A horizontal axis wind turbine wind-approaching angle automatic optimization method, system and device

Abstract

This invention provides an automatic optimization method, system, and device for the windward angle of a horizontal axis wind turbine, comprising the following steps: obtaining a wind turbine power fitting curve; dividing the rated active power into continuous intervals, and combining the wind turbine power fitting curve to obtain multiple continuous wind speed intervals corresponding to the rated active power; obtaining the active power-windward angle function relationship curve corresponding to each operating data interval; obtaining the yaw windward error value of the horizontal axis wind turbine to be processed; and correcting the windward angle of the horizontal axis wind turbine to be processed. This invention can reduce the power loss caused by the yaw windward error of the wind turbine, and improve the power plant efficiency and wind turbine safety and stability.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wind power generation, and particularly relates to a method, system and device for automatically optimizing a yaw angle of a horizontal-axis wind turbine. BACKGROUND

[0002] An upwind horizontal-axis wind turbine generally comprises a tower base, a tower barrel, a nacelle, a rotor, a hub, a yaw system, a braking system, a lubrication system, a variable-pitch drive, a transmission system, a generator and the like. The yaw system mainly comprises a yaw gear ring and bearing, a yaw drive motor, a yaw caliper, a yaw counter, an anemometer, a wind vane and a yaw controller. The main function of the yaw system is to automatically yaw to wind, and since the wind direction is random and dynamically changing, the rotor needs to follow the wind direction at all times to obtain the most wind energy. The yaw system drives the rotor to track the wind direction by means of the yaw motor, so that the rotor is always perpendicular to the incoming wind direction, thereby ensuring that the wind turbine can obtain the maximum wind energy even when the wind direction changes. This function is extremely important for the operating characteristics of the wind turbine. The yaw system is irreplaceable and crucial for the safety, reliability and stability of the wind turbine.

[0003] For an upwind horizontal-axis wind turbine, the wind turbine generally installs an anemometer at the tail of the nacelle to detect the wind speed and wind direction signals in time, so that the main control system can control the start and stop of the wind turbine and the yaw system can yaw to wind, thereby ensuring the continuous and stable operation of the wind turbine and the optimal absorption of wind energy, and maximizing the power generation of the wind turbine. The anemometer is generally installed on a wind measuring mast, and the height thereof is about 1 meter. In actual application, the installation modes of different models are different due to the differences in the structural arrangement of the top of the nacelle.

[0004] The yaw system is an important part of the control system of the wind turbine. The yaw-to-wind error of the wind turbine has a great influence on the power generation, and a large yaw-to-wind error will lead to a reduction in the utilization rate of wind energy and a reduction in power generation. The measurement of the wind direction in the yaw system is mainly completed by the wind vane, and the measurement accuracy of the wind vane is reduced due to the influence of the environment and installation errors during the operation of the wind turbine, so the wind direction cannot be accurately measured, which affects the yaw control and the power generation of the wind turbine. During the normal operation and power generation of the wind turbine, a series of reasons such as vortex generated by the rotation of the rotor and inaccurate installation of the anemometer can lead to inaccurate measurement of the wind speed and wind direction, affect the accuracy of the input data of the yaw controller, and thus cause the yaw-to-wind error. The existence of the yaw-to-wind error has a negative impact on the efficiency, blade load and operation and maintenance cost of the wind turbine. Statistical data shows that when the average yaw error is 15°, the power generation loss of the wind turbine can reach 5% to 13%.

[0005] The wind vane is installed on the top of the cabin, and the zero mark is parallel to the wind wheel axis. When the wind vane accurately measures the wind direction, the angle between the wind vane zero mark and the wind wheel axis is 0°. When the angle is not 0°, the angle between the wind vane zero mark and the wind wheel axis is the yaw-to-wind error angle. The yaw-to-wind error is caused by many reasons, such as the wind direction sensor is not zeroed during the initial installation and debugging of the wind turbine, the wind vane measurement accuracy is not high enough, the wind condition at the site of the unit is complex, and the environment is harsh. Based on the above reasons, the wind direction sensor often cannot accurately measure the wind direction. That is, there is a certain angle deviation between the wind direction measured by the wind vane and the true wind direction, as shown in FIG. 1, wherein φ is the true yaw-to-wind angle; α is the yaw-to-wind angle measured by the wind vane; and θ is the yaw-to-wind error. Figure 1

[0006] The wind energy utilization rate of the wind turbine depends on whether the yaw system can correctly track the wind direction. The yaw system executes yaw action based on the wind direction information measured by the wind vane. Due to various factors, the accuracy and timeliness of the yaw system are affected, and there is a certain wind deviation during operation. Not only does this reduce the wind energy capture capacity of the unit, but it also causes uneven stress on the wind wheel, resulting in an increase in the operating load of the unit. Improving the performance of the yaw system of the wind turbine can effectively increase the power generation capacity of the unit and reduce the asymmetric load. By analyzing the operating data of the wind turbine and evaluating the yaw-to-wind error during operation, the wind measuring device can be corrected and adjusted to improve the accuracy of the yaw control of the wind turbine and thus increase the power generation capacity of the wind turbine.

[0007] At present, the yaw system has the following defects in wind deviation:

[0008] (1) There is no effective real-time monitoring method and technology for the wind deviation of the wind turbine;

[0009] (2) The installation position of the zero mark of the wind vane is checked to determine whether it is correct, but this method ignores the monitoring error of the wind vane itself;

[0010] (3) Offline laser radar is often used to monitor the wind deviation of the wind turbine, which is high in cost and long in cycle, and cannot be continuously monitored for a long time. SUMMARY

[0011] The present application provides a horizontal axis wind turbine yaw angle automatic optimization method, system and device, which solves the above-mentioned deficiencies in the prior art.

[0012] In order to achieve the above-mentioned purposes, the technical scheme adopted by the present application is as follows:

[0013] The present application provides a horizontal axis wind turbine yaw angle automatic optimization method, which comprises the following steps:

[0014] ​Step 1, obtaining first historical operation data and second historical operation data of a horizontal axis wind turbine to be processed, wherein the first historical operation data comprises minute-level average wind speed and minute-level average active power, and the second historical operation data comprises minute-level average yaw-to-wind angle of the wind turbine and wind turbine state;

[0015] Step 2, obtaining a wind turbine power fitting curve according to the obtained first historical operation data;

[0016] Step 3, dividing the rated active power of the horizontal axis wind turbine to be processed into continuous intervals, and combining the wind turbine power fitting curve obtained in step 2 to obtain a plurality of continuous wind speed intervals corresponding to the rated active power;

[0017] Step 4, dividing the second historical operation data according to the plurality of wind speed intervals obtained in step 3 to obtain a plurality of operation data intervals;

[0018] Step 5, obtaining an active power-to-wind angle function relationship curve corresponding to each operation data interval;

[0019] Step 6, obtaining a yaw-to-wind error value of the horizontal axis wind turbine to be processed according to the active power-to-wind angle function relationship curves corresponding to all the operation data intervals;

[0020] Step 7, correcting the yaw-to-wind angle of the horizontal axis wind turbine to be processed according to the obtained yaw-to-wind error value, and completing automatic optimization of the yaw-to-wind angle of the horizontal axis wind turbine to be processed.

[0021] Preferably, in step 2, before obtaining the wind turbine power fitting curve according to the obtained first historical operation data, the abnormal data in the first historical operation data is removed.

[0022] Preferably, in step 4, before dividing the second historical operation data, the abnormal data in the second historical operation data is removed.

[0023] Preferably, in step 5, the active power-to-wind angle function relationship curve corresponding to each operation data interval is obtained by the following specific method:

[0024] S51, sorting the wind-to-wind angle in each operation data interval to obtain each sorted operation data interval;

[0025] S52, selecting the operation data with a wind-to-wind angle of-25°-25° in each sorted operation data interval to obtain a plurality of new operation data intervals;

[0026] S53, dividing each new operation data interval at a set angle interval to obtain a plurality of wind-to-wind angle intervals corresponding to each new operation data interval;

[0027] S54, calculating the corresponding active power average value in each wind-against angle interval;

[0028] S55, fitting the active power- wind-against angle function relationship curve corresponding to each operation data interval according to the obtained active power average value.

[0029] Preferably, in step 6, the yawing wind-against error value of the horizontal axis wind turbine to be processed is obtained according to the active power- wind-against angle function relationship curve corresponding to all the operation data intervals, and the specific method is:

[0030] S61, performing accumulation calculation and curve fitting on the obtained active power- wind-against angle function relationship curve corresponding to all the operation data intervals to obtain the angle data corresponding to the maximum value of the accumulated active power data;

[0031] S62, calculating the absolute value of the angle data corresponding to the maximum value of the accumulated active power data, and taking the absolute value as the wind turbine yawing wind-against error value.

[0032] Preferably, in step 7, the wind-against angle of the horizontal axis wind turbine to be processed is corrected according to the obtained yawing wind-against error value, and the automatic optimization of the wind-against angle of the horizontal axis wind turbine to be processed is completed, and the specific method is:

[0033] The wind turbine yawing wind-against error value is compared with the set early warning value, and the wind turbine yawing wind-against is automatically corrected according to the comparison result in combination with the wind vane zero position and the real-time collected wind direction.

[0034] A horizontal axis wind turbine wind-against angle automatic optimization system, comprising:

[0035] A data acquisition unit is configured to acquire first historical operation data and second historical operation data of a horizontal axis wind turbine to be processed, wherein the first historical operation data comprises minute-level average wind speed and minute-level active power average value, and the second historical operation data comprises minute-level wind turbine yawing wind-against angle average value and wind turbine state.

[0036] A curve fitting unit is configured to obtain a wind turbine power fitting curve according to the obtained first historical operation data.

[0037] A wind speed interval acquisition unit is configured to divide the rated active power of the horizontal axis wind turbine to be processed into continuous intervals, and obtain a plurality of continuous wind speed intervals corresponding to the rated active power in combination with the obtained wind turbine power fitting curve.

[0038] A data division unit is configured to divide the second historical operation data according to the obtained plurality of wind speed intervals to obtain a plurality of operation data intervals.

[0039] a function curve acquisition unit configured to acquire an active power versus wind-attack angle function curve corresponding to each operation data interval;

[0040] a yaw-attack error value acquisition unit configured to acquire a yaw-attack error value of the horizontal-axis wind turbine to be processed according to the acquired active power versus wind-attack angle function curves corresponding to all operation data intervals;

[0041] an angle correction unit configured to correct the wind-attack angle of the horizontal-axis wind turbine to be processed according to the acquired yaw-attack error value, and complete the automatic optimization of the wind-attack angle of the horizontal-axis wind turbine to be processed.

[0042] Preferably, the function curve acquisition unit comprises:

[0043] a sorting module configured to sort the wind-attack angles in each operation data interval, and acquire each sorted operation data interval;

[0044] a data selection module configured to select operation data with a wind-attack angle of -25°-25° in each sorted operation data interval, and acquire a plurality of new operation data intervals;

[0045] a data division module configured to divide each new operation data interval at intervals of a set angle, and acquire a plurality of wind-attack angle intervals corresponding to each new operation data interval;

[0046] an active power average value calculation module configured to calculate an active power average value in each wind-attack angle interval;

[0047] a function curve acquisition module configured to acquire an active power versus wind-attack angle function curve corresponding to each operation data interval according to the acquired active power average values.

[0048] Preferably, the yaw-attack error value acquisition unit comprises:

[0049] a curve accumulation module configured to accumulate and calculate the acquired active power versus wind-attack angle function curves corresponding to all operation data intervals, and perform curve fitting to acquire angle data corresponding to a maximum value of accumulated active power data;

[0050] a wind-turbine yaw-attack error value acquisition module configured to calculate an absolute value of the angle data corresponding to the maximum value of accumulated active power data, and take the absolute value as a wind-turbine yaw-attack error value.

[0051] A horizontal-axis wind turbine wind-attack angle automatic optimization device, comprising a processor and a computer program capable of running on the processor, wherein the processor executes the computer program to realize the steps of the method.

[0052] Compared with the prior art, the present application has the beneficial effects that:

[0053] The horizontal axis wind turbine wind-approaching angle automatic optimization method provided by the present application realizes fine data analysis by dividing the wind speed by equal power, thereby improving the balance of the divided data, reducing the influencing factors of the power data dispersion of each bin, further reducing the influence of wind speed turbulence on the calculation results, and improving the accuracy of the yaw wind-approaching error calculation; then the wind turbine yaw wind-approaching error is automatically corrected, the loss of electric quantity caused by the wind turbine yaw wind-approaching error is reduced, and the efficiency of the power station and the safety and stability of the wind turbine are improved. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 is a flowchart of the present application;

[0055] Figure 2 is a curve accumulation graph of the present application. DETAILED DESCRIPTION

[0056] In the following description, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, persons skilled in the art will understand that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0057] In the present application, the reference to "one embodiment" or "some embodiments" means that the specific feature, structure or characteristic described in connection with this embodiment is included in one or more embodiments of the present application. Therefore, the statements "in one embodiment", "in some embodiments", "in other some embodiments", "in yet some embodiments" and the like appearing in various places in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically stated. The terms "comprise", "include", "have" and their conjugates mean "including but not limited to", unless otherwise specifically stated.

[0058] Embodiment 1

[0059] The horizontal axis wind turbine wind-approaching angle automatic optimization method provided by the present application comprises the following steps:

[0060] Step 1, obtaining the first historical operation data and the second historical operation data of the horizontal axis wind turbine to be processed, wherein the first historical operation data comprises the minute-level average wind speed and the minute-level active power average value, and the second historical operation data comprises the minute-level wind turbine yaw wind-approaching angle average value and the wind turbine state;

[0061] Step 2, obtaining a fan power fitting curve according to the obtained first historical operation data;

[0062] Step 3, dividing the rated active power of the horizontal axis wind turbine to be processed into continuous intervals, and combining the fan power fitting curve obtained in step 2 to obtain a plurality of continuous wind speed intervals corresponding to the rated active power;

[0063] Step 4, dividing the second historical operation data according to the plurality of wind speed intervals obtained in step 3 to obtain a plurality of operation data intervals;

[0064] Step 5, obtaining an active power-into-the-wind angle function relationship curve corresponding to each operation data interval;

[0065] Step 6, obtaining a yaw-into-the-wind error value of the horizontal axis wind turbine to be processed according to the active power-into-the-wind angle function relationship curves corresponding to all the operation data intervals;

[0066] Step 7, correcting the into-the-wind angle of the horizontal axis wind turbine to be processed according to the obtained yaw-into-the-wind error value, and completing the automatic optimization of the into-the-wind angle of the horizontal axis wind turbine to be processed.

[0067] Embodiment 2

[0068] The horizontal axis wind turbine into-the-wind angle automatic optimization method provided in this embodiment includes the following steps:

[0069] Step 1, automatically collecting first historical operation data and second historical operation data of a horizontal axis wind turbine to be processed within a period T from a wind farm SCADA system, wherein the first historical operation data includes minute-level average wind speed and minute-level active power average value, and the second historical operation data includes minute-level wind turbine yaw-into-the-wind angle average value and wind turbine state;

[0070] Step 2, removing operation data during maintenance and fault shutdown, operation data during power limitation, operation data below the cut-in wind speed and above the rated wind speed in the first historical operation data, and obtaining new operation data only retaining data within the cut-in wind speed to the rated wind speed interval and under normal operation of the wind turbine, and obtaining a wind turbine power fitting curve within the period T according to “GB / T18451.2 / IEC61400-12-1” “Wind Turbine Generator Power Performance Test” and combining the new operation data;

[0071] Step 3, dividing the rated active power of the horizontal axis wind turbine to be processed into ten continuous intervals, and combining the wind turbine power fitting curve to obtain ten continuous wind speed intervals corresponding to the rated active power;

[0072] Step 4, in order to ensure the objectivity and rationality of the yaw-to-wind error analysis, the second historical operation data collected is preprocessed: the operation data during maintenance and fault shutdown, the operation data during power limitation, the operation data below the cut-in wind speed and above the rated wind speed are removed, and only the data of the wind turbine in normal operation within the cut-in wind speed to the rated wind speed interval are reserved after the preprocessing.

[0073] Step 5, the preprocessed data is divided according to the above-mentioned ten wind speed intervals, and a plurality of operation data intervals are obtained.

[0074] Step 6, the wind-attack angle in each operation data interval is sorted, and each sorted operation data interval is obtained.

[0075] Step 7, the operation data with the wind-attack angle of-25° to 25° is selected in each sorted operation data interval, a plurality of new operation data intervals are obtained, and each new operation data interval is divided into fifty wind-attack angle intervals corresponding to the new operation data interval at an interval of 1°.

[0076] Step 8, the average active power value in each wind-attack angle interval is calculated.

[0077] Step 9, the active power-wind-attack angle function relationship curve corresponding to each operation data interval is fitted according to the obtained average active power value.

[0078] Step 10, ten active power-wind-attack angle function relationship curves are calculated and curve-fitted, and the accumulation curve diagram as shown in FIG. 1 is obtained. Figure 2

[0079] Step 11, the wind-attack angle corresponding to the maximum active power value is obtained from the accumulation curve diagram, and the absolute value of the wind-attack angle is calculated, and the absolute value is taken as the yaw-to-wind error value of the horizontal axis wind turbine to be processed.

[0080] Step 12, the yaw-to-wind error value is compared with the set early warning value (set at 6-8 degrees according to the wind turbine operation data experience value), and if the value exceeds the threshold value, a pre-warning prompt is given, and the wind direction vane zero position and the real-time collected wind direction are combined to automatically complete the automatic correction of the wind turbine yaw-to-wind, and the wind-attack angle automatic optimization of the horizontal axis wind turbine to be processed is completed.

[0081] Example 3

[0082] The horizontal axis wind turbine wind-attack angle automatic optimization system provided in the embodiment comprises:

[0083] ​The data acquisition unit is configured to acquire first historical operation data and second historical operation data of the horizontal axis wind turbine to be processed, wherein the first historical operation data comprises minute-level average wind speed and minute-level average active power, and the second historical operation data comprises minute-level average yaw-to-wind angle of the wind turbine and wind turbine state;

[0084] The curve fitting unit is configured to acquire a wind turbine power fitting curve according to the obtained first historical operation data.

[0085] The wind speed interval acquisition unit is configured to divide the rated active power of the horizontal axis wind turbine to be processed into continuous intervals, and acquire a plurality of continuous wind speed intervals corresponding to the rated active power in combination with the obtained wind turbine power fitting curve.

[0086] The data division unit is configured to divide the second historical operation data according to the obtained plurality of wind speed intervals, and acquire a plurality of operation data intervals.

[0087] The function curve acquisition unit is configured to acquire an active power-to-wind angle function relationship curve corresponding to each operation data interval.

[0088] The yaw-to-wind error value acquisition unit is configured to acquire a yaw-to-wind error value of the horizontal axis wind turbine to be processed according to the active power-to-wind angle function relationship curves corresponding to all the operation data intervals.

[0089] The angle correction unit is configured to correct the wind-to-angle of the horizontal axis wind turbine to be processed according to the obtained yaw-to-wind error value, and complete automatic optimization of the wind-to-angle of the horizontal axis wind turbine to be processed.

[0090] The function curve acquisition unit comprises:

[0091] The sorting module is configured to sort the wind-to-angles in each operation data interval, and acquire each sorted operation data interval.

[0092] The data selection module is configured to select operation data with a wind-to-angle of -25° to 25° in each sorted operation data interval, and acquire a plurality of new operation data intervals.

[0093] The data division module is configured to divide each new operation data interval at a set angle interval, and acquire a plurality of wind-to-angle intervals corresponding to each new operation data interval.

[0094] The active power average value calculation module is configured to calculate an active power average value in each wind-to-angle interval.

[0095] The function curve acquisition module is configured to fit an active power-to-wind angle function relationship curve corresponding to each operation data interval according to the obtained active power average value.

[0096] The yaw-to-wind error value acquisition unit comprises:

[0097] The curve accumulation module is configured to accumulate and fit the active power-to-wind angle function relationship curves corresponding to all the obtained operation data intervals to obtain angle data corresponding to the maximum value of the accumulated active power data.

[0098] The wind turbine yaw-to-wind error value acquisition module is configured to calculate the absolute value of the angle data corresponding to the maximum value of the accumulated active power data and take the absolute value as the wind turbine yaw-to-wind error value.

[0099] Embodiment 4

[0100] The horizontal axis wind turbine wind-approaching angle automatic optimization device can be a desktop computer, a notebook computer, a palm computer, a cloud server, or other computing devices. The horizontal axis wind turbine wind-approaching angle automatic optimization device can include, but is not limited to, a processor and a memory.

[0101] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like.

[0102] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for automatically optimizing the wind-approaching angle of a horizontal-axis wind turbine, characterized in that, The method comprises the following steps: Step 1, obtaining first historical operation data and second historical operation data of a horizontal axis wind turbine to be processed, wherein the first historical operation data comprises minute-level average wind speed and minute-level average active power, and the second historical operation data comprises minute-level average yaw-to-wind angle of the wind turbine and wind turbine state; Step 2, obtaining a wind turbine power fitting curve according to the obtained first historical operation data; Step 3, dividing the rated active power of the horizontal axis wind turbine to be processed into continuous intervals, and combining the wind turbine power fitting curve obtained in step 2 to obtain a plurality of continuous wind speed intervals corresponding to the rated active power; Step 4, dividing the second historical operation data according to the plurality of wind speed intervals obtained in step 3 to obtain a plurality of operation data intervals; Step 5, obtaining an active power-to-wind angle function relationship curve corresponding to each operation data interval; Step 6, obtaining a yaw-to-wind error value of the horizontal axis wind turbine to be processed according to the active power-to-wind angle function relationship curves corresponding to all the operation data intervals; Step 7, correcting the wind-approaching angle of the horizontal axis wind turbine to be processed according to the obtained yaw-to-wind error value to complete automatic optimization of the wind-approaching angle of the horizontal axis wind turbine to be processed. In step 5, the active power-to-wind angle function relationship curve corresponding to each operation data interval is obtained by the following specific method: S51, sorting the wind-approaching angles in each operation data interval to obtain each sorted operation data interval; S52, selecting operation data with a wind-approaching angle of-25°~25° in each sorted operation data interval to obtain a plurality of new operation data intervals; S53, dividing each new operation data interval at a set angle interval to obtain a plurality of wind-approaching angle intervals corresponding to each new operation data interval; S54, calculating the average active power in each wind-approaching angle interval; S55, fitting the active power-to-wind angle function relationship curve corresponding to each operation data interval according to the obtained average active power; In step 6, the yaw-to-wind error value of the horizontal axis wind turbine to be processed is obtained according to the active power-to-wind angle function relationship curves corresponding to all the operation data intervals by the following specific method: S61, performing accumulation calculation and curve fitting on the obtained active power-to-wind angle function relationship curves corresponding to all the operation data intervals to obtain angle data corresponding to the maximum value of the accumulated active power data; S62, calculating the absolute value of the angle data corresponding to the maximum value of the accumulated active power data, and taking the absolute value as the yaw-to-wind error value of the wind turbine.

2. A method for automatic optimization of the wind- facing angle of a horizontal-axis wind turbine according to claim 1, characterized in that, In step 2, before obtaining the wind turbine power fitting curve according to the obtained first historical operation data, the abnormal data in the first historical operation data is removed.

3. A method for automatic optimization of the wind- facing angle of a horizontal-axis wind turbine according to claim 1, characterized in that, In step 4, before dividing the second historical operation data, the abnormal data in the second historical operation data is removed.

4. A method for automatic optimization of the wind- seeking angle of a horizontal axis wind turbine according to claim 1, characterized in that, In step 7, the wind-approaching angle of the horizontal axis wind turbine to be processed is corrected according to the obtained yaw-to-wind error value to complete automatic optimization of the wind-approaching angle of the horizontal axis wind turbine to be processed by the following specific method: The yawing wind error value of the fan is compared with a set early warning value, and the fan yawing wind is automatically corrected according to the comparison result in combination with the wind vane zero position and the real-time collected wind direction.

5. A horizontal axis wind turbine automatic wind seeking angle optimization system, characterized in that, The method comprises the following steps: a data acquisition unit is configured to acquire first historical operation data and second historical operation data of a horizontal axis wind turbine, wherein the first historical operation data comprises minute-level average wind speed and minute-level average active power, and the second historical operation data comprises minute-level average yawing wind angle of the fan and a state of the fan; a curve fitting unit is configured to acquire a fan power fitting curve according to the obtained first historical operation data; a wind speed interval acquisition unit is configured to divide a rated active power of the horizontal axis wind turbine into continuous intervals, and acquire a plurality of continuous wind speed intervals corresponding to the rated active power in combination with the obtained fan power fitting curve; a data division unit is configured to divide the second historical operation data according to the obtained plurality of wind speed intervals to obtain a plurality of operation data intervals; a function curve acquisition unit is configured to acquire an active power-yawing wind angle function relationship curve corresponding to each operation data interval; a yawing wind error value acquisition unit is configured to acquire a yawing wind error value of the horizontal axis wind turbine according to the active power-yawing wind angle function relationship curves corresponding to all the operation data intervals; an angle correction unit is configured to correct a yawing wind angle of the horizontal axis wind turbine according to the obtained yawing wind error value, and complete automatic optimization of the yawing wind angle of the horizontal axis wind turbine; the function curve acquisition unit comprises: a sorting module configured to sort the yawing wind angles in each operation data interval to obtain each sorted operation data interval; a data selection module configured to select operation data with a yawing wind angle of-25° to 25° in each sorted operation data interval to obtain a plurality of new operation data intervals; a data division module configured to divide each new operation data interval at a set angle interval to obtain a plurality of yawing wind angle intervals corresponding to each new operation data interval; an active power average value calculation module configured to calculate an active power average value in each yawing wind angle interval; a function curve acquisition module configured to fit an active power-yawing wind angle function relationship curve corresponding to each operation data interval according to the obtained active power average value; the yawing wind error value acquisition unit comprises: a curve accumulation module configured to accumulate and calculate the active power-yawing wind angle function relationship curves corresponding to all the operation data intervals to obtain angle data corresponding to a maximum value of accumulated active power data; 6. A horizontal axis wind turbine automatic search optimization device for wind angle, comprising a processor and a computer program capable of running on the processor, characterized in that, a fan yawing wind error value acquisition module configured to calculate an absolute value of the angle data corresponding to the maximum value of the accumulated active power data, and take the absolute value as the fan yawing wind error value. The processor executes the computer program to realize the steps of the method according to any one of claims 1-4.

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