Self-adaptive wind direction variable pitch control method for optimizing performance of vertical axis wind turbine

Abstract

The invention discloses a theoretical method for optimizing the performance of a vertical axis wind turbine, and belongs to the technical field of wind power generation. The method comprises the following steps: firstly, constructing a blade variable pitch control model based on wind direction dynamic adaptation, equivalently incorporating a wind direction angle into blade pitch angle calculation, simulating a jumping process of an optimal pitch angle under different azimuth angles by using a Sigmoid activation function, and generating a continuous and smooth adaptive pitch angle control function; therefore, the attack angle of the blade is adjusted in real time to be maintained within the optimal range. Secondly, a self-adaptive multi-flow-pipe model is provided on the basis of a double-multi-flow-pipe model, a wind flow field is subdivided into eight areas (equivalent to four parallel double-brake-disc multi-flow-pipe models), and interference factors and average power coefficients of all the areas are iteratively solved by combining the blade element theory and the momentum theorem; the dynamic stall phenomenon can be effectively restrained, the lift characteristic, torque output and operation stability of the wind driven generator in the full working condition range are remarkably improved, and the method is particularly suitable for a distributed wind power system in the urban environment.

Description

Technical Field

[0001] This invention specifically relates to an adaptive wind direction pitch control method for blades of vertical axis wind turbines, particularly suitable for urban distributed wind power systems, and belongs to the field of wind power engineering technology. Background Technology

[0002] In the current context of energy transition and the advancement of zero-carbon goals, wind energy, due to its clean and renewable characteristics, is increasingly becoming an important component of the energy strategies of many countries. According to the recent Urban Renewable Energy Report released by the International Renewable Energy Agency, despite the rapid development of wind power technology, its application in urban areas still faces many bottlenecks, including limited installation space, incompatibility between the size of traditional wind turbines and low-wind-speed turbulent environments, as well as challenges such as noise and visual pollution.

[0003] Vertical axis wind turbines, with their simple blade structure, reasonable generator arrangement, strong adaptability to low wind speeds, and good noise control, have shown significant potential for application in urban environments. However, during operation, the blade angle of attack changes rapidly with rotation, easily causing complex aerodynamic disturbances, leading to dynamic stall and vortex shedding, which severely restricts their aerodynamic performance and actual efficiency, becoming a major obstacle to their large-scale promotion in urban environments. Summary of the Invention

[0004] To address this technical bottleneck, this invention proposes a blade pitch control model for vertical axis wind turbines based on dynamic wind direction adaptation. This model adjusts the blade angle of attack in real time to keep it within the stall critical range and optimizes the azimuth angle to near the optimal pitch angle, thereby significantly improving lift characteristics and torque output across the entire operating range and ultimately maximizing the operating efficiency of the vertical axis wind turbine. This model effectively expands the operating tip speed ratio range of vertical axis wind turbines, improves the overall performance robustness and annual power generation potential of the turbine, and provides a reliable technical solution for urban wind power applications.

[0005] Compared with the prior art, the beneficial effects of the present invention are:

[0006] 1. This invention can control the vertical axis wind turbine blades to maintain the optimal pitch angle at the corresponding azimuth angle, thereby maximizing lift and torque across the entire speed range and significantly improving the efficiency of the wind turbine.

[0007] 2. This invention can reduce the fluctuation range of the wind energy utilization coefficient of vertical axis wind turbines, thereby improving the stability of output torque and the mechanical reliability of the whole machine.

[0008] 3. By optimizing operational stability and wind energy conversion efficiency, this invention can maintain high power output stability even under low wind speed conditions, verifying its feasibility as a core technology for next-generation vertical axis wind turbines. It is especially suitable for applications with high reliability requirements, such as off-grid distributed energy systems and extreme climate environments.

[0009] 4. This invention enables vertical axis wind turbines to maintain a high lift-to-drag ratio under low tip speed ratio conditions to improve efficiency; and effectively suppresses dynamic stall under high tip speed ratio conditions to reduce aerodynamic losses, thereby expanding the high-efficiency operating range of the wind turbine. Attached Figure Description

[0010] To facilitate understanding of the technical solutions of the embodiments of this application, the accompanying drawings are briefly described below.

[0011] Figure 1 This is a schematic diagram of the wind direction equivalent of the present invention.

[0012] Figure 2 This is a simulation model diagram of the NACA0015 blade with an angle of attack of 0°.

[0013] Figure 3 This is a curve showing the periodic variation of the power coefficient of the NACA0015 blade of the present invention with the adaptive pitch angle.

[0014] Figure 4 This is a schematic diagram of a dual multi-flow tube model.

[0015] Figure 5 This is a schematic diagram of the adaptive multi-pipe flow model of the present invention.

[0016] Figure 6 This is a flowchart of the adaptive multi-pipe flow model algorithm of the present invention. Detailed Implementation

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments; the embodiments are only used to illustrate the implementation of the present invention and do not constitute a limitation on the scope of protection of the claims.

[0018] Vertical axis wind turbines experience dramatic changes in the angle of attack of their blades during a single rotation. Therefore, symmetrical airfoils from the NACA airfoil database are typically selected based on the turbine's operating conditions and design specifications. This embodiment uses the standard airfoil NACA0015 as the research object. As shown in Table 1, the aerodynamic design of the blade airfoil is performed under operating conditions with an incoming flow velocity of 10 m / s and a tip speed ratio of 2.1.

[0019] Table 1 Model Parameters

[0020]

[0021] Step (1): Calculation of the equivalent and optimal angle of attack for the wind direction angle. This embodiment calculates the wind direction angle... Equivalent inlet blade pitch angle This facilitates subsequent calculations and simulations, such as... Figure 1 As shown. Under constant wind direction, the turbine efficiency is best when the pitch angle equals the optimal angle of attack. When the wind direction changes, the turbine efficiency is best when the adaptive pitch angle is given by equation [equation missing]. The adaptive pitch angle equation is:

[0022]

[0023] in, It is the best angle of attack with the wind direction horizontal. It is the wind direction angle. It is the wind direction compensation angle. For the corresponding azimuth angle The pitch angle jump variable is n, where n is the total number of jump points.

[0024] Under conditions of no wind direction change, a blade model with an angle of attack ranging from -20° to 20° was established using STAR-CCM+. Figure 2 This is a blade model with an angle of attack of 0°. The lift-to-drag ratios were obtained by selecting steady, coupled fluid, and Spalart-Allmaras turbulence models, respectively. When the lift-to-drag ratio The angle of attack corresponding to the maximum, i.e., the wind direction angle. Optimal angle of attack at 0° .

[0025] Step (2): Calculate the power coefficient of the blade at different pitch angles. In this embodiment, the blade pitch angles are set in 1° intervals. Simulations are performed using separated flow, implicit unsteady-state, and K-Omega turbulence models. Velocity contour maps are observed, and simulations are stopped when stall occurs. The pitch angles are controlled within the stall range to obtain the following results: Figure 3 The wind power coefficient curve corresponding to the shown azimuth angle. Figure 3 It can be seen that the power coefficient of the NACA0015 blade varies significantly with azimuth angle at different pitch angles, indicating that the optimal angle corresponding to the adaptive pitch angle is also different at different azimuth angles. The variation law of the adaptive pitch angle with azimuth angle is summarized and listed in Table 2.

[0026] Table 2. Variation of blade power coefficient with adaptive pitch angle period and corresponding jump variables of the adaptive pitch angle jump term.

[0027] Azimuth Optimal pitch angle (average) Jump term jump variables 1°-60° -7° 1° +2° 61°-90° 3° 61° +12° 91°-120° -1° 91° +8° 121°-180° -3° 121° +6° 181°-240° -10° 181° -1° 241°-270° -10° 241° -1° 271°-300° -11° 271° -2° 301°-360° -11° 301° -2°

[0028] Based on the above simulation, the optimal angle of attack in this embodiment under the condition of no wind direction change is... The jump term is found using the optimal angle of attack as the baseline, as shown in Table 1. This embodiment uses the sigmoid activation function to simulate piecewise jumps, which takes the following form:

[0029]

[0030] In the formula, k controls the steepness of the jump. Considering that the angle change is small and the actual control reaction time is insufficient, the control jump is slowed down, and k = -0.01 is taken. It is the independent variable of the function.

[0031] By superimposing jump terms from different intervals, the piecewise constant is converted into a continuous function, enabling wind-adaptive pitch angles. For example:

[0032]

[0033] In the formula, The azimuth angle of the blade.

[0034] Step (3): Based on the patterns obtained in Step 2, optimize the dual-multi-pipe model method and propose an adaptive multi-pipe flow model. The dual-multi-pipe model ignores wind direction changes and is divided into upper and lower regions, as shown below. Figure 4 As shown, this embodiment optimizes upon this, dividing the flow field into 8 regions. The adaptive multi-pipe flow model is equivalent to 4 dual-disc multi-pipe models, as follows. Figure 5 As shown. Based on the dual-multi-flow tube model, blade element theory, and momentum theorem, the average wind power coefficient of the blades in upwind zone 1 can be obtained as:

[0035]

[0036] In the formula, For the number of leaves, For the chord length of the leaf, The wind turbine rotation speed, For the incoming wind speed, The induced velocity in the upwind zone 1, denoted as the thrust coefficient of the blade.

[0037] Similarly, the average wind power coefficient of the blades in upwind zones 2 to 4 is:

[0038]

[0039]

[0040]

[0041] Similarly, the average wind power coefficient of the blades in downwind zones 1 to 4 is:

[0042]

[0043]

[0044]

[0045]

[0046] In the formula, The induced velocity in downwind zone 1, The induced velocity in downwind zone 2, The induced velocity in downwind zone 3, The induced velocity is in zone 4 downwind.

[0047] The sum of the average power coefficients of the eight regions is the average power of the vertical axis wind turbine. for:

[0048]

[0049] Step (4): Implement the algorithm for the adaptive multi-pipe flow model. In this embodiment, the wind turbine flow domain is divided into four dual-disc systems for separate calculations, significantly improving the accuracy of the calculation results. Simultaneously, the angle-of-attack calculation formula is improved. This improved formula can dynamically adjust the pitch direction according to different operating conditions, thereby effectively optimizing the blade lift-to-drag ratio and improving the wind energy utilization coefficient. Finally, the algorithm for the adaptive multi-pipe flow model is implemented, and its process is as follows: Figure 6 As shown, firstly, the interference factor of blade elements in the flow tubes at various points in the wind turbine model is calculated, in order to... Figure 5 Taking the flow tubes in the upper-middle wind zone 1 and the lower wind zone 1 as an example, according to Figure 6 The process shown involves inputting multiple initial values, sequentially solving for each parameter, and iterating multiple times using the least squares method until the residual is less than 10. -6 The output results yield the interference factors of blade elements in each flow tube of the upwind and downwind regions 1. Similarly, the interference factors of blade elements in each flow tube of the other six regions are obtained. Next, after obtaining the interference factors, the average power coefficients of the upwind and downwind regions are obtained according to the solution formula of this embodiment. Finally, the average power coefficient of the entire wind turbine is calculated.

[0050] The descriptions in the embodiments of this invention are merely examples of implementations of the inventive concept. The scope of protection of this invention should not be considered as limited to the specific forms described in the embodiments. This invention can be modified in many ways, and all such modifications and variations are within the scope of protection claimed by this invention.

Claims

1. A theoretical method for optimizing the performance of a vertical axis wind turbine, characterized in that, The method includes the following steps: Step 1: Establish an aerodynamic parameter model of the blade airfoil of a vertical axis wind turbine, and set the operating conditions of the incoming wind speed and the tip speed ratio; Step 2: Construct a blade pitch control model based on dynamic wind direction adaptation; the control model incorporates the wind direction angle into the blade pitch angle, calculates the optimal angle of attack under no wind direction change as the benchmark value, and determines the optimal pitch angle in each azimuth angle interval according to the power coefficient variation law under different azimuth angles; Step 3: Use the Sigmoid activation function to simulate the jump of the optimal pitch angle between different azimuth angle intervals, and construct a continuous wind direction adaptive pitch angle control function by superimposing the jump terms of different intervals. Step 4: Based on the adaptive pitch angle control function, establish an adaptive multi-flow tube model; the adaptive multi-flow tube model divides the flow field of the wind turbine into multiple regions, and calculates the induced velocity and disturbance factor of each region respectively; Step 5: Calculate the blade average wind power coefficient of each region using the adaptive multi-flow tube model, and sum them to obtain the overall average power coefficient of the vertical axis wind turbine, thereby evaluating and optimizing the aerodynamic performance of the wind turbine.

2. The theoretical method for optimizing the performance of a vertical axis wind turbine according to claim 1, characterized in that, The Sigmoid activation function described in step 3 is in the following form: Where k is a coefficient that controls the steepness of the transition. The wind direction adaptive pitch angle control function is the independent variable of the function. The expression is: Where θ is the blade azimuth angle. The optimal angle of attack is when the wind direction is horizontal, where φ is the wind direction angle. For wind direction compensation angle, For the corresponding azimuth angle The pitch angle jump variable is n, where n is the total number of jump points.

3. The theoretical method for optimizing the performance of a vertical axis wind turbine according to claim 2, characterized in that, The coefficient k is set to -0.01 to achieve a smooth transition of angle changes.

4. The theoretical method for optimizing the performance of a vertical axis wind turbine according to claim 2, characterized in that, For the NACA0015 airfoil blade, the wind direction adaptive pitch angle control function The specific configuration is as follows: The azimuth angle θ is measured in degrees.

5. The theoretical method for optimizing the performance of a vertical axis wind turbine according to claim 1, characterized in that, The adaptive multi-flow tube model described in step 4 is an improvement on the dual multi-flow tube model. It divides the wind turbine flow field into 8 regions, including 4 regions in the upwind region (upwind region 1 to upwind region 4) and 4 regions in the downwind region (downwind region 1 to downwind region 4). The model is equivalent to 4 parallel dual brake disc multi-flow tube models.

6. The theoretical method for optimizing the performance of a vertical axis wind turbine according to claim 5, characterized in that, The formula for calculating the average wind power coefficient of the blades in upwind zone 1 in step 5 is as follows: Where N is the number of blades, c is the blade chord length, and ω is the rotor speed. For the incoming wind speed, The induced velocity in the upwind zone 1, The thrust coefficient of the blade is given. The formula for calculating the average wind power coefficient in upwind zones 2 to 4 and downwind zones 1 to 4 is the same, only the induced velocity parameter and the integration interval are different.

7. The theoretical method for optimizing the performance of a vertical axis wind turbine according to claim 6, characterized in that, The overall average power coefficient of the vertical axis wind turbine The sum of the average power coefficients of the eight regions:

8. The theoretical method for optimizing the performance of a vertical axis wind turbine according to claim 1, characterized in that, The evaluation and optimization process described in step 5 includes iterative calculation steps: 1) Initialize the interference factor of leaf element in each flow tube; 2) Calculate the local relative wind speed and effective angle of attack based on the current interference factors; 3) Obtain the lift coefficient and drag coefficient by referring to tables using airfoil aerodynamic data; 4) Update the disturbance factor according to the momentum theorem; 5) Perform multiple iterations using the least squares method until the residual is less than a preset threshold (10). -6 The output shows the converged interference factor and power coefficient.

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