Full-wind-condition blade wind speed real-time soft measurement method and system based on wind power model

Abstract

The invention provides a full-wind-condition blade wind speed real-time soft measurement method and system based on a wind power model, and belongs to the technical field of wind power generation, and the method comprises the steps: obtaining the rotating speed of a rotor of a wind turbine generator, the electromagnetic power outputted by the wind turbine generator, and the pitch angle of a blade; based on the wind turbine generator rotor speed and the electromagnetic power output by the wind turbine generator, the wind power captured by the wind turbine generator is obtained in real time through an equivalent rotor motion differential equation of the wind turbine generator; obtaining a function value of the intermediate function expression by using a wind power expression captured by a second wind turbine generator based on the wind power captured by the wind turbine generator which is obtained in real time; based on the function value of the obtained intermediate function expression and the pitch angle of the blade, searching the intermediate function table to obtain a blade tip speed ratio; and on the basis of the obtained tip speed ratio and the rotor rotating speed of the wind turbine generator, an average wind speed measurement value is calculated by using a tip speed ratio expression.

Description

Technical Field

[0001] This invention belongs to the technical field of wind power generation, and particularly relates to a real-time soft measurement method and system for blade wind speed under all wind conditions based on a wind power model. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Wind energy, as a green and clean energy source, is the renewable energy source with the greatest potential for large-scale development and commercial application today. Wind power generation, with its mature technology, low cost, and large-scale development and utilization advantages, has become the fastest-growing and most competitive power generation technology among new energy sources. Accurate wind speed measurement plays a crucial role in evaluating the power characteristics of wind turbine units, assessing power generation benefits, and ensuring the safe and efficient operation of the control system.

[0004] Regarding wind speed measurement, one method is to use an anemometer. However, because the anemometer is installed at the rear of the nacelle, the measurement results are easily affected by factors such as blade obstruction and turbulence. Moreover, the measured wind speed signal is a single-point wind speed and cannot accurately reflect the average wind speed blowing across the turbine blades. Therefore, the measured wind speed is difficult to meet the wind speed measurement requirements of wind turbine units. Another method is to use lidar for speed measurement. While lidar installed on the nacelle or in the hub can accurately measure the average wind speed of the blades, the investment in hardware such as sensors increases costs, and its reliability remains an issue worth exploring.

[0005] In addition, although existing technologies disclose technical solutions for soft wind speed measurement, current soft wind speed measurement methods that do not require hardware installation achieve indirect wind speed measurement by establishing a nonlinear functional relationship between the wind speed to be measured and measurable or easily measurable variables such as rotor speed, electromagnetic power, and blade pitch angle, relying on software algorithms. Currently, soft measurement modeling methods can be summarized as: (1) state estimation modeling, such as Kalman filtering; (2) data-driven modeling, such as regression analysis, neural networks, and support vector machines; (3) mechanism modeling; and (4) hybrid modeling.

[0006] Existing state estimation and data-driven modeling methods both employ a logical structure of offline parameter identification and online self-calibration. Due to limitations inherent in this logical structure and the dependence of the algorithm's training set on historical data, the relative error between measured and actual wind speed values ​​is generally around 10%. -2 Order of magnitude. Existing hybrid modeling methods require the addition of anemometers or lidar, increasing unit costs and maintenance expenses. Furthermore, mechanistic modeling methods are affected by the complex nonlinear functional relationship between the measured wind speed and measurable variables, and have not yet developed into engineering-practical modeling methods. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this invention provides a real-time soft measurement method and system for blade wind speed under all wind conditions based on a wind power model. This method can measure the average wind speed blowing across the blades in real time across all wind conditions without requiring hardware installation. Furthermore, according to specific application embodiments, the relative error between the measured wind speed value and the actual value can be reduced to 10%. -3 Order of magnitude.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: Firstly, a real-time soft measurement method for blade wind speed under all wind conditions based on a wind power model is disclosed, including: Obtain the rotor speed of the wind turbine, the electromagnetic power output of the wind turbine, and the blade pitch angle; Based on the rotor speed and electromagnetic power output of the wind turbine, the wind power captured by the wind turbine is obtained in real time using the equivalent rotor motion differential equation of the wind turbine. Then, based on the wind power captured by the wind turbine in real time, the function value of the intermediate function expression is obtained by using the expression of the wind power captured by the second wind turbine. Based on the function value of the obtained intermediate function expression and the blade pitch angle, the tip speed ratio is obtained by looking up the intermediate function table. The process of constructing the intermediate function table is as follows: consult the equipment data provided by the wind turbine manufacturer to obtain the functional relationship between the wind energy utilization coefficient and the tip speed ratio and the blade pitch angle in a table, i.e., the wind energy utilization coefficient table. Based on the wind energy utilization coefficient table, the function is obtained through calculation. f ( l , β (Ratio to tip speed) l Pitch angle β The functional relationships are presented in a table, i.e., an intermediate function table; Based on the obtained tip speed ratio and wind turbine rotor speed, the average wind speed measurement value is calculated using the tip speed ratio expression.

[0009] As a further technical solution, it also includes constructing a wind power model, which is used to represent the relationship between the wind power captured by the wind turbine and the wind speed, specifically including the expression for the wind power captured by the first wind turbine and the expression for the tip speed ratio.

[0010] As a further technical solution, the tip speed ratio expression is substituted into the wind power expression captured by the first wind turbine to obtain the wind power expression captured by the second wind turbine and the intermediate function expression.

[0011] As a further technical solution, it is also necessary to construct the equivalent rotor motion differential equation of the wind turbine.

[0012] As a further technical solution, the wind energy utilization coefficient table is used to represent the functional relationship between the wind energy utilization coefficient and the tip speed ratio and the blade pitch angle, presented in a table.

[0013] As a further technical solution, the functional relationship between the function and the tip speed ratio and the blade pitch angle is calculated based on the intermediate function expression and presented in a table.

[0014] Secondly, a real-time soft measurement system for blade wind speed under all wind conditions based on a wind power model is disclosed, including: The data acquisition module is configured to acquire the rotor speed of the wind turbine, the electromagnetic power output by the wind turbine, and the blade pitch angle. The wind power calculation module is configured to: obtain the wind power captured by the wind turbine in real time based on the rotor speed of the wind turbine and the electromagnetic power output by the wind turbine, using the equivalent rotor motion differential equation of the wind turbine. The function value calculation module is configured to: obtain the function value of the intermediate function expression based on the wind power captured by the wind turbine in real time and the wind power expression captured by the second wind turbine. The tip speed ratio acquisition module is configured to: obtain the tip speed ratio by looking up the intermediate function table based on the function value of the acquired intermediate function expression and the blade pitch angle; The process of constructing the intermediate function table is as follows: consult the equipment data provided by the wind turbine manufacturer to obtain the functional relationship between the wind energy utilization coefficient and the tip speed ratio and the blade pitch angle in a table, i.e., the wind energy utilization coefficient table. Based on the wind energy utilization coefficient table, the function is obtained through calculation. f ( l , β (Ratio to tip speed) l Pitch angle β The functional relationships are presented in a table, i.e., an intermediate function table; The average wind speed measurement calculation module is configured to calculate the average wind speed measurement value based on the acquired tip speed ratio and wind turbine rotor speed using the tip speed ratio expression.

[0015] The above one or more technical solutions have the following beneficial effects: The soft measurement scheme based on the wind turbine mechanism model proposed in this invention, on the one hand, utilizes the functional relationship between the wind speed to be measured and measurable quantities such as rotor speed, electromagnetic power, and blade pitch angle to indirectly obtain the wind speed measurement value through transformation. This replaces the function of a hardware anemometer with software, eliminating the need for sensor installation. On the other hand, existing soft measurement modeling methods based on state estimation and data-driven approaches all employ a prediction and correction logic structure, while the method proposed in this invention derives the relationship between the wind speed to be measured and measurable quantities based on the wind turbine mechanism model, and calculates the average wind speed blowing over the blades in real time. This can significantly reduce the relative error between the measured wind speed value and the actual value, and improve measurement accuracy.

[0016] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0018] Figure 1 This is a flowchart illustrating the method of an embodiment of the present invention; Figure 2 A diagram showing the actual and measured wind speeds; Figure 3 Schematic diagram of absolute wind speed error; Figure 4 Schematic diagram of relative wind speed error. Detailed Implementation

[0019] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0021] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0022] In the existing technology, patent CN 115013258 B has several core problems. Its hardware dependence has not been completely resolved. Although it claims to reduce the number of LiDAR units, it still essentially requires the installation of LiDAR on some wind turbines to obtain the measured operational data needed for modeling. This means that newly built wind farms need to bear the additional costs of purchasing and installing LiDAR, and when retrofitting old wind farms, it is necessary to damage the original unit structure for installation. It does not truly achieve the core requirement of "de-emphasizing additional hardware" for soft measurement. The model accuracy of this patent has extremely strong data dependence and stability defects. It relies entirely on the completeness of wind condition coverage and sample balance of LiDAR samples. Under extreme wind conditions such as typhoons and strong turbulence, the model is prone to failure due to missing samples. Moreover, when the wind farm is replaced or the unit model is adjusted, the original samples will completely lose their adaptability, requiring the re-collection of data and completion of the entire modeling process, resulting in extremely high adaptation costs. The complexity of its modeling and operation is equally prominent, requiring technicians to master multiple professional technologies such as nonlinear feature selection (MIC), high-dimensional clustering, deep learning BiLSTM, and signal processing EMD / PSD. Small and medium-sized wind farms often lack corresponding technical team support, and the performance of the units will naturally degrade during long-term operation, requiring periodic re-collection of samples to update the model, further increasing the operation and maintenance burden. In addition, the core logic of this patent is contradictory. Although it mentions a dynamic mechanism model, it is essentially still fitting the input-output mapping relationship through lidar sample data, and has not truly broken through the data dependency limitation. If the lidar sample itself has measurement errors such as wake interference, it will be directly transmitted to the soft measurement results. Moreover, for new operating conditions that do not appear in the samples, the model cannot effectively cope with them, and the accuracy will drop sharply or even become completely invalid.

[0023] From the perspective of application scenarios, the scope of application of patent CN 115013258 B is clearly limited: it is only applicable to wind farms that are already equipped with lidar and have stable wind conditions (such as low-turbulence plains), and cannot cover wind farms with complex terrains such as mountains and canyons. It is also not applicable to newly built or old wind farms that do not have lidar deployment and are unwilling to invest additional hardware costs. Its multi-step data processing and rolling prediction mode result in insufficient real-time performance, and can only be used for short-term or ultra-short-term wind speed prediction. It is difficult to adapt to real-time control scenarios that require millisecond-level feedback, such as wind turbine pitch control and power regulation. In addition, the patent has strict requirements for operation and maintenance capabilities, and can only be implemented by large wind farms or energy companies with professional algorithm teams. Small and medium-sized wind farms cannot promote its application due to technical capabilities and cost limitations. In multi-turbine mixed wind farms, separate models need to be built for each turbine model, which further increases the operation and maintenance burden. Under extreme wind conditions or transitional operating conditions, the model will completely lose its value because lidar cannot collect data stably or the input variables change abruptly.

[0024] Patent CN115013258 B employs a hybrid approach combining data-driven methods with partial mechanistic models. Its core relies on measured data collected by lidar as modeling samples, essentially not eliminating the need for additional hardware. New wind farms or retrofits of older wind farms still require investment in lidar procurement and installation, and subsequent maintenance incurs additional costs. The modeling process of this patent is extremely complex, involving multiple steps such as delay order determination, high-dimensional parametric machine learning clustering, hyperplane partitioning, multi-domain model training, empirical mode decomposition, and power spectral density analysis. This not only demands exceptional expertise from the technical team, requiring mastery of nonlinear feature selection, deep learning, and signal processing technologies, but also exhibits strong data dependence. The quality of the lidar samples and the completeness of wind condition coverage directly determine the model accuracy. If the wind farm, turbine model, or turbine parameters are changed, the original samples become completely invalid, necessitating re-collection of data and repetition of the modeling process, resulting in high adaptation costs. More importantly, although the patent mentions a "dynamic mechanism model," its core is still to establish an input-output mapping relationship through data fitting. If there is a measurement error in the lidar sample, it will be directly transmitted to the soft measurement result. It cannot cope with extreme wind conditions (such as typhoons and strong turbulence) or transitional operating conditions (such as wind turbine start-up and shutdown, and large-scale adjustment of pitch angle) that do not appear in the sample. At this time, the model accuracy will drop sharply or even fail completely. At the same time, it does not clearly quantify the measurement error and only verifies the performance through trend comparison, which is difficult to meet the high-precision requirements of wind turbine power characteristic evaluation and real-time control.

[0025] Example 1 See appendix Figure 1 As shown, this embodiment discloses a real-time soft measurement method for blade wind speed under all wind conditions based on a wind power model, including: Step 1: Obtain the rotor speed of the wind turbine, the electromagnetic power output of the wind turbine, and the blade pitch angle; Step 2: Based on the rotor speed and electromagnetic power output of the wind turbine, the wind power captured by the wind turbine is obtained in real time using the equivalent rotor motion differential equation of the wind turbine. Step 3: Based on the wind power captured by the wind turbine in real time, the function value of the intermediate function expression is obtained by using the wind power expression captured by the second wind turbine. Step 4: Based on the function value of the obtained intermediate function expression and the blade pitch angle, look up the intermediate function table to obtain the tip speed ratio; Step 5: Based on the obtained tip speed ratio and wind turbine rotor speed, calculate the average wind speed measurement value using the tip speed ratio expression.

[0026] By repeating steps one through five at each moment, the average wind speed of the wind turbine blades can be measured in real time.

[0027] The acquisition of the three parameters in step one relies on the sensors, monitoring modules, and control systems that come with the wind turbine from the factory. No additional hardware is required, and the inherent monitoring resources of the unit are fully reused. The specific methods are as follows: (1) Rotor speed of wind turbine: The speed signal is collected in real time by the speed sensor (such as photoelectric encoder or Hall effect sensor) pre-installed on the main shaft or generator end of the unit. After the sensor converts the mechanical speed into an electrical signal, it is transmitted to the main control system of the wind turbine (such as PLC or SCADA system). The real-time rotor speed data can be read directly from the control system. The sampling frequency can match the control requirements of the unit.

[0028] (2) Electromagnetic power output by wind turbine: The three-phase voltage and current signals are collected in real time by the voltage transformer and current transformer pre-installed on the generator output side. The main control system calculates the electromagnetic power in real time based on the electromagnetic power calculation formula (P=√3UIcosφ, where cosφ is the power factor, which is fed back in real time by the unit's reactive power compensation system). The data is transmitted and read synchronously with the speed signal, without the need for additional manual intervention.

[0029] (3) Blade pitch angle: The actual blade deflection angle is fed back in real time by the angle sensor (such as potentiometer or absolute encoder) pre-installed in the blade pitch system. The pitch system communicates with the main control system in real time and directly calls the real-time pitch angle data from the main control system. This angle sensor is the core component of the pitch system and is used for closed-loop control of the blade angle. It is a standard component of the unit and does not require additional installation or modification.

[0030] In summary, the acquisition of the three parameters all rely on the existing monitoring hardware and control system of the wind turbine. Data acquisition, transmission and calculation are inherent processes during normal operation of the unit, without the need for additional hardware investment or complex extra operations, thus ensuring the real-time nature of the data.

[0031] In one implementation example, a mechanistic model of the wind turbine is also included, which is used to represent the relationship between the wind power captured by the wind turbine and the wind speed. Specifically, it includes the expression for the wind power captured by the first wind turbine and the expression for the tip speed ratio.

[0032] The expressions for the wind power captured by the first wind turbine and the tip speed ratio are as follows: (1) (2) In the formula, P m This indicates the wind power captured by the wind turbine. r Indicates air density; R Indicates the radius of the wind turbine blades; A This indicates the swept area of ​​the fan blades; vThis indicates the average wind speed that passes over the fan blades; oh r Indicates the rotor speed; l Indicates the tip speed ratio; β Indicates the blade pitch angle; C p ( l , β () represents the wind energy utilization coefficient; air density r Sweeping area of ​​blades A With blade radius R For a given wind turbine, these are all fixed values.

[0033] It should be noted that the wind energy utilization coefficient C p ( l , β (This refers to the tip speed ratio) l and pitch angle β The comprehensive function is the wind energy utilization factor provided by the wind turbine manufacturer at the time of manufacture. C p Speed ​​ratio with leaf tip l Pitch angle β The functional relationships between them are presented in a table, with some data shown in Table 1, which is a table of wind energy utilization coefficients. For example, the tip speed ratio... l =6.5, pitch angle β At 4°, the wind energy utilization coefficient C p =0.34842.

[0034] Table 1

[0035] Substituting the tip speed ratio expression into the wind power expression captured by the first wind turbine, we obtain the wind power expression captured by the second wind turbine and the intermediate function expression.

[0036] The wind power captured by the second wind turbine and the intermediate function expression are as follows: (3) (4) In the formula, P m This indicates the wind power captured by the wind turbine. r Indicates air density; R Indicates the radius of the wind turbine blades; A This indicates the swept area of ​​the fan blades; oh r Indicates the rotor speed; l Indicates the tip speed ratio; βIndicates the blade pitch angle; C p ( l , β () represents the wind energy utilization coefficient; air density r Sweeping area of ​​blades A With blade radius R For a given wind turbine, these are all fixed values. In one implementation example, it also includes constructing the rotor motion differential equations equivalent to those of the wind turbine.

[0037] (5) In the formula, J This represents the equivalent rotational inertia of the wind turbine rotor. For a given wind turbine, this parameter is a fixed value. oh r Indicates the rotor speed; P m This indicates the wind power captured by the wind turbine. P e This indicates the electromagnetic power output of the wind turbine.

[0038] The wind energy utilization coefficient table is used to represent the functional relationship between the wind energy utilization coefficient and the tip speed ratio and blade pitch angle, presented in a tabular form.

[0039] By consulting the equipment data provided by the wind turbine manufacturer, the functional relationship between the wind energy utilization coefficient and the tip speed ratio and blade pitch angle is presented in a table, and some data are shown in Table 1.

[0040] Based on the wind energy utilization coefficient table, i.e., Table 1, the result is obtained by formula (4). f ( l , β (Ratio to tip speed) l Pitch angle β The functional relationships are presented in a table, i.e., an intermediate function table.

[0041] Taking the first line as an example, the first line represents... l When =2.5, C p about β The function divides each function value by 2.5. 3 You can get the current l =2.5 f about β The function is... Similarly, each row is divided by the function corresponding to its row. l cubed, we get f The table, i.e., the intermediate function table.

[0042] A real-time soft measurement method for average blade wind speed under all wind conditions based on a wind turbine mechanism model. Compared to sensor-based wind speed measurement methods, this method can accurately measure the average blade wind speed without the need for sensor installation. Compared to other soft measurement methods, this method innovatively proposes a soft measurement modeling method based on a mechanism model, breaking through the logical structure of prediction and correction in other methods, eliminating dependence on historical data, and improving the model's generalization ability and measurement accuracy.

[0043] Specific application example: A simulation model was built in DIgSILENT / PowerFactory. The main parameters of the wind turbine model used are shown in Table 2.

[0044] Table 2 Main parameters of the model

[0045] Based on different wind conditions, grid-connected wind turbines can be divided into three regions: the maximum wind energy tracking zone, the constant speed zone, and the constant power zone. For this simulation model, the wind speed ranges for these three regions are [7.6160, 11.4240], [11.4240, 16.1896], and [16.1896, 28.9827], respectively, in m / s. The effectiveness of the proposed method is verified using 24-hour actual wind speed data from a wind farm in eastern coastal China. The simulation duration is 1000s, and the wind speed variation range covers the above three regions. A comparison chart of the measured and actual wind speed values ​​is shown below. Figure 2 As shown, in addition, the results of the absolute error and relative error of the wind speed measurement are as follows: Figure 3 , Figure 4 As shown.

[0046] from Figure 3 , Figure 4 It can be seen that, except for a few data points with abrupt changes in wind speed, the absolute value of the absolute error of the vast majority (99.98%) of the data points is less than 0.05 m / s, and the relative error of 98.38% of the data points is less than 0.2%, indicating that the proposed real-time soft measurement method for the average wind speed of wind turbine blades is effective.

[0047] This embodiment presents a real-time soft measurement scheme for blade wind speed under all wind conditions based on a wind turbine mechanism model. The core logic is purely mechanism-driven, requiring no additional hardware or historical sample data. It utilizes only the inherent measurable parameters of the wind turbine (rotor speed, electromagnetic power, and pitch angle), combined with the rotor motion differential equation derived from the wind turbine's aerodynamic characteristics, the wind power expression, and the wind energy utilization coefficient table provided by the manufacturer. Through formula transformation and real-time calculation, the average blade wind speed can be obtained. This not only completely eliminates the dependence on additional hardware such as lidar but also overcomes the prediction-correction logic limitations of traditional state estimation and data-driven methods. It achieves real-time measurement under all wind conditions, with measurement accuracy significantly superior to existing technologies, reducing the relative error to 10%. - The error is on the order of 3, with 98.38% of the data points having a relative error of less than 0.2%. It is simple to implement in engineering and requires no complex algorithm training and maintenance.

[0048] Example 2 The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0049] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0050] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above method.

[0051] Example 4 The purpose of this embodiment is to provide a real-time soft measurement system for blade wind speed under all wind conditions based on a wind power model, including: The data acquisition module is configured to acquire the rotor speed of the wind turbine, the electromagnetic power output by the wind turbine, and the blade pitch angle. The wind power calculation module is configured to: obtain the wind power captured by the wind turbine in real time based on the rotor speed of the wind turbine and the electromagnetic power output by the wind turbine, using the equivalent rotor motion differential equation of the wind turbine. The function value calculation module is configured to: obtain the function value of the intermediate function expression based on the wind power captured by the wind turbine in real time and the wind power expression captured by the second wind turbine. The tip speed ratio acquisition module is configured to: obtain the tip speed ratio by looking up the intermediate function table based on the function value of the acquired intermediate function expression and the blade pitch angle; The process of constructing the intermediate function table is as follows: consult the equipment data provided by the wind turbine manufacturer to obtain the functional relationship between the wind energy utilization coefficient and the tip speed ratio and the blade pitch angle in a table, i.e., the wind energy utilization coefficient table. Based on the wind energy utilization coefficient table, the function is obtained through calculation. f ( l , β (Ratio to tip speed) l Pitch angle β The functional relationships are presented in a table, i.e., an intermediate function table; The average wind speed measurement calculation module is configured to calculate the average wind speed measurement value based on the acquired tip speed ratio and wind turbine rotor speed using the tip speed ratio expression.

[0052] Example 5 The purpose of this embodiment is to provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods and functions involved in any of the above embodiments. The steps and methods involved in the apparatus of the above embodiments correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0053] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0054] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A real-time soft measurement method for blade wind speed under all wind conditions based on a wind power model, characterized by: include: Obtain the rotor speed of the wind turbine, the electromagnetic power output of the wind turbine, and the blade pitch angle; Based on the rotor speed and electromagnetic power output of the wind turbine, the wind power captured by the wind turbine is obtained in real time using the equivalent rotor motion differential equation of the wind turbine. Then, based on the wind power captured by the wind turbine in real time, the function value of the intermediate function expression is obtained by using the expression of the wind power captured by the second wind turbine. Based on the function value of the obtained intermediate function expression and the blade pitch angle, the tip speed ratio is obtained by looking up the intermediate function table. The process of constructing the intermediate function table is as follows: consult the equipment data provided by the wind turbine manufacturer to obtain the functional relationship between the wind energy utilization coefficient and the tip speed ratio and the blade pitch angle in a table, i.e., the wind energy utilization coefficient table. Based on the wind energy utilization coefficient table, the function is obtained through calculation. f ( λ , β (Ratio to tip speed) λ Pitch angle β The functional relationships are presented in a table, i.e., an intermediate function table; Based on the obtained tip speed ratio and wind turbine rotor speed, the average wind speed measurement value is calculated using the tip speed ratio expression.

2. The real-time soft measurement method for blade wind speed under all wind conditions based on a wind power model as described in claim 1, characterized in that, It also includes constructing a wind power model, which is used to represent the relationship between the wind power captured by the wind turbine and the wind speed, specifically including the expression for the wind power captured by the first wind turbine and the expression for the tip speed ratio.

3. The real-time soft measurement method for blade wind speed under all wind conditions based on a wind power model as described in claim 2, characterized in that, Substituting the tip speed ratio expression into the wind power expression captured by the first wind turbine, we obtain the wind power expression captured by the second wind turbine and the intermediate function expression.

4. The real-time soft measurement method for blade wind speed under all wind conditions based on a wind power model as described in claim 1, characterized in that, It also includes constructing the equivalent rotor motion differential equations for wind turbine units.

5. The real-time soft measurement method for blade wind speed under all wind conditions based on a wind power model as described in claim 3, characterized in that, The functional relationship between the intermediate function and the tip speed ratio and the blade pitch angle is calculated based on the intermediate function expression and presented in a table.

6. A real-time soft measurement system for blade wind speed under all wind conditions based on a wind power model, characterized in that: include: The data acquisition module is configured to acquire the rotor speed of the wind turbine, the electromagnetic power output by the wind turbine, and the blade pitch angle. The wind power calculation module is configured to: obtain the wind power captured by the wind turbine in real time based on the rotor speed of the wind turbine and the electromagnetic power output by the wind turbine, using the equivalent rotor motion differential equation of the wind turbine. The function value calculation module is configured to: obtain the function value of the intermediate function expression based on the wind power captured by the wind turbine in real time and the wind power expression captured by the second wind turbine. The tip speed ratio acquisition module is configured to: obtain the tip speed ratio by looking up the intermediate function table based on the function value of the acquired intermediate function expression and the blade pitch angle; The process of constructing the intermediate function table is as follows: consult the equipment data provided by the wind turbine manufacturer to obtain the functional relationship between the wind energy utilization coefficient and the tip speed ratio and the blade pitch angle in a table, i.e., the wind energy utilization coefficient table. Based on the wind energy utilization coefficient table, the function is obtained through calculation. f ( λ , β (Ratio to tip speed) λ Pitch angle β The functional relationships are presented in a table, i.e., an intermediate function table; The average wind speed measurement calculation module is configured to calculate the average wind speed measurement value based on the acquired tip speed ratio and wind turbine rotor speed using the tip speed ratio expression.

7. The real-time soft measurement system for blade wind speed under all wind conditions based on a wind power model as described in claim 6, characterized in that, It also includes constructing a wind power model, which is used to represent the relationship between the wind power captured by the wind turbine and the wind speed, specifically including the expression for the wind power captured by the first wind turbine and the expression for the tip speed ratio.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method described in any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it performs the steps of the method described in any one of claims 1-5.

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