A wind turbine static yaw error dynamic identification method and device
By performing simulation analysis and steady-state normalization on the wind turbine model, yaw error is identified and dynamically compensated, solving the problems of low measurement accuracy, high cost, and long cycle in the existing technology, and improving the power generation capacity and output of the wind turbine.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA ELECTRIC POWER RESEARCH INSTITUTE CO LTD
- Filing Date
- 2022-11-25
- Publication Date
- 2026-07-14
AI Technical Summary
Existing methods for identifying yaw error in wind turbines suffer from low measurement accuracy, high cost, long cycle time, and unsustainability, thus failing to effectively improve power generation.
By simulating and analyzing the wind turbine model, a functional expression model of the relationship between wind speed, air density, turbulence intensity, inflow angle and power output is established. Steady normalization processing is performed to identify and dynamically compensate for yaw error.
It enables accurate identification and dynamic compensation of yaw error of wind turbine units, improving power generation capacity and output, reducing costs and increasing reliability.
Smart Images
Figure CN115898787B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of new energy power generation technology, specifically to a method and device for dynamic identification of static yaw error of wind turbine units. Background Technology
[0002] Currently, the wind power industry is experiencing rapid development, with China ranking first in the world in terms of both installed capacity and total output. However, as wind turbines age, structural components deteriorate, and electrical components experience performance degradation, leading to significant changes in their operating characteristics. This directly manifests as a continuous decrease in power generation capacity and output. Wind turbine power generation is a primary concern for wind farm operators and investors. For wind turbines operating for extended periods, optimizing their power curves and increasing power generation remains a constant goal for wind farm owners.
[0003] Wind turbine power generation is a crucial indicator of its performance. For wind farm owners, power generation directly translates to overall farm revenue. With the rapid development of my country's wind power industry, wind turbines are operating for increasingly longer periods, leading to a growing degradation in turbine output and power generation. Particularly, for wind turbines with unified control strategies and logic, their power output is significantly influenced by numerous factors under specific site and environmental conditions. These factors primarily include air density, yaw error, turbulence intensity, and inflow angle. Improving the power output of long-term in-service wind turbines requires research and development based on the characteristics of these influencing factors. Considering that parameters such as air density, turbulence intensity, and inflow angle are environmental conditions that cannot be artificially manipulated, wind turbine power generation improvement based on yaw error identification is a hot research topic.
[0004] Considering the impact of yaw error on wind turbine power generation, it is necessary to identify the yaw error of wind turbines and compensate for it in the wind turbine control system in order to reduce the impact of yaw error on wind turbine power generation.
[0005] One existing solution to the problem of identifying yaw error in wind turbines is to install a lidar anemometer on the top of the wind turbine nacelle to measure the incoming wind direction and compare it with the wind direction signal of the wind turbine itself. The deviation is then analyzed by linear fitting, which is defined as the yaw error of the wind turbine system.
[0006] Existing wind turbine yaw error identification technologies based on nacelle radar anemometers have the following drawbacks and shortcomings:
[0007] 1. The cabin radar anemometer mainly uses the optical Doppler principle, measuring the wind speed and direction of the incoming airflow by observing the propagation of light waves through an aerosol medium. The measured wind speed and direction signals are affected by the horizontal influence of aerosols in the air, making the accuracy of the anemometer measurement impossible to fully guarantee. In other words, the accuracy of key airflow data such as wind speed and direction cannot be comprehensively assured.
[0008] 2. The cost of lidar anemometers is relatively high. Under the current trend of "grid parity" and the continuous compression of the cost per kilowatt-hour of wind turbines, the additional cost of lidar anemometers is high for wind turbine manufacturers and operators.
[0009] 3. The method for identifying yaw error in wind turbines based on lidar anemometers requires a certain period of field testing, while simultaneously collecting data from the wind turbine's own control systems, such as wind speed and direction, for statistical comparative analysis. Considering the need to collect data across different wind speed ranges, the data collection period is typically 3-6 months, resulting in a long testing cycle and high time costs.
[0010] 4. Wind turbine yaw error exhibits time-varying characteristics, meaning that as the operating time of a wind turbine increases, its yaw error changes over time. This indicates that yaw error identification and compensation for wind turbines requires continuous and uninterrupted implementation. Current methods for identifying yaw error using lidar anemometers are often one-time measurements and cannot provide continuous yaw error identification and compensation.
[0011] To address the issue of yaw error identification in wind turbines, one existing solution is to use SCADA data from wind turbine operation to statistically analyze the power output characteristics of the turbines in different wind speed ranges, and then compare and analyze these characteristics to identify and determine the yaw error of the wind turbines.
[0012] Based on SCADA data of wind turbine operation, the power output characteristics during different wind speeds are analyzed to estimate the yaw error of the turbine. The implementation process is relatively simple and low-cost, but it still has the following disadvantages and shortcomings:
[0013] 1. The yaw error identification process relies on the actual SCADA data of the wind turbine, and the accuracy and precision of the data have a significant impact on the analysis results;
[0014] 2. This method analyzes the power output characteristics of the wind turbine across different wind speed ranges and estimates potential yaw errors through comparative analysis. However, it neglects the influence of air density, inflow angle, and turbulence intensity on the wind turbine's power output characteristics. The power output characteristics obtained through statistical analysis of a single wind speed range are actually coupled with the effects of other factors such as air density, turbulence intensity, and inflow angle. This directly leads to unreliable yaw error identification results, with the possibility of overcompensation and undercompensation. Summary of the Invention
[0015] To overcome the above-mentioned defects, this invention proposes a method and device for dynamic identification of static yaw error of wind turbine units.
[0016] Firstly, a method for dynamically identifying the static yaw error of a wind turbine is provided, the method comprising:
[0017] Step 1. Perform simulation analysis on the wind turbine model to obtain the wind speed, wind turbine power output characteristic parameters, and the functional expression model of the relationship between power output;
[0018] Step 2. Normalize the power output characteristic parameters of the wind turbine in the function expression model in turn to obtain the function expression model of wind speed and power output after the wind turbine power output characteristic parameters are stabilized and normalized.
[0019] Step 3. Perform interval analysis on the functional expression model between wind speed and power output after the wind turbine power output characteristic parameters are normalized to obtain the initial yaw error of the wind turbine.
[0020] Step 4. Determine the yaw error index of the wind turbine based on the initial yaw error of the wind turbine, and determine whether the yaw error index meets the convergence condition. If yes, return to step 2; otherwise, output the initial yaw error of the wind turbine.
[0021] Preferably, the power output characteristic parameters include at least one of the following: air density, turbulence intensity, and inflow angle.
[0022] Furthermore, the functional expression model relating wind speed, wind turbine power output characteristic parameters, and power output includes:
[0023] The mathematical model for the functional relationship between wind speed, air density, and power output is as follows:
[0024] P=f(v,ρ,TI,α)
[0025] The mathematical model for the functional relationship between wind speed, turbulence intensity, and power output is as follows:
[0026] P=g(v,ρ,TI,α)
[0027] The mathematical model for the functional relationship between wind speed, inflow angle, and power output is as follows:
[0028] P=h(v,ρ,TI,α)
[0029] In the above formula, P is the power output of the wind turbine, v is the wind speed, ρ is the air density, TI is the turbulence intensity, α is the inflow angle, f is the functional expression between wind speed, air density and power output, g is the functional expression between wind speed, turbulence intensity and power output, and h is the functional expression between wind speed, inflow angle and power output.
[0030] Furthermore, the functional expression model of the relationship between wind speed and power output after the wind turbine power output characteristic parameters have been normalized includes:
[0031] The mathematical model for the functional expression of wind speed versus power output after steady-state normalization of air density is as follows:
[0032] P=f(v1,1.225,TI,α)×p
[0033] The mathematical model for the functional relationship between air density, turbulence intensity, and wind speed after steady-state normalization, and power output is as follows:
[0034] P=g(v2,1.225,10%,α)×q
[0035] The mathematical model for the functional relationship between air density, turbulence intensity, inflow angle, and wind speed after steady-state normalization, and power output is as follows:
[0036] P=h(v3,1.225,10%,0)×r
[0037] In the above formula, v1 represents the air density of 1.225 kg / m³. 3 The corresponding wind speed, where p is the air density of 1.225 kg / m³. 3 The transformation matrix between the time and the functional expression model of wind speed, air density, and power output, where v2 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10%, where q is the air density of 1.225 kg / m³. 3 The transformation matrix between the functional expression model of wind speed, turbulence intensity, and power output when the turbulence intensity is 10%, and v3 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10% and an inflow angle of 0, where r is the air density of 1.225 kg / m³. 3The transformation matrix between the functional expression model of wind speed, inflow angle and power output when the turbulence intensity is 10% and the inflow angle is 0.
[0038] Furthermore, step 3 includes:
[0039] The wind speed interval-power characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the first initial yaw error of the wind turbine.
[0040] The wind speed interval-power coefficient characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the second initial yaw error of the wind turbine.
[0041] By using the wind speed interval-power generation characteristic analysis method, the functional expression model between wind speed and power output after the steady-state normalization of the power output characteristic parameters of the wind turbine is analyzed in intervals to obtain the yaw error corresponding to the wind speed-power curve, and this yaw error is used as the third initial yaw error of the wind turbine.
[0042] Furthermore, the formula for calculating the yaw error index of the wind turbine is as follows:
[0043]
[0044] In the above formula, W is the yaw error index of the wind turbine, w i Let be the initial yaw error of the i-th yaw.
[0045] Furthermore, the convergence condition is W ≤ 0.1.
[0046] Secondly, a dynamic identification device for static yaw error of a wind turbine is provided, the dynamic identification device for static yaw error of a wind turbine includes:
[0047] The simulation module is used to perform simulation analysis on the wind turbine model to obtain the wind speed, wind turbine power output characteristic parameters and the functional expression model between power output.
[0048] The first analysis module is used to normalize the power output characteristic parameters of the wind turbine in the function expression model in turn, so as to obtain the function expression model between wind speed and power output after the wind turbine power output characteristic parameters are stabilized and normalized.
[0049] The second analysis module is used to perform interval analysis on the functional expression model between wind speed and power output after the wind turbine power output characteristic parameters are stabilized and normalized, so as to obtain the initial yaw error of the wind turbine.
[0050] The third analysis module is used to determine the yaw error index of the wind turbine based on the initial yaw error of the wind turbine, and to determine whether the yaw error index meets the convergence condition. If it does, the module returns to step 2; otherwise, it outputs the initial yaw error of the wind turbine.
[0051] Preferably, the power output characteristic parameters include at least one of the following: air density, turbulence intensity, and inflow angle.
[0052] Furthermore, the functional expression model relating wind speed, wind turbine power output characteristic parameters, and power output includes:
[0053] The mathematical model for the functional relationship between wind speed, air density, and power output is as follows:
[0054] P=f(v,ρ,TI,α)
[0055] The mathematical model for the functional relationship between wind speed, turbulence intensity, and power output is as follows:
[0056] P=g(v,ρ,TI,α)
[0057] The mathematical model for the functional relationship between wind speed, inflow angle, and power output is as follows:
[0058] P=h(v,ρ,TI,α)
[0059] In the above formula, P is the power output of the wind turbine, v is the wind speed, ρ is the air density, TI is the turbulence intensity, α is the inflow angle, f is the functional expression between wind speed, air density and power output, g is the functional expression between wind speed, turbulence intensity and power output, and h is the functional expression between wind speed, inflow angle and power output.
[0060] Furthermore, the functional expression model of the relationship between wind speed and power output after the wind turbine power output characteristic parameters have been normalized includes:
[0061] The mathematical model for the functional expression of wind speed versus power output after steady-state normalization of air density is as follows:
[0062] P=f(v1,1.225,TI,α)×p
[0063] The mathematical model for the functional relationship between air density, turbulence intensity, and wind speed after steady-state normalization, and power output is as follows:
[0064] P=g(v2,1.225,10%,α)×q
[0065] The mathematical model for the functional relationship between air density, turbulence intensity, inflow angle, and wind speed after steady-state normalization, and power output is as follows:
[0066] P=h(v3,1.225,10%,0)×r
[0067] In the above formula, v1 represents the air density of 1.225 kg / m³. 3 The corresponding wind speed, where p is the air density of 1.225 kg / m³. 3 The transformation matrix between the time and the functional expression model of wind speed, air density, and power output, where v2 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10%, where q is the air density of 1.225 kg / m³. 3 The transformation matrix between the functional expression model of wind speed, turbulence intensity, and power output when the turbulence intensity is 10%, and v3 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10% and an inflow angle of 0, where r is the air density of 1.225 kg / m³. 3 The transformation matrix between the functional expression model of wind speed, inflow angle and power output when the turbulence intensity is 10% and the inflow angle is 0.
[0068] Furthermore, the second analysis module is specifically used for:
[0069] The wind speed interval-power characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the first initial yaw error of the wind turbine.
[0070] The wind speed interval-power coefficient characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the second initial yaw error of the wind turbine.
[0071] By using the wind speed interval-power generation characteristic analysis method, the functional expression model between wind speed and power output after the steady-state normalization of the power output characteristic parameters of the wind turbine is analyzed in intervals to obtain the yaw error corresponding to the wind speed-power curve, and this yaw error is used as the third initial yaw error of the wind turbine.
[0072] Furthermore, the formula for calculating the yaw error index of the wind turbine is as follows:
[0073]
[0074] In the above formula, W is the yaw error index of the wind turbine, w i Let be the initial yaw error of the i-th yaw.
[0075] Furthermore, the convergence condition is W ≤ 0.1.
[0076] Thirdly, a computer device is provided, comprising: one or more processors;
[0077] The processor is used to store one or more programs;
[0078] When the one or more programs are executed by the one or more processors, the dynamic identification method for static yaw error of wind turbines is implemented.
[0079] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed, the method for dynamic identification of static yaw error of wind turbine is implemented.
[0080] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects:
[0081] This invention provides a method for dynamic identification of static yaw error of wind turbine generators. The method includes: Step 1. Simulating and analyzing a wind turbine generator model to obtain wind speed, wind turbine generator power output characteristic parameters, and a functional expression model of the relationship between power output; Step 2. Normalizing the wind turbine generator power output characteristic parameters in the functional expression model to obtain a functional expression model of wind speed and power output after the wind turbine generator power output characteristic parameters have been normalized; Step 3. Performing interval analysis on the functional expression model of wind speed and power output after the wind turbine generator power output characteristic parameters have been normalized to obtain the initial yaw error of the wind turbine generator; Step 4. Determining the yaw error index of the wind turbine generator based on the initial yaw error, and judging whether the yaw error index meets the convergence condition. If yes, return to Step 2; otherwise, output the initial yaw error of the wind turbine generator. The technical solution provided by this invention can solve the problem of accurate identification and dynamic compensation of yaw error of wind turbine units in long-term operation. By identifying the yaw error of wind turbine units in real time, dynamic compensation can be implemented. This solves the problems of high cost, long cycle, poor reliability and accuracy and unsustainability of existing yaw error identification methods, thereby continuously improving the power generation capacity and power output of wind turbine units, and has significant social and economic benefits. Attached Figure Description
[0082] Figure 1 This is a schematic diagram of the main steps of the dynamic identification method for static yaw error of wind turbines according to an embodiment of the present invention;
[0083] Figure 2 This is a schematic diagram of yaw error according to an embodiment of the present invention;
[0084] Figure 3 This is a slice surface plot of the power output characteristic-air density function according to an embodiment of the present invention;
[0085] Figure 4 This is a slice surface plot of the power output characteristics-turbulence intensity function according to an embodiment of the present invention;
[0086] Figure 5 This is a slice surface plot of the power output characteristic-inflow angle function according to an embodiment of the present invention;
[0087] Figure 6 This is a block diagram of the main structure of the dynamic identification device for static yaw error of wind turbine generators according to an embodiment of the present invention. Detailed Implementation
[0088] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.
[0089] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0090] As disclosed in the background section, the wind power industry is currently experiencing rapid development, with its installed capacity and total volume ranking first in the world. However, with the continuous increase in the operating time of wind turbines, aging of structural components and degradation of electrical components occur frequently, leading to significant changes in the operating characteristics of the wind turbines. This directly manifests as a continuous decrease in the power generation capacity and output of the wind turbines. Wind turbine power generation is the primary concern for wind farm operators and investors. For wind turbines operating for extended periods, optimizing the operating power curve and increasing power generation is a continuous goal pursued by wind farm owners.
[0091] Wind turbine power generation is a crucial indicator of its performance. For wind farm owners, power generation directly translates to overall farm revenue. With the rapid development of my country's wind power industry, wind turbines are operating for increasingly longer periods, leading to a growing degradation in turbine output and power generation. Particularly, for wind turbines with unified control strategies and logic, their power output is significantly influenced by numerous factors under specific site and environmental conditions. These factors primarily include air density, yaw error, turbulence intensity, and inflow angle. Improving the power output of long-term in-service wind turbines requires research and development based on the characteristics of these influencing factors. Considering that parameters such as air density, turbulence intensity, and inflow angle are environmental conditions that cannot be artificially manipulated, wind turbine power generation improvement based on yaw error identification is a hot research topic.
[0092] Considering the impact of yaw error on wind turbine power generation, it is necessary to identify the yaw error of wind turbines and compensate for it in the wind turbine control system in order to reduce the impact of yaw error on wind turbine power generation.
[0093] One existing solution to the problem of identifying yaw error in wind turbines is to install a lidar anemometer on the top of the wind turbine nacelle to measure the incoming wind direction and compare it with the wind direction signal of the wind turbine itself. The deviation is then analyzed by linear fitting, which is defined as the yaw error of the wind turbine system.
[0094] Existing wind turbine yaw error identification technologies based on nacelle radar anemometers have the following drawbacks and shortcomings:
[0095] 1. The cabin radar anemometer mainly uses the optical Doppler principle, measuring the wind speed and direction of the incoming airflow by observing the propagation of light waves through an aerosol medium. The measured wind speed and direction signals are affected by the horizontal influence of aerosols in the air, making the accuracy of the anemometer measurement impossible to fully guarantee. In other words, the accuracy of key airflow data such as wind speed and direction cannot be comprehensively assured.
[0096] 2. The cost of lidar anemometers is relatively high. Under the current trend of "grid parity" and the continuous compression of the cost per kilowatt-hour of wind turbines, the additional cost of lidar anemometers is high for wind turbine manufacturers and operators.
[0097] 3. The method for identifying yaw error in wind turbines based on lidar anemometers requires a certain period of field testing, while simultaneously collecting data from the wind turbine's own control systems, such as wind speed and direction, for statistical comparative analysis. Considering the need to collect data across different wind speed ranges, the data collection period is typically 3-6 months, resulting in a long testing cycle and high time costs.
[0098] 4. Wind turbine yaw error exhibits time-varying characteristics, meaning that as the operating time of a wind turbine increases, its yaw error changes over time. This indicates that yaw error identification and compensation for wind turbines requires continuous and uninterrupted implementation. Current methods for identifying yaw error using lidar anemometers are often one-time measurements and cannot provide continuous yaw error identification and compensation.
[0099] To address the issue of yaw error identification in wind turbines, one existing solution is to use SCADA data from wind turbine operation to statistically analyze the power output characteristics of the turbines in different wind speed ranges, and then compare and analyze these characteristics to identify and determine the yaw error of the wind turbines.
[0100] Based on SCADA data of wind turbine operation, the power output characteristics during different wind speeds are analyzed to estimate the yaw error of the turbine. The implementation process is relatively simple and low-cost, but it still has the following disadvantages and shortcomings:
[0101] 1. The yaw error identification process relies on the actual SCADA data of the wind turbine, and the accuracy and precision of the data have a significant impact on the analysis results;
[0102] 2. This method analyzes the power output characteristics of the wind turbine across different wind speed ranges and estimates potential yaw errors through comparative analysis. However, it neglects the influence of air density, inflow angle, and turbulence intensity on the wind turbine's power output characteristics. The power output characteristics obtained through statistical analysis of a single wind speed range are actually coupled with the effects of other factors such as air density, turbulence intensity, and inflow angle. This directly leads to unreliable yaw error identification results, with the possibility of overcompensation and undercompensation.
[0103] To address the aforementioned issues, this invention provides a dynamic identification method for static yaw error of wind turbines. The method includes: Step 1. Simulating and analyzing a wind turbine model to obtain wind speed, wind turbine power output characteristic parameters, and a functional expression model of the relationship between power output; Step 2. Normalizing the wind turbine power output characteristic parameters in the functional expression model to obtain a function expression model of wind speed and power output after the wind turbine power output characteristic parameters have been normalized; Step 3. Performing interval analysis on the function expression model of wind speed and power output after the wind turbine power output characteristic parameters have been normalized to obtain the initial yaw error of the wind turbine; Step 4. Determining the yaw error index of the wind turbine based on the initial yaw error, and determining whether the yaw error index meets the convergence condition. If yes, returning to Step 2; otherwise, outputting the initial yaw error of the wind turbine. The technical solution provided by this invention can solve the problem of accurate identification and dynamic compensation of yaw error in long-term in-service wind turbines. By identifying the yaw error of the wind turbine in real time, dynamic compensation can be implemented, solving the problems of high cost, long cycle, poor reliability and accuracy, and unsustainability of existing yaw error identification methods. This continuously improves the power generation capacity and output of wind turbines, resulting in significant social and economic benefits. The above solution is described in detail below.
[0104] Example 1
[0105] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a dynamic identification method for static yaw error of wind turbines according to an embodiment of the present invention. Figure 1 As shown, the dynamic identification method for static yaw error of wind turbine units in this embodiment of the invention mainly includes the following steps:
[0106] Step 1. Perform simulation analysis on the wind turbine model to obtain the wind speed, wind turbine power output characteristic parameters, and the functional expression model of the relationship between power output;
[0107] Step 2. Normalize the power output characteristic parameters of the wind turbine in the function expression model in turn to obtain the function expression model of wind speed and power output after the wind turbine power output characteristic parameters are stabilized and normalized.
[0108] Step 3. Perform interval analysis on the functional expression model between wind speed and power output after the wind turbine power output characteristic parameters are normalized to obtain the initial yaw error of the wind turbine.
[0109] Step 4. Determine the yaw error index of the wind turbine based on the initial yaw error of the wind turbine, and determine whether the yaw error index meets the convergence condition. If yes, return to step 2; otherwise, output the initial yaw error of the wind turbine.
[0110] Yaw error: A wind turbine is a power generation device that converts wind energy into electrical energy. Because the direction of incoming winds changes in real time, the wind turbine rotor needs to constantly yaw to adjust its angle relative to the incoming wind direction in order to capture wind energy to the maximum extent. Ideally, the wind turbine's nose is directly opposite the direction of the incoming wind, at which point the yaw error is 0°. In actual operation, due to various reasons, the wind turbine's nose may not be directly opposite the direction of the incoming wind, resulting in a certain deviation, which is defined as yaw error. Figure 2 The definition of yaw error is given.
[0111] In this embodiment, the power output characteristic parameters include at least one of the following: air density, turbulence intensity, and inflow angle.
[0112] In one implementation, the simulation of the wind turbine power output model and the modeling of key parameters are mainly aimed at analyzing the influence weights of factors such as air density, turbulence intensity, and inflow angle on the power output characteristics of the turbine through matrix quantization simulation, thereby establishing the correlation function between the power output of the wind turbine and each influencing factor.
[0113] Based on the BLADED design model of the wind turbine, the power output characteristics under different air density conditions, different turbulence intensities, and different inflow angles were simulated to obtain a database of the output characteristics of this type of wind turbine under different input conditions. The specific simulation settings are shown in Table 1 below.
[0114] Table 1
[0115] Serial Number Feature parameters Simulation range Simulation interval 1 air density <![CDATA[0.8 kg / m 3 -1.3kg / m 3 ]]> <![CDATA[0.01kg / m 3 ]]> 2 Turbulence intensity 0%-40% 2% 3 Inflow angle -20°-20° 2° 4 wind speed 3m / s-25m / s 0.5m / s
[0116] Statistical analysis of the simulation results yielded functional models relating air density, turbulence intensity, inflow angle, and power output:
[0117] The mathematical model for the functional relationship between wind speed, air density, and power output is as follows:
[0118] P=f(v,ρ,TI,α)
[0119] The mathematical model for the functional relationship between wind speed, turbulence intensity, and power output is as follows:
[0120] P=g(v,ρ,TI,α)
[0121] The mathematical model for the functional relationship between wind speed, inflow angle, and power output is as follows:
[0122] P=h(v,ρ,TI,α)
[0123] In the above formula, P is the power output of the wind turbine, v is the wind speed, ρ is the air density, TI is the turbulence intensity, α is the inflow angle, f is the functional expression between wind speed, air density and power output, g is the functional expression between wind speed, turbulence intensity and power output, and h is the functional expression between wind speed, inflow angle and power output.
[0124] Figure 3 , 4 Figures 5 and 6 respectively present typical power output characteristics as slice surfaces of air density function, power output characteristics as slice surfaces of turbulence intensity function, and power output characteristics as slice surfaces of inflow angle function.
[0125] Furthermore, in the embodiments provided by this invention, the SCADA operation data of the wind turbine can also be processed and filtered. This mainly involves collecting historical operation data of the wind turbine, with a time period of at least one year. The data mainly includes statistical values (including maximum, minimum, average, and standard deviation) of parameters such as wind speed, power, pitch angle, rotor speed, wind direction, yaw error, and turbine operation status indicators. In this step, based on the actual situation of the data in each signal channel, shutdown data, abnormal signal channel data, and power rationing operation data need to be filtered out to clean the basic database and ensure data reliability.
[0126] In one embodiment, the present invention normalizes the influence factors of air density, turbulence intensity, and inflow angle on power output, and normalizes the wind speed under different air densities, turbulence intensities, and inflow angles to a unified air density level. The functional expression model between the wind speed and power output after the steady-state normalization of the wind turbine power output characteristic parameters includes:
[0127] The mathematical model for the functional expression of wind speed versus power output after steady-state normalization of air density is as follows:
[0128] P=f(v1,1.225,TI,α)×p
[0129] The mathematical model for the functional relationship between air density, turbulence intensity, and wind speed after steady-state normalization, and power output is as follows:
[0130] P=g(v2,1.225,10%,α)×q
[0131] The mathematical model for the functional relationship between air density, turbulence intensity, inflow angle, and wind speed after steady-state normalization, and power output is as follows:
[0132] P=h(v3,1.225,10%,0)×r
[0133] In the above formula, v1 represents the air density of 1.225 kg / m³. 3 The corresponding wind speed, where p is the air density of 1.225 kg / m³. 3 The transformation matrix between the time and the functional expression model of wind speed, air density, and power output, where v2 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10%, where q is the air density of 1.225 kg / m³. 3 The transformation matrix between the functional expression model of wind speed, turbulence intensity, and power output when the turbulence intensity is 10%, and v3 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10% and an inflow angle of 0, where r is the air density of 1.225 kg / m³. 3 The transformation matrix between the functional expression model of wind speed, inflow angle and power output when the turbulence intensity is 10% and the inflow angle is 0.
[0134] After completing the normalization of air density, turbulence intensity, and inflow angle in sequence, the original operating wind speed data of the wind turbine was transformed to form new operating data of the wind turbine. This operating data achieves steady-state normalization of the main factors affecting the power output of the unit: air density, turbulence intensity, and inflow angle.
[0135] Next, we analyzed the SCADA operating data of the wind turbine after steady-state normalization, using three independent analysis methods to identify three yaw error results. The three independent analysis methods are described below:
[0136] The wind speed interval-power characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the first initial yaw error of the wind turbine.
[0137] In one implementation, the wind speed range-power characteristic analysis method mainly involves dividing the steady-state normalized SCADA operating data of the wind turbine into intervals according to wind speeds of 3 m / s to 25 m / s, with intervals defined at 0.5 m / s intervals, and ranges defined as 2.75 m / s to 3.25 m / s, 3.25 m / s to 3.75 m / s, and so on. Simultaneously, the yaw error is divided into intervals from -40° to 40°, with intervals defined at 2° intervals. The data is then processed separately to generate power output characteristic curves for different yaw error intervals and different wind speed intervals. The yaw error interval corresponding to the peak value of the curve is defined as the static yaw error. Finally, the first initial yaw error of the turbine based on the wind speed range-power characteristics is identified.
[0138] The wind speed interval-power coefficient characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the second initial yaw error of the wind turbine.
[0139] In one implementation, the analysis method based on wind speed range-power coefficient characteristics mainly involves dividing the fully steady-state normalized SCADA operating data of the wind turbine into intervals according to wind speeds of 3 m / s to 25 m / s, with intervals defined at 0.5 m / s intervals, and ranges defined as 2.75 m / s to 3.25 m / s, 3.25 m / s to 3.75 m / s, and so on. Simultaneously, the yaw error is divided into intervals from -40° to 40°, with intervals defined at 2° intervals. The data is then processed separately to generate power coefficient output characteristic curves for different yaw error intervals and different wind speed intervals. The yaw error interval corresponding to the peak value of the power coefficient characteristic curve is defined as the static yaw error. Finally, the second initial yaw error of the turbine based on wind speed range-power coefficient characteristics is identified.
[0140] By using the wind speed interval-power generation characteristic analysis method, the functional expression model between wind speed and power output after the steady-state normalization of the power output characteristic parameters of the wind turbine is analyzed in intervals to obtain the yaw error corresponding to the wind speed-power curve, and this yaw error is used as the third initial yaw error of the wind turbine.
[0141] In one implementation, the analysis method based on wind speed range-power generation characteristics mainly involves dividing the fully steady-state normalized SCADA operating data of the wind turbine into intervals according to wind speeds of 3 m / s to 25 m / s, with intervals defined at 0.5 m / s intervals, and ranges defined as 2.75 m / s to 3.25 m / s, 3.25 m / s to 3.75 m / s, and so on. Simultaneously, the yaw error is divided into intervals from -40° to 40°, with intervals defined at 2° intervals. The data is then processed separately to generate statistical output characteristic curves of power generation for different yaw error intervals and different wind speed intervals. The yaw error interval corresponding to the peak value of the power generation curve is defined as the static yaw error. Finally, the third initial yaw error of the turbine based on wind speed range-power generation characteristics is identified.
[0142] For the yaw error identified by the three independent methods mentioned above, the yaw error index of the wind turbine is determined by the following formula:
[0143]
[0144] In the above formula, W is the yaw error index of the wind turbine, w i Let be the initial yaw error of the i-th yaw.
[0145] The convergence condition is W ≤ 0.1.
[0146] In this embodiment, the static yaw error result obtained by the above method can also be transmitted to the wind turbine main control system in the form of parameters using protocols such as TCP / IP and MODBUS to modify the zero point position of the turbine's yaw against the wind and realize dynamic compensation for the static yaw error.
[0147] This invention uses algorithms and models to solve the problem of accurate identification and dynamic compensation of yaw error in long-term in-service wind turbines. By identifying the yaw error of wind turbines in real time and implementing dynamic compensation, it solves the problems of high cost, long cycle, poor reliability and accuracy and unsustainability of existing yaw error identification methods. This continuously improves the power generation capacity and output of wind turbines, resulting in significant social and economic benefits.
[0148] Taking a single 8MW offshore wind turbine as an example, assuming the turbine operates at full capacity for 4000 hours per year, the grid-connected electricity price is 0.85 yuan / kWh, and the turbine itself has a 10-degree yaw error, after implementing this patent, it is preliminarily estimated that a single wind turbine can increase power generation by 9.65 × 10⁵ kWh per year, resulting in an increase in revenue of approximately 820,000 yuan. Furthermore, assuming the entire site has 25 8MW turbines, the total power generation of the entire site can increase by 2.41 × 10⁷ kWh per year, resulting in an increase in revenue of approximately 20.5 million yuan.
[0149] Example 2
[0150] Based on the same inventive concept, this invention also provides a dynamic identification device for static yaw error of wind turbine units, such as... Figure 6 As shown, the dynamic identification device for static yaw error of the wind turbine includes:
[0151] The simulation module is used to perform simulation analysis on the wind turbine model to obtain the wind speed, wind turbine power output characteristic parameters and the functional expression model between power output.
[0152] The first analysis module is used to normalize the power output characteristic parameters of the wind turbine in the function expression model in turn, so as to obtain the function expression model between wind speed and power output after the wind turbine power output characteristic parameters are stabilized and normalized.
[0153] The second analysis module is used to perform interval analysis on the functional expression model between wind speed and power output after the wind turbine power output characteristic parameters are stabilized and normalized, so as to obtain the initial yaw error of the wind turbine.
[0154] The third analysis module is used to determine the yaw error index of the wind turbine based on the initial yaw error of the wind turbine, and to determine whether the yaw error index meets the convergence condition. If it does, the module returns to step 2; otherwise, it outputs the initial yaw error of the wind turbine.
[0155] Preferably, the power output characteristic parameters include at least one of the following: air density, turbulence intensity, and inflow angle.
[0156] Furthermore, the functional expression model relating wind speed, wind turbine power output characteristic parameters, and power output includes:
[0157] The mathematical model for the functional relationship between wind speed, air density, and power output is as follows:
[0158] P=f(v,ρ,TI,α)
[0159] The mathematical model for the functional relationship between wind speed, turbulence intensity, and power output is as follows:
[0160] P=g(v,ρ,TI,α)
[0161] The mathematical model for the functional relationship between wind speed, inflow angle, and power output is as follows:
[0162] P=h(v,ρ,TI,α)
[0163] In the above formula, P is the power output of the wind turbine, v is the wind speed, ρ is the air density, TI is the turbulence intensity, α is the inflow angle, f is the functional expression between wind speed, air density and power output, g is the functional expression between wind speed, turbulence intensity and power output, and h is the functional expression between wind speed, inflow angle and power output.
[0164] Furthermore, the functional expression model of the relationship between wind speed and power output after the wind turbine power output characteristic parameters have been normalized includes:
[0165] The mathematical model for the functional expression of wind speed versus power output after steady-state normalization of air density is as follows:
[0166] P=f(v1,1.225,TI,α)×p
[0167] The mathematical model for the functional relationship between air density, turbulence intensity, and wind speed after steady-state normalization, and power output is as follows:
[0168] P=g(v2,1.225,10%,α)×q
[0169] The mathematical model for the functional relationship between air density, turbulence intensity, inflow angle, and wind speed after steady-state normalization, and power output is as follows:
[0170] P=h(v3,1.225,10%,0)×r
[0171] In the above formula, v1 represents the air density of 1.225 kg / m³. 3 The corresponding wind speed, where p is the air density of 1.225 kg / m³. 3 The transformation matrix between the time and the functional expression model of wind speed, air density, and power output, where v2 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10%, where q is the air density of 1.225 kg / m³. 3 The transformation matrix between the functional expression model of wind speed, turbulence intensity, and power output when the turbulence intensity is 10%, and v3 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10% and an inflow angle of 0, where r is the air density of 1.225 kg / m³. 3 The transformation matrix between the functional expression model of wind speed, inflow angle and power output when the turbulence intensity is 10% and the inflow angle is 0.
[0172] Furthermore, the second analysis module is specifically used for:
[0173] The wind speed interval-power characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the first initial yaw error of the wind turbine.
[0174] The wind speed interval-power coefficient characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the second initial yaw error of the wind turbine.
[0175] By using the wind speed interval-power generation characteristic analysis method, the functional expression model between wind speed and power output after the steady-state normalization of the power output characteristic parameters of the wind turbine is analyzed in intervals to obtain the yaw error corresponding to the wind speed-power curve, and this yaw error is used as the third initial yaw error of the wind turbine.
[0176] Furthermore, the formula for calculating the yaw error index of the wind turbine is as follows:
[0177]
[0178] In the above formula, W is the yaw error index of the wind turbine, w i Let be the initial yaw error of the i-th yaw.
[0179] Furthermore, the convergence condition is W ≤ 0.1.
[0180] Example 3
[0181] Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, thereby realizing the steps of the dynamic identification method for static yaw error of a wind turbine in the above embodiments.
[0182] Example 4
[0183] Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the dynamic identification method for static yaw error of a wind turbine in the above embodiments.
[0184] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0185] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0186] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0187] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A method for dynamic identification of static yaw error of wind turbine generators, characterized in that, The method includes: Step 1. Perform simulation analysis on the wind turbine model to obtain the wind speed, wind turbine power output characteristic parameters, and the functional expression model of the relationship between power output; Step 2. Normalize the power output characteristic parameters of the wind turbine in the function expression model in turn to obtain the function expression model of wind speed and power output after the wind turbine power output characteristic parameters are stabilized and normalized. Step 3. Perform interval analysis on the functional expression model between wind speed and power output after the wind turbine power output characteristic parameters are normalized to obtain the initial yaw error of the wind turbine. Step 4. Determine the yaw error index of the wind turbine based on the initial yaw error of the wind turbine, and determine whether the yaw error index meets the convergence condition. If yes, return to step 2; otherwise, output the initial yaw error of the wind turbine. The power output characteristic parameters include at least one of the following: air density, turbulence intensity, and inflow angle; The functional expression model for the relationship between wind speed, wind turbine power output characteristic parameters, and power output includes: The mathematical model for the functional relationship between wind speed, air density, and power output is as follows: P=f(v,ρ,TI,α) The mathematical model for the functional relationship between wind speed, turbulence intensity, and power output is as follows: P=g(v,ρ,TI,α) The mathematical model for the functional relationship between wind speed, inflow angle, and power output is as follows: P=h(v,ρ,TI,α) In the above formula, P is the power output of the wind turbine, v is the wind speed, ρ is the air density, TI is the turbulence intensity, α is the inflow angle, f is the functional expression between wind speed, air density and power output, g is the functional expression between wind speed, turbulence intensity and power output, and h is the functional expression between wind speed, inflow angle and power output. The functional expression model of the relationship between wind speed and power output after the steady-state normalization of the wind turbine power output characteristic parameters includes: The mathematical model for the functional expression of wind speed versus power output after steady-state normalization of air density is as follows: P=f(v1,1.225,TI,α)×p The mathematical model for the functional relationship between air density, turbulence intensity, and wind speed after steady-state normalization, and power output is as follows: P=g(v2,1.225,10%,α)×q The mathematical model for the functional relationship between air density, turbulence intensity, inflow angle, and wind speed after steady-state normalization, and power output is as follows: P=h(v3,1.225,10%,0)×r In the above formula, v1 represents the air density of 1.225 kg / m³. 3 The corresponding wind speed, where p is the air density of 1.225 kg / m³. 3 The transformation matrix between the time and the functional expression model of wind speed, air density, and power output, where v2 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10%, where q is the air density of 1.225 kg / m³. 3 The transformation matrix between the functional expression model of wind speed, turbulence intensity, and power output when the turbulence intensity is 10%, and v3 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10% and an inflow angle of 0, where r is the air density of 1.225 kg / m³. 3 The transformation matrix between the functional expression model of wind speed, inflow angle and power output when the turbulence intensity is 10% and the inflow angle is 0.
2. The method as described in claim 1, characterized in that, Step 3 includes: The wind speed interval-power characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the first initial yaw error of the wind turbine. The wind speed interval-power coefficient characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the second initial yaw error of the wind turbine. By using the wind speed interval-power generation characteristic analysis method, the functional expression model between wind speed and power output after the steady-state normalization of the power output characteristic parameters of the wind turbine is analyzed in intervals to obtain the yaw error corresponding to the wind speed-power curve, and this yaw error is used as the third initial yaw error of the wind turbine.
3. The method as described in claim 2, characterized in that, The formula for calculating the yaw error index of the wind turbine is as follows: In the above formula, W is the yaw error index of the wind turbine, w i Let be the initial yaw error of the i-th yaw.
4. The method as described in claim 3, characterized in that, The convergence condition is W≤0.
1.
5. A dynamic identification device for static yaw error of a wind turbine, characterized in that, The device includes: The simulation module is used to perform simulation analysis on the wind turbine model to obtain the wind speed, wind turbine power output characteristic parameters and the functional expression model between power output. The first analysis module is used to normalize the power output characteristic parameters of the wind turbine in the function expression model in turn, so as to obtain the function expression model between wind speed and power output after the wind turbine power output characteristic parameters are stabilized and normalized. The second analysis module is used to perform interval analysis on the functional expression model between wind speed and power output after the wind turbine power output characteristic parameters are stabilized and normalized, so as to obtain the initial yaw error of the wind turbine. The third analysis module is used to determine the yaw error index of the wind turbine based on the initial yaw error of the wind turbine, and to determine whether the yaw error index meets the convergence condition. If it does, return to step 2; otherwise, output the initial yaw error of the wind turbine. The power output characteristic parameters include at least one of the following: air density, turbulence intensity, and inflow angle; The functional expression model for the relationship between wind speed, wind turbine power output characteristic parameters, and power output includes: The mathematical model for the functional relationship between wind speed, air density, and power output is as follows: P=f(v,ρ,TI,α) The mathematical model for the functional relationship between wind speed, turbulence intensity, and power output is as follows: P=g(v,ρ,TI,α) The mathematical model for the functional relationship between wind speed, inflow angle, and power output is as follows: P=h(v,ρ,TI,α) In the above formula, P is the power output of the wind turbine, v is the wind speed, ρ is the air density, TI is the turbulence intensity, α is the inflow angle, f is the functional expression between wind speed, air density and power output, g is the functional expression between wind speed, turbulence intensity and power output, and h is the functional expression between wind speed, inflow angle and power output. The functional expression model of the relationship between wind speed and power output after the steady-state normalization of the wind turbine power output characteristic parameters includes: The mathematical model for the functional expression of wind speed versus power output after steady-state normalization of air density is as follows: P=f(v1,1.225,TI,α)×p The mathematical model for the functional relationship between air density, turbulence intensity, and wind speed after steady-state normalization, and power output is as follows: P=g(v2,1.225,10%,α)×q The mathematical model for the functional relationship between air density, turbulence intensity, inflow angle, and wind speed after steady-state normalization, and power output is as follows: P=h(v3,1.225,10%,0)×r In the above formula, v1 represents the air density of 1.225 kg / m³. 3 The corresponding wind speed, where p is the air density of 1.225 kg / m³. 3 The transformation matrix between the time and the functional expression model of wind speed, air density, and power output, where v2 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10%, where q is the air density of 1.225 kg / m³. 3 The transformation matrix between the functional expression model of wind speed, turbulence intensity, and power output when the turbulence intensity is 10%, and v3 is the air density of 1.225 kg / m³. 3 The wind speed corresponding to a turbulence intensity of 10% and an inflow angle of 0, where r is the air density of 1.225 kg / m³. 3 The transformation matrix between the functional expression model of wind speed, inflow angle and power output when the turbulence intensity is 10% and the inflow angle is 0.
6. The apparatus as claimed in claim 5, characterized in that, The second analysis module is specifically used for: The wind speed interval-power characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the first initial yaw error of the wind turbine. The wind speed interval-power coefficient characteristic analysis method is used to perform interval analysis on the functional expression model between wind speed and power output after the steady-normalized wind speed and power output characteristic parameters of the wind turbine, to obtain the yaw error corresponding to the wind speed-power curve, and to use the yaw error as the second initial yaw error of the wind turbine. By using the wind speed interval-power generation characteristic analysis method, the functional expression model between wind speed and power output after the steady-state normalization of the power output characteristic parameters of the wind turbine is analyzed in intervals to obtain the yaw error corresponding to the wind speed-power curve, and this yaw error is used as the third initial yaw error of the wind turbine.
7. The apparatus as claimed in claim 6, characterized in that, The formula for calculating the yaw error index of the wind turbine is as follows: In the above formula, W is the yaw error index of the wind turbine, w i Let be the initial yaw error of the i-th yaw.
8. The apparatus as claimed in claim 7, characterized in that, The convergence condition is W≤0.
1.
9. A computer device, characterized in that, include: One or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the dynamic identification method for static yaw error of wind turbines as described in any one of claims 1 to 4 is implemented.
10. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the dynamic identification method for static yaw error of wind turbine as described in any one of claims 1 to 4.