CN108488038B

CN108488038B - A kind of Yaw control method of wind power generating set

Info

Publication number: CN108488038B
Application number: CN201810259908.2A
Authority: CN
Inventors: 董密; 李力; 宋冬然; 田小雨
Original assignee: Central South University
Current assignee: Changsha Victory Electricity Tech Co ltd
Priority date: 2018-03-27
Filing date: 2018-03-27
Publication date: 2019-05-24
Anticipated expiration: 2038-03-27
Also published as: CN108488038A

Abstract

A kind of Yaw control method of wind power generating set, comprising: Step 1: calculating separately wind speed average value and the Mathematics models in preset duration according to the wind speed and direction got, historical wind speed data and history wind direction data are obtained, according to the air speed data and wind direction data of historical wind speed data and history wind direction data prediction subsequent time；Step 2: determining control parameter according to the air speed data of subsequent time, and yaw control is carried out to wind power generating set using control parameter and wind direction data.Compared to traditional Yaw control method, the yaw number of this method increases relative to Traditional control strategy, but the number improved is concentrated mainly on middle high wind speed area, therefore power loss coefficient is substantially reduced.This method can effectively reduce the yaw error in middle high wind speed area, to reduce power loss coefficient (improving the utilization rate of wind energy).

Description

Yaw control method of wind generating set

Technical Field

The invention relates to the technical field of wind power generation, in particular to a yaw control method of a wind generating set.

Background

Currently, with the depletion of traditional fossil fuels and the increasing demand for energy, there is an increasing emphasis on the development and utilization of renewable, green, clean energy sources. Wind power generation is regarded as one of the power generation modes of green renewable energy, is valued by various national industries and academic circles, is mature day by day in wind power generation technology, and is relatively low in cost in renewable energy, so that the wind power generation has a wide development prospect.

The yaw regulator is a wind adjusting device of the wind generating set, the axis of a wind wheel of the fan is always consistent with the wind direction, and the control precision of the regulator has obvious influence on the generating performance of the wind generating set. Modern large wind generating sets operate on the premise that yaw errors exist.

On the one hand, the existence of the yaw error can lead to the reduction of the wind energy acquisition amount, and according to related data, the annual average energy loss caused by the yaw error is 2.7%, and when the yaw error is 20 degrees, the annual energy loss amount can reach 11%. On the other hand, the presence of yaw errors also causes an increase in component loads, which results in unstable yaw causing the genset to oscillate and cause a shutdown.

With the gradual increase of modern fan blades, the influence brought by the yaw regulator is gradually highlighted. The data show that the yaw system caused a failure rate of 12.5% and the down time caused by yaw failure was 13.3%. Therefore, it is necessary to intensively study a control device and a control strategy for active yaw of a large wind turbine generator system.

Disclosure of Invention

In order to solve the above problem, the present invention provides a yaw control method for a wind turbine generator system, the yaw control method comprising:

step one, respectively calculating a wind speed average value and a wind direction average value within a preset time according to the acquired wind speed and wind direction to obtain historical wind speed data and historical wind direction data, and predicting the wind speed data and the wind direction data at the next moment according to the historical wind speed data and the historical wind direction data;

and step two, determining control parameters according to the wind speed data at the next moment, and performing yaw control on the wind generating set by using the control parameters and the wind direction data.

According to an embodiment of the present invention, in the step one, the preset time period is 10s, 30s or 60 s.

According to an embodiment of the present invention, in the step one, the step of predicting wind speed data and wind direction data at the next time comprises:

decomposing the wind vector according to the historical wind speed data and the historical wind direction data to obtain historical wind vector abscissa data and historical wind vector ordinate data;

determining wind vector abscissa data and wind vector ordinate data of the next moment according to the historical wind vector abscissa data and the historical wind vector ordinate data by using an ARMA (autoregressive moving average) model;

and respectively determining wind speed data and wind direction data at the next moment according to the wind vector abscissa data and the wind vector ordinate data at the next moment.

According to one embodiment of the invention, the wind vector is decomposed according to the following expression:

wherein,andrespectively representing the wind vector abscissa data and the wind vector ordinate data at the time t,the wind speed data is represented by a representation of,indicating wind direction data at time t.

According to one embodiment of the invention, the wind speed data at the next moment is determined according to the following expression:

wherein,representing the wind speed data at time t +1,andrespectively representing the abscissa data and ordinate data of the wind vector at the time t + 1.

According to one embodiment of the invention, the wind direction data at the next moment is determined according to the following expression:

wherein,representing the wind direction data at time t +1,andrespectively representing the abscissa data and ordinate data of the wind vector at the time t + 1.

According to an embodiment of the present invention, in the first step, the step of predicting wind direction data at the next time includes:

performing circular variable transformation on the historical wind direction data to obtain a sine value and a cosine value of the historical wind direction data;

and determining the sine value and the cosine value of the wind direction data at the next moment according to the sine value and the cosine value of the historical wind direction data by using an ARMA model, and determining the wind direction data at the next moment according to the sine value and the cosine value of the wind direction data at the next moment.

According to one embodiment of the invention, the historical wind direction data is subjected to circular variable transformation according to the following expression:

wherein,andrespectively representing the sine and cosine values of the wind direction data at time t,indicating wind direction data at time t.

According to an embodiment of the present invention, the wind direction data at the next time is determined according to the following expression:

wherein,representing the wind direction data at time t +1,andrespectively representing the sine and cosine values of the wind direction data at time t + 1.

According to an embodiment of the invention, in the first step, the ARMA model is used to determine the wind direction data of the next time according to the historical wind direction data.

According to an embodiment of the invention, in the first step, the ARMA model is used to determine the wind speed data at the next moment according to the historical wind speed data.

According to one embodiment of the invention, the step of determining wind speed data for the next moment in time comprises:

step a, performing trend-removing processing on the historical wind speed data to obtain trend-removing wind speed data;

b, determining a trailing truncation mode according to an autocorrelation function and a partial autocorrelation function of the detrending wind speed data;

step c, based on the tailing truncation mode, utilizing a preset criterion to determine the order of the ARMA model, and determining an auto-regression order, a sliding mean order and a difference order;

and d, calculating the wind speed data at the next moment according to the detrended wind speed data by utilizing the autoregressive order, the sliding mean order and the difference order based on the ARMA model.

According to an embodiment of the present invention, in the second step, a wind speed interval to which the wind speed data at the next time belongs is determined, and the control parameter is determined according to the wind speed interval to which the wind speed data belongs.

According to an embodiment of the invention, in the second step, if the wind speed data at the next moment is less than the preset cut-in wind speed, the wind generating set is controlled to be in a shutdown state.

According to an embodiment of the invention, in the second step, if the wind direction data at the next moment is greater than or equal to the preset cut-out wind speed, the wind generating set is controlled to yaw to the downwind position and be in a shutdown state.

According to one embodiment of the present invention, in the second step,

if the wind speed data at the next moment is greater than or equal to the preset cut-in wind speed and less than a first preset wind speed threshold value, keeping the control parameter as the original control parameter;

and/or if the wind speed data at the next moment is greater than or equal to the first preset wind speed threshold and less than a preset cut-out wind speed, reducing the original control parameter by a specific value to obtain the required control parameter.

According to an embodiment of the invention, in the second step, a plurality of wind speed intervals are included between the first preset wind speed threshold and the preset rated wind speed, wherein for the wind speed intervals, the larger the wind speed is, the smaller the control parameter corresponding to the wind speed interval is.

According to an embodiment of the invention, in the second step, if the wind speed data at the next moment is greater than or equal to a preset rated wind speed and less than a preset cut-out wind speed, yaw control is performed on the wind generating set according to the wind direction data at the next moment so that a yaw error of the wind generating set is within a preset error range.

Compared with the traditional yaw control method, the yaw frequency of the yaw control method provided by the invention is improved relative to the traditional control strategy, but the improved frequency is mainly concentrated in a medium and high wind speed area, so that the power loss coefficient is obviously reduced. The partition prediction control method provided by the invention can effectively reduce the yaw error of the medium and high wind speed area, thereby reducing the power loss coefficient (namely improving the utilization rate of wind energy).

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required in the description of the embodiments or the prior art:

FIG. 1 is a schematic view of a wind turbine generator set with an active yaw modulator;

FIG. 2 is a schematic diagram of a yaw system driving motor rotating forward to turn a wind turbine nacelle clockwise;

FIG. 3 is a schematic diagram of a yaw system driving motor rotating forward to turn a wind turbine nacelle counterclockwise;

FIG. 4 is a schematic flow chart of an implementation of a prior art yaw logic control algorithm;

5-7 show distribution diagrams of wind speed and wind direction of a wind farm in the south;

FIGS. 8-10 are schematic diagrams of actual operational results under a conventional yaw control strategy;

FIG. 11 is a flow chart illustrating an implementation of a wind speed independent prediction method according to an embodiment of the invention;

FIGS. 12 and 13 are schematic diagrams of the autocorrelation function and the partial correlation function of the wind speed series 10s mean according to one embodiment of the present invention;

FIG. 14 is a flowchart illustrating an implementation of a wind speed and direction prediction method according to an embodiment of the present invention;

FIG. 15 is a flowchart illustrating an implementation of a wind speed and direction prediction method according to an embodiment of the present invention;

FIG. 16 is a diagram illustrating the original wind direction and the average wind direction for different durations of time according to one embodiment of the present invention;

FIG. 17 shows a schematic diagram of the raw wind speed and the average wind speed over different time periods for one embodiment of the present invention;

FIGS. 18 and 19 are schematic diagrams illustrating 10s wind direction prediction results and wind speed prediction results obtained by different prediction methods according to an embodiment of the present invention;

FIGS. 20 and 21 are diagrams illustrating the 30s wind direction prediction result and the wind speed prediction result obtained by different prediction methods according to an embodiment of the present invention;

FIGS. 22 and 23 are schematic diagrams illustrating 60s wind direction prediction results and wind speed prediction results obtained by different prediction methods according to an embodiment of the present invention;

FIG. 24 is a schematic flow chart illustrating an implementation of a yaw control method of a wind turbine generator system according to an embodiment of the invention;

FIG. 25 illustrates an ideal operating power profile for a wind turbine generator set according to an embodiment of the present invention;

FIG. 26 illustrates a plot of nacelle position under different control strategies for one embodiment of the present invention;

FIGS. 27-30 show yaw error profiles over different wind speed intervals for different control strategies according to an embodiment of the invention.

Detailed Description

The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.

In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without some of these specific details or with other methods described herein.

Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions and, although a logical order is illustrated in the flow charts, in some cases, the steps illustrated or described may be performed in an order different than here.

The control of the current yaw system mainly focuses on power control, such as Maximum Power Point Tracking (MPPT) control. Due to the early limit of measurement technology, the hill climbing method is mostly adopted for yaw control. However, since the MPP of the fan is not only related to the wind direction but also related to the wind speed, the MPP cannot be accurately located, so the method still has a controversy in the industry.

With the development of measurement technology, researchers have proposed yaw control methods and logic control methods combining PID and fuzzy control, which are active yaw control based on wind direction measurement, which is also a yaw control method commonly used in the industry at present. But because the measurement of the wind direction is always mixed with interference noise and abnormal values, and meanwhile, the wind direction is continuously changed and is different from the future wind direction. Therefore, such active yaw control based on wind direction feedback does not significantly improve the control performance of the yaw system.

In recent years, researchers have proposed that wind speed and direction 150m directly in front of the impeller be detected by laser radar, and based on this, predictive control of the yaw system is proposed. This yaw control strategy based on advanced measurement techniques can improve wind energy capture and reduce loads in certain extreme wind directions. However, this wind measurement technique is still in the experimental phase due to its high cost.

The wind direction is also important for the wind generating set to obtain the maximum power, and the yaw control based on wind direction prediction provides possibility for obtaining the maximum power output because the axis of the fan is consistent with the wind direction. Bao et al propose a method based on circular regression and Bayesian averaging to perform bias correction on the prediction data obtained from the weather forecast model. Ergin Erdem et al propose a prediction method based on the ARMA wind speed and direction combination. Kalsuner et al propose a method for predicting wind vectors based on "similar days". The prediction of wind speed and direction, which are both crucial for the capture rate of wind energy, are two distinct attributes, and there is today little research into how to predict multiple wind attributes simultaneously and to use the predictions for yaw system control.

On the basis of the original ARMA-based wind speed and direction independent prediction method, a new ARMA-model-based wind speed and direction prediction method is provided.

FIG. 1 is a schematic view of a wind turbine generator set with an active yaw regulator.

The wind turbine shown in fig. 1 includes: a motor module 101, a pitch control module 102, an aerodynamic system module 103, a frequency converter control module 104, a yaw control module 105 and a tower and gear module. Wind in the air is rotated by a wind turbine blade in the aerodynamic system module 103 to convert wind energy into mechanical energy to drive a generator rotor in the motor module 101 to rotate, and then the variable frequency and the variable voltage generated by the generator are converted into fixed frequency and fixed voltage which can be accepted by a power grid through the variable frequency control module 104 by applying a space vector control technology.

According to Betz theory in aerodynamics, the power P which can be obtained and output by the wind generating set from wind_aComprises the following steps:

V_e＝V₀cos(θ_e)＝V₀cos(θ_w-θ_np) (2)

where ρ represents the air density, A_rIndicates the swept area of the wind wheel, C_pRepresenting the wind energy utilization coefficient, V, of a wind turbine_eExpressed as effective wind speed, V₀Representing the free stream wind speed, θ_eRepresenting yaw error, theta_wAnd theta_npRespectively representing the wind direction and the north angle of the wind turbine nacelle.

According to the expression (1) and the expression (2), the power P captured by the wind turbine_aEffective value V of wind speed_eIs proportional to the 3 rd power, which indicates the yaw error theta_eLarger wind turbine captured power P_aThe smaller.

The active yaw system actively aligns the axis of the nacelle with the wind direction, that is, the wind wheel is adjusted to the windward position through the yaw direction adjusting device according to the calculated average value of the wind direction detected by the wind vane in a period of time. When the position of the cabin of the wind turbine changes, the absolute value encoder records the current adjustment angle, then yaw braking is started, and the possibility of capturing the maximum wind energy by the series of active yaw adjustment actions as a wind generating set is provided.

Therefore, to improve the efficiency of the wind turbine, the yaw system always requires aligning the wind direction by turning the nacelle perpendicular on the tower according to the shortest path, so the relationship between the shortest path for yaw adjustment and the yaw angle is as follows:

(1) under the condition that the angle difference between the position of the wind turbine cabin and the wind direction is less than 180 degrees, the calculation formula of the yaw angle is as follows:

θ_e＝θ_w-θ_np (3)

at this time, the yaw system drives the motor to rotate forward, so that the direction of the cabin of the wind turbine is adjusted clockwise, and the schematic diagram is shown in fig. 2;

(2) under the condition that the angle difference between the position of the wind turbine cabin and the wind direction is larger than 180 degrees, the calculation formula of the yaw angle is as follows:

θ_e＝360°-|θ_w-θ_np| (4)

at this time, the yaw system driving motor is reversed, so that the wind turbine nacelle is turned counterclockwise, and the schematic diagram is shown in fig. 3.

Currently, the yaw error under the active yaw control strategy based on wind direction feedback is mainly distributed in a centralized way at [ -15 degrees, 15 degrees ]. When the wind direction changes beyond the set range, the yaw system adjusts the position of the cabin. The yaw logic control algorithm commonly used in the industry is described below by taking a certain 1.5MWCMYWP wind turbine as an example, and the implementation flow diagram thereof is shown in fig. 4.

As can be seen from fig. 4, in the implementation process of the conventional active yaw control logic algorithm, the original wind direction measurement data is firstly filtered, and then the average yaw error value within the set time is calculated according to the filtered wind direction data.

Specifically, the control algorithm calculates the average value of the yaw error in the set time according to the following expression:

wherein,represents the average value of the deviation error within 10s,indicating the average value of the yaw error over 30s,representing the mean value of the yaw error over 60 s.

The algorithm will then determine whether the calculated average yaw error value is outside a predetermined corresponding range. Wherein if the preset range is not exceeded, the yawing system is not operated. If the yaw error average value exceeds the preset range, the algorithm further judges whether the time that the yaw error average value exceeds the preset range exceeds the set delay time length. Wherein if the time that the yaw error average value exceeds the preset range does not exceed the set time delay duration, the same yaw system does not act.

If the time that the average value of the yaw errors exceeds the preset range exceeds the set delay time length, the algorithm calculates the running time length t of the yaw system at the moment_yaw. Specifically, the algorithm can calculate the running time t of the yaw system according to the following expression_yaw：

t_yaw＝θ_e/v_yaw (6)

Wherein v is_yawThe operating speed of the yawing system (i.e. the rotational speed of the yawing system) is indicated.

In obtaining a deviationOperating time t of navigation system_yawThen, the algorithm can also be used for calculating the running time t of the yaw system_yawTo control the yaw system to act.

However, wind is the movement of air relative to the earth's surface, its formation is influenced by many factors, including geographical location, meteorological conditions, and it has significant day-to-day and year-to-year effects. In addition, there is a certain relationship between wind speed and wind direction, and fig. 5 to 7 show distribution diagrams of wind speed and wind direction in a certain wind farm in the south.

As can be seen from fig. 5 to 7, the wind direction changes more frequently in the low wind speed region, and the wind direction tends to be stable as the wind speed increases. In addition, the wind speed and the wind direction in each place have distinct regional characteristics, and the regional wind speed and the wind direction characteristics are shown in table 1.

TABLE 1

As can be seen from FIGS. 5-7 and Table 1, the wind speed during this time period is mainly 9-15m/s, accounting for 90.48%. The wind direction to the north is mainly concentrated at 280-330 degrees, mainly northwest, and occupies 83.71 percent of the total amount. The mean wind speed was 10.18m/s and the standard deviation of the wind speed was 4.02.

The foregoing data is used to analyze the actual operation results under the conventional yaw control strategy, and the results are shown in fig. 8 to 10. According to fig. 8 to 10, under the control of the conventional yaw strategy, the mean value and the standard deviation of the yaw error of the wind turbine gradually decrease along with the increase of the wind speed. In the wind speed area lower than 2.5m/s, because the wind speed is low, the yawing system is not started, and therefore the yawing error in the area is large; in a low wind speed region below a rated value, the average value of the yaw error in the section of 2.5-4m/s is large, and then the yaw error gradually stabilizes; in the high wind speed area above the rated value, the average value of the yaw error is stable.

Through analysis, the inventor finds that the traditional yaw control algorithm has the following problems:

(1) the yaw wind alignment precision of the unit is low. The existing control strategy is based on wind direction feedback control and completely depends on the accuracy of wind direction measurement. However, the accuracy of the wind direction is closely related to the installation position of the wind vane besides the measurement accuracy of the wind vane sensor. The wind wheel of the wind generating set positioned in the upwind direction rotates to generate wake turbulence, so that the wind vane positioned in the downwind direction continuously swings, the accuracy of wind direction measurement and the service life of wind measuring equipment are reduced, an ideal wind direction input signal cannot be obtained by a yaw control system, and the wind precision of the set is low.

(2) The yaw control lags. The yaw error used by existing yaw control strategies is an average over a period of time calculated and reflecting historical yaw conditions.

(3) The same control strategy is adopted in the whole wind speed area, and wind speed is not considered by purely depending on wind direction data. According to the above studies, there is a certain relationship between wind speed and wind direction. The yaw strategy of most wind turbines on site does not distinguish wind speeds, so that the tolerance range and the delay time of a yaw error angle are fixed values.

(4) The yaw system adaptation level is low. In an actual wind field, the influence of the geographical position of a wind turbine on a yaw system is also very large, such as the influence between the terrain and different positions. At present, the units of different machine positions and even different wind power plants adopt the same control strategy, and the difference of wind conditions of the wind power plants and the performance difference among the units are ignored.

(5) As can be seen from fig. 8 to 10, with the conventional yaw control strategy, although the yaw error is stable in the high wind speed region, the average value of the yaw error exceeds the set 8 °.

As can be seen, the conventional yaw control strategy is not satisfactory in effect, and therefore, it is necessary to optimize the yaw control system.

The invention firstly provides a method for predicting wind speed and wind direction, which can realize short-time independent prediction of the wind speed and the wind direction. Since the implementation principle and implementation flow of the method for predicting the wind speed and the wind direction are the same, the method will be described only by taking the prediction of the wind speed as an example.

Fig. 11 is a schematic flow chart illustrating an implementation process of predicting wind speed in the present embodiment.

FIG. 11 is a schematic flow chart illustrating an implementation of independent wind speed prediction in the present embodiment.

As shown in fig. 11, in the present embodiment, the method first obtains historical wind speed data in step S1101. It should be noted that the historical wind speed data acquired in step S1101 of the method refers to a wind speed average value (for example, a wind speed average value within 10S, 30S, or 60S) within a preset time period corresponding to a plurality of time instants (including the current time instant) included in a time period preferably having a certain length (the length may be configured to be different reasonable values according to actual needs). The average value of the wind speed within 10s corresponding to the current moment represents the average value of the wind speed within 10s before the current moment.

Of course, in different embodiments of the present invention, the preset time duration may be configured to different reasonable values (for example, reasonable values within 5s to 240 s) according to actual needs, and the specific value of the preset time duration is not limited in the present invention.

Because the method is based on the ARMA model to predict the wind speed, and the ARMA model requires stable data, after obtaining the historical wind speed data, the method can perform the detrending processing on the historical wind speed data in step S1102 so as to obtain the detrending wind speed data in order to ensure the stability of the data.

Specifically, in this embodiment, the method preferably performs the detrending process on the wind speed data at the historical time in step S1102 according to the following expression:

wherein,representing the detrended wind speed data at time t,a wind speed data value representing the time t,representing historical wind speed trend values (i.e., averages).

In this embodiment, the historical wind speed data averagePreferably, it means an average of all wind speed data before the current time or an average of wind speed data within a certain time period before the current time.

After completing a detrending process, the method may also perform stationarity detection on the detrended wind speed data in step S1102. If the detrended wind speed data is not stable, the method differentiates the detrended wind speed data again and performs stability detection again until the obtained detrended wind speed data is stable.

In the embodiment, the method preferably adopts a method of referring to an ordered differential operator (namely ▽ ═ 1-B) to carry out the original non-stationary time sequence { y-_tApply a first order difference transform. Namely, there are:

▽y_t＝(1-B)y_t＝y_t-y_t-1 (8)

wherein, ▽ y_tRepresenting the difference between the data at time t (i.e., the current time) and at time t-1 (i.e., the previous time), and B represents y_tAnd y_t-1Coefficient of proportionality of y_tAnd y_t-1Representing data at time t (i.e., the current time) and time t-1 (i.e., the previous time), respectively.

The d order difference can obtain:

▽^dy_t＝(1-B)^dy_t (9)

wherein, ▽^dy_tRepresenting the d-order difference operator.

The stationary sequence obtained after the difference can be described by using the models of AR, MA and ARMA, and the original time sequence can be expressed as:

wherein,representing a lag operator polynomial, theta (B) representing a prediction error lag operator polynomial, a_tIndicating the prediction error.

This is the cumulative autoregressive-moving average model ARIMA (p, d, q).

If it is necessary to keep the data sequence smooth, it is also necessary to require that the roots of equations phi (B) 0 and theta (B) 0 are all located outside the unit circle, i.e. the modulus values of the roots are all greater than 1. Wherein,

wherein, if the module values of the root of the equation are all larger than 1, the wind speed sequence is stable. And if the stability and reversibility check fails, the difference order can be properly adjusted to correct until the adjusted wind speed sequence is stable.

Of course, in other embodiments of the invention, the method may also use other reasonable ways to detect the stationarity of the detrended wind speed data, and the invention is not limited thereto.

In this embodiment, by performing the de-trending process on the historical wind speed data, the method may also determine the difference order d in the ARMA model.

After the detrending process is completed, the method determines a tail truncation mode in step S1103 according to the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the detrended wind speed data obtained in step S1102.

Specifically, in this embodiment, the autocorrelation function and the partial autocorrelation function may be respectively expressed as:

where ρ is_kWhich means that the autocorrelation coefficient is calculated with a lag number k,andrespectively show the detrended at the i momentAnd the detrended data time, phi, of time i + k_kkRepresents a partial correlation coefficient with a lag number k, phi_k-1,jRepresents the jth regression coefficient in the k-1 order autoregressive process.

Specifically, in this embodiment, the method determines whether the autocorrelation function of the detrended wind speed data can remain zero after reaching a certain order. If the self-correlation function of the detrended wind speed data has the tailing property, the method can judge the self-correlation function of the detrended wind speed data has the tailing property, and otherwise, the self-correlation function of the detrended wind speed data can be judged to have the tailing property.

Similarly, the method may also determine whether the partial autocorrelation function of the detrended wind speed data can remain zero after reaching a particular order. If the partial autocorrelation function of the detrended wind speed data can be judged to have the tailing property, otherwise, the partial autocorrelation function of the detrended wind speed data can be judged to have the tailing property.

By judging whether the autocorrelation function and the partial correlation function of the trending-removed wind speed data are in a trailing type or a tail truncation type, the method provided by the embodiment can determine the trailing tail truncation mode of the trending-removed wind speed data.

Fig. 12 and 13 show schematic diagrams of the autocorrelation function and the partial correlation function of the average value of the velocity sequence 10s in this embodiment, respectively. As can be seen in fig. 12 and 13, the autocorrelation function and the partial correlation function of the detrended wind speed data are both of the trailing type.

As shown in fig. 11 again, in this embodiment, after determining the tail-biting pattern of the outgoing trending wind speed data, the method determines the order of the ARMA model based on the determined tail-biting pattern in step S1104, so as to determine the automatic order, the moving average order and the difference order. Wherein, the difference order is the number of differences determined in the difference process of step S1102. If the wind speed data is relatively smooth, the difference processing is not needed in the de-trending process, so that the difference number (i.e., the difference order d) is equal to zero.

After determining the auto-regression order, the sliding mean order, and the difference order in the ARMA model, in this embodiment, the method predicts the wind speed data one step ahead of time according to the detrended wind speed data by using the auto-regression order, the sliding mean order, and the difference order determined in step S1104 based on the ARMA model in step S1105, so as to calculate the wind speed data at the next time.

Specifically, in the present embodiment, the method preferably determines the wind speed data at the next time according to the following expression:

wherein, y_t+1Data representing time t +1 (i.e. the next time), y_tData representing time t (i.e. the current time), y_t-iData representing time t-i, delta represents a constant term,denotes the ith autoregressive coefficient, phi_jDenotes a j-th moving average coefficient, p denotes an order of autoregressive, q denotes an order of a moving average, e_tAn error term (i.e., the difference between the predicted value and the observed value at time t) representing time t (i.e., the current time).

For wind speed data, namely:

wherein,representing wind speed data at time t +1 (i.e., the next time).

Therefore, the wind speed data at the next moment is predicted according to the historical wind speed data.

Based on the same principle and process, the wind speed and direction prediction method provided by the invention can also predict the wind direction data at the next moment according to the historical wind direction data.

The invention also provides a wind speed and direction prediction method, which can predict the wind direction data at the next moment by using a wind direction circular transformation mode under the condition that the ARMA model is used for determining the wind speed data at the next moment according to the historical wind speed data.

Fig. 14 shows a flow chart of an implementation of the wind speed and direction prediction method provided by the present embodiment.

As shown in fig. 14, in the present embodiment, the method obtains historical wind speed data and wind direction data in step S1401. It should be noted that the historical wind speed data acquired in step S1101 preferably refers to a wind speed average value (for example, a wind speed average value within 10S, 30S or 60S) within a preset time period corresponding to a plurality of time instants (including the current time instant). The average value of the wind speed within 10s corresponding to the current moment represents the average value of the wind speed within 10s before the current moment.

In step S1402, the method predicts wind speed data at a next time based on historical wind speed data using an ARMA model. In this embodiment, the specific principle and process of predicting the wind speed data at the next time according to the historical wind speed data by using the ARMA model are similar to those described in the above steps S1102 to S1105, and therefore the details of this part are not described herein again.

The wind direction is a circular variable, so the method provided by the embodiment adopts a prediction method more suitable for the circular variable to predict the wind direction data at the next moment. Specifically, in this embodiment, in step S1403, the method performs circular variable transformation on the historical wind direction data, so as to obtain a sine value and a cosine value of the historical wind direction data.

Specifically, the method preferably transforms the historical wind direction data according to the following expression:

Based on the expression (17), the method can obtain the sine value and the cosine value of the wind direction data at the current time and at each time before the current time.

After determining the sine value and the cosine value of the wind direction data at the current time (i.e., time t), the method determines the sine value of the wind direction data at the next time (i.e., time t + 1) according to the sine value and the cosine value of the wind direction data at the current time in step S1404And cosine value

Specifically, in the present embodiment, the method preferably utilizes the ARMA models to base history on, respectivelyDetermining the sine value of the wind direction data at the next moment according to the sine value and the cosine value of the wind direction dataAnd cosine valueThe specific principle and process are the same as those described in fig. 11, and therefore, the details of this part will not be described herein again.

As shown in fig. 14, in the present embodiment, the sine value of the wind direction data at the next time is obtainedAnd cosine valueThe method then proceeds to step S1405 based on the sine of the wind direction data at a timeAnd cosine valueDetermining wind direction data for a next time instant

Specifically, in the present embodiment, the method preferably determines the wind direction data at the next time according to the following expression

Wherein,indicating wind direction data at time t +1 (i.e. the next time),andrespectively representing the sine and cosine values of the wind direction data at time t + 1.

It should be noted that in other embodiments of the present invention, the method may also use other reasonable ways to predict the sine value of the wind direction data at the next time according to the predicted sine valueAnd cosine valueDetermining wind direction data for a next time instant

It should be noted that in other embodiments of the present invention, the prediction of the wind speed data may be configured according to actual needs, that is, the wind speed data is acquired and predicted if needed, and the wind speed data is not acquired and predicted if not needed, and the present invention is not limited thereto. Furthermore, in other embodiments of the present invention, the method may also use other reasonable ways to predict the wind speed data according to actual needs, and the present invention is not limited thereto.

The invention provides a novel wind speed and direction prediction method, which takes wind speed and wind direction as a vector and gives the wind vector to predict wind speed data and wind direction data at the next moment.

Fig. 15 shows a flow chart of an implementation of the wind speed and direction prediction method provided by the present embodiment.

As shown in fig. 15, in the present embodiment, the wind speed and direction prediction method obtains the historical wind speed data and the historical wind direction data of the area to be analyzed in step S1501. It should be noted that the historical wind speed data acquired in step S1101 preferably refers to a wind speed average value (for example, a wind speed average value within 10S, 30S or 60S) within a preset time period corresponding to a plurality of time instants (including the current time instant). The average value of the wind speed within 10s corresponding to the current moment represents the average value of the wind speed within 10s before the current moment.

After obtaining the historical wind speed data and the historical wind direction data, the method decomposes the wind vector according to the historical wind speed data and the historical wind direction data to obtain a historical wind vector abscissa and a historical wind vector ordinate in step S1502.

Specifically, in the present embodiment, the method preferably decomposes the wind vector according to the following expression:

Based on the expression (19), the method can obtain the wind vector abscissa data and the wind vector ordinate data of the current time and each time before the current time.

Of course, in other embodiments of the present invention, the method may also use other reasonable ways to decompose the wind vector, and the present invention is not limited thereto.

After obtaining the historical wind vector abscissa and the wind vector ordinate, the method determines the wind vector abscissa at the next moment according to the historical wind vector abscissa and the historical wind vector ordinate by using the ARMA model in step S1503Ordinate of the vector of the wind

In this embodiment, the method determines the abscissa of the wind vector at the next moment by using the ARMA modelOrdinate of the vector of the windThe specific principle and process of the method are similar to those shown in fig. 11, and the historical wind speed data is replaced by the historical wind vector abscissa and the historical wind vector ordinate based on the method shown in fig. 11, so that the wind vector abscissa at the next moment can be determined respectivelyOrdinate of the vector of the windThis process will not be described in detail herein.

As shown in fig. 15, in the present embodiment, the method will be based on the abscissa of the wind vector at the next time obtained in step S1503 in step S1504Ordinate of the vector of the windWind speed data and wind direction data at the next moment are determined.

Specifically, in the present embodiment, the method preferably determines the wind speed data at the next time according to the following expression

Determining wind direction data at the next moment according to the following expression:

wherein,indicating wind direction data at time t +1 (i.e., the next time).

It should be noted that in other embodiments of the present invention, the method may also use other reasonable ways to determine the abscissa of the wind vector at the next timeOrdinate of the vector of the windWind speed data and wind direction data for the next moment are determined, but the invention is not limited thereto.

In order to verify the effectiveness and advantages of the wind speed and direction prediction method provided by the invention, 86400 points are used in the embodiment of wind speed and direction Data recorded by SCADA (supervisory Control and Data Acquisition System) of a certain wind field in the south within 24 hours. Fig. 16 shows the original wind direction and the average wind direction at different time periods, fig. 17 shows the original wind speed and the average wind speed at different time periods, fig. 18 and 19 show the 10s wind direction prediction result and the wind speed prediction result obtained by different prediction methods, respectively, fig. 20 and 21 show the 30s wind direction prediction result and the wind speed prediction result obtained by different prediction methods, respectively, and fig. 22 and 23 show the 60s wind direction prediction result and the wind speed prediction result obtained by different prediction methods, respectively.

As can be seen from fig. 16 and 17, since the fluctuation of the wind speed raw data and the wind direction raw data is relatively large, calculating the average values of 10s, 30s, and 60s is advantageous for filtering and reducing the influence of the abnormal value. Furthermore, the value will start from 0 when the wind vane is measuring wind direction over 360 °. This causes the wind direction shown in fig. 16 to fluctuate greatly in the event of 20-22H, and the accuracy is not high when the wind direction is predicted by the ARMA model alone (especially at the circle in fig. 18, 20 and 22), but the wind direction prediction using the circular variable method can reflect the continuity of the wind direction more, and the abrupt change at the circle is not caused, so that the wind direction prediction accuracy is higher. For wind speed prediction, it can be seen from fig. 19, 21 and 23 that the results obtained by using the individual prediction method are more stable than the original data.

In order to evaluate the accuracy of the proposed short-term wind speed and direction prediction method, in this embodiment, three expressions, namely, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Square Error (MSE), may be used to compare the prediction results, and the statistical results are shown in table 2. The calculation expressions of the Mean Absolute Error (MAE), the Mean Absolute Percent Error (MAPE) and the Mean Square Error (MSE) are respectively as follows:

wherein N represents the number of data, x_iThe actual value is represented by a value that is,indicating the predicted value.

TABLE 2

As can be seen from fig. 16 to 23 and table 2, the accuracy of the wind direction prediction result obtained by using the ARMA prediction model alone is lower than that of the wind vector method and the circular variable method for wind direction prediction

Compared with the existing wind speed and wind direction prediction method, the method provided by the invention can enable the wind direction prediction result to be more accurate and stable, so that a data basis is provided for yaw control of the wind generating set.

Fig. 24 is a schematic flow chart illustrating an implementation of the yaw control method of the wind turbine generator system provided by the embodiment.

As shown in fig. 24, in the present embodiment, in step S2401, the yaw control method first calculates a wind speed average value and a wind direction average value within a preset time period according to the acquired wind speed and wind direction, so as to obtain historical wind speed data and wind direction data.

In the present embodiment, the preset time period is preferably 10s, 30s and/or 60 s. Of course, in different embodiments of the present invention, the preset time duration may be configured to different reasonable values (for example, reasonable values within 5s to 240 s) according to actual needs, and the specific value of the preset time duration is not limited in the present invention.

After obtaining the historical wind speed data and wind direction data, the method predicts the wind speed data and wind direction data at the next moment according to the historical wind speed data and wind direction data in step S2402. In this embodiment, the method preferably employs a circular variational method to predict the wind speed data and the wind direction data at the next time, wherein the specific principle and process of predicting the wind speed data and the wind direction data based on the circular variational method have been described in detail in the above description, and therefore details of this part are not repeated herein.

Of course, in other embodiments of the present invention, the method may also use other reasonable ways to predict the wind speed data and the wind direction data at the next time according to the historical wind speed data and the wind direction data according to the actual situation, and the present invention is not limited thereto. For example, in one embodiment of the present invention, the method may also use a manner of predicting wind speed data and wind direction data respectively as shown in fig. 11 to obtain wind speed data and wind direction data at the next time, or use a manner of predicting based on a wind vector method as shown in fig. 15 to obtain wind speed data and wind direction data at the next time.

As shown in fig. 24, in this embodiment, after the wind speed data and the wind direction data at the next time are determined, the method determines the control parameters according to the predicted wind speed data at the next time in step S2403, and then performs yaw control on the wind-powered light-emitting point unit according to the control parameters determined in step S2403 and the wind direction book determined in step S2402 in step S2404.

In this embodiment, the method preferably determines a wind speed interval to which the predicted wind speed data at the next time belongs in step S2403, and determines the control parameter according to the wind speed interval to which the wind speed data belongs.

Specifically, as shown in fig. 25, in the present embodiment, the method cuts the controllable wind speed region of the wind turbine generator set into the wind speed v_{cut_in}Rated wind speed v_nAnd cut-out wind speed v_{cut_out}Is divided into 4 segments for the boundary.

Preferably if the wind speed v measured by the relevant sensor is low_w＜v_{cut_in}(e.g. v)_w< 2.5m/s), which in turn means the wind speed v_w(i.e. predicted wind speed data for the next time) at the cut-in wind speed v_{cut_in}The following region. Since this region contains less wind energy, the method preferably controls the wind energy installation in this wind speed region to be in a standstill state.

Preferably, if the wind speed v_w＞v_{cut_out}(e.g. v)_w> 25m/s), which means the wind speed v_w(i.e. predicted wind speed data for the next moment) at the cut-out wind speed v_{cut_out}The above region. Since the wind speed in the area is high, and the excessive wind speed has a large influence on the load of the wind generating set, so that the safety, reliability and service life of the wind generating set are influenced, in the embodiment, the method preferably controls the wind generating set to yaw to the downwind position in the wind speed area and be in the shutdown state.

Preferably, if the wind speed v_wGreater than or equal to a preset cut-in wind speed and less than a first preset wind speed threshold value, i.e. v exists_{cut_in}≤v_w＜v₁Then the method will keep the control parameters unchanged from the original control parameters. Whereas if the wind speed v is_wGreater than or equal to a first preset wind speed threshold value and less than a preset cut-out wind speed, i.e. v exists₁≤v_w＜v_{cut_out}Then the method reduces the original control parameter by a specified value to obtain the desired control parameter. It is noted that in various embodiments of the present invention, theThe first preset wind speed threshold value may be configured to be different reasonable values according to the actual wind energy condition, but the invention is not limited thereto. It should also be noted that in other embodiments of the present invention, when v is_{cut_in}≤v_w＜v₁Or v₁≤v_w＜v_{cut_out}However, the method may also employ other reasonable ways to configure the control parameters, and the invention is not limited thereto as such.

For example, if the first preset wind speed threshold value is configured to be 4m/s, since the wind energy in the wind speed interval [2.5m/s,4m/s) occupies 9.64% of the total wind energy, the average value and standard deviation of the wind error are large, and the wind direction in the area is unstable. Meanwhile, because the energy acquired by the wind generating set from the wind is small in the wind speed interval, in this embodiment, the method preferably delays the time T_setAnd/or yaw start-up error angle v_setThe control parameters are kept unchanged from the original control parameters, i.e. the control parameters set according to the existing yaw control method.

And for the wind speed interval [4m/s,25m/s), in this embodiment, the method preferably reduces the original control parameter by a specific value, so as to obtain a new control parameter suitable for the wind speed interval. The off-air control method can control the wind driven generator set according to the determined new control parameters.

In this embodiment, a plurality of wind speed intervals are included between the first preset wind speed threshold and the preset rated wind speed, wherein for the wind speed intervals, the larger the wind speed is, the smaller the control parameter corresponding to the wind speed interval is.

For example, if 4m/s ≦ v_wThe wind energy contained in the wind speed interval occupies 13.61% of the total wind energy and is in a low wind speed section below the rated wind speed, so the average value and the standard deviation of wind errors under the traditional yaw control are smaller, the wind speed interval can be improved by the traditional yaw controller compared with the wind speed interval of [2.5m/s,4m/s), and the yaw control performance still needs to be improved. Therefore, the method provided by the embodiment will delay the timeInter T_setAnd/or yaw start-up error angle v_setAnd reducing the original control parameter value of the control parameter by a specific value, so that the yaw control performance can meet the requirement of the wind speed interval.

If 9m/s is less than or equal to v_wAnd the wind energy contained in the wind speed interval occupies 33.07% of the total wind energy and is in a medium and high wind speed section below the rated wind speed, the average value and the standard deviation of the wind error under the traditional yaw control can be continuously smaller than those in the previous wind speed interval, but the yaw control performance still needs to be improved. Therefore, in this embodiment, the method will delay the time T_setAnd/or yaw start-up error angle v_setAnd the original control parameter value of the control parameter is continuously reduced, so that the yaw control performance can meet the requirement of the wind speed interval.

In the present embodiment, if the wind speed v is_wAnd if the wind speed is greater than or equal to the preset cut-in wind speed and less than the rated wind speed, the method preferably aims to realize the tracking of the optimal power curve and the capture of the maximum wind energy by adjusting the tip speed ratio of the wind turbine. At this time, the pitch angle is preferably set to 0 °. Of course, in other embodiments of the invention, the pitch angle may also be configured to other reasonable values according to actual needs, and the invention is not limited thereto.

In this embodiment, if the wind speed data is greater than or equal to the preset rated wind speed and less than the preset cut-out wind speed, the method performs yaw control on the wind turbine generator system according to the wind direction data at the next moment so that the yaw error of the wind turbine generator system is within the preset error range. Specifically, in this embodiment, if the wind speed data is greater than or equal to the preset rated wind speed and less than the preset cut-out wind speed, the method will adjust the pitch angle to change the wind energy obtaining coefficient, so as to obtain stable output power and protect the unit equipment.

For example, if 12m/s ≦ v_wAnd the wind energy contained in the wind speed interval is 40.32 percent of the total wind energy and the wind direction in the wind speed interval is steadily enhanced < 25 m/s. Due to rated wind speedAlthough the yaw error of the wind speed interval does not affect the power generation, the excessive yaw error will affect the whole load of the wind generating set, resulting in an excessive variation amplitude of the average induced wind speed, so in this embodiment, the method will configure the yaw starting error angle to [ -8 °, -8 ° ]]Therefore, the yaw error of the wind generating set can be kept at the angle of-8 degrees and 8 degrees through the yaw control of the wind generating set]。

It can be seen that, for each wind speed interval included in the cut-in wind speed to the cut-off wind speed, the method provided by the embodiment preferably sets the control parameter corresponding to each wind speed interval individually, and the specific setting result can be shown in table 3.

TABLE 3

With the yaw control method of the wind generating set provided by the embodiment, the wind speed data (for example, the average value of 10s, 30s and/or 60s wind speeds) and the wind direction data (for example, the average value of 10s, 30s and/or 60s wind directions) used in the yaw control can be predicted in advance, and then the operation of the yaw system is controlled by judging the predicted wind speed data and the predicted wind direction data with the corresponding threshold values.

In order to verify the effectiveness of the partition control strategy based on wind speed and wind direction prediction, the embodiment adopts wind speed data and wind direction data as shown in fig. 16 to 23, respectively controls the traditional control strategy and the partition control strategy based on wind speed and wind direction prediction provided by the invention under Matlab/Simulink environment, and analyzes the experimental result. In addition, in order to clearly express the effect of the partitioning strategy, the present embodiment analyzes five aspects, namely, the average value of the yaw error, the root mean square of the yaw error, the yaw time, the number of times of yaw, and the power loss coefficient.

The average yaw error value is calculated by adopting the following expression:

the yaw error root mean square is calculated by adopting the following expression:

the yaw time is calculated by the following expression:

the yaw frequency is calculated by adopting the following expression:

the power loss coefficient is calculated in practical engineering experience by using the following expression

Wherein, theta_yeDenotes that N denotes the number of yaw errors, t_yawWhich is indicative of the time of yaw,is represented by C_yawRepresenting the number of drifts, ξ representing the power loss coefficient, P_redIndicating reduced power, P_realWhich represents the power that is output in the ideal case,representing an equivalent yaw error.

Equivalent yaw errorIt can be calculated according to the following expression:

wherein,is the average value of errors in the jth yaw error region, which represents the probability of the yaw error region.

FIG. 26 illustrates the nacelle position under both the conventional control strategy and the zone control strategy provided by the present invention. The results of fig. 26 were counted for each wind speed division to obtain the yaw error distribution diagrams shown in fig. 27 to 30.

Table 4 shows the statistics under different yaw control methods.

TABLE 4

As can be seen from the statistics results of fig. 26 to fig. 30 and table 4, in a low wind speed interval (e.g., [2.5m/s,4m/s)), the yaw control strategy adopted by the yaw control method provided by the present embodiment is consistent with the conventional strategy, so that the yaw error distribution is not changed.

In a medium-low wind speed interval (for example, [4m/s,9m/s ]) below the rated wind speed, the yaw error obtained by the yaw control method provided by the embodiment is reduced compared with that obtained by the traditional method, the wind precision is higher, and the yaw error is improved from 75.20% to 76.04% in an interval of [ -8 °, -8 ° ].

In the middle and high wind speed area (for example [9m/s,12m/s)) below the rated wind speed, the yaw error obtained by the yaw control method provided by the embodiment is remarkably reduced compared with that obtained by the traditional method, and the yaw error is improved from 81.75% to 82.62% in the range of [ -8 °, -8 ° ].

In a high wind speed region (for example, [12m/s,25m/s ]) above the rated wind speed, the yaw error obtained by the yaw control method provided by the embodiment is remarkably reduced compared with that obtained by the traditional method, the yaw error is improved from 83.83% to 84.79% in the range of [ -8 °, -8 ° ], and the error distribution and the yaw error distribution are more concentrated.

Compared with the traditional yaw control method, the yaw frequency of the yaw control method provided by the invention is improved relative to the traditional control strategy, but the improved frequency is mainly concentrated in a medium and high wind speed area, so that the power loss coefficient is obviously reduced.

Therefore, the partition prediction control method provided by the invention can effectively reduce the yaw error of the medium and high wind speed area, thereby reducing the power loss coefficient (namely improving the utilization rate of wind energy).

It is to be understood that the disclosed embodiments of the invention are not limited to the particular structures or process steps disclosed herein, but extend to equivalents thereof as would be understood by those skilled in the relevant art. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment" or "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.

While the above examples are illustrative of the principles of the present invention in one or more applications, it will be apparent to those of ordinary skill in the art that various changes in form, usage and details of implementation can be made without departing from the principles and concepts of the invention. Accordingly, the invention is defined by the appended claims.

Claims

1. A yaw control method of a wind generating set is characterized by comprising the following steps:

determining control parameters according to the wind speed data at the next moment, and performing yaw control on the wind generating set by using the control parameters and the wind direction data;

wherein, in the first step, the step of predicting wind speed data and wind direction data at the next moment comprises:

respectively determining wind speed data and wind direction data at the next moment according to the wind vector abscissa data and the wind vector ordinate data at the next moment;

or,

2. The method of claim 1, wherein in the step one, the preset time period is 10s, 30s or 60 s.

3. The method of claim 1, wherein the wind vector is decomposed according to the expression:

4. The method of claim 1, wherein the wind speed data for the next time is determined according to the following expression:

5. The method of claim 1, wherein the wind direction data for the next time is determined according to the following expression:

6. The method of claim 1, wherein the historical wind direction data is circularly variable transformed according to the following expression:

7. The method of claim 1, wherein the wind direction data for the next time instant is determined according to the following expression:

wherein,wind direction data representing time t +1，Andrespectively representing the sine and cosine values of the wind direction data at time t + 1.

8. The method of claim 6, wherein in step one, the ARMA model is used to determine wind speed data at a next time based on historical wind speed data.

9. The method of claim 8, wherein the step of determining wind speed data for a next time comprises:

10. The method according to any one of claims 1 to 9, wherein in the second step, a wind speed interval to which the wind speed data of the next moment belongs is determined, and the control parameter is determined according to the wind speed interval to which the wind speed data belongs.

11. The method according to claim 10, wherein in the second step, if the wind speed data at the next moment is less than the preset cut-in wind speed, the wind turbine generator set is controlled to be in a shutdown state.

12. The method according to claim 10, wherein in step two, if the wind direction data at the next moment is greater than or equal to a preset cut-out wind speed, the wind turbine generator set is controlled to yaw to a downwind position and be in a shutdown state.

13. The method of claim 10, wherein, in step two,

14. The method according to claim 13, wherein in the second step, a plurality of wind speed intervals are included between the first preset wind speed threshold value and the preset rated wind speed, and for the wind speed intervals, the larger the wind speed is, the smaller the control parameter corresponding to the wind speed interval is.

15. The method according to claim 10, wherein in the second step, if the wind speed data at the next moment is greater than or equal to a preset rated wind speed and less than a preset cut-out wind speed, the wind turbine generator set is yaw-controlled according to the wind direction data at the next moment so that the yaw error of the wind turbine generator set is within a preset error range.