Dynamic correction method and system for engineering wake model based on measured wind speed of wind farm

Abstract

The application discloses a kind of engineering wake model dynamic correction method and system based on wind farm measured wind speed, the method of the present application includes according to the time delay characteristics of wake propagation constructs dynamic wake model, establishes optimization model;Dynamic single wake model and dynamic superposition model in dynamic wake model are carried out first-order Taylor expansion, the incremental form of dynamic wake model is obtained, and the discrete state space matrix of historical wind speed prediction value is formed;According to the discrete state space matrix of the historical wind speed prediction value, the optimization model is converted into discrete form, and the constraint condition of setting wake expansion coefficient and superposition correction coefficient is converted into standard quadratic programming problem and is solved to obtain optimal model parameter to realize the dynamic real-time correction of wake model.The present application aims to optimize the parameters of engineering wake model dynamically according to historical wind speed measured value, to improve the calculation accuracy of engineering wake model, while ensuring the speed and timeliness of wake calculation.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and in particular relates to a method and system for dynamically correcting an engineering wake model based on the measured wind speed of a wind farm. Background Art

[0002] As offshore wind farms continue to expand in size and number, the wake effect within the farm is becoming increasingly pronounced, significantly impacting the economic operation of offshore wind farms. The presence of the wake effect leads to complex aerodynamic coupling between upstream and downstream wind turbines, which must be considered when optimizing and controlling offshore wind farm operations.

[0003] In order to characterize the dynamic characteristics of the wake model, mathematical and physical methods are often used to model it to obtain the wake model. Depending on the method used, the wake model is divided into two categories: one is the numerical wake model obtained by computational fluid dynamics method, and the other is the engineering wake model expressed by mathematical formulas. The numerical wake model can accurately characterize the dynamic characteristics of the wake effect, but it requires a lot of computing time and computing resources and is not suitable for engineering applications. The engineering wake model can quickly calculate the wake distribution, but the calculation accuracy is limited. Existing wake models are difficult to balance calculation speed and calculation accuracy, which poses a challenge to the optimization control of large-scale wind farm operations considering the wake effect.

[0004] Currently, research is exploring the optimization of engineering wake model parameters using statistical or data-driven approaches to improve computational accuracy while maintaining the extremely fast computational speed of engineering wake models. However, statistical methods often require the development of complex optimization models, which inherently require long computational times to optimize parameters. Furthermore, they cannot leverage real-time wind speed data for dynamic optimization. Data-driven methods lack explicit models, making them difficult to apply to wind farm optimization control.

[0005] Therefore, in the current field of engineering wake model correction, it is necessary to further consider balancing calculation speed and accuracy, using real-time measured wind speed data for dynamic correction, etc., so as to provide a more applicable method and system for the dynamic correction of engineering wake models based on wind farm wind speed measured data. Summary of the Invention

[0006] Technical problem to be solved by the present invention: In response to the above-mentioned problems in the prior art, a method and system for dynamically correcting an engineering wake model based on the measured wind speed of a wind farm are provided. The present invention aims to improve the calculation accuracy of the engineering wake model by dynamically optimizing the parameters of the engineering wake model according to the measured historical wind speed values, while ensuring the speed and timeliness of the wake calculation.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0008] A dynamic correction method for an engineering wake model based on measured wind speeds at a wind farm includes:

[0009] S1. Based on the time delay characteristics of wake propagation, a wake delay time is introduced into the traditional Jensen wake model and the energy conservation wake superposition model to establish a dynamic single wake model and a dynamic superposition model. A superposition correction coefficient α is introduced into the dynamic superposition model to obtain a dynamic wake model for wind speed correction jointly formed by the dynamic superposition model and the dynamic single wake model;

[0010] S2. Based on the dynamic wake model, the historical wind speed forecast value is calculated by using the historical measured values ​​of inflow wind speed, historical thrust coefficient, and the wake expansion coefficient and superposition correction coefficient of the Jensen wake model at the previous moment. The wake expansion coefficient and superposition correction coefficient of the engineering wake model at the current moment are corrected using the first N historical wind speed measured values ​​and historical wind speed forecast values ​​of the organic groups in the wind farm to establish an optimization model.

[0011] S3. Performing a first-order Taylor expansion on the dynamic single wake model and the dynamic superposition model in the dynamic wake model to obtain an incremental form of the dynamic wake model and form a discrete state space matrix of the historical wind speed prediction value;

[0012] S4. According to the discrete state space matrix of the historical wind speed forecast value, the optimization model is converted into a discrete form, and the constraints of the wake expansion coefficient and the superposition correction coefficient are set to convert it into a standard quadratic programming problem. The standard quadratic programming problem is solved to obtain the optimal model parameters to achieve dynamic real-time correction of the Jensen wake model.

[0013] Optionally, the function expressions for establishing the dynamic single wake model and the dynamic superposition model in step S1 are:

[0014]

[0015] In the above formula, is the predicted value of the wake wind speed caused by wind turbine i to wind turbine n at time t0, is the predicted value of the inflow wind speed of upstream wind turbine i at time t0, C t,i (t0) is the thrust coefficient of wind turbine i at time t0, k i (t0) is the expansion coefficient of the wake generated by wind turbine i at time t0, d in is the geographical distance between the upstream wind turbine i and the downstream wind turbine n along the wind direction, R is the rotor radius of the wind turbine, is the rotor coverage area of ​​wind turbine n affected by the wake effect of wind turbine i, A0 is the swept area of ​​the wind turbine rotor, is the wind turbine n at t0+τ 1,n Wind speed at the moment, v ∞ (t0) is the inflow wind speed of the most upstream wind turbine at time t0, α n (t0) is the superposition correction coefficient introduced at time t0, is wind turbine i at t0+τ 1,n The wind speed at the moment, is the relationship between wind turbine i and wind turbine n at t0+τ 1,n The predicted value of the wake wind speed at time τ 1,n is the wake delay time between the most upstream wind turbine 1 and the downstream wind turbine n, and τ 1,n =d in / v ∞ , v ∞ is the inflow wind speed of the most upstream wind turbine, and Γ is the set of upstream turbines that cause wake effects on wind turbine n.

[0016] Optionally, in step S2, the dynamic wake model is based on the historical wind speed measured value, the historical thrust coefficient and the wake expansion coefficient of the Jensen wake model at the previous moment, and the superposition correction coefficient, when calculating the historical wind speed prediction value, including the N historical inflow wind speed measured values ​​v before time t0. ∞ (t0-1)~v ∞ (t0-N) and its corresponding historical thrust coefficient C t (t0-1)~

[0017] C t (t0-N), N is the size of the historical window, and the wake expansion coefficient and superposition correction coefficient of the Jensen wake model at the previous moment are combined to calculate the previous N historical wind speed forecast values, and any previous T-th historical wind speed forecast value V pre,n The function expression of (t0-T) is:

[0018]

[0019] In the above formula, C t,i (t0-T) is the Tth historical thrust coefficient, k i (t0-T) is the first T wake expansion coefficient of upstream wind turbine i, d in is the geographical distance between the upstream wind turbine i and the downstream wind turbine n along the wind direction, R is the rotor radius of the wind turbine, is the rotor coverage area of ​​wind turbine n affected by the wake effect of wind turbine i, A0 is the swept area of ​​the wind turbine rotor, v ∞ (t0-T) is the measured value of the inflow wind speed in the Tth historical period, α n(t0-T) is the first T-th superposition correction coefficient of downstream wind turbine n, V pre,i (t0-T+τ i,n ) is the wind turbine i at t0-T+τ 1,n The historical wind speed forecast value at time is the relationship between wind turbine i and wind turbine n at t0-T+τ 1,n The predicted value of the wake wind speed at time τ 1,n is the wake delay time between the most upstream wind turbine 1 and the downstream wind turbine n.

[0020] Optionally, the optimization model function expression established in step S2 is:

[0021]

[0022] In the above formula, N wt is the number of wind turbines, V pre,i (t0-T+τ i,n ) is the wind turbine i at t0-T+τ 1,n The historical wind speed forecast value at time V meas,i (t0) is the historical wind speed measured value of wind turbine i at time t0, τ 1,n is the wake delay time between the most upstream wind turbine 1 and the downstream wind turbine n.

[0023] Optionally, when performing first-order Taylor expansion on the dynamic single wake model and the dynamic superposition model in the dynamic wake model in step S3, the function expression of the first-order Taylor expansion of the dynamic single wake model is:

[0024]

[0025] In the above formula, and are the predicted values ​​of the wake wind speed caused by wind turbine i to wind turbine n. Expansion coefficient k of the wake generated by wind turbine i i , the predicted value of the inflow wind speed of upstream wind turbine i The partial derivative of Δk i (t0) and They are k i (t0) and The function expression of the first-order Taylor expansion of the dynamic superposition model is:

[0026]

[0027] In the above formula, and are the predicted values ​​of the inflow wind speed of upstream wind turbine n respectively Predicted value of the wake wind speed caused by wind turbine i to wind turbine n Predicted value of inflow wind speed of upstream wind turbine i And the superposition correction coefficient α introduced n The partial derivative of is the wind turbine n at t0+τ 1,n The wind speed increment at the moment, Δα n (t0) is the increment of the superposition correction coefficient at time t0, is the relationship between wind turbine i and wind turbine n at t0-T+τ 1,n The predicted value increment of the wake wind speed caused by time is, is wind turbine i at t0+τ 1,n The wind speed increment at that moment, is the relationship between wind turbine i and wind turbine n at t0-T+τ 1,,n The predicted value of the wake wind speed at time τ 1,n is the wake delay time between the most upstream wind turbine 1 and the downstream wind turbine n; the incremental function expression of the dynamic wake model is obtained as follows:

[0028]

[0029] In the above formula, is the wind speed prediction increment matrix at time t0, Δx(t0) is the wake model parameter increment matrix at time t0, M(t0) and N(t0) are the coefficient matrices at time t0, and:

[0030]

[0031] Δx(t0)=[Δk(t0),Δα(t0)] T ,

[0032] Δk(t0)=[Δk1(t0),Δk2(t0),…,Δk n-1 (t0)] T ,

[0033] Δα(t0)=[Δα3(t0),Δα4(t0),…,Δα n (t0)] T ,

[0034] M(t0)=[M1(t0),M2(t0),…,M n (t0)] T ,

[0035] N(t0)=[N1(t0),N2(t0),…,N n (t0)]T ,

[0036] In the above formula, They are wind turbines 2~n at t0+τ 1,2 ~t0+τ 1,n The wind speed increment at time t0, Δk(t0) and Δα(t0) are the increments of the wake expansion coefficient and correction coefficient at time t0, Δk1(t0)~Δk n-1 (t0) is the wake expansion coefficient increment of the wake effect from unit 1 to unit n-1 at time t0, Δα3(t0)~Δα n (t0) is the correction coefficient increment of the wake superposition model of unit 3 to unit n at time t0, M1(t0)~M n (t0) is an element in the coefficient matrix M(t0), N1(t0)~N n (t0) is an element in the coefficient matrix N(t0), and has:

[0037]

[0038] In the above formula, M i and N i are the i-th element in M(t0) and N(t0) respectively; the function expression of the discrete state space matrix of the historical wind speed forecast value is finally formed as follows:

[0039]

[0040] In the above formula, is a matrix composed of the wind speed prediction increment matrix from t0-1 to t0-N, I is an N×(n-1)-dimensional unit matrix, N and M are matrices composed of the coefficient matrices from t0-1 to t0-N, Δx is the optimization variable, and:

[0041]

[0042] M=[M(t0-1),M(t0-2),…,M(t0-N)] T ,

[0043] N=diag[N(t0-1),N(t0-2),…,N(t0-N)],

[0044] In the above formula, is the wind speed prediction increment matrix from t0-1 to t0-N, M(t0-1) to M(t0-N) and N(t0-1) to N(t0-N) are the coefficient matrices from t0-1 to t0-N.

[0045] Optionally, in step S4, the function expression of the discrete form in which the optimization model is converted is:

[0046]

[0047] In the above formula, N is the size of the historical window, Γ is the set of upstream units that cause wake impact on wind turbine n, is wind turbine i at t0-T+τ 1,n The wind speed increment at the moment, V pre,i (t0-T+τ i,n ) is the wind turbine i at t0-T+τ 1,n The historical wind speed forecast value at time V meas,i (t0-T) is the historical wind speed measured value of wind turbine i at time t0-T, Δx(t0) and Δx(t0-1) are the optimization variables at time t0 and t0-1 respectively, Q v and Q x are weight parameters respectively.

[0048] Optionally, the function expression of the constraint conditions of the wake expansion coefficient and the superposition correction coefficient set in step S4 is:

[0049]

[0050] In the above formula, k init is the initial value of the wake expansion coefficient, Δk i (t0) is the increment of wake expansion coefficient at time t0, k i (t0-1) is the wake expansion coefficient at time t0-1, Δα i (t0) is the increment of the superposition correction coefficient at time t0, α i (t0-1) is the superposition correction coefficient at time t0-1.

[0051] In addition, the present invention also provides a dynamic correction system for an engineering wake model based on the actual measured wind speed of a wind farm, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the dynamic correction method for an engineering wake model based on the actual measured wind speed of a wind farm.

[0052] In addition, the present invention also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the dynamic correction method of the engineering wake model based on the measured wind speed of the wind farm through a processor.

[0053] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the method for dynamically correcting the engineering wake model based on the measured wind speed of the wind farm through a processor.

[0054] Compared with existing technologies, the present invention offers the following key advantages: By leveraging historical wind speed measurements from large-scale offshore wind farms, the present invention achieves dynamic, real-time optimization of engineering wake model parameters. This approach, while maintaining the extremely fast computational speed of the engineering wake model, improves computational accuracy, resulting in more accurate wind speed predictions from the engineering wake model. By converting the engineering wake model into an incremental form through a first-order Taylor expansion, the present invention transforms the high-dimensional, highly nonlinear optimization model into a standard quadratic programming problem. This ensures the speed of solving the optimization problem, meets the real-time requirements of wind farm operation optimization control, and ensures the speed and timeliness of wake calculations. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Schematic diagram of the basic process of the method of the embodiment of the present invention.

[0056] Figure 2 FIG. 4 is a structural diagram of a simulation system in a simulation case according to an embodiment of the present invention.

[0057] Figure 3 This is a simulation verification diagram of the engineering wake model correction of an embodiment of the present invention.

[0058] Figure 4 This is a diagram showing parameter optimization results of the engineering wake model correction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0060] like Figure 1 As shown, the method for dynamically correcting the engineering wake model based on the measured wind speed of the wind farm in this embodiment includes:

[0061] S1. Based on the time delay characteristics of wake propagation, a wake delay time is introduced into the traditional Jensen wake model and the energy conservation wake superposition model to establish a dynamic single wake model and a dynamic superposition model. A superposition correction coefficient α is introduced into the dynamic superposition model to obtain a dynamic wake model for wind speed correction jointly formed by the dynamic superposition model and the dynamic single wake model;

[0062] S2. Based on the dynamic wake model, the historical wind speed forecast value is calculated by using the historical measured values ​​of inflow wind speed, historical thrust coefficient, and the wake expansion coefficient and superposition correction coefficient of the Jensen wake model at the previous moment. The wake expansion coefficient and superposition correction coefficient of the engineering wake model at the current moment are corrected using the first N historical wind speed measured values ​​and historical wind speed forecast values ​​of the organic groups in the wind farm to establish an optimization model.

[0063] S3. Performing a first-order Taylor expansion on the dynamic single wake model and the dynamic superposition model in the dynamic wake model to obtain an incremental form of the dynamic wake model and form a discrete state space matrix of the historical wind speed prediction value;

[0064] S4. According to the discrete state space matrix of the historical wind speed forecast value, the optimization model is converted into a discrete form, and the constraints of the wake expansion coefficient and the superposition correction coefficient are set to convert it into a standard quadratic programming problem. The standard quadratic programming problem is solved to obtain the optimal model parameters to achieve dynamic real-time correction of the Jensen wake model.

[0065] According to the average inflow wind speed V ∞ The geographical distance d along the wind direction from the upstream wind turbine i and the downstream wind turbine n in Calculate the wake delay time τ in , the specific function expression is: τ in =d in / v ∞ The function expressions for establishing the dynamic single wake model and the dynamic superposition model in step S1 of this embodiment are:

[0066]

[0067] In the above formula, is the predicted value of the wake wind speed caused by wind turbine i to wind turbine n at time t0, is the predicted value of the inflow wind speed of upstream wind turbine i at time t0, C t,i (t0) is the thrust coefficient of wind turbine i at time t0, k i (t0) is the expansion coefficient of the wake generated by wind turbine i at time t0, d in is the geographical distance between the upstream wind turbine i and the downstream wind turbine n along the wind direction, R is the rotor radius of the wind turbine, is the rotor coverage area of ​​wind turbine n affected by the wake effect of wind turbine i, A0 is the swept area of ​​the wind turbine rotor, is the wind turbine n at t0+τ 1,n Wind speed at the moment, v ∞ (t0) is the inflow wind speed of the most upstream wind turbine at time t0, α n (t0) is the superposition correction coefficient introduced at time t0, is wind turbine i at t0+τ 1,n The wind speed at the moment, is the relationship between wind turbine i and wind turbine n at t0+τ 1,n The predicted value of the wake wind speed at time τ 1,n is the wake delay time between the most upstream wind turbine 1 and the downstream wind turbine n, and τ1,n =d in / v ∞ , v ∞ is the inflow wind speed of the most upstream wind turbine, and Γ is the set of upstream turbines that cause wake effects on wind turbine n. The ^ at the top of the variable character represents the wind speed prediction value calculated by the dynamic wake model, for example represents the predicted value of the wake wind speed caused by wind turbine i to wind turbine n, represents the predicted value of the inflow wind speed of upstream wind turbine i. The obtained dynamic single wake model and dynamic superposition model together constitute the dynamic wake model used for wind speed correction.

[0068] In step S2 of this embodiment, the dynamic wake model is based on the historical inflow wind speed measured value, the historical thrust coefficient and the wake expansion coefficient of the Jensen wake model at the previous moment, and the superposition correction coefficient. When calculating the historical wind speed prediction value, it includes the N historical inflow wind speed measured values ​​v before time t0. ∞ (t0-1)~v ∞ (t0-N) and its corresponding historical thrust coefficient C t (t0-1)~

[0069] C t (t0-N), N is the size of the historical window, and the wake expansion coefficient and superposition correction coefficient of the Jensen wake model at the previous moment are combined to calculate the previous N historical wind speed forecast values, and any previous T-th historical wind speed forecast value V pre,n The function expression of (t0-T) is:

[0070]

[0071] In the above formula, C t,i (t0-T) is the Tth historical thrust coefficient, k i (t0-T) is the first T wake expansion coefficient of upstream wind turbine i, d in is the geographical distance between the upstream wind turbine i and the downstream wind turbine n along the wind direction, R is the rotor radius of the wind turbine, is the rotor coverage area of ​​wind turbine n affected by the wake effect of wind turbine i, A0 is the swept area of ​​the wind turbine rotor, v ∞ (t0-T) is the measured value of the inflow wind speed in the Tth historical period, α n (t0-T) is the first T-th superposition correction coefficient of downstream wind turbine n, V pre,i (t0-T+τ i,n ) is the wind turbine i at t0-T+τ 1,n The historical wind speed forecast value at time is the relationship between wind turbine i and wind turbine n at t0-T+τ1,n The predicted value of the wake wind speed at time τ 1,n is the wake delay time between the most upstream wind turbine 1 and the downstream wind turbine n.

[0072] Based on the dynamic wake model, the historical inflow wind speed measured value (V ∞ (t0-1)~V ∞ (t0-N)), historical thrust coefficient (C t (t0-1)~C t (t0-N)) and the wake expansion coefficient k(t0-1) of the engineering wake model at the previous moment, and the superposition correction coefficient α(t0-1) to calculate the historical wind speed forecast value (V pre (t0-1)~V pre (t0-N)). Use the first N historical wind speed measured values ​​of the wind farm organic group (V meas (t0-1)~V meas The wake expansion coefficient k(t0) and k(t0) of the engineering wake model at the current moment are corrected by using the historical wind speed prediction value to establish an optimization model. The function expression of the optimization model established in step S2 of this embodiment is:

[0073]

[0074] In the above formula, N is the number of historical wind speed measurements used for correction (correction step size), N wt is the number of wind turbines, V pre,i (t0-T+τ i,n ) is the wind turbine i at t0-T+τ 1,n The historical wind speed forecast value at time V meas,i (t0) is the historical wind speed measured value of wind turbine i at time t0, τ 1,n is the wake delay time between the most upstream wind turbine 1 and the downstream wind turbine n.

[0075] In step S3 of this embodiment, when the dynamic single wake model and the dynamic superposition model in the dynamic wake model are subjected to first-order Taylor expansion, the function expression of the first-order Taylor expansion of the dynamic single wake model is:

[0076]

[0077] In the above formula, and are the predicted values ​​of the wake wind speed caused by wind turbine i to wind turbine n. Expansion coefficient k of the wake generated by wind turbine i i , the predicted value of the inflow wind speed of upstream wind turbine i The partial derivative of Δki (t0) and They are k i (t0) and increment.

[0078] In step S3 of this embodiment, the function expression of performing first-order Taylor expansion on the dynamic superposition model is:

[0079]

[0080] In the above formula, and are the predicted values ​​of the inflow wind speed of upstream wind turbine n respectively Predicted value of the wake wind speed caused by wind turbine i to wind turbine n Predicted value of inflow wind speed of upstream wind turbine i And the superposition correction coefficient α introduced n The partial derivative of is the wind turbine n at t0+τ 1,n The wind speed increment at the moment, Δα n (t0) is the increment of the superposition correction coefficient at time t0, is the relationship between wind turbine i and wind turbine n at t0-T+τ 1,n The predicted value increment of the wake wind speed caused by time is, is wind turbine i at t0+τ 1,n The wind speed increment at that moment, is the relationship between wind turbine i and wind turbine n at t0-T+τ 1,n The predicted value of the wake wind speed at time τ 1,n is the wake delay time between the most upstream wind turbine 1 and the downstream wind turbine n;

[0081] The incremental form of the dynamic wake model can be obtained by the functional expressions of the two first-order Taylor expansions mentioned above. In this embodiment, the functional expression for obtaining the incremental form of the dynamic wake model is:

[0082]

[0083] In the above formula, is the wind speed prediction increment matrix at time t0, Δx(t0) is the wake model parameter increment matrix at time t0, M(t0) and N(t0) are the coefficient matrices at time t0, and:

[0084]

[0085] Δx(t0)=[Δk(t0),Δα(t0)] T ,

[0086] Δk(t0)=[Δk1(t0),Δk2(t0),…,Δk n-1 (t0)] T ,

[0087] Δα(t0)=[Δα3(t0),Δα4(t0),…,Δα n (t0)] T ,

[0088] M(t0)=[M1(t0),M2(t0),…,M n (t0)] T ,

[0089] N(t0)=[N1(t0),N2(t0),…,N n (t0)] T ,

[0090] In the above formula, They are wind turbines 2~n at t0+τ 1,2 ~t0+τ 1,n The wind speed increment at time t0, Δk(t0) and Δα(t0) are the increments of the wake expansion coefficient and correction coefficient at time t0, Δk1(t0)~Δk n-1 (t0) is the wake expansion coefficient increment of the wake effect from unit 1 to unit n-1 at time t0, Δα3(t0)~Δα n (t0) is the correction coefficient increment of the wake superposition model of unit 3 to unit n at time t0, M1(t0)~M n (t0) is an element in the coefficient matrix M(t0), N1(t0)~N n (t0) is an element in the coefficient matrix N(t0), and has:

[0091]

[0092] In the above formula, M i and N i are the i-th element in M(t0) and N(t0) respectively;

[0093] Using N historical wind speed measurements to correct the model, the discrete state space matrix of the historical wind speed forecast values ​​from time t0-1 to time t0-N is written according to the above formula. The function expression of the discrete state space matrix of the historical wind speed forecast values ​​is finally formed as follows:

[0094]

[0095] In the above formula, is a matrix composed of wind speed prediction increment matrices at t0-1-t0-N, I is an N x (n-1) dimensional unit matrix, N and M are matrices composed of coefficient matrices at t0-1-t0-N, Δx is an optimization variable, and has:

[0096]

[0097] M = [M(t0-1), M(t0-2), …, M(t0-N)] T ,

[0098] N = diag[N(t0-1), N(t0-2), …, N(t0-N)],

[0099] In the above formula, is a wind speed prediction increment matrix at t0-1-t0-N, M(t0-1)-M(t0-N) and N(t0-1)-N(t0-N) are coefficient matrices at t0-1-t0-N.

[0100] The optimal model can be converted into a discrete form based on a discrete state space matrix, and the function expression of the discrete form of the optimal model converted in step S4 of the embodiment is:

[0101]

[0102] In the above formula, N is a historical window size, Γ is a set of upstream units that have a wake effect on wind turbine n, is a wind speed increment of wind turbine i at t0-T+τ 1,n , pre,i is a historical wind speed prediction value of wind turbine i at t0-T+τ i,n , 1,n is a historical wind speed prediction value of wind turbine i at t0-T+τ meas,i , Δx(t0) and Δx(t0-1) are optimization variables at t0 and t0-1 respectively, Q v and Q x are weight parameters.

[0103] In order to ensure the rationality of the optimization variable Δx, the constraint conditions of the wake expansion coefficient k and the superposition correction coefficient α are set, and specifically, the function expression of the constraint conditions of the wake expansion coefficient and the superposition correction coefficient set in step S4 of the embodiment is:

[0104]

[0105] In the above formula, k init is an initial value of the wake expansion coefficient, Δk i(t0) is the increment of wake expansion coefficient at time t0, k i (t0-1) is the wake expansion coefficient at time t0-1, Δα i (t0) is the increment of the superposition correction coefficient at time t0, α i (t0-1) is the superposition correction coefficient at time t0-1. Based on the discrete form of the optimization model and the constraints of the optimization variables, the optimization model is converted into a standard quadratic programming problem. The optimal wake expansion coefficient k and superposition correction coefficient α are solved to achieve dynamic real-time correction of the engineering wake model.

[0106] In order to verify the effectiveness of the dynamic correction method of the engineering wake model based on the wind speed measured in the wind farm in this embodiment, a dynamic correction simulation system of the engineering wake model based on the wind speed measured data of the wind farm was built by using SimWindFarm software and MATLAB / Simulink software. Figure 2 This simulation system treats SimWindFarm as a real offshore wind farm, consisting of six NREL 5MW wind turbines aligned with the wind direction, with each turbine spaced 630 meters apart. The output of the inflow wind speed for each turbine serves as the measured wind speed data for the wind farm.

[0107] In order to compare the effects of wake model correction under different average wind speeds and different turbulence intensities, low wind speed (10m / s) low turbulence (3%), low wind speed high turbulence (10%), high wind speed (12m / s) low turbulence, and high wind speed high turbulence environments are used as simulation conditions. The effect of the dynamic correction of the engineering wake model can be reflected by the measured wind speed values ​​of the wind farm and the calculated values ​​of the engineering wake model. Taking wind turbines 1 to 6 as the research objects, the comparison results of the measured wind speed values ​​of the wind farm, the calculated wind speed values ​​of the revised engineering wake model, and the calculated wind speed values ​​of the engineering wake model before correction under the low wind speed and high turbulence environment are shown as follows. Figure 3 , Table 1 and Table 2, the revised engineering wake model parameters are as follows Figure 4 As shown, Figure 4 (a) is the correction result of the wake expansion coefficient, k1~k5 are the wake expansion coefficients of wind turbines 1~5; Figure 4 (b) is the correction result of the superposition correction coefficient, and a3~a6 are the superposition correction coefficients of wind turbines 3~6.

[0108] Table 1: Root mean square error between the calculated and measured wind speeds for wind turbines 1 to 6

[0109] Correction step length Fan 2 Fan 3 Fan 4 Fan 5 Fan 6 average value Calculation time 30 0.114 0.0129 0.0744 0.178 0.126 0.101 0.0136 60 0.105 0.0562 0.0137 0.0639 0.0126 0.05 0.0264 120 0.0338 0.048 0.0232 0.0145 0.0183 0.0276 0.0736 300 0.031 0.0167 0.0204 0.0166 0.0234 0.0216 0.443 600 0.0213 0.0158 0.0252 0.0183 0.0286 0.0218 2.528 No correction 0.398 0.442 0.392 0.331 0.297 0.372 /

[0110] In Table 1, the units of correction step size and calculation time are both seconds.

[0111] Table 2: Average RMS error between calculated and measured wind speeds for all wind turbines

[0112] environment 30 60 120 300 600 No correction Low wind speed and low turbulence 0.102 0.0479 0.0236 0.0146 0.0149 0.372 Low wind speed and high turbulence 0.101 0.0502 0.0276 0.0216 0.0218 0.372 High wind speed and low turbulence 0.236 0.145 0.0919 0.0628 0.0614 0.459 High wind speed and high turbulence 0.365 0.309 0.227 0.159 0.157 0.463

[0113] In Table 2, 30, 60, 120, 300, and 600 are the correction step sizes.

[0114] pass Figure 3 and Figure 4 It can be seen that the wind speed calculation accuracy of the engineering wake model corrected by the dynamic correction method of the engineering wake model based on the measured wind speed of the wind farm in this embodiment is higher. Under different correction step sizes, the root mean square error between the calculated wind speed values ​​and the measured wind speed values ​​of wind turbines 1 to 6 is shown in Table 1. It can be seen from Table 1 that the smaller the correction step size, the shorter the calculation time, but the worse the correction effect. Under four different environments and different correction step sizes, the average value of the root mean square error between the calculated wind speed values ​​and the measured wind speed values ​​of all wind turbines in the wind farm is shown in Table 2. It can be seen that higher average wind speed and turbulence intensity mean greater natural wind speed fluctuations, so low wind speed and low turbulence environments have better correction effects. In addition, the larger the correction step size, the better the correction effect.

[0115] In summary, the method for dynamically correcting the engineering wake model based on the measured wind speed of a wind farm in this embodiment includes introducing a wake delay time into the traditional Jensen wake model and the energy conservation wake superposition model according to the time delay characteristics of wake propagation, establishing a dynamic single wake model and a dynamic superposition model. In order to further improve the correction accuracy of the engineering wake model, a superposition correction coefficient α is introduced into the dynamic superposition model, which together with the dynamic single wake model constitutes a dynamic wake model for wind speed correction. Based on the established dynamic wake model, the measured historical inflow wind speed value (V ∞ (t0-1)~V ∞ (t0-N)), historical thrust coefficient (C t (t0-1)~C t (t0-N)) and the wake expansion coefficient k(t0-1) of the engineering wake model at the previous moment, and the superposition correction coefficient α(t0-1) are used to calculate the historical wind speed prediction value (Vpre(t0-1)~Vpre(t0-N)), and the previous N historical wind speed measured values ​​of the organic groups of the wind farm (V meas (t0-1)~V meas(t0-N)) and the historical wind speed prediction value are used to correct the wake expansion coefficient k(t0) and of the engineering wake model at the current moment to establish an optimization model; a first-order Taylor expansion is performed on the established dynamic single wake model, and a first-order Taylor expansion is performed on the established dynamic superposition model to obtain an incremental form of the dynamic wake model, forming a discrete state space matrix of the historical wind speed prediction value; according to the obtained discrete state space matrix of the historical wind speed prediction value, the obtained optimization model is converted into a discrete form, and the constraints of the wake expansion coefficient k and the superposition correction coefficient α are set, and the problem is converted into a standard quadratic programming problem, and the optimal wake model parameters are obtained by solving the problem to realize dynamic real-time correction of the engineering wake model. The dynamic correction method of the engineering wake model based on the measured wind speed of the wind farm in this embodiment has the advantages of high correction accuracy and high real-time performance.

[0116] In addition, this embodiment also provides a dynamic correction system for an engineering wake model based on the measured wind speed of a wind farm, comprising an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the dynamic correction method for an engineering wake model based on the measured wind speed of a wind farm. Corresponding to the above correction method, the dynamic correction system for an engineering wake model based on the measured wind speed of a wind farm in this embodiment also has the advantages described above for the dynamic correction method for an engineering wake model based on the measured wind speed of a wind farm. In addition, this embodiment also provides a dynamic correction system for an engineering wake model based on the measured wind speed of a wind farm, comprising a data acquisition and storage system and a solver, wherein the solver is loaded with a computer program, and when the computer program is executed by the solver, the computer program executes the dynamic correction method for an engineering wake model based on the measured wind speed of a wind farm as described above. Corresponding to the above correction method, the dynamic correction system for an engineering wake model based on the measured wind speed of a wind farm in this embodiment also has the advantages described above for the dynamic correction method for an engineering wake model based on the measured wind speed of a wind farm.

[0117] In addition, this embodiment further provides a computer-readable storage medium having a computer program or instructions stored therein, the computer program or instructions being programmed or configured to execute, via a processor, the method for dynamically correcting an engineering wake model based on the measured wind speed of a wind farm. In addition, this embodiment further provides a computer program product comprising the computer program or instructions being programmed or configured to execute, via a processor, the method for dynamically correcting an engineering wake model based on the measured wind speed of a wind farm.

[0118] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0119] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic correction method for an engineering wake model based on the measured wind speed of a wind farm, characterized in that: include: S1. According to the time delay characteristics of wake propagation, the traditional Jensen wake model and the energy conservation wake superposition model are introduced with the wake delay time to establish a dynamic single wake model and a dynamic superposition model, and the superposition correction coefficient is introduced into the dynamic superposition model. α , the dynamic superposition model and the dynamic single wake model together constitute the dynamic wake model for wind speed correction; S2, based on the dynamic wake model, the historical wind speed forecast value is calculated by the historical inflow wind speed measured value, historical thrust coefficient and the wake expansion coefficient of the Jensen wake model at the previous moment, and the superposition correction coefficient. N The historical wind speed measured values ​​and historical wind speed forecast values ​​are used to correct the wake expansion coefficient and superposition correction coefficient of the engineering wake model at the current moment to establish an optimization model; S3. Performing a first-order Taylor expansion on the dynamic single wake model and the dynamic superposition model in the dynamic wake model to obtain an incremental form of the dynamic wake model and form a discrete state space matrix of the historical wind speed prediction value; S4. According to the discrete state space matrix of the historical wind speed forecast value, the optimization model is converted into a discrete form, and the constraints of the wake expansion coefficient and the superposition correction coefficient are set to convert it into a standard quadratic programming problem. The standard quadratic programming problem is solved to obtain the optimal model parameters to achieve dynamic real-time correction of the Jensen wake model.

2. The method for dynamic correction of engineering wake model based on wind farm measured wind speed according to claim 1, characterized in that: The function expressions for establishing the dynamic single wake model and the dynamic superposition model in step S1 are: ， ， In the above formula, For wind turbines i For wind turbines n exist The predicted value of the wake wind speed at the moment, For upstream wind turbines i exist The predicted value of the inflow wind speed at time , For wind turbines i exist The thrust coefficient at time For wind turbines i exist The expansion coefficient of the wake generated at this moment, For upstream wind turbines i , downstream wind turbines n Geographic distance along the wind direction, R is the impeller radius of the wind turbine, For wind turbines i The wake effect caused by the wind turbine n Affected impeller coverage area, is the swept area of ​​the wind turbine rotor, For wind turbines n exist The wind speed at the moment, For the most upstream wind turbine The inflow wind speed at the moment, For the introduction The superposition correction coefficient at the moment, For wind turbines i exist The wind speed at the moment, For wind turbines i For wind turbines n exist The predicted value of the wake wind speed at the moment, The most upstream wind turbine 1 and the downstream wind turbine n The wake delay time between , is the inflow wind speed of the most upstream wind turbine, For wind turbines n The collection of upstream units that cause wake effects.

3. The method for dynamic correction of engineering wake model based on wind farm measured wind speed according to claim 2, characterized in that: In step S2, the dynamic wake model is used to calculate the historical wind speed forecast value through the historical inflow wind speed measured value, the historical thrust coefficient and the wake expansion coefficient and superposition correction coefficient of the Jensen wake model at the previous moment, including the following: N historical measured values ​​of inflow wind speed before the moment ~ and its corresponding historical thrust coefficient ~ , N is the size of the historical window, combined with the wake expansion coefficient and superposition correction coefficient of the Jensen wake model at the previous moment to calculate the previous N historical wind speed forecast values, and any previous T-th historical wind speed forecast value The function expression is: ， In the above formula, is the Tth historical thrust coefficient, For upstream wind turbines i The first T wake expansion coefficients, For upstream wind turbines i , downstream wind turbines n Geographic distance along the wind direction, R is the impeller radius of the wind turbine, For wind turbines i The wake effect caused by the wind turbine n Affected impeller coverage area, is the swept area of ​​the wind turbine rotor, is the Tth measured value of the historical inflow wind speed, For downstream wind turbines n The first T superposition correction coefficients, For wind turbines i exist The historical wind speed forecast value at time For wind turbines i For wind turbines n exist The predicted value of the wake wind speed at the moment, The most upstream wind turbine 1 and the downstream wind turbine n The wake delay time between .

4. The method for dynamic correction of engineering wake model based on wind farm measured wind speed according to claim 3, characterized in that: The optimization model function expression established in step S2 is: ， In the above formula, is the number of wind turbines, For wind turbines i exist The historical wind speed forecast value at time For wind turbines i exist The historical wind speed measured value at the time, The most upstream wind turbine 1 and the downstream wind turbine n The wake delay time between .

5. The method for dynamic correction of engineering wake model based on wind farm measured wind speed according to claim 1, characterized in that: When the dynamic single wake model and the dynamic superposition model in the dynamic wake model are subjected to first-order Taylor expansion in step S3, the function expression of the first-order Taylor expansion of the dynamic single wake model is: ， In the above formula, and Wind turbines i For wind turbines n Predicted value of the wind speed causing the wake For wind turbines i Expansion coefficient for wake generation k i , upstream wind turbines i Predicted value of inflow wind speed The partial derivative of 、 and They are 、 and The function expression of the first-order Taylor expansion of the dynamic superposition model is: ， In the above formula, 、 and Upstream wind turbines n Predicted value of inflow wind speed The superposition correction factor introduced α n , wind turbines i For wind turbines n Predicted value of the wind speed causing the wake and upstream wind turbines i Predicted value of inflow wind speed The partial derivative of For wind turbines n exist The wind speed increment at that moment, for The increment of the superposition correction coefficient at the moment, For wind turbines i For wind turbines n exist The predicted value increment of the wake wind speed caused by time is, For wind turbines i exist The wind speed increment at that moment, For wind turbines i For wind turbines n exist The predicted value of the wake wind speed at the moment, The most upstream wind turbine 1 and the downstream wind turbine n The wake delay time between the two is obtained; the function expression of the incremental form of the dynamic wake model is: ， In the above formula, for The incremental matrix of wind speed prediction at time t, for The wake model parameter increment matrix at time , and for The coefficient matrix of time, and there are: ， D x = [ , ] T , D k ( t 0) = [D k 1( t 0), D k 2( t 0), …, D k n-1 ( t 0)] T , D α ( t 0) = [D α 3( t 0), D α 4( t 0), …, D α n ( t 0)] T , M ( t 0) = [ M 1( t 0), M 2( t 0), …, M n ( t 0)] T ， N ( t 0) = [ N 1( t 0), N 2( t 0), …, N n ( t 0)] T ， In the above formula, ~ They are wind turbines 2~ n exist ~ The wind speed increment at that moment, and for The increment of wake expansion coefficient and correction coefficient at time Δ k 1( t 0)~Δ k n-1 ( t 0) for The increment of the wake expansion coefficient from unit 1 to unit n-1 at time instant, Δ α 3( t 0)~Δ α n ( t 0) The correction coefficient increment of the wake superposition model of units 3 to n at time, M 1( t 0)~ M n ( t 0) is the coefficient matrix M ( t 0), N 1( t 0)~ N n ( t 0) is the coefficient matrix N ( t 0) and have: ， ， In the above formula, and They are and The function expression of the discrete state space matrix that ultimately forms the historical wind speed forecast value is: ， In the above formula, for t 0-1~ t 0- N The matrix composed of the incremental matrix of wind speed prediction at the moment, for N ×( n -1)-dimensional identity matrix, 、 for t 0-1~ t 0- N The matrix composed of the coefficient matrix of the moment, is the optimization variable, and there are: ， ， ， In the above formula, for t 0-1~ t 0- N The incremental matrix of wind speed prediction at time t, M ( t 0-1)~ M ( t 0- N ) 、 N ( t 0-1)~ N ( t 0- N )for t 0-1~ t 0- N The coefficient matrix at time t.

6. The method for dynamic correction of engineering wake model based on wind farm measured wind speed according to claim 5, characterized in that: The function expression of the discrete form in which the optimization model is converted in step S4 is: ， In the above formula, N is the size of the historical window, For wind turbines n The collection of upstream units causing wake effects, For wind turbines i exist The wind speed increment at that moment, For wind turbines i exist The historical wind speed forecast value at time For wind turbines i exist The historical wind speed measured value at the time, and They are and The optimization variable at time, Q v and Q x are weight parameters respectively.

7. The method for dynamically correcting an engineering wake model based on wind farm measured wind speed according to claim 6, characterized in that: The function expression of the constraint conditions of the wake expansion coefficient and the superposition correction coefficient set in step S4 is: ， In the above formula, is the initial value of the wake expansion coefficient, for The increment of the wake expansion coefficient at time , for The wake expansion coefficient at time , for The increment of the superposition correction coefficient at the moment, for The superposition correction factor at the moment.

8. A dynamic correction system for an engineering wake model based on measured wind speed at a wind farm, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the method for dynamically correcting an engineering wake model based on the measured wind speed of a wind farm as recited in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute, through a processor, the method for dynamically correcting an engineering wake model based on the measured wind speed of a wind farm as recited in any one of claims 1 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute, through a processor, the method for dynamically correcting an engineering wake model based on the measured wind speed of a wind farm as recited in any one of claims 1 to 7.

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