, 18: 12.37, 19: 0.0, 2: 14.61, 20: 15.69, 21: 17.92, 22: 23.96, 23: 13.3, 24: 17.86, 25:} 0.0, 26: 19.56, 27: 12.26, 28: 17.84, 29: 0.0, 3: 25.37, 30: 8.89, 31: 18.32, 32:0.0, 33: 20.58, 34: 11.32, 35: 17.01, 36: 0.0, 37: 12.9, 38: 28.02, 39:15.61, 4: 13.07, 40: 0.0, 41: 18.68, 42: 19.92, 43: 27.18, 44: 17.43, 45:19.47, 46: 26.2, 47: The initial total power of the simulation was 50306.55kW, and the optimized total power was 57353.63kW, an improvement of 14.01%, taking 60.24s. (The values ​​for the simulation parameters are: 0.0, 48: 18.47, 49: 15.72, 5: 19.2, 50: 19.6, 51: 15.1, 52: 18.09, 53: 0.0, 54: 16.19, 55: 25.32, 56: 14.77, 57: 19.53, 58: 15.12, 6:14.58, 7: 18.64, 8: 23.93, 9: 13.69).

[0109] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A distributed yaw control method integrating a three-dimensional analytical wake model and dynamic grouping, characterized in that, The specific steps are as follows: Step 1, Data Acquisition and Parameter Extraction: Use the wind farm SCADA system to collect wind speed, wind direction, power and yaw position data for each wind turbine, and extract the layout information of the wind farm in the Cartesian coordinate system, as well as the rotor diameter, hub height and standard power curve of each wind turbine. Step 2, Determination of main inflow wind speed and direction and rotation of coordinate system: Establish a Cartesian coordinate system with due north as 0°, construct the wind speed and wind direction vector of each wind turbine with wind speed as the modulus and wind direction as the angle, and calculate its coordinate components in the Cartesian coordinate system. The coordinate components of all wind turbine units are superimposed and arithmetically averaged to calculate the wind speed and direction of the main inflow of the wind farm at the sampling time. The plane rectangular coordinate system is rotated with the main inflow wind direction as the positive x-axis to obtain the coordinates of each wind turbine unit in the new coordinate system. The upstream and downstream order of the wind turbine units is determined according to the x-coordinate values ​​from smallest to largest. Step 3, Construction of wake influence weight matrix: Based on the wake deflection model and wake diameter, calculate the influence area of ​​the wake of the upstream wind turbine at the rotor plane of the downstream wind turbine in the order of upstream and downstream; combine the straight distance between the upstream and downstream wind turbines to calculate the weight coefficients and construct the weight coefficient matrix of the entire wind farm. Step 4, Equivalent wind speed calculation: Based on the upstream and downstream sequence, calculate the wake loss velocity caused by the upstream wind turbine based on the three-dimensional asymmetric double Gaussian analytical wake model; calculate the equivalent wind speed at the rotor plane of the downstream wind turbine based on the influence area and wake superposition model. Step 5, Dynamic Grouping and Shared Node Identification: Define each wind turbine as a node and construct a directed graph using the weight coefficient matrix as the edge feature; The dynamically adjusted group detection algorithm groups all nodes; for nodes whose edge features exceed a preset threshold and do not belong to the same group, they are copied to each other's group and marked as shared nodes. Step 6, Distributed Yaw Optimization Solution: Based on the alternating direction multiplier method, with the goal of maximizing the total power of the wind farm, yaw angle optimization is performed independently for each group; a penalty term is introduced into the optimization model to constrain the yaw angle of shared nodes to remain consistent in different groups, and the yaw angle optimization range of all wind turbines is limited to a certain range. The optimal yaw angle for each wind turbine is obtained through iterative solution.

2. The ADMM distributed yaw control method integrating a three-dimensional analytical wake model and dynamic grouping as described in claim 1, characterized in that, Step 1, data acquisition and parameter extraction, the specific method is as follows: The SCADA system was used to collect average data of the wind turbine every 10 minutes, including sampling time, wind speed, wind direction, and yaw position. A Cartesian coordinate system is established with a specific wind turbine as the origin, the east direction as the positive x-axis, and the north direction as the positive y-axis. The coordinate position of each wind turbine in the Cartesian coordinate system is then extracted. Based on the model of the wind turbine, extract the rotor diameter, hub height, rated power, and corresponding standard power curve for that model.

3. The ADMM distributed yaw control method integrating a three-dimensional analytical wake model and dynamic grouping as described in claim 1, characterized in that, Step 2, determining the main inflow wind speed and direction and rotating the coordinate system, the specific method is as follows: Establish a Cartesian coordinate system with true north as 0°. Construct the wind speed and wind direction vectors for each wind turbine, using wind speed as the modulus and wind direction as the angle. Calculate its coordinate components in this Cartesian coordinate system. The specific formula is as follows: ； Where i is the wind turbine index. Let be the wind speed of wind turbine i. The wind direction is for wind turbine i. Let be the coordinate components of the wind speed and direction vector of wind turbine i in the Cartesian coordinate system; By superimposing the coordinate components of all wind turbines and taking the arithmetic mean, the wind speed and direction of the main inflow at the sampling time are calculated. The specific formula is as follows: ； ； ； ； in, These are the coordinate components of the wind speed and direction vector of the main inflow into the wind farm. It refers to the number of wind turbines in the wind farm. It is the wind speed of the main inflow into the wind farm. The wind direction of the main inflow to the wind farm before the angle was changed. It is the direction of the main inflow wind to the wind farm after the angle is changed; Rotate the Cartesian coordinate system with the main inflow wind direction as the positive x-axis to obtain the coordinates of each wind turbine in the new coordinate system. The specific formula is as follows: ； in, and These are the initial x and y coordinates of wind turbine i, respectively. and These are the x and y coordinates of wind turbine i in the new rectangular coordinate system; The upstream and downstream sequence of wind turbine units is determined based on the x-coordinate values ​​in the new coordinate system, from smallest to largest.

4. The ADMM distributed yaw control method integrating a three-dimensional analytical wake model and dynamic grouping as described in claim 1, characterized in that, Step 3, constructing the wake influence weight matrix, the specific method is as follows: Based on the wake deflection model and wake diameter, the influence area of ​​the wake of the upstream wind turbine j on the rotor plane of the downstream wind turbine i is calculated sequentially according to the upstream and downstream order of the wind turbines. The specific formula is as follows: ； ； ； ； ； Where j represents the upstream wind turbine and i represents the downstream wind turbine. and These are the x-coordinates of wind turbine units j and i in the new Cartesian coordinate system, respectively. and These are the y-coordinates of wind turbine units j and i in the new Cartesian coordinate system. It is the difference in the x-coordinates of wind turbine units j and i. It is the difference between the y-coordinates of wind turbine units j and i. It is the wake expansion coefficient of wind turbine j. It is the thrust coefficient of wind turbine j. It is the rotor diameter of wind turbine j. It is the wake diameter of wind turbine j at wind turbine i. It is the yaw angle of wind turbine j. It is the initial wake deflection angle of wind turbine j. These are the undetermined parameters for the wake deflection model of wind turbine j. It is the distance from the wake deflection centerline of wind turbine j; Based on the positional relationship between the wake of upstream wind turbine j and downstream wind turbine i, the influence area of ​​the wake of upstream wind turbine j at the rotor plane of downstream wind turbine i is calculated in three cases: (1) If ,but ; (2) If or ,but ; (3) If or or ,but The calculation formula is as follows: ； ； ； ； ； in, It is the rotor diameter of wind turbine i. and These are the hub heights of wind turbine units j and i, respectively. It is the height difference at the hub of wind turbine units j and i. It is the wake radius of wind turbine j at wind turbine i. It is the rotor radius of wind turbine i. It is the projection of the rotor radius of wind turbine i onto the wind direction perpendicular to the main inflow of the wind farm. It is the yaw angle of wind turbine i. It is the straight-line distance between the hubs of wind turbine units j and i. It is the area of ​​influence of the wake of the upstream wind turbine j at the rotor plane of the downstream wind turbine i; Based on the straight-line distance between upstream and downstream wind turbines, weighting coefficients are calculated, and a weighting coefficient matrix for the entire wind farm is constructed. The specific formula is as follows: ； ； ； in, It is the projected area of ​​wind turbine i in the wind direction perpendicular to the main inflow of the wind farm. It is the yaw angle of wind turbine i. It is the weighting coefficient of wind turbine j with respect to i. It is the weighting coefficient matrix of the wind farm.

5. The ADMM distributed yaw control method integrating a three-dimensional analytical wake model and dynamic grouping as described in claim 1, characterized in that, Step 4, equivalent wind speed calculation, the specific method is as follows: Based on the upstream and downstream sequence of the wind turbines, the wake loss velocity at the downstream wind turbine i is calculated using a three-dimensional asymmetric double Gaussian analytical wake model. The specific formula is as follows: ； Where x is the axial distance from the current wind turbine, y is the lateral distance from the current wind turbine, and z is the longitudinal distance from the current wind turbine. This is the current inflow wind speed of the wind turbine. It is the wake velocity deficit normalization coefficient related to the axial distance x and the lateral distance y. and These represent the standard deviations of the Gaussian distribution, corresponding to the right and left sides of the wake cross-section plane, respectively. It is the standard deviation of the Gaussian distribution in the vertical direction. It is the vertical gap between the minimum wake velocity and the center of the wake; Based on the superimposed area matrix and the wake superposition model, the equivalent wind speed at the i-rotor plane of the downstream wind turbine is calculated using the following formula: ； ； in, It is the wind speed of the main inflow into the wind farm. This refers to the number of upstream wind turbines that have a wake effect on wind turbine i. It is the wake loss velocity of wind turbine i under the influence of the wake of wind turbine j. It is the wind speed of wind turbine j. It is the ratio of the area of ​​influence of the wake of upstream wind turbine j at the rotor plane of downstream wind turbine i to the projected area of ​​the rotor plane of wind turbine i in the wind direction perpendicular to the main inflow of the wind farm. It is the equivalent wind speed at the rotor plane of the wind turbine unit. It is the projected area of ​​wind turbine i in the wind direction perpendicular to the main inflow of the wind farm. It is the area of ​​influence of the wake of the upstream wind turbine j on the rotor plane of the downstream wind turbine i.

6. The ADMM distributed yaw control method integrating a three-dimensional analytical wake model and dynamic grouping as described in claim 1, characterized in that, Step 5, Dynamic Grouping and Shared Node Identification, the specific method is as follows: Each wind turbine is defined as a node, and the weights in the weighting matrix W are used to define the nodes. Construct a directed graph G and its transpose graph based on the edge features from node j to node i. ; Initialize each node as an independent group; traverse the edge features of each node in the directed graph G with other nodes, and calculate the edge feature threshold of the current node i. The fit of the node to which it belongs If the maximum fit exceeds the fit threshold If the current node is moved to the group with the highest fit, the specific formula for calculating the fit is: ； ； ； ； in, Let G be the edge characteristics of wind turbine j and wind turbine i in the directed graph G. It represents the edge characteristics of wind turbine i in the directed graph G. It represents the goodness of fit with the community of nodes whose edge features exceed the edge feature threshold of the current node i. The value is the sum of the edge features of the current node i within the community. It is the threshold of the edge features. It is the fit threshold; After the initial partitioning is completed, based on the directed transpose graph Iterate through the edge features of each node, and for each edge whose feature is greater than the edge feature threshold,... For nodes that are not in the same group, add the current node to the group that the current node belongs to, and mark the current node as a shared node.

7. The ADMM distributed yaw control method integrating a three-dimensional analytical wake model and dynamic grouping as described in claim 1, characterized in that, Step 6, Distributed yaw optimization solution, the specific method is as follows: The output power of each wind turbine in yaw mode is defined by the following formula: ； An optimization function is constructed with the objective of maximizing the total power of the wind farm. The specific formula is as follows: ； in, It is air density. It is the planar area of ​​the wind turbine i. It is the equivalent wind speed at the rotor plane of wind turbine i. This is the power coefficient of wind turbine unit i. It is the yaw angle of wind turbine i. These are all the groups into which wind turbine units are classified. It is one of the groups. Group 'output power' Group The optimization function, and These are the yaw angle and equivalent wind speed of the wind turbines within the group; The ADMM optimization algorithm is used to perform distributed optimization for each group. A penalty factor is introduced to constrain the yaw angle of shared nodes in different groups, and the range of the yaw angle is limited to ±30°. The specific formula is as follows: ； ； in, It is a penalty factor constraint for shared nodes. It is a group that shares nodes with the current group. It is one of the groups that have shared nodes. It is a shared node between the two groups. It is one of the shared nodes. It refers to the yaw angle of wind turbines within a specific group. It is the yaw angle of the shared node. yes A copy in the global variable; The optimization problem described above can be defined in the form of an augmented Lagrange function, with the specific formula as follows: ； in, It is a penalty parameter. It is a dual variable. It is a global variable; The ADMM algorithm is solved iteratively by updating the Lagrange function variable in each group sequentially. The specific formula is as follows: ； ； ； in, and These are the global and dual variables from the previous iteration, respectively.

8. An ADMM distributed yaw control system integrating a three-dimensional analytical wake model and dynamic grouping, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the ADMM distributed yaw control method according to any one of claims 1-7, which integrates a three-dimensional analytical wake model and dynamic grouping.

9. A computer device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the ADMM distributed yaw control method according to any one of claims 1-7, which integrates a three-dimensional analytical wake model and dynamic grouping.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the ADMM distributed yaw control method according to any one of claims 1-7, which integrates a three-dimensional analytical wake model and dynamic grouping.

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