Fan yaw automatic correction method and device based on static and dynamic deviation coupling

Abstract

The invention discloses a fan yaw automatic correction method and device based on static and dynamic deviation coupling. The fan yaw automatic correction method comprises the steps that historical operation data of a fan SCADA system is read within a preset time period; working condition segmentation is carried out on the operation data, and shutdown, power limiting, full power generation and climbing working conditions are identified; dividing the climbing working condition data into bins according to wind facing angle intervals, constructing wind speed-power curves of all the intervals, and comparing curve areas; performing yaw deviation quantitative analysis on the climbing condition data; wind direction signals of an existing ultrasonic anemorumbometer and a newly-added mechanical anemorumbometer of the fan are collected, and the mean value of the two wind direction signals is taken as a dynamic reference wind direction; and coupling the target yaw deviation angle with the dynamic reference wind direction to generate a corrected wind direction value, converting the corrected wind direction value into an analog current signal, outputting the analog current signal to a DCS (Distributed Control System), and driving a fan yaw system to automatically execute wind correction. According to the invention, through dual-sensor data fusion and comprehensive quantitative modeling of static and dynamic deviation, the wind facing precision and the power generation efficiency of the wind turbine are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine operation optimization control, and in particular to a method and device for automatic wind turbine yaw correction based on static and dynamic deviation coupling. Background Art

[0002] In wind power generation systems, the function of the wind turbine yaw system is to ensure that the wind turbine impeller is continuously and accurately aligned with the wind direction to maximize the capture of wind energy. However, during the operation of the wind turbine, due to factors such as rapid changes in wind direction, mechanical wear, sensor errors and complex environmental interference, the yaw system often has deviations to varying degrees, making it difficult to accurately follow the real-time wind direction, resulting in a significant decrease in the actual operating efficiency of the wind turbine.

[0003] Currently, there are two mainstream wind turbine automatic yaw correction technologies: the first is to use a single sensor signal or a simple algorithm to achieve yaw correction, such as adding an ultrasonic anemometer to the wind turbine. This method is simple to implement, but the correction accuracy is limited; the other method uses a complex intelligent yaw control strategy. Although this method can achieve high-precision correction, it requires significant adjustments to the existing control system and software architecture of the wind turbine, which is costly. It is difficult to apply it to existing wind farms that have been built and operated, and its applicability is poor.

[0004] CN115573858A discloses a method for correcting yaw-to-wind error based on wind vane signal calibration. Although verification of the yaw correction effect and analysis of power generation efficiency improvement are achieved by adding a correction module to the wind vane output end, the method only uses a single wind vane signal for error compensation. When the wind speed changes drastically or there are errors in the wind vane signal itself, the stability of the correction result is difficult to guarantee.

[0005] CN106503406A discloses a method for automatically correcting wind turbine yaw based on statistical analysis of power peaks. This method analyzes a large number of scattered data points to obtain the yaw angle offset corresponding to the power peak. This method has the advantages of simplicity and low computational complexity. However, it fails to consider the dynamic error characteristics caused by simultaneous changes in wind speed and direction, and does not incorporate multi-sensor signal fusion methods, resulting in limited correction accuracy under complex operating conditions. Therefore, the above-mentioned prior art methods still have shortcomings in terms of comprehensive analysis of dynamic and static deviations, sensor data fusion, and operating condition segmentation and refined correction.

[0006] Therefore, the existing wind turbine yaw automatic correction technology generally has the problems of failure to effectively couple and analyze static and dynamic deviations and high dependence on a single sensor. To address the above problems, the present invention proposes a wind turbine yaw automatic correction method based on static and dynamic deviation coupling. Through refined working condition data analysis, dual sensor data fusion and comprehensive quantitative modeling of static and dynamic deviations, the wind turbine's wind accuracy and power generation efficiency are significantly improved. Summary of the Invention

[0007] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract of the specification and the title of the invention of this application to avoid blurring the purpose of this section, the abstract of the specification and the title of the invention, and such simplifications or omissions cannot be used to limit the scope of the invention.

[0008] In view of the above existing problems, the present invention is proposed.

[0009] To solve the above technical problems, the present invention provides the following technical solutions: Step 1: reading historical operation data of the wind turbine SCADA system within a preset time period, wherein the historical operation data at least includes wind speed and direction, wind angle, output power, cumulative power generation and three-propeller pitch angle;

[0010] Step 2: dividing the operating data into operating conditions according to the three-propeller pitch angle and the output power, identifying shutdown, power limit, full power and climbing operating conditions, and filtering climbing operating condition data;

[0011] Step 3: Bin the climbing condition data according to the wind angle interval, construct the wind speed-power curve for each interval, and compare the curve areas. If the curve area of the interval (-2°, 2°) is the largest, there is no significant yaw deviation. Otherwise, proceed to step 4.

[0012] Step 4: Quantitatively analyze the yaw deviation of the climbing condition data:

[0013] Establish output power and cos by wind speed 2 The regression model between θ is solved by the least square method to obtain the first yaw deviation angle;

[0014] A regression model between wind speed and θ2 is established based on power bins, and the second yaw deviation angle is obtained using the least squares method.

[0015] Taking a weighted average of the first yaw deviation angle and the second yaw deviation angle to obtain a target yaw deviation angle;

[0016] Step 5: Collect the wind direction signal of the existing ultrasonic anemometer and the wind direction signal of the newly added mechanical anemometer, and take the average of the two as the dynamic reference wind direction;

[0017] Step 6: Coupling the target yaw deviation angle with the dynamic reference wind direction to generate a correction wind direction value, and converting the correction wind direction value into a 4-20mA analog current signal and outputting it to the DCS system to drive the wind turbine yaw system to automatically perform wind correction.

[0018] As a preferred solution of the method for automatic correction of wind turbine yaw based on static-dynamic deviation coupling of the present invention, the preset time period is 5 to 30 minutes.

[0019] As a preferred solution of the wind turbine yaw automatic correction method based on static and dynamic deviation coupling of the present invention, the operating data is divided into working conditions, including:

[0020] When the pitch angle is greater than 60°, it is judged as shutdown;

[0021] When the output power is greater than the rated power, it is considered as full power.

[0022] When the pitch angle is between 3° and 60° and the output power is less than the rated power, it is determined to be power-limited;

[0023] The rest of the cases are judged as climbing condition data.

[0024] As a preferred solution of the wind turbine yaw automatic correction method based on static and dynamic deviation coupling of the present invention, the climbing condition data is divided into bins according to the wind angle interval, including:

[0025] The wind angle interval is divided into five intervals: (-10°, -6°], (-6°, -2°], (-2°, 2°], (2°, 6°], and (6°, 10°], and a wind speed step size of 0.5 m / s is used to bin the data in each wind angle interval.

[0026] As an optimal solution of the wind turbine yaw automatic correction method based on static and dynamic deviation coupling described in the present invention, the weighting coefficients of the first yaw deviation angle and the second yaw deviation angle are respectively selected as the normalized values of the central wind speed of the wind speed bin and the central power of the power bin.

[0027] As a preferred solution of the wind turbine yaw automatic correction method based on static and dynamic deviation coupling described in the present invention, the ultrasonic anemometer and the mechanical anemometer are synchronously sampled through an input module, and the sampling period is less than 1s.

[0028] As a preferred solution of the automatic yaw correction method of a wind turbine based on static and dynamic deviation coupling described in the present invention, the corrected wind direction value is calculated by the edge computing terminal, converted into a 4-20mA signal through the output module and connected to the wind direction interface of the existing DCS, without modifying the original yaw control strategy.

[0029] As a preferred solution of the wind turbine yaw automatic correction device based on static and dynamic deviation coupling described in the present invention, it includes a mechanical anemometer, an input module, a network communication module, an edge computing terminal, an output module and a power supply module; wherein:

[0030] The mechanical anemometer and wind direction meter and the original ultrasonic anemometer and wind direction meter of the fan respectively output wind direction signals to the input module;

[0031] The network communication module is used to read the operation data from the SCADA system and send it to the edge computing terminal;

[0032] The edge computing terminal is used to run the algorithms of steps 3 to 6 in the wind turbine yaw automatic correction method based on static and dynamic deviation coupling, and output the corrected wind direction value;

[0033] The output module is used to convert the corrected wind direction value into a 4-20mA signal;

[0034] The power supply module is used to provide 24V DC power to each module.

[0035] As a preferred solution of the wind turbine yaw automatic correction device based on static and dynamic deviation coupling of the present invention, it also includes: one or more processors;

[0036] A memory stores operable instructions, which, when executed by the one or more processors, cause the one or more processors to perform operations, including the process of the aforementioned method for automatic correction of wind turbine yaw based on static and dynamic deviation coupling.

[0037] As a preferred embodiment of a computer-readable medium for storing software according to the present invention, the software includes instructions that can be executed by one or more computers, and the instructions enable the one or more computers to perform operations through such execution, and the operations include the process of the aforementioned method for automatic correction of wind turbine yaw based on static and dynamic deviation coupling.

[0038] Beneficial effects of the present invention:

[0039] 1. By reading the historical operating data in the wind turbine SCADA system within a preset time period, the basic data required for subsequent operating condition classification and yaw deviation analysis is obtained, providing accurate and sufficient data support for the entire correction process, improving the accuracy of subsequent operating condition classification, reducing analysis errors, and improving the reliability of the overall method;

[0040] 2. By segmenting operating data into operating conditions based on the three-propeller pitch angle and output power, the system accurately identifies four typical operating conditions: shutdown, power curtailment, full power generation, and ramp-up. Furthermore, data from the ramp-up operating condition, which is the most sensitive to wind turbines and best reflects yaw deviation characteristics, is specifically selected. This prevents interference and misleading data from other operating conditions in the quantitative analysis of yaw deviation. The refined operating condition analysis reduces the impact of non-sensitive conditions on the results, significantly improving the accuracy of subsequent yaw deviation assessments.

[0041] 3. By quickly pre-judging the yaw state, preliminary diagnosis can be achieved, avoiding unnecessary complex calculations and analysis, reducing the system's calculation burden, improving overall analysis efficiency, and making quick decisions;

[0042] 4. By using two independent perspectives to model and comprehensively evaluate yaw error, the analysis results of the two different dimensional models are mutually verified and cross-corroborated, thereby improving the accuracy and robustness of yaw error assessment;

[0043] 5. By simultaneously collecting wind direction data from the wind turbine's existing ultrasonic anemometer and wind direction meter and the newly added mechanical anemometer, and fusing and averaging the wind direction values measured by the two different principles, a dynamic reference wind direction is established. This effectively eliminates the impact of a single type of sensor error on the actual wind direction measurement accuracy, improves wind direction measurement stability, suppresses interference from single sensor errors, and improves wind direction measurement reliability and accuracy.

[0044] 6. By coupling the target yaw deviation angle with the dynamic reference wind direction, a correction wind direction value is formed that can be used to directly correct the actual yaw of the wind turbine. This value is then converted into an industrial standard 4-20mA analog current signal and output to the DCS system, automatically driving the wind turbine yaw system for real-time correction, achieving precise automatic yaw control without modifying the internal software logic of the existing control system. It has the advantages of easy implementation, economy and practicality, and improves the accuracy, convenience and compatibility of automatic yaw correction with existing systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort. Among them:

[0046] Figure 1 Schematic diagram of the flow of the automatic yaw correction method of a wind turbine based on static and dynamic deviation coupling shown in the present invention;

[0047] Figure 2 This is a schematic diagram of the module structure distribution of the wind turbine yaw automatic correction device based on static and dynamic deviation coupling shown in the present invention. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0049] Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without making any creative work should fall within the scope of protection of the present invention.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0051] According to an embodiment of the present invention, Figure 1 The flowchart shown in FIG. 1 is a method for automatically correcting wind turbine yaw based on static and dynamic deviation coupling, comprising:

[0052] S1. Read historical operation data of the wind turbine SCADA system within a preset time period. The historical operation data at least includes wind speed and direction, wind angle, output power, cumulative power generation, and three-propeller pitch angles 1, 2, and 3.

[0053] As an example, the time period is preset to be 5 to 30 minutes.

[0054] As an example, the three pitch angles are pitch angle 1, pitch angle 2, and pitch angle 3.

[0055] S2. Divide the operating data into operating conditions according to the three-propeller pitch angle and output power, identify shutdown, power limit, full power and climbing conditions, and filter the climbing condition data.

[0056] The operating conditions are divided into four types: shutdown, power restriction, full power generation, and ramp-up.

[0057] When the pitch angle is greater than 60°, it is judged as shutdown;

[0058] When the output power is greater than the rated power, it is considered as full power.

[0059] When the pitch angle is between 3° and 60° and the output power is less than the rated power, it is determined to be power-limited;

[0060] The rest of the cases are judged as climbing conditions.

[0061] S3. Divide the climbing condition data into bins according to the wind angle interval, construct the wind speed-power curve for each interval, and compare the curve areas. If the curve area of the interval (-2°, 2°] is the largest, there is no significant yaw deviation. Otherwise, proceed to step 4. It should be noted that this step is divided into the wind angle intervals of (-10, -6], (-6, -2], (-2, 2], (2, 6], and (6, 10]). The calculation process of the wind speed-power curve within a single wind angle interval is as follows:

[0062] The climbing condition data is divided into several wind speed intervals according to wind speed. The mathematical expression is:

[0063]

[0064] Take the median (P v(i) ), get the wind speed power curve array under the wind angle {median(P v(i) ),v (i)}, calculate the corresponding power curve area as

[0065]

[0066] Where n is the number of compartments, v(i) is the lower limit of the wind speed in the i-th interval, and v (i+1) is the upper limit of wind speed in the ith interval, P is the output power, and the step interval of each interval is 0.5 m / s.

[0067] In an optional embodiment, it is identified whether the uppermost curve of the wind speed-power curve family is in the interval of (-2, 2], that is, whether the area of the power curve in the (-2, 2] wind angle interval is the largest among the five wind angle intervals. If so, it indicates that the wind turbine is accurately facing the wind, and the return is 1; if not, it indicates that there is a deviation in the wind, and step S4 is executed to perform quantitative analysis of the deviation.

[0068] S4. Quantitative analysis of yaw deviation is performed on the climbing condition data. It should be noted that:

[0069] S4.1. Establish output power and cos by wind speed 2 The regression model between θ is solved by the least square method to obtain the first yaw deviation angle:

[0070] The climbing condition is divided into several wind speed intervals according to wind speed.

[0071] The square of the cosine of the wind deviation angle θ is proportional to the output power. 2 θ performs regression fitting on the output power P and wind deviation angle in each wind speed bin:

[0072]

[0073] Among them, λ j is the proportional coefficient of the j-th wind speed bin, is the wind deviation angle of the j-th wind speed bin;

[0074] The least squares method is used for regression fitting to calculate the parameter λ of each wind speed bin. j and in, is the wind deviation of the wind speed bin;

[0075] Let the least squares objective defined by the least squares method be:

[0076]

[0077] Among them, P j i is the power of the i-th data point in the j-th wind speed bin, is the wind angle of the i-th data point in the j-th wind speed bin, N j is the number of data points in the j-th wind speed bin;

[0078] When the Levenberg-Marquardt algorithm (an iterative method for nonlinear least squares optimization) is used to calculate the minimum S, λ is obtained. j and

[0079] The weighted average of the calculation results is performed to obtain the yaw deviation angle value based on wind speed bins, that is, the first yaw deviation angle, and its mathematical expression formula is as follows:

[0080]

[0081] Among them, α 风速 is the wind deviation angle calculated based on wind speed bins, w i is the median wind speed of each wind speed bin, is the wind deviation angle corresponding to each wind speed bin, i is the wind speed bin number, and n is the number of wind speed bins;

[0082] S4.2. Establish wind speed and θ by power bin 2 The regression model between and is solved by the least square method to obtain the second yaw deviation angle:

[0083] The climbing condition is divided into several power intervals according to power. The mathematical expression is:

[0084] BIN (i) ={v P(i) ,P (i)<P<P (i+1)}

[0085] Among them, P (i) is the power lower limit of the i-th interval, P (i+1) is the power upper limit of the i-th interval, and the step interval of each interval is 100KW;

[0086] The square of the wind deviation angle θ is proportional to the wind speed. 2 Perform regression fitting on the wind speed v and wind deviation angle θ in each power bin:

[0087]

[0088] Among them, μ j is the proportional coefficient of the j-th power bin, is the wind deviation angle of the jth power bin;

[0089] The least squares method is used for regression fitting to calculate the parameter μ of each power bin. j and in, The wind deviation of this power bin;

[0090] Let the least squares objective defined by the least squares method be:

[0091]

[0092] Among them, v j i is the wind speed of the ith data point in the jth power bin, is the wind angle of the i-th data point in the j-th power bin, N j is the number of data points in the jth power bin;

[0093] When the Levenberg-Marquardt algorithm is used to calculate the minimum S, μ is obtained. j and

[0094] The calculation results are averaged to obtain the yaw deviation angle value based on power bins, that is, the second yaw deviation angle, which is expressed as follows:

[0095]

[0096] Among them, β 风速 It is the wind deviation angle calculated based on the power bin. is the wind deviation angle corresponding to each wind speed bin, i is the power bin number, and n is the number of power bins;

[0097] S4.3. Taking a weighted average of the first yaw deviation angle and the second yaw deviation angle to obtain a target yaw deviation angle;

[0098] For example, the average of the wind deviation based on wind speed binning and the wind deviation based on power binning is taken as the wind turbine deviation quantification result:

[0099]

[0100] Where, σ is the target yaw deviation angle;

[0101] The result is output to the edge terminal to correct the wind direction result.

[0102] S5. Collect the wind direction signal of the existing ultrasonic anemometer and wind direction instrument of the wind turbine and the wind direction signal of the newly added mechanical anemometer and wind direction instrument, and take the average of the two as the dynamic reference wind direction.

[0103] S6. The target yaw deviation angle is coupled with the dynamic reference wind direction to generate a correction wind direction value, and the correction wind direction value is converted into a 4-20mA analog current signal and output to the DCS system to drive the wind turbine yaw system to automatically perform wind correction.

[0104] The input module obtains the original ultrasonic anemometer signal and the wind direction signals ω1 and ω2 emitted by the mechanical anemometer, and transmits them to the edge computing terminal;

[0105] The program in the edge computing terminal calculates the average of the two input wind direction signals as the dynamic reference value, and combines it with the static deviation analysis result as the corrected wind direction measurement value W:

[0106]

[0107] The corrected wind direction value is sent to the output module, which converts the wind direction value into a 4-20mA standard signal and connects it to the original DCS wind direction signal interface.

[0108] It should be noted that the quantitative analysis method of the static deviation of wind turbine yaw to wind provided by the present invention greatly reduces the requirements for the number of wind turbine measurement point types for wind deviation analysis. It can provide a more accurate quantitative evaluation value of the wind deviation angle while using less data, thereby reducing the difficulty of wind deviation analysis and improving the universality of the method.

[0109] In summary, the present invention, through the coordinated cooperation of the above steps, systematically solves the technical difficulties of accurately acquiring wind turbine operating data, accurately identifying sensitive working conditions, accurately quantifying static and dynamic yaw deviations, enhancing the reliability of wind direction measurement, and conveniently implementing automatic yaw correction. It comprehensively improves the correction accuracy of the wind turbine yaw system and the operating economy of the wind farm, and effectively improves the power generation efficiency.

[0110] In the application of the above embodiments, other aspects disclosed in the embodiments of the present invention further provide a wind turbine yaw automatic correction device based on static and dynamic deviation coupling, including a mechanical anemometer, an input module, an edge computing terminal, an output module, a network communication module, and a power supply module; wherein:

[0111] Mechanical anemometer is used to measure wind speed and direction when the wind turbine is running;

[0112] The input module is used to receive the wind speed and direction signals from the wind turbine's original ultrasonic anemometer and wind direction instrument and the mechanical anemometer and wind direction instrument;

[0113] The network communication module is used to read the data of the fan in a time period in the SCADA and transmit it to the edge computing terminal;

[0114] The edge computing terminal is used to execute the wind turbine yaw static deviation quantitative analysis algorithm in the wind turbine yaw automatic correction method based on static and dynamic deviation coupling, and automatically output the correction data;

[0115] The output module is used to receive the correction data from the edge computing terminal and convert it into a 4-20mA signal to input into the central control DCS system;

[0116] The power module is used to provide 24V power to other modules.

[0117] It should also be noted that the correction device also includes one or more processors and memory;

[0118] The memory is used to store operable instructions, which, when executed by the one or more processors, cause the one or more processors to perform operations, including the process of the wind turbine yaw automatic correction method based on static and dynamic deviation coupling of the aforementioned embodiment, in particular Figure 1 The process of the method shown.

[0119] In other aspects disclosed in the embodiments of the present invention, a computer-readable medium storing software is provided. The software includes instructions that can be executed by one or more computers. The execution of these instructions causes the one or more computers to perform operations. These operations include the process of the wind turbine yaw automatic correction method based on static and dynamic deviation coupling of the aforementioned embodiment, especially Figure 1 The process of the method shown.

[0120] The wind turbine yaw automatic correction device provided by the present invention calculates the correction value through the algorithm model of the edge computing terminal. The correction value takes into account the static deviation under different wind speed-power conditions and the dynamic deviation caused by the measurement error of the original anemometer. The wind correction is achieved by connecting to the DCS system through the original wind direction signal interface. This method does not require major modifications to the original DCS system and yaw control strategy, greatly reducing costs and enhancing the convenience of device application.

[0121] It should be noted that the embodiment of the present invention determines whether the current wind direction has a wind deviation and quantifies it through the analysis of the static deviation of the wind turbine yaw to the wind based on working condition segmentation and data partitioning, and adjusts and corrects the real-time wind direction through the edge terminal to realize automatic correction of the wind turbine yaw with coupled static and dynamic deviations.

[0122] Preferably, an embodiment of the present invention provides a method and device for automatic correction of wind turbine yaw based on static and dynamic deviation coupling, which can perform self-learning evaluation of the wind turbine's recent yaw deviation to wind based on the wind turbine's recent operating data, determine the deviation angle value of the wind turbine's recent yaw to wind, and drive the yaw correction device to achieve wind direction correction.

[0123] It should be appreciated that embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory.

[0124] The method may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes a computer to operate in a specific and predefined manner.

[0125] Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system, however, the program can be implemented in assembly or machine language if desired.

[0126] In any case, the language may be a compiled or interpreted language.

[0127] Furthermore, the program can be run on an application specific integrated circuit programmed for this purpose.

[0128] The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that collectively executes on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that can be executed by one or more processors.

[0129] Further, the method may be implemented in any type of computing platform operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device.

[0130] Aspects of the present invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage media, RAM, ROM, etc., such that it can be read by a programmable computer and, when the storage medium or device is read by the computer, can be used to configure and operate the computer to perform the processes described herein.

[0131] Additionally, the machine-readable code, or portions thereof, can be transmitted over a wired or wireless network.

[0132] The invention described herein includes these and other various types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor.

[0133] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for automatic yaw correction of a wind turbine based on static and dynamic deviation coupling, characterized in that: include: Step 1: reading historical operation data of the wind turbine SCADA system within a preset time period, wherein the historical operation data at least includes wind speed and direction, angle to wind, output power, cumulative power generation and three-propeller pitch angle; Step 2: dividing the operating data into operating conditions according to the three-propeller pitch angle and the output power, identifying shutdown, power limit, full power and climbing operating conditions, and filtering climbing operating condition data; Step 3: Bin the climbing condition data according to the wind angle interval, construct the wind speed-power curve for each interval, and compare the curve areas. If the curve area of the interval (-2°, 2°) is the largest, there is no significant yaw deviation. Otherwise, proceed to step 4. Step 4: Quantitatively analyze the yaw deviation of the climbing condition data: Establish output power and cos by wind speed 2 The regression model between θ is solved by the least square method to obtain the first yaw deviation angle; A regression model between wind speed and θ2 is established based on power bins, and the second yaw deviation angle is obtained using the least squares method. Taking a weighted average of the first yaw deviation angle and the second yaw deviation angle to obtain a target yaw deviation angle; Step 5: Collect the wind direction signal of the existing ultrasonic anemometer and the wind direction signal of the newly added mechanical anemometer, and take the average of the two as the dynamic reference wind direction; Step 6: Coupling the target yaw deviation angle with the dynamic reference wind direction to generate a correction wind direction value, and converting the correction wind direction value into a 4-20mA analog current signal and outputting it to the DCS system to drive the wind turbine yaw system to automatically perform wind correction.

2. The method for automatically correcting wind turbine yaw based on static and dynamic deviation coupling according to claim 1 is characterized in that: The preset time period is 5 to 30 minutes.

3. The method for automatically correcting wind turbine yaw based on static and dynamic deviation coupling according to claim 1 or 2, characterized in that: The operating data is divided into working conditions, including: When the pitch angle is greater than 60°, it is judged as shutdown; When the output power is greater than the rated power, it is considered as full power. When the pitch angle is between 3° and 60° and the output power is less than the rated power, it is determined to be power-limited; The rest of the cases are judged as climbing condition data.

4. The method for automatically correcting wind turbine yaw based on static and dynamic deviation coupling according to claim 3 is characterized in that: The climbing condition data is divided into bins according to the wind angle interval, including: The wind angle interval is divided into five intervals: (-10°, -6°], (-6°, -2°], (-2°, 2°], (2°, 6°], and (6°, 10°], and a wind speed step size of 0.5 m / s is used to bin the data in each wind angle interval.

5. The method for automatically correcting wind turbine yaw based on static and dynamic deviation coupling according to claim 1, characterized in that: The weighting coefficients of the first yaw deviation angle and the second yaw deviation angle are respectively selected from the normalized values of the central wind speed of the wind speed bin and the central power of the power bin.

6. The method for automatically correcting wind turbine yaw based on static and dynamic deviation coupling according to claim 1, characterized in that: The ultrasonic anemometer and the mechanical anemometer are sampled synchronously via an input module, and the sampling period is less than 1s.

7. The method for automatically correcting wind turbine yaw based on static and dynamic deviation coupling according to claim 1, characterized in that: The corrected wind direction value is calculated by the edge computing terminal, converted into a 4-20mA signal through the output module and connected to the wind direction interface of the existing DCS, without modifying the original yaw control strategy.

8. A wind turbine yaw automatic correction device based on static and dynamic deviation coupling, characterized in that: It includes a mechanical anemometer, an input module, a network communication module, an edge computing terminal, an output module and a power module; among which: The mechanical anemometer and wind direction meter and the original ultrasonic anemometer and wind direction meter of the fan respectively output wind direction signals to the input module; The network communication module is used to read the operation data from the SCADA system and send it to the edge computing terminal; The edge computing terminal is used to run the algorithms of steps 3 to 6 in the wind turbine yaw automatic correction method based on static and dynamic deviation coupling, and output the corrected wind direction value; The output module is used to convert the corrected wind direction value into a 4-20mA signal; The power supply module is used to provide 24V DC power to each module.

9. The wind turbine yaw automatic correction device based on static and dynamic deviation coupling according to claim 8 is characterized in that: Also includes: one or more processors; A memory storing operable instructions, wherein when the instructions are executed by the one or more processors, the one or more processors are caused to perform operations, wherein the operations include the process of the method for automatic correction of wind turbine yaw based on static and dynamic deviation coupling as described in any one of claims 1 to 7.

10. A computer-readable medium storing software, characterized in that: The software includes instructions that can be executed by one or more computers, and the instructions, through such execution, enable the one or more computers to perform operations, and the operations include the process of the automatic wind turbine yaw correction method based on static and dynamic deviation coupling as described in any one of claims 1 to 7.

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