Offshore wind farm automatic generation control flow field dynamic modeling method

Abstract

The application discloses a kind of offshore wind farm automatic power generation control flow field dynamic modeling methods, comprising the following steps: data sampling is carried out using CFD simulation, obtains data set;Data is collected and arranged;Eigen-orthogonal decomposition is used for order reduction processing;Offshore wind farm automatic power generation control flow field dynamic model based on yaw, variable pitch, speed regulation process and its disturbance process is constructed.The application can effectively overcome the shortcomings that the data set obtained by high-precision wind farm flow field dynamic simulation is large in structure and difficult to apply directly, and the dynamic reduced-order model of the wind farm flow field obtained can greatly compress the data structure, retain the main characteristics of the flow field, and has high wake prediction accuracy.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and more specifically to a method for dynamic modeling of the flow field in automatic power generation control of offshore wind farms. Background Technology

[0002] Common wind farm flow field models used for wake control are typically empirical static wake models such as the Jensen model. These models usually have low accuracy and cannot reflect the delay caused by wind speed changes propagating within the wind farm. To quickly simulate the changes in wind farm flow field caused by changes in wind speed, direction, and turbine status, it is necessary to establish dynamic flow field control models for offshore wind farms. However, most existing dynamic wind farm flow field models are based on numerical models using flow field CFD, generating a large amount of dynamic process flow field evolution datasets, but these are difficult to apply to control applications. Summary of the Invention

[0003] Purpose of the invention: The purpose of this invention is to provide a dynamic modeling method for the flow field of offshore wind farm automatic power generation control that can solve the problem that the dataset obtained from high-precision wind farm flow field dynamic simulation in the prior art is too large in structure to be directly applied to the automatic power generation control of offshore wind farms.

[0004] Summary of the Invention: The present invention provides a method for dynamic modeling of the flow field in automatic power generation control of offshore wind farms, comprising the following steps:

[0005] (1) Use CFD simulation to sample data and obtain a dataset;

[0006] (2) After collecting the required data, collect and organize the data;

[0007] (3) Use intrinsic orthogonal decomposition to reduce the order;

[0008] (4) Construct a dynamic model of the flow field for automatic power generation control of offshore wind farms based on the yaw, pitch, and speed regulation processes and their disturbance processes.

[0009] In step (1), the data sampling using CFD simulation to obtain the dataset specifically includes: implementing solid modeling of the wind turbine unit through ICEM software, importing the wind turbine unit model into XFlow software, setting the input wind speed, and ensuring that the wind direction is consistent with the arrangement direction of the unit, and also setting the simulation sampling time interval.

[0010] In step (1), the data sampled using CFD simulation includes the x, y, z components of the wind speed at each grid point on the horizontal plane at the hub height, the x, y, z components of the wind speed at each grid point on the vertical plane passing through the center of the unit, the output power of each unit, and the yaw angle of the first unit.

[0011] In step (2), after collecting the required data, it is organized in the following format: Assume there are n sampling time points, and the two wind speed data sampling planes have a total of N grid points; at the i-th sampling time point, the x, y, z components of the wind speed at each grid point of the two planes are used to form a vector x. i (i = 1, 2, ..., n-1) is a 3N-dimensional column vector; the collected data matrix is ​​shown in the following form:

[0012]

[0013] In the formula, U θ Represents the yaw matrix, u i (i = 1, 2, ..., n-1) represents the yaw vector of the i-th sampling point, denoted as m-dimensional; Y represents the output matrix, y i (i = 1, 2, ..., n-1) represents the vector composed of the power of each unit at the i-th sampling point; X and X' are formed by x i The matrix formed by these equations is defined by equation (1).

[0014] In step (3), the process of reducing the order using intrinsic orthogonal decomposition specifically involves: selecting an appropriate model order r based on suitable model accuracy and complexity, and reducing the order of the collected wind field data; ultimately obtaining a discrete state-space model in the following form:

[0015]

[0016] The relationship between the state vectors before and after the order reduction is defined as follows:

[0017]

[0018] In the formula, P∈R r×n , where is the projection subspace matrix.

[0019] In step (4), the construction of the dynamic model of the automatic power generation control flow field of the offshore wind farm based on the yaw, pitch, and speed regulation processes and their disturbance processes specifically involves:

[0020] Perform singular value decomposition on X obtained in step (2): X = U∑V T Where U represents the left singular vector, and R is... 3N×3N ∑ represents an orthogonal matrix; ∑ denotes a singular value matrix, which is R 3N×(n-1) A diagonal matrix; V is a singular vector, which is R. (n-1)×(n-1) The orthogonal matrix; by retaining only the r-order subset of the POD pattern, a high degree of compression of the original data is achieved while still ensuring modeling accuracy. At this point, X can be approximated as:

[0021]

[0022] make Since Ur is a real orthogonal matrix, then PP T =I r I r ∈R r×r It is the identity matrix;

[0023] From equation (2), we get

[0024]

[0025] Substituting equations (3) and (5) into (6), we obtain the state space matrix:

[0026]

[0027] Substituting the state space matrix into equation (2) yields the dynamic reduced-order model of the wind farm flow field.

[0028] Working Principle: This invention employs model reduction methods based on mode identification, such as Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD). Through data compression, it proposes an efficient and reliable method for reducing the order of the dynamic process of the flow field in the automatic power generation control of offshore wind farms. A reduced-order state-space model is constructed for yaw, pitch, and speed regulation processes and their disturbances. The resulting dynamic reduced-order model of the wind farm flow field significantly compresses the data structure while retaining the main characteristics of the flow field, exhibiting high wake prediction accuracy. Although the reduced-order wake model does not capture small-scale wake fluctuations and offsets, it can reflect the overall distribution characteristics of the wake. The reduced-order wake dynamic model is of great significance for the rapid calculation and application of dynamic wakes in offshore wind farms, especially in wake control based on offshore wind farms.

[0029] Beneficial effects: Compared with the prior art, the beneficial effects of this invention are as follows: the dynamic reduced-order model of the wind farm flow field obtained by combining the analysis results can greatly compress the data structure, retain the main features of the flow field, and have high wake prediction accuracy. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the process of the present invention;

[0031] Figure 2 This is a layout diagram of a single-row wind farm turbine unit in this invention;

[0032] Figure 3 This is a diagram showing the setting of the yaw angle changing over time in this invention;

[0033] Figure 4 This is a diagram showing the computational domain settings in this invention. Detailed Implementation

[0034] The technical solution of the present invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0035] The following example demonstrates how to construct a dynamic flow field model for a wind farm based on the yaw adjustment process of only the first turbine unit.

[0036] For this embodiment, as Figure 2 As shown, a wind farm consists of three identical turbines arranged in a single row, with a rotor diameter of D and a hub height of H = 70m. The three turbines are arranged at equal intervals of d. During the simulation, the input wind speed and direction are set to be consistent with the arrangement of the three turbines. During the simulation, if... Figure 3 As shown, the yaw angle of the first unit changes stepwise within the range of 0° to 30°. After the angle changes, it is maintained for a certain period of time to allow the wind farm flow field to fully develop. The simulation duration and yaw angle settings are shown in Table 1.

[0037] Table 1 Simulation Duration and Yaw Angle Settings

[0038]

[0039] like Figure 1 As shown, the method for dynamic modeling of the flow field for automatic power generation control in offshore wind farms according to the present invention specifically includes the following steps:

[0040] (1) Data sampling was carried out using CFD simulation to obtain a dataset; specifically, the entity model of the wind turbine was realized by using ICEM software, the wind turbine model was imported into XFlow software, and the wake field of the three turbines arranged in series with the upstream turbine being numerically simulated.

[0041] like Figure 4 The diagram shows the computational domain setup. The dimensions of the computational domain are set to (12D+2d)×4D×4D, and the distance from the rotor plane to the inflow boundary is 2D, where D is the rotor diameter. The inlet of the computational domain is set as a velocity inlet, the outlet as a free outlet, the bottom surface as a ground wall, and the remaining surfaces as periodic boundaries. The WALE subgrid turbulence model is used, with model parameters C... w The value is set to 0.2. The calculation time step is set to 0.1s, and the total physical calculation time is 800s. An adaptive grid arrangement is used for grid partitioning, the wind turbine is finely densified, and the wake is solved using dynamic adaptive tracking optimization.

[0042] Probes were set at different locations in the flow field to sample data. The simulation sampling time interval was 0.1s. The sampled data included: the x, y, and z components of the wind speed at each grid point on the horizontal plane at the hub height; the x, y, and z components of the wind speed at each grid point on the vertical plane passing through the center of the three units; the output power of each unit; and the yaw angle of the first unit.

[0043] (2) After collecting the required data, the data is collected and organized; specifically, it is organized in the following format: Assume there are n sampling time points, and the two wind speed data sampling planes have a total of N grid points. At the i-th sampling time point, the x, y, z components of the wind speed at each grid point of the two planes are combined to form a vector x. i (i = 1, 2, ..., n-1) is a 3N-dimensional column vector. The collected data matrix is ​​in the form of:

[0044]

[0045] In the formula, U θ Represents the yaw matrix, u i (i = 1, 2, ..., n-1) represents the yaw vector of the i-th sampling point, denoted as m-dimensional; Y represents the output matrix, y i (i = 1, 2, ..., n-1) represents a vector consisting of the power of each unit at the i-th sampling point. X and X' are derived from x i The matrix formed by these equations is defined by equation (1).

[0046] (3) Intrinsic orthogonal decomposition is used for order reduction; specifically, based on appropriate model accuracy and complexity, a suitable model order r is selected to reduce the order of the collected wind field data. The final discrete state-space model is obtained in the following form:

[0047]

[0048] The relationship between the state vectors before and after the order reduction is defined as follows:

[0049]

[0050] In the formula, P∈R r×n Let be the projection subspace matrix.

[0051] (4) Construct a dynamic model of the flow field for automatic power generation control of offshore wind farms based on yaw, pitch, speed regulation processes and their disturbance processes; specifically, first perform singular value decomposition on X obtained in step (2): X=U∑V T Where U is the left singular vector, and R is... 3N×3N The orthogonal matrix; ∑ is the singular value matrix, which is R 3N×(n-1) The diagonal matrix; V is a singular vector, which is R. (n -1)×(n-1) The orthogonal matrix. By retaining only the r-order subset of the POD patterns, a high degree of compression of the original data can be achieved while still maintaining satisfactory modeling accuracy. In this case, the approximation of X can be written as:

[0052]

[0053] make Due to U r If PP is a real orthogonal matrix, then PP T =I r I r ∈R r×r It is an identity matrix.

[0054] From equation (2), we get

[0055]

[0056] Substituting equations (3) and (5) into (6), we obtain the state space matrix:

[0057]

[0058] Substituting the state space matrix into equation (2) yields the dynamic reduced-order model of the wind farm flow field.

Claims

1. A method for dynamic modeling of the flow field in automatic power generation control of offshore wind farms, characterized in that: Includes the following steps: Step (1) Use CFD simulation to sample data and obtain a dataset; After collecting the required data in step (2), the data is collected and organized as follows: Assuming there is a total There are [number] sampling time points, and the two wind speed data sampling planes have a total of [number] sampling time points. The grid point; in the grid point; The wind speeds at each grid point in the two planes were collected at each sampling time point. , , Components form a vector , It is A column vector; the collected data matrix is ​​shown in the following form: （1） In the formula, Represents the yaw matrix. Indicates the first The yaw vector of the unit at each sampling point , set as dimension; Indicates the output matrix; Indicates the first A vector composed of the power of each unit at each sampling point. ; and It is by The matrix formed by this matrix is ​​defined by equation (1); Step (3) employs intrinsic orthogonal decomposition for order reduction. Specifically, based on the model's accuracy and complexity, the model order r is selected, and the collected wind field data is reduced in order. The final discrete state-space model is obtained in the following form: （2） The relationship between the state vectors before and after the order reduction is defined as follows: （3） In the formula, , is the projection subspace matrix; Step (4) Constructs a dynamic model of the flow field for automatic power generation control of offshore wind farms based on yaw, pitch, and speed regulation processes and their disturbance processes, specifically as follows: The result obtained in step (2) Perform singular value decomposition: ,in, Describing a left singular vector is an orthogonal matrix; Describing a singular value matrix is a diagonal matrix; For singular vectors, it is The orthogonal matrix; by retaining only the POD mode A subset of the order of magnitude is used to achieve a high degree of compression of the original data while still maintaining modeling accuracy. The approximation is written as: （4） make (5) because If is a real orthogonal matrix, then , It is the identity matrix; From equation (2), we get （6） Substituting equations (3) and (5) into (6), we obtain the state space matrix: （7） Substituting the state space matrix into equation (2) yields the dynamic reduced-order model of the wind farm flow field.

2. The method for dynamic modeling of the flow field for automatic power generation control in offshore wind farms according to claim 1, characterized in that: In step (1), the data sampling using CFD simulation to obtain the dataset specifically includes: implementing solid modeling of the wind turbine unit through ICEM software, importing the wind turbine unit model into XFlow software, setting the input wind speed, and ensuring that the wind direction is consistent with the arrangement direction of the unit, and also setting the simulation sampling time interval.

3. The method for dynamic modeling of the flow field for automatic power generation control in offshore wind farms according to claim 1, characterized in that: In step (1), the data sampled using CFD simulation includes the wind speed at each grid point on the horizontal plane at the hub height. , , Components, wind speeds at each grid point on the vertical plane passing through the center of the unit. , , Components, output power of each unit, and yaw angle of the first unit.

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