Method for calculating power of wind farm considering coupling influence of additional turbulence and blocking effect

Abstract

The application provides a wind farm power generation calculation method considering coupling influence of additional turbulence and blocking effect, comprising: step 1, obtaining wind farm information and inflow wind data; step 2, numbering each wind turbine in the field according to the front and rear order along the inflow wind direction; step 3, quantifying wake loss considering additional turbulence influence, combining wind speed variation under the influence of blocking effect, calculating effective wind speed and thrust coefficient of each wind turbine; step 4, taking the result of step 3 as input, calculating the wind speed variation of each wind turbine caused by blocking effect; step 5, according to the result of step 4, updating the wind speed variation of step 3, and through iterative calculation, obtaining the effective wind speed meeting the convergence standard; step 6, according to the effective wind speed of step 5, calculating the power generation. The application realizes decoupling of additional turbulence and blocking effect, first proposes a power generation calculation method which can efficiently and accurately simulate the common influence of the two, and significantly improves the calculation precision.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and more specifically to a method for calculating the power output of a wind farm that takes into account the coupled effects of additional turbulence and blockage. Background Technology

[0002] Wind farms contain numerous wind turbines, and these turbines interact in complex ways during operation. Studies show that in the wake region of a wind turbine, in addition to wind speed losses due to energy absorption, the turbulence intensity is significantly higher than that in the inflow. This incremental turbulence, known as "additional turbulence," affects the wake evolution behind the rotor, particularly the recovery of the aforementioned wind speed losses. Ignoring this effect would lead to an underestimation of the wind speed at the downwind turbine, consequently affecting the accuracy of wind farm power calculations.

[0003] Furthermore, the "blocking effect" has received widespread attention in the field of wind power technology in recent years. An induced velocity zone exists in front of the rotor of any isolated wind turbine, within which wind speed decreases. Similarly, the inflow wind speed in front of a wind farm differs from the free flow velocity, being even lower. This reduction in wind speed due to the presence of the wind farm itself stems from the "blocking effect." In wind power engineering practice, the "blocking effect" is particularly pronounced on the first row of wind turbines in the field. Although they are not affected by the wakes of other wind turbines, their power generation is significantly different. When there are more downwind turbines, the power generation of the first row is even lower. Extensive statistical data shows that the power loss due to the "blocking effect" can reach 3%, especially under stable atmospheric conditions, where the strong interaction between the wind farm and the atmospheric turbulent boundary layer further amplifies the impact of the "blocking effect." Therefore, it is essential to consider the "blocking effect" when calculating wind farm power generation.

[0004] As described above, both "additional turbulence" and "blocking effect" have a significant impact on wind farm power generation. Generally, the former tends to reduce wake interference between turbines, increasing power generation, while the latter, conversely, reduces the incoming wind speed perceived by the turbines, leading to decreased power generation. Patent document CN 116720027 A discloses a method and apparatus for calculating wind farm power generation considering the "blocking effect," in which the influence area of ​​the "blocking effect" is defined with a preset distance as the radius. This approach lacks physical basis and may lead to an underestimation of the impact of the "blocking effect." More importantly, the method does not consider "additional turbulence" and its coupling with the "blocking effect," resulting in significant deviations in power calculation and thus affecting the reliability of the economic assessment results of wind power projects.

[0005] In summary, existing research on how to accurately quantify the complex nonlinear coupling effects of "additional turbulence" and "blocking effect" remains lacking, and efficient and reliable technical solutions are urgently needed. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by providing a wind farm power calculation method that takes into account the coupled effects of additional turbulence and blockage, thereby improving the accuracy of power calculation.

[0007] The technical solution adopted in this invention is: a wind farm power calculation method considering the coupled effects of additional turbulence and blockage, comprising the following steps: Step 1: Obtain wind farm information and inflow wind data; Step 2: Number each wind turbine in the field according to the front-to-back sequence along the inflow direction; Step 3: Following the order of numbers from smallest to largest, the wake effect calculation model that considers the influence of additional turbulence on wake evolution is used to quantify the wind speed loss at each wind turbine in the field after considering the additional turbulence and wake interference from other wind turbines. Combined with the wind speed change caused by the blocking effect, the effective wind speed of each wind turbine is obtained, and the thrust coefficient is obtained through interpolation calculation. Step 4: Using the effective wind speed and thrust coefficient from Step 3 as inputs to the blockage effect calculation model, quantify the wind speed change caused by the blockage effect at each wind turbine in descending order of number. Step 5: Based on the calculation results of Step 4, update the wind speed change mentioned in Step 3 and perform iterative calculations until the difference between the wind speed changes at each wind turbine in the two calculations is less than the set threshold, and output the effective wind speed of each wind turbine at this time. Step 6: Based on the effective wind speed output in Step 5, and combined with the wind speed-aerodynamic parameter interpolation list of each wind turbine model, calculate the output power, and then obtain the wind farm's power generation through summation calculation. Step 3 includes the following specific steps: Step 3.1: Obtain the sequence of wind speed changes caused by the blocking effect. , The corresponding numbers are as follows The wind speed changes at each wind turbine location related to the blocking effect; in the initial calculation, All values ​​are set to 0; Step 3.2: Following the wind turbine numbers in ascending order from step 2, the wake effect calculation model, which considers the influence of additional turbulence on wake evolution, is used to quantify the wind speed loss at each wind turbine location within the field after considering the additional turbulence and wake interference from other wind turbines. This is combined with the sequence described in step 3.1. middle The value of is used to reflect the impact of the blockage effect and to calculate the effective wind speed at each wind turbine. Step 3.3: Continuing from the effective wind speed calculated in Step 3.2, and combining it with the wind speed-aerodynamic parameter list of the corresponding model, the thrust coefficient of each wind turbine is obtained through interpolation calculation. Step 3.4: Integrate the calculation results from steps 3.2 and 3.3 to form a sequence. and sequence , The corresponding field numbers are as follows: The effective wind speed of the wind turbine The corresponding field numbers are as follows: The thrust coefficient of the wind turbine; Step 4 includes the following specific steps: Step 4.1: Using the effective wind speed and thrust coefficient from Step 3 as input, and following the order of wind turbine numbers from largest to smallest, use the blocking effect calculation model to quantify the wind speed change caused by the blocking effect at each wind turbine in the field. Step 4.2: Integrate the calculation results from Step 4.1 to form a new sequence of wind speed changes, represented as follows: , The numbers obtained from step 4.1 are respectively... The change in wind speed at each wind turbine; Step 5 includes the following steps: Step 5.1, calculate the wind speed change sequence. and The relative change of each data element at each position is calculated, and it is checked one by one whether it is less than a set threshold. If the threshold is met, the calculation stops and proceeds to step 5.2; otherwise, the sequence is used. Update sequence Repeat steps 3 and 4 until the convergence criteria are met; Step 5.2: Output the effective wind speed of each wind turbine in the field.

[0008] Through the aforementioned technical means and a complete process of "joint calculation of wake and blockage - blockage quantification - iterative convergence - power calculation," the decoupled calculation of the coupled effects of additional turbulence and blockage is achieved. The bidirectional feedback and iterative optimization in steps 3 and 4 solve the problem of existing technologies being unable to quantify the nonlinear coupling relationship between the two, making the effective wind speed calculation more closely resemble the actual flow field, thereby significantly improving the accuracy of wind farm power generation calculation. The sub-steps of step 3 clarify the initial value setting of the blockage effect, the calculation of the effective wind speed and thrust coefficient, and the integration of results, providing standardized and accurate input parameters for subsequent blockage effect calculations and iterative processes. The sub-steps of step 4 achieve accurate quantification and sequence updating of the wind speed change due to the blockage effect, providing data support for iterative convergence. The iterative convergence mechanism in step 5 ensures the stability and accuracy of the calculation results, ultimately providing reliable data support for the economic evaluation of wind power projects.

[0009] In some embodiments, in step 2, any point within the wind farm is taken as the origin of the coordinate system, and the due east direction is defined as... The positive axis and the due north direction are In the positive direction of the axis, a two-dimensional rectangular coordinate system is established, called the geodetic coordinate system, to determine the coordinates of each wind turbine in this coordinate system;

[0010] Keeping the origin of the coordinate system fixed, rotate the coordinate system following the inflow direction from step 1, so that in the new coordinate system, i.e., the relative coordinate system... The positive axis points in the direction of the inflow;

[0011] Based on the coordinate transformation matrix expressed in equation (1), calculate the coordinates of each wind turbine in the relative coordinate system: (1) (2) in, and These represent the x-coordinate and y-coordinate in the geodetic coordinate system and the relative coordinate system, respectively. For the incoming wind direction, Let be the angle between the inflow wind direction and the positive x-axis in the geodetic coordinate system. By comparing the abscissas of each wind turbine in the relative coordinate system, their relative positions can be determined, and they can be sorted and numbered accordingly. The first wind turbine is designated as number 1, and the numbers of each downwind turbine are sequentially increased. Let the total number of wind turbines in the wind farm be . The numbering sequence is then represented as .

[0012] By employing the aforementioned technical means and establishing a conversion mechanism between the geodetic coordinate system and the relative coordinate system, precise matching between the wind turbine position and the inflow wind direction is achieved. This solves the problem in existing technologies where the wind turbine position sequence does not correspond to the inflow wind direction, leading to calculation errors in wake and blockage effects. The combined calculation of the coordinate transformation matrix and the wind direction angle can accurately quantify the relative position of each wind turbine along the inflow wind direction, providing a clear logical basis for subsequent sequential calculations of wake and blockage effects. This avoids flow field calculation errors caused by ambiguous position quantification and ensures the orderliness and accuracy of subsequent calculation steps.

[0013] In some embodiments, step 6 includes the following steps: Step 6.1: Based on the effective wind speed output in Step 5, and combined with the wind speed-aerodynamic parameter list of each wind turbine model, the output power of each turbine is obtained through interpolation calculation. Step 6.2: The power generation of the wind farm is obtained by summing the output power of each wind turbine.

[0014] By using the above-mentioned technical means, the accurate and effective wind speed after the previous iteration convergence is converted into the actual power generation result, which solves the problem of power generation prediction distortion caused by wind speed calculation deviation in the existing technology.

[0015] The beneficial effects of this invention are as follows: This invention uses effective wind speed and thrust coefficient as an intermediate bridge to decouple the complex coupling relationship between additional turbulence and blockage effect into two independently executable steps: wake effect calculation (step 3) and blockage effect calculation (step 4). Through the iterative and convergence judgment of steps 3 and 4, the quantitative simulation of the coupling effect between the two is realized for the first time, filling the technical gap in existing research that cannot accurately quantify the nonlinear coupling relationship between the two. Compared with the shortcomings of existing technologies that can only consider a single effect or ignore the coupling effect, this invention greatly improves the rationality and accuracy of wind farm flow field calculation.

[0016] This invention, through iterative steps 3 and 4 and convergence judgment of relative changes, ensures that the effective wind speed, thrust coefficient, additional turbulence, and wind speed changes due to blocking effects continuously approach the true values ​​within the coupling relationship. When the wind speed change due to blocking effects meets a set threshold, the final effective wind speed is output, ensuring the stability and high accuracy of the calculation results considering the coupling effects. This significantly reduces the calculation error of wind farm power generation, providing scientific and technical support for wind farm micro-site selection, farm-level collaborative control, and economic evaluation.

[0017] Based on the blockage effect calculation model in step 4 and the initial value setting and iteration mechanism in step 3, this invention quantifies the blockage effect from the physical essence of flow field evolution. At the same time, by sorting the wind turbines by their numbers based on the inflow wind direction, it ensures that the blockage effect of each wind turbine can be included in the influence of all downstream units, effectively avoiding the problem of the blockage effect being underestimated. This makes the wind speed calculation at key locations such as the first row of wind turbines more in line with the actual engineering scenario and solves the problem of power calculation error caused by the quantification deviation of the blockage effect in the prior art.

[0018] Based on the wake effect calculation model in step 3, this invention incorporates the influence of additional turbulence on wake evolution into the wind speed loss quantification process. By co-calculating the effective wind speed and thrust coefficient, it avoids the problem of underestimation of downwind wind turbine wind speed caused by neglecting additional turbulence in existing technologies. This makes the wake loss calculation more consistent with the actual flow field characteristics, further improving the accuracy of effective wind speed calculation and laying a solid foundation for subsequent power generation calculation. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention, some drawings required in the description of the specific embodiments will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the wind farm power calculation process that takes into account the coupling effects of additional turbulence and blockage.

[0021] Figure 2 This is a schematic diagram of coordinate transformation for a wind turbine.

[0022] Figure 3 A schematic diagram illustrating the complex coupling and correlation of multiple key characteristic quantities affecting the evolution of the wind farm flow field.

[0023] Figure 4 This is a flowchart of the iterative calculation process.

[0024] Figure 5 In the diagram, (a) is a schematic diagram of each wind turbine row used for power data analysis in the wind farm under the condition that the inflow wind direction angle is θ=222° in Example 1, and (b) is the normalized power of each wind turbine row under the condition that the inflow wind direction angle is θ=222°.

[0025] Figure 6 In the diagram, (a) is a schematic diagram of each wind turbine row used for power data analysis in the wind farm under the condition that the inflow wind direction angle is θ=270° in Example 1, and (b) is the normalized power of each wind turbine row under the condition that the inflow wind direction angle is θ=270°.

[0026] Figure 7In the diagram, (a) is a schematic diagram of each wind turbine row used for power data analysis in the wind farm under the condition that the inflow wind direction angle is θ=312° in Example 1, and (b) is the normalized power of each wind turbine row under the condition that the inflow wind direction angle is θ=312°. Detailed Implementation

[0027] The wind farm power calculation method provided by this invention, which takes into account the coupled effects of additional turbulence and blockage, is as follows: Figure 1 As shown, the steps are as follows: Step 1: Obtain wind farm information and inflow wind data; Step 2: Number each wind turbine in the field according to the front-to-back sequence along the inflow direction; Step 3: Following the order of numbers from smallest to largest, the wake effect calculation model that considers the influence of additional turbulence on wake evolution is used to quantify the wind speed loss at each wind turbine in the field after considering the additional turbulence and wake interference from other wind turbines. Combined with the wind speed change caused by the blocking effect, the effective wind speed of each wind turbine is obtained, and the thrust coefficient is obtained through interpolation calculation. Step 4: Using the effective wind speed and thrust coefficient from Step 3 as inputs to the blockage effect calculation model, quantify the wind speed change caused by the blockage effect at each wind turbine in descending order of number. Step 5: Based on the calculation results of Step 4, update the wind speed change mentioned in Step 3 and perform iterative calculations until the difference between the wind speed changes at each wind turbine in the two calculations is less than the set threshold, and output the effective wind speed of each wind turbine at this time. Step 6: Based on the effective wind speed output in Step 5, and combined with the wind speed-aerodynamic parameter interpolation list of each wind turbine model, calculate the output power, and then sum the results to obtain the wind farm's power generation.

[0028] In step 1, the wind farm information includes the location of each wind turbine in the wind farm, the turbine model, and a list of hub height, rotor diameter, and wind speed-aerodynamic parameters for the corresponding turbine model; the inflow wind data includes the wind speed, wind direction, and turbulence intensity at the hub height of the wind turbine.

[0029] In step 2, as Figure 2 As shown, take any point within the wind farm as the origin of the coordinate system, and define the due east direction as... The positive axis (corresponding wind direction angle is 270°), and the due north direction is... Establish a two-dimensional rectangular coordinate system, called the geodetic coordinate system, along the positive axis (corresponding to a wind direction angle of 180°), and determine the position coordinates of each wind turbine in this coordinate system.

[0030] Keeping the origin of the coordinate system fixed, rotate the coordinate system following the inflow direction described in step 1, so that in the new coordinate system, i.e., the relative coordinate system... The positive axis points in the direction of the inflow.

[0031] Based on the coordinate transformation matrix expressed in equation (1), calculate the position coordinates of each wind turbine in the relative coordinate system: (1) (2) in, and These represent the x-coordinate and y-coordinate in the geodetic coordinate system and the relative coordinate system, respectively. For the incoming wind direction, It is the angle between the inflow wind direction and the positive x-axis in the geodetic coordinate system.

[0032] By comparing the x-coordinates of each wind turbine in a relative coordinate system, their relative positions are determined, and they are then sorted and numbered accordingly. The first wind turbine is designated as number 1, and the numbers of the turbines downwind are sequentially increased. Let the total number of wind turbines in the wind farm be... Then the numbering sequence can be represented as .

[0033] For step 3, the specific steps are as follows: Step 3.1: Obtain the sequence of wind speed changes caused by the "blocking effect". The elements therein are numbered sequentially as follows: The wind speed changes at each wind turbine location related to the "blocking effect". In the initial calculation, the sequence... All data elements are set to 0.

[0034] Step 3.2: Following the wind turbine numbers in ascending order, using the "wake effect" calculation model that considers the influence of additional turbulence on wake evolution, the wind speed loss under the influence of wake interference at each wind turbine in the field is quantified sequentially, combined with the sequence described in Step 3.1. The values ​​of the corresponding elements are used to reflect the impact of the blockage effect and to calculate the effective wind speed of each wind turbine. The following example demonstrates the calculation for wind turbine numbered j (1≤j≤N): When j=1, it is the first wind turbine along the inflow direction. Since there are no other turbines in front, it is not affected by the wake interference, and therefore the associated wind speed loss is... Only the inflow velocity needs to be... Based on this, subtract the wind speed change caused by the blocking effect. This allows the determination of the effective wind speed it senses. (3) When j>1, it is upwind. The wind turbines operate within the wake region of the typhoon. Assuming the wind turbine upwind is numbered i, this embodiment uses a linear superposition method to calculate the velocity loss in the overlapping wake region of multiple wind turbines. The wind turbine is calculated using the following formula. Wind speed loss due to wake interference : (4) Furthermore, the wind speed change caused by the "blocking effect" is taken into account. Wind turbines can be obtained Effective wind speed : (5) In equation (4) Indicates the number is When an upwind wind turbine operates in isolation, its wake is at the target wind turbine. The velocity loss at the point, in equation (5) This represents the inflow velocity. In this embodiment, the wake velocity loss threshold is defined according to the one-dimensional momentum theorem, and the modified Gaussian model is used to calculate... : (6) (7) (8) (9) (10) in, , and They represent wind turbines The effective wind speed, thrust coefficient, and rotor diameter, Refers to wind turbine With wind turbine The flow direction spacing, Indicated relative to wind turbine Radial distance of the wind turbine's central axis; parameters Used for quantizing wind turbines The velocity loss and its impact range within the wake region vary with downstream distance, and their values ​​are related to the wind turbine. Turbulence intensity at Positive correlation, the calculation formula is: (11) (12) in, The turbulence intensity in the inflow. For wind turbine The perceived additional turbulence intensity caused by the operation of wind turbines upwind, when i=1, is due to the absence of other wind turbines upwind. When i>1, assume the wind turbine number upwind is ,but The following formula can be used for calculation: (13) in, For wind turbine The area swept by the wind turbine, The representative is located at the wind turbine Upwind wind turbine The additional turbulence intensity generated during isolated operation in wind turbines Size of the location Indicates wind turbine Isolated wake and wind turbine The overlapping area of ​​the wind turbine disks.

[0035] Given that the profile of the additional turbulence intensity in the wake region of a wind turbine approximately exhibits a bimodal Gaussian distribution within the horizontal plane at the hub height, it can be calculated using the following formula. : (14) in, Representative for fitting wind turbine The standard deviation of the bimodal Gaussian function of the additional turbulence intensity profile in an isolated wake in a wind turbine Size of the location Refers to wind turbine With wind turbine The flow direction spacing, Indicated relative to wind turbine The radial distance of the wind turbine's central axis; Represents wind turbine The additional turbulence intensity generated by isolated operation in wind turbines The peak value at that location is estimated in this embodiment using an empirical calculation formula proposed by Crespo et al., which is commonly used in wind power engineering practice: (15) (16) in, , and They represent wind turbines The thrust coefficient, turbulence intensity, and rotor diameter.

[0036] In equation (14) The bimodal Gaussian function used to describe the additional turbulence intensity profile is calculated as follows: (17) in, This represents the distance between the peak point and the center point of the additional turbulence intensity profile. Existing research indicates that tip vortices are a significant driver of additional turbulence intensity. Therefore, near the rotor disk, the peak point approximately appears at the blade tip. As the wake propagates downstream, due to continuous momentum exchange with the surrounding flow field, the peak point gradually moves away from the profile center point, exhibiting an approximately linear expansion with increasing downstream distance; this is known as wake expansion characteristics. Therefore, in this embodiment, the following formula is used to estimate... : (18) In equation (17) and As the weighting coefficient, considering the aforementioned wake expansion characteristics, the calculation formula is: (19) It should be noted that in this embodiment, the Gaussian model and linear superposition method are used as examples to calculate the velocity loss in the wake region of each isolated wind turbine and to quantify the overlapping effect of the wakes of multiple wind turbines in the upwind direction. Other commonly used calculation methods in the field are also within the scope of protection of this invention, such as the Park model and sum of squares superposition method embedded in wind resource calculation software such as WAsP and WindFarmer.

[0037] Similarly, to improve the calculation accuracy of additional turbulence, this embodiment uses a more realistic bimodal Gaussian function to describe the intensity of additional turbulence in the wake region of the wind turbine, and considers the influence of wake expansion characteristics on the spatial evolution of additional turbulence intensity. This is just an example and not a limitation. Other commonly used calculation models in the field are also within the scope of protection of this invention, such as the IEC model commonly used in the current wind power engineering field.

[0038] Step 3.3: Continuing from the effective wind speeds of each wind turbine calculated in Step 3.2, and combining them with the wind speed-aerodynamic parameter list of the respective turbine model, the thrust coefficient is obtained through interpolation calculation.

[0039] An example of thrust coefficient interpolation calculation is as follows: Assume the number is The effective wind speed of the wind turbine is Furthermore, according to data provided by wind turbine manufacturers, their respective models are in The thrust coefficient at wind speed is , The thrust coefficient at wind speed is , the corresponding thrust coefficient can be obtained through linear interpolation, and the calculation formula and result are .

[0040] Similarly, in this embodiment, only the linear interpolation method is used as an example, rather than a limitation. Other commonly used interpolation calculation methods in this field are also within the protection scope of this invention.

[0041] Step 3.4, integrate the calculation results of Step 3.2 and Step 3.3 to form a sequence , , and each element in it corresponds in sequence to the effective wind speed and thrust coefficient of the wind turbines numbered .

[0042] The specific content of said Step 4 is as follows: Step 4.1, taking the effective wind speed and thrust coefficient calculated in Step 3 as inputs, and in the order of decreasing wind turbine numbers, use the "blocking effect" calculation model to sequentially quantify the wind speed change amount caused by the "blocking effect" at each wind turbine in the field. Hereinafter, an example calculation for the wind turbine numbered j (1 ≤ j ≤ N) is given: When j = N, it is the last wind turbine along the inflow wind direction. Since there are no other wind turbines downstream of it, it is not affected by the "blocking effect", and the corresponding wind speed change amount is .

[0043] When j < N, in this embodiment, the induced velocity of all downstream wind turbines at their positions is linearly superimposed to quantify the wind speed change amount it perceives due to the "blocking effect" , and the calculation formula is: (20) Where, is the number of the wind turbine downstream, is the correction coefficient, and the recommended value is 1.8, refers to the magnitude of the induced velocity generated by the wind turbine downstream at the position of the wind turbine when operating in isolation, and in this embodiment, it is calculated by the "vortex column" model: (21) Where, is the vorticity of the wind turbine , and the calculation formula is: (22) Where, and respectively represent the effective wind speed and thrust coefficient of the wind turbine .

[0044] In equation (21) For use in describing wind turbines The geometric factor for the induced velocity spatial distribution is calculated as follows: (twenty three) in, and Let them represent the first and third complete elliptic integrals, respectively. Refers to wind turbine With wind turbine The flow direction spacing, For wind turbine The diameter of the wind turbine, In contrast to wind turbines Radial distance of the wind turbine's central axis, parameters Calculated by the following formula: (twenty four) To quantify the impact of the ground effect, the ground plane is used as a mirror. It is assumed that there is a "virtual wind turbine" below it that is the same type as the wind turbine above the ground plane and is symmetrically distributed. The induced velocity of the latter can also be calculated by referring to equations (20)-(24).

[0045] Similarly, in this embodiment, the "vortex column" model is used only as an example to calculate the induced velocity of each wind turbine in the field, and is not intended to limit it. Other related calculation models are also within the protection scope of this invention.

[0046] Step 4.2: Integrate the calculation results from Step 4.1 to form a wind speed change sequence, which is the same as the sequence described in Step 3.1. To differentiate, the new sequence is represented as The elements therein are numbered sequentially as follows: The change in wind speed at each wind turbine due to the "blocking effect".

[0047] Regarding the specific explanation of step 5, firstly, it is necessary to summarize why iterative solution is required to obtain the wind farm power considering the coupling effect of additional turbulence and blockage effect. According to step 4, the induced velocity of the wind turbine is closely related to its own effective wind speed and thrust coefficient, and affects the wind speed change at the wind turbine upwind through the blockage effect; and as can be seen from step 3, for any wind turbine in the field, its perceived effective wind speed must be obtained by comprehensively considering the blockage effect and wake interference of other wind turbines. Among these, the additional turbulence determines the strength of the wake interference by affecting the wake evolution of the wind turbine, and according to equations (14)-(16), the magnitude of the additional turbulence depends on the thrust coefficient of the wind turbine, which is a function of the effective wind speed of the wind turbine. It can be seen that there is a clear nonlinear coupling relationship between the effective wind speed, thrust coefficient, additional turbulence and induced velocity, such as Figure 3 As shown. In view of this, iterative solution is essential to realize the calculation of wind farm power taking into account the coupled effects of additional turbulence and blockage.

[0048] Based on the above analysis of the mutual constraints among several key characteristic quantities affecting the flow field evolution and power generation of wind farms, this invention proposes the following iterative calculation method. The calculation process is detailed in [link to calculation]. Figure 4 The steps include the following: Step 5.1, following the sequence of wind speed changes caused by the "blocking effect" described in Steps 3 and 4. and Calculate the relative change of data elements at each position in the two sequences (indicated by the first position). Taking a typhoon generator as an example, that is, calculation The algorithm iterates through the sequence and checks whether each value is less than a pre-set threshold S. If the threshold is met, the calculation stops and proceeds to step 5.2; otherwise, the sequence is processed. Update sequence Repeat steps 3 and 4 until the convergence criteria are met.

[0049] Step 5.2: Output the effective wind speed of each wind turbine in the field.

[0050] Step 6 specifically involves: Step 6.1: Based on the effective wind speed output in Step 5, and combined with the wind speed-aerodynamic parameter interpolation list for each wind turbine model, calculate the output power for each turbine. An example is as follows: Assume the number is The effective wind speed of the wind turbine is Furthermore, according to data provided by wind turbine manufacturers, their respective models are in The output power at wind speed is , The output power at wind speed is The corresponding output power can be calculated using linear interpolation. The calculation formula and result are as follows: .

[0051] Step 6.2: The power generation of the wind farm is obtained by summing the output power of each wind turbine in step 6.1.

[0052] The effectiveness and reliability of the present invention will be illustrated below through specific examples: Taking the Horns Rev I wind farm as the research object, the farm contains a total of 80 Vestas 2MW wind turbines, with a rotor diameter of D=80 m and a hub height of H=70 m. The turbines are arranged very regularly, with 8 rows in the north-south direction and 10 columns in the east-west direction. The row spacing and column spacing are both about 7D.

[0053] Under the inflow conditions of a wind speed of 8 m / s at the height of the wind turbine hub and a turbulence intensity of 0.077, the technical path of this invention was adopted, and the threshold S=0.001 mentioned in step 5 was set. The power generation at three typical inflow wind direction angles of θ=222°, 270° and 312° was calculated in sequence. The results were compared with measured data, CFD simulation data and model calculation results when the effects of additional turbulence and blockage were ignored.

[0054] Specifically, referring to the processing method for wind turbine power generation in the Horns Rev I wind farm when acquiring measured data and CFD simulation data, Figure 5 (a) Figure 6 (a) and Figure 7 Figure (a) shows the wind turbine rows for power data analysis at wind direction angles of θ=222°, 270°, and 312°. These rows are represented by black dashed lines connecting multiple adjacent solid black circles, each circle representing one wind turbine. As can be seen, the number of wind turbines in each row is exactly the same at the same wind direction angle. For ease of distinction and explanation, each wind turbine row is numbered according to its sequence and labeled in the figure with the character "R" followed by the serial number. Figure 5 Example (a) in the example contains five wind turbine rows referred to as R1, R2, R3, R4 and R5. Figure 5 (b) Figure 6 (b) and Figure 7 In section (b), the corresponding values ​​are given respectively. Figure 5 (a) Figure 6 (a) and Figure 7 The variation of normalized power for each wind turbine row is shown in (a) of the diagram. The normalized power is defined as the ratio of the actual power of each wind turbine row to the actual power of the first wind turbine in the same wind direction angle. Figure 5For example, in the result of (b), the normalized power of the wind turbine row referred to by "R2" is the ratio of the actual power of the row to the actual power of the wind turbine row referred to by "R1".

[0055] according to Figure 5 (b) Figure 6 (b) and Figure 7 In (b) of the test, regardless of the change in the inflow wind direction angle, the normalized power of each wind turbine row calculated based on the technical solution of the present invention is in good agreement with the measured data and CFD simulation data used as reference. The accuracy is significantly higher than the model calculation results when the effects of additional turbulence and blockage are ignored.

[0056] The technical solutions of the present invention have been described in conjunction with the accompanying drawings. It will be readily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent modifications or substitutions to the relevant technical features, and all such modifications or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for calculating the power output of a wind farm considering the coupled effects of additional turbulence and blockage, characterized in that, Includes the following steps: Step 1: Obtain wind farm information and inflow wind data; Step 2: Number each wind turbine in the field according to the front-to-back sequence along the inflow direction; Step 3: Following the order of numbers from smallest to largest, the wake effect calculation model that considers the influence of additional turbulence on wake evolution is used to quantify the wind speed loss at each wind turbine in the field after considering the additional turbulence and wake interference from other wind turbines. Combined with the wind speed change caused by the blocking effect, the effective wind speed of each wind turbine is obtained, and the thrust coefficient is obtained through interpolation calculation. Step 4: Using the effective wind speed and thrust coefficient from Step 3 as inputs to the blockage effect calculation model, quantify the wind speed change caused by the blockage effect at each wind turbine in descending order of number. Step 5: Based on the calculation results of Step 4, update the wind speed change mentioned in Step 3 and perform iterative calculations until the difference between the wind speed changes at each wind turbine in the two calculations is less than the set threshold, and output the effective wind speed of each wind turbine at this time. Step 6: Based on the effective wind speed output in Step 5, and combined with the wind speed-aerodynamic parameter interpolation list of each wind turbine model, calculate the output power, and then obtain the wind farm's power generation through summation calculation. Step 3 includes the following specific steps: Step 3.1: Obtain the sequence of wind speed changes caused by the blocking effect. , The corresponding numbers are as follows The wind speed changes at each wind turbine location related to the blocking effect; in the initial calculation, All values ​​are set to 0; Step 3.2: Following the wind turbine numbers in ascending order from step 2, the wake effect calculation model, which considers the influence of additional turbulence on wake evolution, is used to quantify the wind speed loss at each wind turbine location within the field after considering the additional turbulence and wake interference from other wind turbines. This is combined with the sequence described in step 3.

1. middle The value of is used to reflect the impact of the blockage effect and to calculate the effective wind speed at each wind turbine. Step 3.3: Continuing from the effective wind speed calculated in Step 3.2, and combining it with the wind speed-aerodynamic parameter list of the corresponding model, the thrust coefficient of each wind turbine is obtained through interpolation calculation. Step 3.4: Integrate the calculation results from steps 3.2 and 3.3 to form a sequence. and sequence , The corresponding field numbers are as follows: The effective wind speed of the wind turbine The corresponding field numbers are as follows: The thrust coefficient of the wind turbine; Step 4 includes the following specific steps: Step 4.1: Using the effective wind speed and thrust coefficient from Step 3 as input, and following the order of wind turbine numbers from largest to smallest, use the blocking effect calculation model to quantify the wind speed change caused by the blocking effect at each wind turbine in the field. Step 4.2: Integrate the calculation results from Step 4.1 to form a new sequence of wind speed changes, represented as follows: , The numbers obtained from step 4.1 are respectively... The change in wind speed at each wind turbine; Step 5 includes the following steps: Step 5.1, calculate the wind speed change sequence. and The relative change of each data element at each position is calculated, and it is checked one by one whether it is less than a set threshold. If the threshold is met, the calculation stops and proceeds to step 5.2; otherwise, the sequence is used. Update sequence Repeat steps 3 and 4 until the convergence criteria are met; Step 5.2: Output the effective wind speed of each wind turbine in the field.

2. The wind farm power calculation method considering the coupled effects of additional turbulence and blockage as described in claim 1, characterized in that, In step 2, any point within the wind farm is taken as the origin of the coordinate system, and the due east direction is defined as... The positive axis and the due north direction are In the positive direction of the axis, a two-dimensional rectangular coordinate system is established, called the geodetic coordinate system, to determine the coordinates of each wind turbine in this coordinate system; Keeping the origin of the coordinate system fixed, rotate the coordinate system following the inflow direction from step 1, so that in the new coordinate system, i.e., the relative coordinate system... The positive axis points in the direction of the inflow; Based on the coordinate transformation matrix expressed in equation (1), calculate the coordinates of each wind turbine in the relative coordinate system: （1） (2) in, and These represent the x-coordinate and y-coordinate in the geodetic coordinate system and the relative coordinate system, respectively. For the incoming wind direction, Let be the angle between the inflow wind direction and the positive x-axis in the geodetic coordinate system. By comparing the abscissas of each wind turbine in the relative coordinate system, their relative positions can be determined, and they can be sorted and numbered accordingly. The first wind turbine is designated as number 1, and the numbers of each downwind turbine are sequentially increased. Let the total number of wind turbines in the wind farm be . The numbering sequence is then represented as .

3. The wind farm power calculation method considering the coupled effects of additional turbulence and blockage as described in claim 2, characterized in that, Step 6 includes the following steps: Step 6.1: Based on the effective wind speed output in Step 5, and combined with the wind speed-aerodynamic parameter list of each wind turbine model, the output power of each turbine is obtained through interpolation calculation. Step 6.2: The power generation of the wind farm is obtained by summing the output power of each wind turbine.

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