CN106150904B - A kind of wind driven generator unit yaw system control performance optimization method and system - Google Patents

Abstract

The present invention provides a method and system for optimizing the control performance of the yaw system of a wind power generating set. The wind direction of the engine room; the absolute wind direction of the future flow is divided into sectors according to the predetermined angle interval, and the wind speed of the future flow is divided into sections according to the predetermined wind speed interval; the first grouping of the incoming wind data is carried out according to the division of sectors and segments; according to the wind direction of the incoming flow relative to the engine room Calculate the yaw error of the corresponding group to obtain the yaw error optimization model; when optimizing the yaw system, input the measured wind speed and absolute wind direction of the incoming flow into the yaw error optimization model, match the corresponding yaw error, and use the corresponding The yaw error correction is applied to the yaw control system input. Different optimization strategies can be adopted for different types of wind turbines at different incoming wind speeds, which improves the optimization accuracy of yaw error.

Description

Method and system for optimizing control performance of yaw system of wind power generating set

technical field

The invention belongs to the field of wind power yaw control, in particular to a method and system for optimizing the control performance of a yaw system of a wind power generating set.

Background technique

With the large-scale development of wind power and the continuous accumulation of detection methods and methods, manufacturers and wind farm owners have put forward higher requirements for the performance of wind turbines. The yaw system is an important part of the control system of horizontal axis wind turbines. Control performance directly determines the safety and economy of wind turbines.

In the prior art, one method of optimizing the control performance of the yaw system is to activate the yaw system as long as there is a yaw deviation. This optimization method has defects such as frequent actions and reduced service life of the yaw system. In addition, the performance optimization method of the yaw system in the prior art usually adopts the same optimization strategy for different types of wind turbines and different incoming wind speeds, which is not targeted, the control effect is not ideal, and the power generation increase is low.

Contents of the invention

The present invention provides a method and system for optimizing the control performance of the yaw system of a wind power generating set, which is used to solve the problem of reducing the service life of related equipment by frequently starting the yaw system in the prior art. Adopting the same optimization strategy is not targeted, the control effect is not ideal, and the power generation increase is low.

In order to solve the above technical problems, a technical solution of the present invention is to provide a method for optimizing the control performance of the yaw system of a wind turbine, including:

In a predetermined period of time, the incoming wind data in front of the nacelle of the wind power generating set to be optimized is obtained at regular intervals, wherein the incoming wind data includes the incoming wind speed, the absolute wind direction of the incoming flow, and the wind direction of the incoming flow relative to the nacelle;

The absolute wind direction of the incoming flow is divided into sectors according to a predetermined angle interval, and the wind speed of the incoming flow is divided into sections according to a predetermined wind speed interval;

Carrying out the first grouping of the incoming wind data according to sub-sectors and segments;

Calculate the yaw error of the corresponding group according to the wind direction of the incoming flow relative to the nacelle described in each set of data, and obtain the yaw error optimization model;

When optimizing the yaw system of the wind power generating set to be optimized, input the measured incoming wind speed and incoming absolute wind direction into the yaw error optimization model, match the corresponding yaw error, and correct the corresponding yaw error to input to the yaw control system.

Another technical solution of the present invention is to provide a control performance optimization system for a yaw system of a wind power generating set, including:

The sampling module is used to obtain the incoming wind data in front of the nacelle of a wind power generating set to be optimized at regular time intervals within a predetermined period of time, wherein the incoming wind data includes the incoming wind speed, the absolute wind direction of the incoming flow, and the relative direction of the incoming flow relative to the nacelle. wind direction;

A dividing module, configured to divide the absolute wind direction of the incoming flow into sectors according to predetermined angle intervals, and divide the incoming wind speed into sections according to predetermined wind speed intervals;

The first grouping module is used to group the incoming wind data for the first time according to the sub-sectors and segments;

The yaw error optimization model calculation module is used to calculate the yaw error of the corresponding group according to the wind direction of the incoming flow relative to the cabin described in each set of data to obtain an optimized model of the yaw error;

The yaw error optimization module is used to input the measured incoming wind speed and incoming absolute wind direction into the yaw error optimization model to match the corresponding yaw error when optimizing the yaw system of the wind turbine to be optimized, The corresponding yaw error is corrected to the input of the yaw control system.

The present invention can adopt different optimization strategies for different types of wind power generators at different incoming wind speeds, can optimize the yaw error of the yaw system in a targeted manner, improves the optimization accuracy of the yaw error, and can achieve an increase in power generation the goal of.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

1 is a flow chart of a method for optimizing the control performance of a yaw system of a wind power generating set according to an embodiment of the present invention;

Fig. 2 is the flow chart of calculating the yaw error optimization model process of an embodiment of the present invention;

Fig. 3 is a flow chart of verifying whether the yaw error optimization model is valid according to an embodiment of the present invention;

Fig. 4 is a flow chart of optimizing the start angle of the yaw system of the wind power generating set according to another embodiment of the present invention;

Fig. 5 is a schematic diagram of division of wind speed segments according to an embodiment of the present invention;

Fig. 6 is a flow chart of verifying whether the starting angle optimization model is valid according to an embodiment of the present invention;

Fig. 7 is a structural diagram of a control performance optimization system for a yaw system of a wind power generating set according to an embodiment of the present invention;

Fig. 8 is a structural diagram of a control performance optimization system for a yaw system of a wind power generating set according to another embodiment of the present invention.

Detailed ways

In order to make the technical features and effects of the present invention more obvious, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings. The present invention can also be described or implemented in other different specific examples. The equivalent transformations done within all belong to the protection category of the present invention.

As shown in FIG. 1 , FIG. 1 is a flowchart of a method for optimizing the control performance of a yaw system of a wind power generating set according to an embodiment of the present invention. In this embodiment, different optimization strategies can be adopted for different types of wind turbines at different incoming wind speeds, and the yaw error of the yaw system can be optimized in a targeted manner, improving the optimization accuracy of the yaw error.

Specifically, the method for optimizing the control performance of the wind turbine yaw system includes:

Step 101, within a predetermined period of time, acquire the incoming wind data in front of the nacelle of a wind turbine to be optimized at regular intervals, wherein the incoming wind data includes incoming wind speed, incoming absolute wind direction, and incoming wind direction relative to the nacelle.

During specific implementation, the predetermined time period is, for example, 30 days. The predetermined time period may also be determined according to the optimization accuracy, and the present invention does not limit its specific value.

The fixed time interval is 10 minutes. The fixed time interval may be determined according to the stability of the data, and the present invention does not limit its specific value.

The data of each variable in the incoming wind data is the average value of the instantaneous value of the variable obtained by sampling in a fixed time interval (for example, the sampling frequency is 1s). Furthermore, in order to ensure that the data is useful, the inflow wind data is collected under the normal operation state of the wind turbine to be optimized, and the data measured under abnormal conditions such as unit failure and maintenance should be eliminated.

In detail, the wind speed of the incoming flow and the wind direction of the incoming flow relative to the nacelle are measured by an anemometer installed on the nacelle of the wind turbine to be optimized. position is calculated.

Step 102, dividing the absolute wind direction of the incoming flow into sectors according to predetermined angle intervals, and dividing the incoming wind speed into sections according to predetermined wind speed intervals.

When dividing sectors, the sector range should be reduced as much as possible so that each sector is affected equally by terrain and obstacles. For units with particularly complex terrain and obstacles, it is recommended that the grouping angle range should be less than 15°. The predetermined angle may be determined according to the terrain and obstacle conditions of the wind power generation unit to be optimized, and the present invention does not limit the value of the predetermined angle.

The predetermined wind speed range can be evenly distributed, such as 6m/s. The predetermined wind speed range can also be low wind speed (starting wind speed-6m/s), medium wind speed (7m/s-rated wind speed) and high wind speed section (above rated wind speed).

Step 103 , grouping the incoming wind data for the first time according to the sub-sectors and sub-wind speeds. Grouped data can be stored in the form of a list.

Step 104, calculate the yaw error of the corresponding group according to the wind direction of the incoming flow relative to the nacelle in each group of data, and obtain the yaw error optimization model.

The yaw error obtained in this step is a comprehensive error caused by the yaw system being affected by the zero installation azimuth of the wind vane (the wind vane in the wind turbine), the wind vane in the nacelle being affected by the wake of the impeller, or the effectiveness of the control strategy is insufficient.

Specifically, as shown in Figure 2, the detailed process of step 104 is:

Step 1041: Count the occurrence probability of the wind directions of the incoming flows relative to the nacelle in each group, and sort the wind directions of the incoming flows relative to the nacelle in each group in descending order of probability.

Step 1042: Calculate the average value of the wind direction of the first N incoming streams relative to the nacelle in each group to obtain the yaw error of the corresponding group.

Step 1043: Summarize the corresponding yaw errors of each group to obtain the yaw error optimization model.

Step 105, when optimizing the yaw system of the wind turbine to be optimized, input the measured incoming wind speed and incoming absolute wind direction into the yaw error optimization model, and match the measured incoming wind speed and incoming absolute wind direction The corresponding yaw error is corrected to the input of the yaw control system.

This implementation adopts the method of comprehensive yaw error, and directly compensates without considering the specific cause of yaw error, which improves the feasibility of yaw optimization and reduces its uncertainty.

In order to extend the yaw error optimization model of a wind power generation unit to be optimized to other wind power generation units of the same type to be optimized, in an embodiment of the present invention, after obtaining the yaw error optimization model, it also includes:

Determine the effective sector of the wind power generating set to be optimized; when the blade normal (i.e. the centerline of the nacelle) of another wind generating set is in the effective sector, apply the yaw error optimization model to the other wind power The generating set may perform yaw error optimization on the other wind generating set according to the yaw error optimization model; wherein, the another generating set is of the same type as the wind generating set to be optimized.

During implementation, the effective sector of the wind power generating set to be optimized can be realized through existing technologies, and will not be repeated here.

In order to verify the accuracy of yaw error optimization, in one embodiment of the present invention, as shown in Figure 3, the following method is used to verify whether the yaw error optimization model is valid:

Step 301 : The yaw system of the wind power generating set to be optimized also includes the guarantee rate of the power characteristic curve before optimization.

Step 302: After the yaw system is optimized, it also includes statistically optimized power characteristic curve guarantee rate.

The guarantee rate of the power characteristic curve is calculated by the following formula:

K: Guarantee rate of power characteristics of the wind turbine;

n: the number of statistical intervals, according to the wind speed range of 0.5m/s as a statistical interval, the center of the interval is an integer multiple of 0.5m/s;

P _i : The average active power value actually output by the fan in the i-th statistical interval when the wind turbine is in the control mode of maximum active power output, the unit is kW;

P _i ': Under standard atmospheric density conditions, the manufacturer guarantees the active power corresponding to the i-th statistical interval, in kW;

The frequency of the i-th statistical interval;

N _i : the number of data whose wind speed falls into the i-th statistical interval;

N: total number of wind speed data.

Step 303: Comparing the guaranteed rate of the optimized power characteristic curve with the guaranteed rate of the power characteristic curve before optimization, if the guaranteed rate of the optimized power characteristic curve is greater than the guaranteed rate of the power characteristic curve before optimization, the yaw error optimization model is valid .

If the power characteristic curve guarantee rate after optimization is lower than the power characteristic curve guarantee rate before optimization, it is necessary to re-optimize the yaw error and perform frequency analysis on the speed of the wind turbine to make the speed control stable before and after yaw error compensation and avoid the occurrence of large Large speed jitter and abnormal vibration of the unit.

In an embodiment of the present invention, by comparing the power characteristic curve before optimization and the power characteristic curve after optimization, when the overall power characteristic curve shifts to the right, the yaw error optimization model is valid. Specifically, the power characteristic curve of the unit to be optimized can be obtained by using existing methods, for example, while obtaining the data of the incoming wind in front of the nacelle of the wind turbine unit to be optimized, the ambient temperature, air pressure and the electric power output by the unit to be optimized are also obtained, combined with The power characteristic curve of the unit to be optimized is counted by the incoming wind data.

In an embodiment of the present invention, as shown in FIG. 4, the method for optimizing the control performance of the yaw system of the wind power generating set further includes:

Step 401: Group the inflow data for the second time according to the low wind speed segment, the medium wind speed segment and the high wind speed segment; wherein, the range of low wind speed is V ₀ <V ≤ V ₁ , and the range of medium wind speed is V ₁ <V ≤ V ₂ , the high wind speed range is V>V ₂ , V ₂ is the rated wind speed, V ₀ is the start-up wind speed, V ₁ is the low wind speed threshold, such as 6m/s, and V is the incoming wind speed.

Specifically, the segmentation basis for the high, medium, and low wind speed segments can be segmented according to different operating characteristics of the units, different control strategies for yaw or pitch angle, and statistical analysis of the unit speed. As shown in Figure 5, an example of segmenting according to the different characteristics of the impeller speed and pitch angle in different operating stages of the unit, the first segment is the stage of "the blade speed increases and the pitch angle remains unchanged", and the second segment is The stage of "the critical change of impeller speed and pitch angle", and the third stage is the stage of "the impeller speed is constant and the pitch angle increases". According to the segments shown in Figure 5, determine the wind speed at the critical point of each segment, so as to obtain V ₁ and V ₂ .

Step 402: Set different yaw start angle optimization criteria for each group of the second grouping to obtain a start angle optimization model.

For example, the starting angle optimization criterion is:

The optimization criterion of the low wind speed starting angle is D _l ± C _l , where D _l is the low wind speed yaw starting angle, and C _l is the adjustment value;

The optimization criterion of the start angle at medium wind speed is D _m -C _m , where D _m is the yaw start angle at medium wind speed, and C _m is the adjustment value;

The optimization criterion for the high wind speed starting angle is D _h ±C _h , where D _h is the high wind speed yaw starting angle, and C _h is the adjustment value.

During implementation, the optimization criterion can also be adjusted according to the optimization accuracy, and the present invention does not specifically limit the optimization criterion.

Step 403: When optimizing the yaw system, input the measured incoming wind speed into the starting angle optimization model to obtain the yaw starting angle corresponding to the measured incoming wind speed. When the wind direction reaches the corresponding yaw start angle, the yaw system action is started.

In order to verify the accuracy of the starting angle optimization, in one embodiment of the present invention, as shown in Figure 6, the following method is used to verify whether the starting angle optimization model is valid:

Step 601: Before the optimization of the yaw system, it also includes statistical analysis of the power characteristic curve guarantee rate before optimization, using the envelope method or probability distribution method to statistically analyze the obtained wind direction and incoming wind speed of the incoming flow relative to the nacelle, and obtain Controls the deadband length.

Specifically, the process of calculating the control dead zone by the envelope method is as follows: set the wind speed of the incoming flow as the abscissa, and the wind direction of the incoming flow relative to the nacelle as the vertical coordinate. The statistical incoming flow is enveloped relative to the data edge of the cabin, and the upper envelope is subtracted from the lower envelope to obtain the length of the control dead zone.

The process of calculating the control dead zone by the probability distribution method is as follows: divide the intervals according to the order of wind speed from small to large, assign the wind direction of the incoming flow relative to the engine room to be calculated in each wind speed interval, and calculate each wind speed interval for each wind speed interval. The probability of occurrence of the wind direction relative to the engine room, and then recursively form the wind direction interval around the wind direction with the highest probability of occurrence relative to the engine room, and calculate the probability of the wind direction interval. When the probability of the wind direction interval is greater than the fixed probability, such as 95% , the wind direction interval difference is the length of the control dead zone.

Step 602: The yaw system optimization period also includes counting the yaw control times of the wind turbine to be optimized and a benchmark wind turbine, the benchmark wind being any wind turbine around the wind turbine to be optimized.

Step 603: After the yaw system is optimized, it also includes statistics of the optimized power characteristic curve guarantee rate, and statistics of the length of the control dead zone after the second wind speed section optimization.

For the calculation method of the control dead zone, refer to step 601, which will not be repeated here.

Step 604: Compare the length of the optimized control dead zone with the length of the control dead zone before optimization, compare the guaranteed rate of the power characteristic curve after optimization with the guaranteed rate of the power characteristic curve before optimization, and compare the guaranteed rate of the wind power generating set to be optimized The yaw control times are compared with the yaw control times of the benchmark fan.

Step 605: If the difference between the optimized control dead zone length in the low wind speed section and the control dead zone length before optimization is less than the first predetermined threshold, the optimized control dead zone length in the medium wind speed section is smaller than the control dead zone length before optimization, and the high The difference between the optimized control dead zone length of the wind speed segment and the control dead zone length before optimization is less than the second predetermined threshold; the optimized power characteristic curve guarantee rate is greater than or equal to the power characteristic curve guarantee rate before optimization; the wind force to be optimized If the yaw control times of the generator set are less than or equal to the yaw control times of the benchmark unit, the start angle optimization model is valid.

During specific implementation, the first predetermined threshold and the second predetermined threshold may be specifically determined according to the unit. When optimizing the start-up angle in the high wind speed section, it is necessary to pay attention to whether the unit generates abnormal vibration, and the second predetermined threshold must be able to ensure that the unit does not experience abnormal vibration.

The invention can individually optimize the yaw error and starting angle of the yaw control of the running wind power generating set, improve the reliability of the yaw system, improve the power characteristic curve of the set, and increase the generating capacity of the set.

As shown in FIG. 7 , FIG. 7 is a structural diagram of a control performance optimization system for a yaw system of a wind power generating set according to an embodiment of the present invention. Specifically, the system 700 includes:

The sampling module 701 is used to acquire the incoming wind data in front of the nacelle of a wind power generating set to be optimized at regular time intervals within a predetermined period of time, wherein the incoming wind data includes the incoming wind speed, the absolute wind direction of the incoming flow, and the incoming flow relative to the nacelle wind direction.

The dividing module 702 is configured to divide the absolute wind direction of the incoming flow into sectors according to predetermined angle intervals, and divide the incoming wind speed into sections according to predetermined wind speed intervals.

The first grouping module 703 is configured to perform a first grouping of the incoming wind data according to sectors and segments.

The yaw error optimization model calculation module 704 is configured to calculate the corresponding set of yaw errors according to the wind direction of the incoming flow relative to the nacelle in each set of data to obtain a yaw error optimization model.

The yaw error optimization module 705 is used to input the measured incoming wind speed and incoming absolute wind direction into the yaw error optimization model to match the corresponding yaw error when optimizing the yaw system of the wind turbine to be optimized , correcting the corresponding yaw error to the input of the yaw control system.

In another embodiment of the present invention, as shown in FIG. 8, the system further includes:

The second grouping module 706 is used to group the incoming flow data for the second time according to the low wind speed segment, the medium wind speed segment and the high wind speed segment; wherein, the range of low wind speed is V ₀ <V ≤ V ₁ , and the range of medium wind speed is V ₁ <V≤V ₂ , the high wind speed range is V>V ₂ , V ₂ is the rated wind speed, V ₀ is the start-up wind speed, V ₁ is the threshold of the low wind speed section, such as 6m/s, and V is the incoming wind speed. The starting angle optimization model calculation module 707 is used to set different yaw starting angle optimization criteria for each group of the second grouping to obtain the starting angle optimization model;

The starting angle optimization module 708 is used for the optimization of the yaw system, inputting the measured incoming wind speed into the starting angle optimization model to obtain the yaw starting angle corresponding to the measured incoming wind speed, to be measured When the wind direction of the incoming flow relative to the nacelle reaches the corresponding yaw start angle, the yaw system action is started.

The present invention can adopt different optimization strategies for different types of wind power generators at different incoming wind speeds, can perform individual optimization on the yaw error and yaw start angle of the yaw control of the running wind power generators, and improve the yaw error And the optimization accuracy of the starting angle, improve the power characteristic curve of the unit while improving the reliability of the yaw system, and increase the power generation of the unit.

The above description is only used to illustrate the technical solution of the present application, and anyone skilled in the art can modify and change the above embodiments without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be determined by the scope of the claims.

Claims (9)

1. a kind of wind driven generator unit yaw system control performance optimization method, which is characterized in that including,

Within a predetermined period of time, the incoming wind number in front of a wind generating set engine room to be optimized is obtained every Fixed Time Interval According to, wherein incoming wind data includes the wind direction of arrives stream wind speed, incoming true wind direction and incoming with respect to cabin；

By the incoming true wind direction, section carries out a point sector by a predetermined angle, by the arrives stream wind speed by predetermined wind speed interval into Row segmentation；

First time grouping is carried out to the incoming wind data according to sector and segmentation is divided；

The yaw error for calculating corresponding group with respect to the wind direction of cabin according to incoming described in every group of data, obtains yaw error optimization Model；

When the wind driven generator unit yaw system optimization to be optimized, the arrives stream wind speed of actual measurement and incoming true wind direction are inputted into institute It states in yaw error Optimized model, matches corresponding yaw error, which is adapted to the wind to be optimized In the input of power generator group yaw system；

Wherein, the yaw error for calculating corresponding group with respect to the wind direction of cabin according to incoming described in every group of data obtains yaw and misses Poor Optimized model includes：

Each incoming in every group is counted with respect to the probability that the wind direction of cabin occurs, by the sequence of probability from big to small to coming in every group The wind direction of the opposite cabin of stream is ranked up；

It calculates every group of top n incoming and is worth to the yaw error of corresponding group with respect to being averaged for the wind direction of cabin；

Every group of corresponding yaw error is gathered to obtain the yaw error Optimized model.

2. wind driven generator unit yaw system control performance optimization method as described in claim 1, which is characterized in that described next Flow the average value that each variable data in wind data is the variable instantaneous value that sampling obtains in Fixed Time Interval.

3. wind driven generator unit yaw system control performance optimization method as described in claim 1, which is characterized in that obtain institute Further include after stating yaw error Optimized model：

Determine effective sector of the wind power generating set to be optimized；

When the blade normal of another wind power generating set is in effective sector, the yaw error Optimized model is suitable for described Another wind power generating set；

Wherein, another generating set is identical as the wind power generating set model to be optimized.

4. wind driven generator unit yaw system control performance optimization method as described in claim 1, which is characterized in that described inclined It further include the power characteristic fraction before statistic op- timization before boat system optimization；

It further include the power characteristic fraction after statistic op- timization after the yaw system optimization；

Compare the power characteristic fraction after optimization and the power characteristic fraction before optimization, if the work(after optimization Rate characteristic curve fraction is more than the power characteristic fraction before optimization, then the yaw error Optimized model is effective.

5. wind driven generator unit yaw system control performance optimization method as described in claim 1, which is characterized in that the side Method further includes：

By low wind speed section, middle wind speed section and high wind speed section second of grouping is carried out to carrying out flow data；

Wherein, low wind speed range is V₀< V≤V₁, middle wind speed ranging from V₁< V≤V₂, high wind speed ranging from V > V₂, V₂It is specified Wind speed, V₀For threshold wind velocity, V₁For low wind speed section threshold value, V is arrives stream wind speed；

The yaws different to every group of setting of second of grouping start angle and optimizing criterion, obtain starting angle and optimizing model；

When the yaw system optimization, the arrives stream wind speed of the actual measurement is inputted in the startup angle and optimizing model, is obtained described The corresponding yaw of arrives stream wind speed of actual measurement starts angle, and incoming to be surveyed reaches the corresponding yaw with respect to the wind direction of cabin and opens When dynamic angle, start the yaw system action.

6. wind driven generator unit yaw system control performance optimization method as claimed in claim 5, which is characterized in that described to open Moving angle and optimizing criterion is：

The Optimality Criteria that low wind speed starts angle is D_l±C_l, wherein D_lIt is yawed for low wind speed and starts angle, C_lFor adjustment amount；

The Optimality Criteria that middle wind speed starts angle is D_m-C_m, wherein D_mIt is yawed for middle wind speed and starts angle, C_mFor adjustment amount；

The Optimality Criteria that high wind speed starts angle is D_h±C_h, wherein D_hIt is yawed for high wind speed and starts angle, C_hFor adjustment amount.

7. wind driven generator unit yaw system control performance optimization method as claimed in claim 6, which is characterized in that described inclined Further include the power characteristic fraction before statistic op- timization before boat system optimization, using envelope method or probability distribution method to obtaining Incoming it is for statistical analysis with respect to the wind direction of cabin and arrives stream wind speed, controlling dead error length before obtaining optimizing；

It further include the yaw control of the statistics wind power generating set to be optimized and a mark post wind turbine during the yaw system optimization Number processed, the mark post wind turbine are the wind power generating set around the wind power generating set to be optimized；

Further include the power characteristic fraction after statistic op- timization, second of grouping gained of statistics after the yaw system optimization Controlling dead error length after the optimization of wind speed section；

By the controlling dead error length after optimization respectively compared with controlling dead error length before optimization, by the power characteristic after optimization Fraction controls number compared with the power characteristic fraction before optimization, by the yaw of the wind power generating set to be optimized Compared with the yaw of mark post wind turbine control number；

If the controlling dead error length after low wind speed section optimization is less than the first predetermined threshold with the controlling dead error length difference before optimization Value, the controlling dead error length after middle wind speed section optimization are less than the controlling dead error length before optimization, the control after the optimization of high wind speed section Dead zone length is less than the second predetermined threshold with the controlling dead error length difference before optimization；Power characteristic fraction after optimization More than or equal to the power characteristic fraction before optimization；The yaw control number of the wind power generating set to be optimized is less than Or the yaw equal to the mark post unit controls number, then it is effective to start angle and optimizing model.

8. the wind driven generator unit yaw system control performance optimization method as described in claim 4 or 7, which is characterized in that institute Power characteristic fraction is stated to calculate by following formula：

K：The power characteristic fraction of wind power generating set；

n：Section number is counted, according to wind speed range be 0.5m/s is a statistics section, the center in section is the whole of 0.5m/s Several times；

P_i：It is under maximum output control model that wind power generating set, which is in active power, and i-th of statistics section inner blower is actually defeated The average active power value gone out, unit kW；

P_i'：Under normal atmosphere density conditions, the active power in i-th of statistics section of correspondence that producer ensures, unit kW；

The frequency in i-th of statistics section；

N_i：Wind speed falls into the data amount check in i-th of statistics section；

N：The total quantity of air speed data.

9. a kind of wind driven generator unit yaw system control performance optimization system, which is characterized in that the system comprises：

Sampling module, within a predetermined period of time, a wind generating set engine room to be optimized being obtained every Fixed Time Interval The incoming wind data in front, wherein incoming wind data includes the wind of arrives stream wind speed, incoming true wind direction and incoming with respect to cabin To；

Division module presses the arrives stream wind speed for by the incoming true wind direction, section to carry out a point sector by a predetermined angle Predetermined wind speed interval is segmented；

First grouping module divides sector and segmentation to carry out first time grouping to the incoming wind data for basis；

Yaw error seismic responses calculated module is corresponded to for being calculated with respect to the wind direction of cabin according to incoming described in every group of data The yaw error of group obtains yaw error Optimized model；

Yaw error optimization module is used in the wind driven generator unit yaw system optimization to be optimized, by the incoming of actual measurement Wind speed and incoming true wind direction input in the yaw error Optimized model, match corresponding yaw error, this is corresponding partially In error correction to the input of the wind driven generator unit yaw system to be optimized of navigating；

Wherein, the yaw error for calculating corresponding group with respect to the wind direction of cabin according to incoming described in every group of data obtains yaw and misses Poor Optimized model includes：

Each incoming in every group is counted with respect to the probability that the wind direction of cabin occurs, by the sequence of probability from big to small to coming in every group The wind direction of the opposite cabin of stream is ranked up；

It calculates every group of top n incoming and is worth to the yaw error of corresponding group with respect to being averaged for the wind direction of cabin；

Every group of corresponding yaw error is gathered to obtain the yaw error Optimized model.