Yaw optimization method based on genetic algorithm and Y-3DJG wake flow model

Abstract

The invention relates to the technical field of wind driven generators, in particular to a yaw optimization method based on a genetic algorithm and a Y-3DJG wake flow model, and the method comprises the steps: obtaining the operation data of a wind power plant; constructing a Y-3DJG wake flow model, and inputting the operation data of the wind power plant into the Y-3DJG wake flow model for calculation to obtain wind turbine generator wake flow distribution after yawing; and by taking the maximum total generated power of the wind power plant as a target and defining a yaw angle variation range as a constraint condition, optimizing wake flow distribution of the yawed wind turbine generators through a genetic algorithm to obtain a yaw angle optimization value of each wind turbine generator, and completing wake flow distribution optimization of the wind turbine generators.

Description

Technical Field

[0001] The invention relates to the technical field of wind turbines, and in particular to a yaw optimization method based on a genetic algorithm and a Y-3DJG wake model. Background Art

[0002] The efficient operation of wind farms depends on accurate wind resource assessment and optimized wind turbine layout. The acquisition and analysis of wind farm operation data is an important step to improve power generation efficiency. The wake effect is a key factor affecting the power generation performance of wind turbines. Wake refers to the weakening of wind energy received by the rear-row fans after the wind energy is extracted by the front-row fans, resulting in a decrease in overall power generation. At present, traditional wind farm optimization technologies usually rely on empirical models and simple wind energy utilization analysis, which not only cannot accurately reflect the complex wake effects, but also have limited optimization effects due to the lack of full use of the real-time operation data of the wind farm. It is difficult to effectively guide the adjustment of the yaw angle of the wind turbine, which in turn leads to the wind farm power generation efficiency failing to meet expectations. Based on this, in response to the above problems, we designed a yaw optimization method based on genetic algorithm and Y-3DJG wake model. Summary of the invention

[0003] The object of the present invention is to provide a yaw optimization method based on a genetic algorithm and a Y-3DJG wake model, which uses the Y-3DJG model to calculate the wake distribution of a wind turbine after yaw, and then optimizes it through a genetic algorithm to obtain an optimized value of the yaw angle of each unit, so as to optimize the wake distribution of the unit, reduce the wake interference between units, enhance the power output of the downstream unit, and slightly degrade the power output of the upstream unit, thereby achieving a net power gain of the wind farm.

[0004] The embodiments of the present invention are implemented by the following technical solutions:

[0005] A yaw optimization method based on a genetic algorithm and a Y-3DJG wake model, the method comprising the following steps:

[0006] Obtaining wind farm operation data;

[0007] Construct the Y-3DJG wake model, input the wind farm operation data into the Y-3DJG wake model for calculation, and obtain the wake distribution of the wind turbine after yaw;

[0008] Taking the maximum total power generation of the wind farm as the goal and defining the yaw angle variation range as the constraint condition, the wake distribution of the wind turbine after yaw is optimized through the genetic algorithm, the yaw angle optimization value of each wind turbine is obtained, and the wake distribution optimization of the wind turbine is completed.

[0009] Optionally, the operating data of the wind farm specifically includes: wind turbine position, wind direction angle, wind speed, thrust coefficient and turbulence intensity.

[0010] Optionally, the construction process of the Y-3DJG wake model is:

[0011] A three-dimensional coordinate system is established with the hub center of the wind turbine as the coordinate origin, the x-axis of the three-dimensional coordinate system is defined as the flow direction of the wake area, the y-axis of the three-dimensional coordinate system is defined as the horizontal direction of the wake area, and the z-axis of the three-dimensional coordinate system is defined as the vertical direction of the wake area.

[0012] Optionally, the calculation formula of the Y-3DJG wake model is:

[0013]

[0014] Where r0 is the initial wake radius, a is the axial induction factor, r z is the wake radius r in the vertical direction z =2.58σ z =k z x+r0,σ z is the standard deviation in the vertical direction, σ y is the standard deviation in the vertical direction, r d is the rotor radius of the wind turbine, C T is the axial thrust coefficient, k z is the wake expansion coefficient in the vertical direction, y d is the eccentricity, β is the empirical coefficient, u is the wake wind speed at the point in three-dimensional space, z hub is the hub height, u hub is the incoming flow velocity at the hub height, and α is the wind shear exponent.

[0015] Optionally, the total power generation of the wind farm is calculated as follows:

[0016]

[0017] Where ρ is the air density, A is the swept area of ​​the wind wheel, u is the inflow wind speed, C P is the power coefficient.

[0018] Optionally, the power generation of a single wind turbine under yaw is calculated based on the total power generation of the wind farm:

[0019]

[0020] Among them, P i is the power generation of the i-th wind turbine under yaw, u i is the incoming wind speed of the i-th wind turbine, γ iis the yaw angle of the i-th wind turbine.

[0021] Optionally, the maximum total wind farm power generation is taken as the target, and the calculation formula is:

[0022]

[0023] Among them, P i (γ) is the output of each wind turbine when considering the wake effect and yaw, n is the number of wind turbines, and they are numbered in the order of the wind direction in which they are exposed to the wind, with the upstream wind turbine numbered first and the downstream wind turbine numbered last, γ is the yaw angle,

[0024] Optionally, the yaw angle variation range is defined as a constraint condition, and the yaw angle variation range is -30°<γ<30°.

[0025] The technical solution of the embodiment of the present invention has at least the following advantages and beneficial effects:

[0026] The embodiment of the present invention uses the Y-3DJG model to calculate the wake distribution of the wind turbine generator set after yaw, and then optimizes it through a genetic algorithm to obtain the optimized value of the yaw angle of each unit, so as to optimize the wake distribution of the unit, reduce the wake interference between units, enhance the power output of the downstream unit, and slightly degrade the power output of the upstream unit, thereby achieving a net power gain of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic flow chart of a yaw optimization method based on a genetic algorithm and a Y-3DJG wake model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0029] like Figure 1 As shown, the present invention provides one embodiment: a yaw optimization method based on a genetic algorithm and a Y-3DJG wake model, the method comprising the steps of:

[0030] Obtaining wind farm operation data;

[0031] Construct the Y-3DJG wake model, input the wind farm operation data into the Y-3DJG wake model for calculation, and obtain the wake distribution of the wind turbine after yaw;

[0032] Taking the maximum total power generation of the wind farm as the goal and defining the yaw angle variation range as the constraint condition, the wake distribution of the wind turbine after yaw is optimized through the genetic algorithm, the yaw angle optimization value of each wind turbine is obtained, and the wake distribution optimization of the wind turbine is completed.

[0033] In this embodiment, this embodiment obtains a model for predicting the wake velocity distribution after the wind turbine yaws by calculating the eccentricity. The wake model adopts a three-dimensional Gaussian wake model based on flow conservation derivation that takes into account the influence of wind shear and turbulence intensity. The model establishes a three-dimensional coordinate system with the center of the wind turbine hub as the coordinate origin. The x-axis represents the flow direction of the wake area; the y-axis represents the horizontal direction of the wake area; and the z-axis represents the vertical direction of the wake area, wherein the wake wind speed at a three-dimensional space point is defined as u=u(x, y, z).

[0034]

[0035] Where r0 is the initial wake radius, a is the axial induction factor, r z is the wake radius r in the vertical direction z =2.58σ z =k z x+r0,σ z is the standard deviation in the vertical direction, σ y is the standard deviation in the vertical direction, r d is the rotor radius of the wind turbine, C T is the axial thrust coefficient, k z is the wake expansion coefficient in the vertical direction. The wake expansion coefficient is 0.075 for onshore wind farms and 0.05 for offshore wind farms. d is the eccentricity, β is an empirical coefficient, which can be 0.09 to 0.125, u is the wake wind speed at a point in three-dimensional space, z hub is the wheel hub height, u hub is the incoming flow velocity at the hub height, and α is the wind shear exponent.

[0036] Specifically, this embodiment defines the following parameter indicators:

[0037] The total power generation of the wind farm is calculated as follows:

[0038]

[0039] Where ρ is the air density, A is the swept area of ​​the wind wheel, u is the inflow wind speed, C P is the power coefficient, C P Depends on wind speed.

[0040] According to the total power generation of the wind farm, the power generation of a single wind turbine under yaw is calculated:

[0041]

[0042] Among them, P i is the power generation of the i-th wind turbine under yaw, u i is the incoming wind speed of the i-th wind turbine, γ i is the yaw angle of the i-th wind turbine.

[0043] In this embodiment, the objective function of the genetic algorithm is:

[0044]

[0045] Among them, P i (γ) is the output of each wind turbine when considering the wake effect and yaw, n is the number of wind turbines, which are numbered in the order of the wind turbines in the current wind direction, with the upstream wind turbines numbered first and the downstream wind turbines numbered last, γ is the yaw angle, and the constraint condition is -30°<γ<30°. In order to save computing costs and ensure accuracy, the population size in the genetic algorithm is 100, the termination iteration number is 300, the crossover probability is 0.7, and the mutation probability is 0.2, which can be changed appropriately according to actual conditions.

[0046] In this embodiment, based on the Y-3DJG engineering wake model and genetic algorithm, the yaw angle of the unit can be effectively optimized, thereby reducing the wake interference between wind turbines and increasing the power generation of the wind turbine by more than 2%.

[0047] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A yaw optimization method based on genetic algorithm and Y-3DJG wake model, characterized in that: The steps of the method include: Obtaining wind farm operation data; Construct the Y-3DJG wake model, input the wind farm operation data into the Y-3DJG wake model for calculation, and obtain the wake distribution of the wind turbine after yaw; Taking the maximum total power generation of the wind farm as the goal and defining the yaw angle variation range as the constraint condition, the wake distribution of the wind turbine after yaw is optimized through the genetic algorithm, the yaw angle optimization value of each wind turbine is obtained, and the wake distribution optimization of the wind turbine is completed.

2. The yaw optimization method based on genetic algorithm and Y-3DJG wake model according to claim 1, characterized in that: The operation data of the wind farm specifically include: wind turbine position, wind direction angle, wind speed, thrust coefficient and turbulence intensity.

3. The yaw optimization method based on genetic algorithm and Y-3DJG wake model according to claim 2, characterized in that: The construction process of the Y-3DJG wake model is as follows: A three-dimensional coordinate system is established with the hub center of the wind turbine as the coordinate origin, the x-axis of the three-dimensional coordinate system is defined as the flow direction of the wake area, the y-axis of the three-dimensional coordinate system is defined as the horizontal direction of the wake area, and the z-axis of the three-dimensional coordinate system is defined as the vertical direction of the wake area.

4. The yaw optimization method based on genetic algorithm and Y-3DJG wake model according to claim 3, characterized in that: The calculation formula of the Y-3DJG wake model is: Where r0 is the initial wake radius, a is the axial induction factor, r z is the wake radius r in the vertical direction z =2.58σ z =k z x+r0,σ z is the standard deviation in the vertical direction, σ y is the standard deviation in the vertical direction, rd is the rotor radius of the wind turbine, C T is the axial thrust coefficient, k z is the wake expansion coefficient in the vertical direction, y d is the eccentricity, β is the empirical coefficient, u is the wake wind speed at the point in three-dimensional space, z hub is the wheel hub height, u hub is the incoming flow velocity at the hub height, and α is the wind shear exponent.

5. The yaw optimization method based on genetic algorithm and Y-3DJG wake model according to claim 4, characterized in that: The total power generation of the wind farm is calculated as follows: Where ρ is the air density, A is the swept area of ​​the wind wheel, u is the inflow wind speed, C P is the power coefficient.

6. The yaw optimization method based on genetic algorithm and Y-3DJG wake model according to claim 5, characterized in that: According to the total power generation of the wind farm, the power generation of a single wind turbine under yaw is calculated: Among them, P i is the power generation of the i-th wind turbine under yaw, u i is the incoming wind speed of the i-th wind turbine, γ i is the yaw angle of the i-th wind turbine.

7. The yaw optimization method based on genetic algorithm and Y-3DJG wake model according to claim 6, characterized in that: The maximum total wind farm power generation is taken as the target, and the calculation formula is: Among them, P i (γ) is the output of each wind turbine when considering wake effect and yaw, n is the number of wind turbines, and they are numbered in the order in which they see the wind in the current wind direction, with the upstream wind turbine numbered first and the downstream wind turbine numbered last, and γ is the yaw angle.

8. The yaw optimization method based on genetic algorithm and Y-3DJG wake model according to claim 7, characterized in that: The yaw angle variation range is defined as a constraint condition, and the yaw angle variation range is -30°<γ<30°.

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