Wind turbine generator downstream wind regime inversion method and system, storage medium and equipment

Abstract

The invention provides a downstream wind regime inversion method and system of a wind turbine generator, a storage medium and equipment. The method comprises the following steps: acquiring basic parameters and operating parameters of the wind turbine generator; constructing a wind turbine generator main shaft torque prediction model, and inverting the vertical distribution of the inflow velocity of the wind turbine generator according to the operation parameters and the real-time main shaft torque of the wind turbine generator; and constructing a wake flow model of the wind turbine generator, and inverting the downstream speed distribution of the wind turbine generator according to the inflow speed vertical distribution of the wind turbine generator to obtain the downstream wind condition of the wind turbine generator. The real-time main shaft torque, the rotating speed, the yaw angle, the pitch angle and other parameters of the wind turbine generator are comprehensively considered, the actual operation state of the wind turbine generator is more comprehensively reflected, and the precision of an inversion result is improved; the wind shear effect is considered in the inversion process, so that the vertical distribution of the inverted inflow velocity better conforms to the actual wind regime, and the accuracy of downstream wind regime prediction is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy, and particularly relates to a method, system, storage medium and device for inverting the downstream wind conditions of a wind turbine generator set. Background Art

[0002] In a wind farm, the wake effect between wind turbine generator sets is an important factor affecting the power generation efficiency of the entire wind farm. When the wind flows through the upstream wind turbine generator set, a wake region will be formed downstream of it, where the wind speed decreases and the turbulence increases. If the downstream wind turbine generator set is within the wake influence range of the upstream unit, its power generation will decrease significantly. Therefore, accurately inverting the downstream wind conditions is of great significance for the reasonable layout of the wind farm, unit selection and power generation prediction.

[0003] However, in the prior art, there are still some limitations. For example, some sensor-based monitoring systems can collect data in real time, but the data accuracy is poor under complex terrains and variable wind conditions; some wind condition inversion models ignore key factors such as wind shear when considering the wake effect, resulting in a certain deviation between the inversion results and the actual wind conditions. Therefore, there is an urgent need for a more accurate, efficient and cost-effective technical method to realize the inversion of downstream wind conditions based on main shaft torque monitoring, so as to improve the overall operation management level and power generation efficiency of the wind farm. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method for inverting the downstream wind conditions of a wind turbine generator set, the method comprising:

[0005] Obtaining the basic parameters and operating parameters of the wind turbine generator set;

[0006] Constructing a main shaft torque prediction model of the wind turbine generator set, and inverting the vertical distribution of the inflow velocity of the wind turbine generator set according to the operating parameters and the real-time main shaft torque of the wind turbine generator set;

[0007] Constructing a wake model of the wind turbine generator set, and inverting the downstream velocity distribution of the wind turbine generator set according to the vertical distribution of the inflow velocity of the wind turbine generator set to obtain the downstream wind conditions of the wind turbine generator set;

[0008] The operating parameters of the wind turbine generator set include: the rotational speed of the wind turbine generator set, the yaw angle, the pitch angle and the real-time main shaft torque.

[0009] Furthermore,

[0010] The basic parameters of the wind turbine generator set include: the rated power of the wind turbine generator set, the tower height of the wind turbine generator set and the blade length;

[0011] Furthermore,

[0012] The main shaft torque prediction model of the wind turbine is constructed based on the aerodynamic model of the wind turbine using the momentum blade element model.

[0013] Furthermore, the inversion of the vertical distribution of the inflow velocity of the wind turbine includes:

[0014] Presetting the formula for the vertical distribution of the inflow velocity of the wind turbine;

[0015] Inverting and optimizing the vertical distribution of the inflow velocity of the wind turbine according to the operating parameters of the wind turbine, the real-time main shaft torque, and the predicted main shaft torque.

[0016] Furthermore,

[0017] The formula for the vertical distribution of the inflow velocity of the wind turbine is expressed as:

[0018]

[0019] where u(y) is the vertical inflow velocity of the wind turbine, u hub is the inflow velocity at the height of the wind turbine tower, z is the height from the ground to the point under consideration, H is the tower height, and α is the wind shear coefficient;

[0020] The inversion and optimization of the vertical distribution of the inflow velocity of the wind turbine is to solve the optimization problem and invert the inflow velocity and the wind shear coefficient at the height of the wind turbine tower. Its formula is expressed as:

[0021]

[0022] where Q s is the real-time main shaft torque, Q m is the predicted main shaft torque, ω is the rotational speed of the wind turbine, φ is the yaw angle of the wind turbine, θ is the pitch angle of the wind turbine, V cutin and V cutout are the cut-in and cut-out wind speeds of the wind turbine respectively, f obj is the objective function of the optimization problem, and s.t. is the abbreviation of subject to, indicating "subject to".

[0023] Furthermore,

[0024] The wake model of the wind turbine is constructed using the Gaussian distribution wake model and considering the wind shear coefficient.

[0025] Furthermore,

[0026] The inversion of the downstream velocity distribution of the wind turbine according to the vertical distribution of the inflow velocity of the wind turbine and using the constructed wake model of the wind turbine is expressed by the formula:

[0027]

[0028] Among them, u(x, y, z) is the wake velocity distribution of the wind farm, C T is the thrust coefficient of the wind turbine, calculated by the momentum blade element theory model, k is the wake width change rate, D is the diameter of the wind turbine.

[0029] A wind condition inversion system downstream of a wind turbine, the system includes: a parameter acquisition module, a model construction module, and an inversion calculation module;

[0030] The parameter acquisition module is used to acquire the basic parameters and operating parameters of the wind turbine;

[0031] The model construction module is used to construct a main shaft torque prediction model of the wind turbine and a wind turbine wake model;

[0032] The inversion module inversely calculates the vertical distribution of the inflow velocity of the wind turbine and the wake velocity distribution of the wind turbine according to the constructed main shaft unit prediction model of the wind turbine and the wind turbine wake model.

[0033] A computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method steps described above are implemented.

[0034] An electronic device includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus;

[0035] The memory is used to store a computer program;

[0036] The processor is used to implement the method steps described above when executing the program stored on the memory.

[0037] A computer program product includes computer programs / instructions, and is characterized in that when the computer programs / instructions are executed by a processor, the steps of the above-mentioned vehicle-human interaction test virtual scene detection method are implemented.

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] 1. The present invention proposes a wind condition inversion method downstream of a wind turbine, which realizes wind condition inversion through main shaft torque monitoring, reduces the dependence on a large number of sensors, reduces the installation and maintenance work of sensors, reduces the complexity and maintenance cost of the system, and improves the reliability and economy of the system.

[0040] 2. By comprehensively considering the operating parameters such as the real-time main shaft torque, rotational speed, yaw angle, and pitch angle of the wind turbine, the actual operating state of the wind turbine can be more comprehensively reflected, thereby improving the accuracy of the inversion result; the wind shear effect is considered during the inversion process, making the vertical distribution of the inverted inflow velocity more in line with the actual wind conditions and improving the accuracy of the downstream wind condition prediction.

[0041] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures pointed out in the specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0043] Figure 1 Shows a flowchart of a method for inverting the downstream wind conditions of a wind turbine.

[0044] Figure 2 Shows a schematic diagram of the modules of a system for inverting the downstream wind conditions of a wind turbine.

[0045] Figure 3 Shows a basic parameter table of the IEA-15MW wind turbine in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0047] As Figure 1 shown, a method for inverting the downstream wind conditions of a wind turbine, the steps of which include,

[0048] S1. Obtain the real-time main shaft torque Q s of the wind turbine, basic parameters, and operating parameters.

[0049] Optionally, the basic parameters include: the rated power of the wind turbine, the tower height of the wind turbine, the blade length, etc.;

[0050] The operating parameters include the rotational speed ω, yaw angle φ, pitch angle θ, etc. of the wind turbine.

[0051] S2. Construct a prediction model for the main shaft torque of the wind turbine, and based on the operating parameters and real-time main shaft torque Q of the wind turbine s , invert the inflow velocity of the wind turbine.

[0052] S2.1. Based on the aerodynamic model of the wind turbine using the momentum blade element model, construct a prediction model for the main shaft torque of the wind turbine, and calculate the predicted main shaft torque Q of the wind turbine m .

[0053] S2.2. Preset the formula for the vertical distribution u(y) of the inflow velocity of the wind turbine, which is expressed as

[0054]

[0055] where u hub is the inflow velocity at the tower height of the wind turbine, z is the height from the ground to the considered point, H is the tower height, and α is the wind shear coefficient.

[0056] S2.3. According to the operating parameters of the wind turbine, real-time main shaft torque Q s and predicted main shaft torque Q m , invert and optimize the vertical distribution u(y) of the inflow velocity of the wind turbine.

[0057] Optionally, inverting and optimizing the vertical distribution of the inflow velocity of the wind turbine means solving an optimization problem to invert the inflow velocity and wind shear coefficient at the tower height of the wind turbine, and its formula is expressed as

[0058]

[0059] where V cutin and V cutout are the cut-in and cut-out wind speeds of the wind turbine respectively, f obj is the objective function of the optimization problem, and s.t. is the abbreviation of subject to, indicating "subject to".

[0060] S3. Construct a wake model for the wind turbine, and based on the vertical distribution of the inflow velocity of the wind turbine, invert the downstream velocity distribution of the wind turbine.

[0061] S3.1. Use the Gaussian distribution wake model and consider the wind shear coefficient to construct a wake model for the wind turbine.

[0062] S3.2. According to the vertical distribution of the inflow velocity of the wind turbine, and using the constructed wake model of the wind turbine, invert the downstream velocity distribution of the wind turbine to obtain the downstream wind conditions of the wind turbine.

[0063] Optionally, the downstream velocity distribution of the wind turbine is inverted, and its formula is expressed as

[0064]

[0065] where u(x, y, z) is the wind farm wake velocity distribution, C T is the thrust coefficient of the wind turbine, calculated by the momentum blade element theory model, k is the wake width change rate, and D is the diameter of the wind turbine.

[0066] Optionally, the wake width change rate is calculated using an empirical formula, and its formula is expressed as

[0067]

[0068] In another embodiment of the present invention, as Figure 3 shown, taking the IEA-15MW wind turbine as an example, the real-time main shaft torque, operating parameters, and basic parameters of the IEA-15MW wind turbine at time t = 0 are obtained.

[0069] Using the wind turbine main shaft torque prediction model constructed in step S2, the predicted main shaft torque of the wind turbine is calculated.

[0070] According to the obtained wind turbine rotational speed ω of 7.55 r / min, yaw angle φ of 5.5°, pitch angle θ of 0.6°, and the calculated predicted main shaft torque of the wind turbine, the vertical distribution of the wind turbine inflow velocity is inverted and optimized to obtain the wind turbine inflow wind speed, which is expressed as

[0071]

[0072] Using the wind turbine wake model constructed in step S3 and calculating according to the wind turbine inflow velocity, the downstream velocity distribution of the wind turbine is inverted to obtain the downstream wind conditions of the wind turbine, which is expressed as

[0073]

[0074] As Figure 2 shown, the present invention also provides a system for inverting the downstream wind conditions of a wind turbine, which includes a parameter acquisition module, a model construction module, and an inversion calculation module.

[0075] Optionally, the parameter acquisition module is used to acquire the basic parameters, real-time main shaft torque, and operating parameters of the wind turbine;

[0076] The model construction module is used to construct a wind turbine main shaft torque prediction model and a wind turbine wake model;

[0077] The inversion module inversely calculates the vertical distribution of the inflow velocity of the wind turbine and the velocity distribution of the wind turbine wake according to the constructed prediction model of the main shaft of the wind turbine and the wind turbine wake model.

[0078] Optionally, the prediction model of the main shaft torque of the wind turbine is an aerodynamic model of the wind turbine based on the momentum blade element model;

[0079] The wind turbine wake model is a Gaussian distribution wake model considering the wind shear coefficient.

[0080] Based on the above-disclosed content, correspondingly, the present invention further provides an electronic device. The electronic device according to an embodiment of the present invention includes at least one processor and at least one storage medium electrically connected, the storage medium being electrically connected to the processor, wherein the storage medium stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

[0081] Based on the same inventive concept, the present invention further provides a storage medium, the storage medium storing instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

[0082] Based on the same inventive concept, the present invention further provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement each process of the embodiment of the method for inversely calculating the wind conditions downstream of a wind farm as described above.

[0083] The above description and the drawings fully illustrate the embodiments of the present invention so that those skilled in the art can practice them. Other embodiments may include structural and other changes. The embodiments only represent possible variations. Unless explicitly required, the individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or substituted for parts and features of other embodiments. The embodiments of the present invention are not limited to the structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A method for inverting wind conditions downstream of a wind turbine, characterized in that: The method comprises, Obtain basic parameters and operating parameters of wind turbines; Construct a wind turbine main shaft torque prediction model, and invert the vertical distribution of wind turbine inflow velocity based on the wind turbine operating parameters and real-time main shaft torque; Construct a wind turbine wake model, and invert the downstream velocity distribution of the wind turbine according to the vertical distribution of the inflow velocity of the wind turbine to obtain the downstream wind conditions of the wind turbine; The wind turbine operating parameters include: wind turbine rotation speed, yaw angle, pitch angle and real-time main shaft torque.

2. The method for inverting wind conditions downstream of a wind turbine according to claim 1, characterized in that: The basic parameters of the wind turbine generator set include: rated power of the wind turbine generator set, tower height of the wind turbine generator set and blade length.

3. The method for inverting wind conditions downstream of a wind turbine according to claim 1, characterized in that: The wind turbine main shaft torque prediction model is constructed based on a wind turbine aerodynamic model of a momentum blade element model.

4. The method for inverting wind conditions downstream of a wind turbine according to claim 1, characterized in that: The inverted vertical distribution of wind turbine inflow velocity includes: Preset the vertical velocity distribution formula of wind turbine inflow; According to the wind turbine rotation speed, yaw angle, pitch angle, real-time main shaft torque and predicted main shaft torque, the vertical distribution of the wind turbine inflow velocity is inversely optimized.

5. The method for inverting wind conditions downstream of a wind turbine according to claim 1, characterized in that: The wind turbine inflow vertical velocity distribution formula is expressed as: Where u(y) is the vertical velocity of the wind turbine inflow, u hub is the inflow velocity at the height of the wind turbine tower, z is the height from the ground to the considered point, H is the tower height, and α is the wind shear coefficient; The inverse optimization of the vertical distribution of the inflow velocity of the wind turbine is to solve the optimization problem and invert the inflow velocity and wind shear coefficient at the height of the wind turbine tower. The formula is expressed as follows: Among them, Q s is the real-time spindle torque, Q m To predict the main shaft torque, ω is the speed of the wind turbine, φ is the yaw angle of the wind turbine, θ is the pitch angle of the wind turbine, V cutin and V cutout are the cut-in and cut-out wind speeds of the wind turbine, respectively, and f obj is the objective function of the optimization problem, and st is the abbreviation of subject to, which means "subject to".

6. The method for inverting wind conditions downstream of a wind turbine according to claim 1, characterized in that: The wind turbine wake model is constructed by using a Gaussian distribution wake model and taking into account a wind shear coefficient.

7. The method for inverting wind conditions downstream of a wind turbine according to claim 1, characterized in that: According to the vertical distribution of the inflow velocity of the wind turbine, and using the constructed wind turbine wake model, the downstream velocity distribution of the wind turbine is inverted, and the formula is expressed as: Among them, u(x,y,z) is the wind farm wake velocity distribution, C T is the thrust coefficient of the wind turbine, calculated by the momentum blade element theory model, k is the rate of change of the wake width, D is the diameter of the wind turbine.

8. A wind turbine downstream wind condition inversion system, characterized in that: The system comprises: a parameter acquisition module, a model building module and an inversion calculation module; The parameter acquisition module is used to obtain the basic parameters and operating parameters of the wind turbine; The model building module is used to build a wind turbine main shaft torque prediction model and a wind turbine wake model; The inversion module inverts the vertical distribution of the wind turbine inflow velocity and the wind turbine wake velocity distribution according to the constructed wind turbine main shaft unit prediction model and wind turbine wake model.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; A processor, for implementing the method steps described in any one of claims 1 to 7 when executing a program stored in a memory.

11. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the human-vehicle interaction test virtual scene detection method as described in any one of claims 1-7 are implemented.

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