WO2019184171A1 - 风力发电机组的偏航控制方法、设备及系统 - Google Patents

风力发电机组的偏航控制方法、设备及系统 Download PDF

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Publication number
WO2019184171A1
WO2019184171A1 PCT/CN2018/097914 CN2018097914W WO2019184171A1 WO 2019184171 A1 WO2019184171 A1 WO 2019184171A1 CN 2018097914 W CN2018097914 W CN 2018097914W WO 2019184171 A1 WO2019184171 A1 WO 2019184171A1
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Prior art keywords
wind
yaw
value
deviation angle
yaw control
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PCT/CN2018/097914
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English (en)
French (fr)
Inventor
欧发顺
王方超
李强
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北京金风科创风电设备有限公司
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Application filed by 北京金风科创风电设备有限公司 filed Critical 北京金风科创风电设备有限公司
Priority to AU2018416808A priority Critical patent/AU2018416808B2/en
Priority to EP18912908.3A priority patent/EP3779184B1/en
Priority to US16/650,245 priority patent/US11174842B2/en
Publication of WO2019184171A1 publication Critical patent/WO2019184171A1/zh

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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/0204—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor for orientation in relation to wind direction
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/045—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with model-based controls
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/82—Forecasts
    • F05B2260/821—Parameter estimation or prediction
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/10—Purpose of the control system
    • F05B2270/20—Purpose of the control system to optimise the performance of a machine
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/32—Wind speeds
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/329—Azimuth or yaw angle
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/335—Output power or torque
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • the present application relates generally to the field of wind power generation technology, and more particularly to a yaw control method, apparatus and system for a wind power generator set.
  • large wind turbines are generally upwind wind turbines equipped with an automatic yaw system.
  • the wind direction detection sensors for example, wind vanes
  • the wind turbine's wind deflection angle is obtained.
  • the yaw motor rotates clockwise or counterclockwise to start the command, and the yaw motor rotates according to the start command to output the low speed and high torque yaw through the yaw reducer.
  • the torque is used to drive the yaw bearing to achieve the yaw to wind action of the wind turbine.
  • the wind deflection angle of the wind turbine is obtained in real time, and when the wind deviation angle is less than a certain threshold, the yaw to wind action is stopped.
  • the present application provides a yaw control method for a wind power generator, the yaw control method comprising: acquiring a current or future ambient wind speed value and a wind deviation angle value of the wind power generator; and estimating the wind power generation unit Determining, according to the wind deviation angle value, a power variation caused by a yaw to wind action according to the ambient wind speed value; determining a yaw control strategy of the wind power generation group according to the estimated power variation amount; An instruction is sent to the wind turbine to cause the wind turbine to perform the determined yaw control strategy.
  • the present application provides a yaw control device for a wind power generator, the yaw control device comprising: a data acquisition module, configured to acquire a current wind speed value and a wind deviation angle value of a current or future wind turbine; An estimation module, configured to estimate, according to the wind wind speed value, a power variation caused by a yaw wind action based on the wind wind angle value; a policy determining module, configured to And an estimated power variation amount, determining a yaw control strategy of the wind power generator; and a sending module, configured to send an instruction to the wind power generator to enable the wind power generator to execute the determined yaw control strategy.
  • the present application provides a computer readable storage medium storing a computer program that, when executed by a processor, implements a yaw control method for a wind turbine as described above.
  • the present application provides a yaw control device for a wind power generator, the yaw control device comprising: a processor; a memory storing a computer program, when the computer program is executed by the processor, implementing the above The yaw control method of the wind turbine.
  • the present application provides a yaw control system for a wind power generator, comprising a plurality of wind power generator sets and a field group control device, wherein the field group control device performs the method as described above to make the plurality of units At least one of the wind turbines performs a corresponding yaw control strategy.
  • FIG. 1 illustrates a flowchart of a yaw control method of a wind power generator set according to an exemplary embodiment of the present application
  • FIG. 2 illustrates a block diagram of a yaw control apparatus of a wind power generator set according to an exemplary embodiment of the present application
  • FIG. 3 illustrates a block diagram of a yaw control system of a wind power plant in accordance with an exemplary embodiment of the present application.
  • FIG. 1 illustrates a flow chart of a yaw control method of a wind power generator set according to an exemplary embodiment of the present application.
  • step S10 current or future ambient wind speed values and wind deviation angle values of the wind turbine are acquired.
  • the ambient wind speed value and the wind deviation angle value of the currently detected wind turbine may be acquired.
  • the ambient wind speed value and the wind deviation angle value of the wind turbine detected at the current time may be obtained, or the mean value of the ambient wind speed value of the wind turbine detected in the current time period (for example, 1 second) may be obtained.
  • the mean of the wind deviation angle values may be obtained.
  • the predicted ambient wind speed value and the wind bias angle value of the predicted wind turbine may be obtained.
  • the predicted wind speed value and the wind deviation angle value after a period of time (for example, 7 seconds) of the wind turbine may be obtained, or the predicted wind speed value of the wind turbine in the future period may be obtained.
  • the first operational data of the wind turbine may be input to a wind speed prediction model corresponding to the wind turbine to predict a future ambient wind speed value by the wind speed prediction model; and the wind power generation
  • the second operational data of the generating unit is input to a wind deviation angle prediction model corresponding to the wind power generator to predict a future wind deviation angle value by the wind deviation angle prediction model, wherein the first operation
  • the data includes at least an ambient wind speed value
  • the second operational data includes at least a wind bias angle value.
  • the first operational data of the wind turbine at each sampling time point within the first predetermined time period at which the current time is the end point may be input to the wind speed prediction model corresponding to the wind power generator to pass the a wind speed prediction model to obtain an ambient wind speed value of the wind power generator after a second predetermined time period; and to perform a second operation of each wind power generator set at each sampling time point within a first predetermined time period with the current time as a termination point
  • the data is input to a wind deviation angle prediction model corresponding to the wind power generator to obtain a wind deviation angle value of the wind power generator after the second predetermined time period by the wind deviation angle prediction model.
  • the first operational data of the wind turbine may be collected at a certain sampling period (eg, 20 milliseconds, 1 second, or 7 seconds) (ie, the first operation based on the time series) Data)
  • a certain sampling period eg, 20 milliseconds, 1 second, or 7 seconds
  • the wind speed prediction model corresponding to the wind turbine is trained, and the wind speed prediction model trained can predict the future wind speed value of the wind turbine.
  • the second operational data of the wind turbine collected at a certain sampling period (for example, 20 milliseconds, 1 second or 7 seconds) (ie, time series based)
  • the second operational data is used as a training set to train the wind deviation angle prediction model corresponding to the wind turbine, and the trained wind deviation angle prediction model can predict the future wind deviation angle value of the wind turbine.
  • the length of the second predetermined time period may be related to the sampling period of the training samples, and the shorter the sampling period of the training samples, the shorter the length of the second predetermined time period.
  • the length of the second predetermined time period may be the same as the sampling period of the training sample, for example, when the training sample corresponding to the 7 sec sampling period is used to train the wind speed prediction model corresponding to the wind turbine, the wind speed prediction model can be predicted Ambient wind speed value after 7 seconds of wind turbine.
  • the first operational data and the second operational data may be wind parameters of the wind turbine operating and/or operational parameters of the wind turbine itself.
  • the first operational data may include, in addition to the ambient wind speed value, a wind deviation angle value, a generator speed, a generator torque, an output power, an acceleration of the nacelle along the X-axis direction and/or the Y-axis direction. At least one of a pitch angle of the blade and a position of the nacelle.
  • the second operational data may include, in addition to the wind deviation angle value, an ambient wind speed value, a rotational speed of the generator, a torque of the generator, an output power, an acceleration of the nacelle along the X-axis direction and/or the Y-axis direction. At least one of a pitch angle of the blade and a position of the nacelle. It should be understood that the first operational data and the second operational data may be identical.
  • the wind speed prediction model/wind deviation angle prediction model corresponding to the wind turbine can be used for future long-term prediction of the ambient wind speed value/wind deviation angle value in the future.
  • the wind speed prediction model/wind deviation angle prediction model corresponding to the wind turbine can be used to predict the ambient wind speed value/wind deviation angle value at a time interval of 7 seconds in the next 5 minutes, for example, predictable Ambient wind speed value after 7 seconds / wind deviation angle value, ambient wind speed value after 14 seconds / wind deviation angle value, ambient wind speed value after 21 seconds / wind deviation angle value, ..., environment after 5 minutes Wind speed value / wind deviation angle value.
  • the wind speed prediction model corresponding to the wind turbine and the wind deviation angle prediction model can be trained in an online manner.
  • the wind speed prediction model can be directly trained using the first operational data of the currently collected wind turbine, and the second operational data of the currently collected wind turbine can be directly used to train the wind deviation angle prediction model in real time. .
  • the wind speed prediction model and the wind deviation angle prediction model corresponding to the wind turbine may be trained in an off-line manner.
  • the first operational data of the wind turbine in a historical period can be obtained to train the wind speed prediction model at one time
  • the second operational data of the wind turbine during the historical period can be obtained to train the wind deviation angle at one time. Forecast model.
  • step S10 when it is determined that the prediction accuracy of the wind speed prediction model and the prediction accuracy of the wind deviation angle prediction model satisfy a preset condition, the predicted future wind speed of the wind turbine set may be obtained. a value and a wind deviation angle value; when it is determined that the prediction accuracy of the wind speed prediction model and the prediction accuracy of the wind deviation angle prediction model do not satisfy a preset condition, acquiring the currently detected wind turbine Ambient wind speed value and wind deviation angle value.
  • the prediction accuracy of the wind speed prediction model may be based on a deviation between an ambient wind speed value of some moments (ie, test samples) obtained by the wind speed prediction model and an actual detected ambient wind speed value at the moments. determine.
  • the prediction accuracy of the wind deviation angle prediction model may be based on a deviation between a wind deviation angle value at some moments obtained by the pair of wind deviation angle prediction models and an actual detected wind deviation angle value at the moments. determine.
  • each sampling time point within a period of time with the current time as the termination point may be taken as a test sample.
  • the prediction accuracy of the wind speed prediction model and the prediction accuracy of the wind deviation angle prediction model can be measured in various appropriate ways.
  • Mean Absolute Error MAE
  • Mean Absolute Percentage MAE
  • Standard Deviation of Average Absolute Error SDMAE
  • Standard Deviation of Average Absolute Error SDMAPE
  • the formula (1) can be used to calculate the MAE of the wind speed prediction model.
  • x i is the wind speed detection value of the i-th test sample.
  • the wind speed predicted value obtained by the wind speed prediction model for the i-th test sample, 1 ⁇ i ⁇ n, n is the number of test samples included in the test set.
  • the formula (2) can be used to calculate the MAPE for the wind deviation angle prediction model.
  • y j is the wind deviation angle detection value of the jth test sample
  • the wind deviation angle prediction value obtained by the wind deviation angle prediction model 1 ⁇ j ⁇ m
  • m is the number of test samples included in the test set.
  • the prediction accuracy of the wind speed prediction model when the prediction accuracy of the wind speed prediction model is higher than the first preset threshold, and the prediction accuracy of the wind deviation angle prediction model is higher than the second preset threshold, the prediction accuracy of the wind speed prediction model may be determined. And the prediction accuracy of the wind deviation angle prediction model satisfies a preset condition.
  • formula (3) can be used to calculate the prediction accuracy of the wind speed prediction model and the weighted result P all of the prediction accuracy of the wind deviation angle prediction model
  • p(f(x 1 )) indicates the prediction accuracy of the wind speed prediction model f(x 1 ) (for example, may be the MAE of the wind speed prediction model), and p(f(x 2 )) indicates the pair
  • the prediction accuracy of the wind deviation angle prediction model f(x 2 ) (for example, may be MAE for the wind deviation angle prediction model)
  • w 1 indicates the weight corresponding to the wind speed prediction model f(x 1 )
  • w 2 indicates the pair
  • the wind speed prediction model and the wind may continue to be optimized and/or trained.
  • the deviation angle prediction model can satisfy the preset condition until the prediction accuracy of the wind speed prediction model and the prediction accuracy of the wind deviation angle prediction model.
  • step S20 the wind power generator is estimated to perform a power variation caused by the yaw wind action based on the wind deviation angle value under the ambient wind speed value.
  • the wind turbine may be estimated that, under the ambient wind speed value, the wind turbine generates an output power boost amount caused by a yaw wind action based on the wind deviation angle value and/or performs the yaw pair The amount of power loss caused by wind action.
  • the wind power generator can be used to estimate the output power boosting amount ⁇ P(v) of the wind turbine after the wind is set against the wind according to the wind deviation angle value ⁇ under the ambient wind speed value v by various appropriate methods. .
  • ⁇ P(v) can be calculated by equation (4):
  • P e (v) indicates the design output power of the wind turbine when the ambient wind speed value is v under standard conditions.
  • a full-shot phase ie, a constant power mode of operation
  • P(v) ⁇ P e (v) + P const1 it may be determined that the wind turbine is in a full-shot phase at an ambient wind speed value v, wherein P(v) indicates that the wind turbine is at an ambient wind speed value v
  • the output power below, P const1 indicates the preset margin.
  • the value of P(v) may be the currently detected output power value; when the future ambient wind speed is acquired in step S10
  • the value of P(v) can be obtained based on the obtained ambient wind speed value v and the operating power curve of the wind turbine.
  • the power loss amount P loss caused by the wind turbine generating wind against the wind based on the wind deflection angle value ⁇ under the ambient wind speed value v can be estimated by various appropriate methods.
  • P loss can be easily estimated by equation (5):
  • n indicates the number of yaw motors in the wind turbine
  • P e ' indicates the rated power of each yaw motor
  • P const2 indicates the power loss margin used to calculate P loss
  • ⁇ is the loss factor The value ranges from 0 to 1, preferably from 0 to 0.3.
  • ⁇ can be set according to the time when the wind turbine is in a yaw state every hour.
  • a yaw control strategy of the wind turbine is determined based on the estimated amount of power change.
  • the yaw control parameter of the yaw control strategy of the wind turbine may be determined based on the estimated amount of power change.
  • an average wind bias angle calculation time coefficient and/or a yaw time delay constant for yaw control of the wind turbine may be determined based on the estimated output power boost amount and/or power loss amount.
  • the average wind-offset angle calculation time coefficient and/or the yaw time delay constant of the yaw control strategy of the wind turbine may be determined based on the estimated output power boost amount and/or power loss amount.
  • the average wind deviation angle calculation time coefficient indicates the length of the time period to which the wind deviation angle value used for calculating the average wind deviation angle value belongs.
  • an average wind deviation angle value may be obtained by averaging the wind deviation angle values within a certain period of time (for example, 30 seconds or 60 seconds) to determine whether it is necessary to perform based on the average pair wind deviation angle value.
  • Yaw for example, determines that yaw is required when the average wind deviation angle value is greater than a preset yaw threshold.
  • the yaw time delay constant indicates a time difference between a time at which the yaw wind action is performed and a time at which the yaw wind action is actually started.
  • the yaw time delay constant may be a parameter of a delay on switch in a programmable logic controller (PLC) for yaw control when the yaw condition is met (ie, the yaw pair wind action is determined) It is necessary to delay the switch by the delay switch (ie, the value of the yaw time delay constant) before starting the yaw wind action.
  • PLC programmable logic controller
  • the yaw control strategy of the wind turbine may be determined as one of mode one, mode two, and mode three according to the estimated output power boost amount and/or power loss amount, wherein mode one is: average Calculating the time coefficient for the wind deviation angle is the first preset value, and/or the yaw time delay constant is the second preset value; the mode 2 is: the average wind deviation angle calculation time coefficient is the third preset value, and / Or the yaw time delay constant is the fourth preset value; the mode three is: the average wind deviation angle calculation time coefficient is the fifth preset value, and/or the yaw time delay constant is the sixth preset value, wherein The first preset value > the fifth preset value > the third preset value; and/or the second preset value > the sixth preset value > the fourth preset value.
  • the output power of the wind turbine is small, and the wind direction and wind speed change greatly at this time.
  • the wind turbine will Frequent yaw, and the amount of power generated by the wind turbine caused by the yaw to wind action may be lower than the amount of power consumed by the yaw action. Therefore, under low wind speed, it can be increased.
  • the large average calculates the time coefficient and/or the yaw time delay constant for the wind deviation angle to reduce the number of yaws to avoid additional loss of power.
  • the yaw control strategy of the wind turbine may be determined to be mode one when ⁇ P(v) ⁇ P loss .
  • the yaw wind has a greater impact on the wind turbine's power output: for the same wind bias angle Value, the amount of power generation gain obtained after yaw to wind at this stage is far greater than the amount of power generation obtained after yaw to wind in the low wind speed section; and, at this stage, wind power is generated by yaw to wind action
  • the amount of power generated by the unit against the wind is higher than the amount of electricity consumed by the yaw.
  • the time coefficient and/or the yaw time delay constant can be calculated by reducing the average wind deviation angle to increase the number of yaws, so that the wind turbine is more sensitive to the wind deviation angle, and the wind power generation is excavated as much as possible.
  • the power generation potential of the unit may be determined to be mode two when P loss ⁇ ⁇ P(v) ⁇ P e (v).
  • the wind direction may have changed during the yaw process, which may cause the yaw motor to overcome the extra thrust applied to the impeller plane caused by the sudden change of the wind direction, which may cause the yaw motor to overload and report the fault.
  • the wind power generation The yaw load of the unit will increase. Therefore, under high wind speeds, the number of yaws can be reduced by increasing the average wind rate deviation angle and/or the yaw time delay constant.
  • the yaw control strategy of the wind turbine may be determined to be mode three when P(v) ⁇ P e (v) + P const1 .
  • an instruction is sent to the wind turbine to cause the wind turbine to perform the determined yaw control strategy.
  • the instructions may include a yaw control parameter corresponding to the determined yaw control strategy, for example, the command may include an average wind deviation angle calculation time coefficient and/or a yaw time delay corresponding to the determined yaw control strategy constant.
  • the variation law of the wind direction at different wind speed sections is fully considered, and the power generation gain and loss caused by the yaw wind force of the wind power generator set under different wind speed sections (that is, different working condition conditions) is fully considered.
  • the yaw control strategy it can reduce the loss of the wind turbine itself, reduce the load of the whole machine under certain extreme working conditions, and explore the power generation potential of the wind turbine.
  • the current yaw system is a follow-up system with large time lag and large inertia. Generally speaking, it takes at least several seconds to several minutes to complete a yaw action on the wind. Since the wind direction is changing at any time, the existing yaw system cannot Track the wind direction in real time and keep the wind turbines facing the wind at any time. Under certain special working conditions, for example, the wind direction under large wind speed conditions is abrupt. If the wind turbine fails to yaw the wind in time, the wind will increase. The load of the generator set.
  • the wind turbine when the predicted future ambient wind speed value and the wind deviation angle value are acquired in step S10, the wind turbine may be caused to perform the determined yaw control strategy based on the prediction in step S40.
  • the future wind deviation angle value is used to perform early yaw control on the wind turbine to make the wind turbine more accurate to the wind, thereby improving the power generation of the wind turbine and reducing the wind direction of the wind turbine. Load.
  • the yaw wind action based on the wind deflection angle value may be calculated based on the yaw rate of the wind turbine.
  • the time required, and based on the time required and the future time determine at which point in time the yaw needs to be initiated, and then yaw is initiated at a determined point in time so that the wind turbine is facing the wind at this future time.
  • the transition of the yaw control from the passive control to the active control is realized, so that the wind turbine is more precise in wind, thereby improving the power generation of the wind turbine and reducing the wind turbine generation.
  • FIG. 2 illustrates a block diagram of a yaw control apparatus of a wind power generator set according to an exemplary embodiment of the present application.
  • the yaw control device of the wind power generator includes: a data acquisition module 10, an estimation module 20, a policy determination module 30, and a transmission module 40.
  • the data acquisition module 10 is configured to acquire current or future ambient wind speed values and wind deviation angle values of the wind power generator set.
  • the data acquisition module 10 may acquire the currently detected ambient wind speed value and wind bias angle value of the wind turbine.
  • the data acquisition module 10 may obtain the predicted future wind speed value and wind deviation angle value of the wind turbine.
  • the data acquisition module 10 may input the first operational data of the wind turbine to an wind speed prediction model corresponding to the wind turbine to predict a future ambient wind speed value by the wind speed prediction model, where The first operational data includes at least an ambient wind speed value; the data acquisition module 10 may input the second operational data of the wind power generator to a wind deviation angle prediction model corresponding to the wind power generator to pass the pair The wind deviation angle prediction model predicts a future wind deviation angle value, wherein the second operational data includes at least a wind deviation angle value.
  • the first operational data may include, in addition to ambient wind speed values, wind bias angle values, generator speed, generator torque, output power, cabin acceleration, blade pitch angle, cabin position. At least one of them.
  • the second operational data may include, in addition to the wind deviation angle value, an ambient wind speed value, a rotational speed of the generator, a torque of the generator, an output power, an acceleration of the nacelle, a pitch angle of the blade, and a position of the nacelle. At least one of them.
  • the data acquisition module 10 may obtain the predicted future wind speed of the wind turbine set when it is determined that the prediction accuracy of the wind speed prediction model and the prediction accuracy of the wind deviation angle prediction model satisfy a preset condition. a value and a wind deviation angle value; when it is determined that the prediction accuracy of the wind speed prediction model and the prediction accuracy of the wind deviation angle prediction model do not satisfy a preset condition, acquiring the currently detected wind turbine Ambient wind speed value and wind deviation angle value.
  • the estimating module 20 is configured to estimate, according to the wind wind speed value, the power variation caused by the yaw wind action based on the wind deviation angle value.
  • the estimating module 20 may estimate, according to the wind wind speed value, the output power boosting amount and/or the performing of the yaw wind action based on the wind bias angle value. The amount of power loss caused by yaw to wind action.
  • the policy determination module 30 is configured to determine a yaw control strategy of the wind turbine based on the estimated amount of power change.
  • the policy determination module 30 may determine an average wind bias angle calculation time factor and/or yaw time for the yaw control of the wind turbine based on the estimated output power boost amount and/or power loss amount.
  • a delay constant wherein the average wind deviation angle calculation time coefficient indicates a length of a time period to which the wind deviation angle value used for calculating the average wind deviation angle value belongs, the yaw time delay constant indicating determination to proceed The time difference between the time when the yaw is on the wind and the time when the yaw is on the wind.
  • the policy determination module 30 may determine the yaw control strategy of the wind power generator as one of mode one, mode two, and mode three according to the estimated output power boost amount and/or power loss amount, where the mode One is: the average time difference coefficient for the wind deviation angle is the first preset value, and/or the yaw time delay constant is the second preset value; the second mode is: the average wind direction deviation angle calculation time coefficient is the third preset The value, and/or the yaw time delay constant is a fourth preset value; the mode three is: the average wind deviation angle calculation time coefficient is the fifth preset value, and/or the yaw time delay constant is the sixth preset value.
  • the policy determination module 30 may determine the yaw control strategy of the wind turbine set to mode one when ⁇ P(v) ⁇ P loss .
  • the policy determination module 30 may determine the yaw control strategy of the wind turbine as mode two when P loss ⁇ ⁇ P(v) ⁇ P e (v).
  • the policy determination module 30 may determine the yaw control strategy of the wind turbine set to mode three when P(v) ⁇ P e (v) + P const1 .
  • ⁇ P(v) indicates the amount of output power boost caused by the yaw wind action
  • P loss indicates the amount of power loss caused by the yaw wind action
  • P(v) indicates the wind power
  • P e (v) indicates the design output power of the wind turbine set when the ambient wind speed value is the obtained ambient wind speed value under the standard condition
  • P const1 indicates the preset margin degree.
  • the transmitting module 40 is configured to send an instruction to the wind turbine to cause the wind turbine to perform the determined yaw control strategy.
  • FIG. 3 is a schematic diagram of a yaw control system of a wind power generator implementing an exemplary embodiment of the present application.
  • the yaw control system of the wind turbine includes a plurality of wind turbines (ie, wind turbine 1, wind turbine 2, ..., wind turbine n) and a field control device 50.
  • components of the farm group control device 50 may include, but are not limited to, one or more processors or processing units 501, system memory 502, connecting different system components (including system memory 502 and The bus 503 of the processing unit 501).
  • Bus 503 represents one or more of a variety of bus structures.
  • these bus structures include, but are not limited to, Industrial Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA Bus, Video Electronics Standards Association (VESA) local bus, and peripheral component interconnects ( PCI) bus.
  • ISA Industrial Architecture
  • MAC Micro Channel Architecture
  • VESA Video Electronics Standards Association
  • PCI peripheral component interconnects
  • the farm group control device 50 may also include one or more computer system readable media. These media can be any available media that can be accessed by the field control device 50, including volatile and non-volatile media, removable media, or non-removable media.
  • System memory 502 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 504 and/or cache memory 505.
  • System memory 502 can further include other removable/non-removable, volatile/non-volatile computer system storage media.
  • system memory 502 can also include a storage system 506, which can be used to read and write non-removable, non-volatile magnetic media (not shown in FIG. 3, commonly referred to as a "hard disk drive"). .
  • system memory 502 may also include a disk drive for reading and writing to a removable non-volatile disk (eg, a floppy disk), and a removable non-volatile disk (eg, a CD-ROM, Optical disc drives for reading and writing DVD-ROM or other optical media.
  • each drive can be coupled to bus 503 via one or more data medium interfaces.
  • System memory 502 can include at least one program product, wherein the program product has at least one program module 507 configured to perform multiple functions of various embodiments of the present application.
  • Programs/utilities 508 having at least one program module 507 can be stored, for example, in system memory 502, including but not limited to: an operating system, one or more applications, other program modules, and program data, Moreover, implementations of the network environment can be included in each or some of these examples.
  • Program module 507 typically performs the functions and/or methods in the embodiments described herein to cause at least one of the plurality of wind turbines to perform a yaw control strategy determined therewith.
  • the farm group control device 50 can also be in communication with the display 60 and one or more other external devices 70 (e.g., a keyboard, pointing device, etc.), as well as with one or more enabling the user to interact with the field control device 50.
  • the device communicates and/or communicates with any device (e.g., a network card, modem, etc.) that enables the field control device 50 to communicate with one or more other computing devices. This communication can take place via an input/output (I/O) interface 509.
  • the farm control device 50 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and/or a public network (e.g., the Internet) via the network adapter 510.
  • networks e.g., a local area network (LAN), a wide area network (WAN), and/or a public network (e.g., the Internet
  • network adapter 510 can communicate with other modules of field group control device 50 via bus 503. It should be understood that although not shown in FIG. 3, other hardware and/or software modules may be utilized in connection with a computer system including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tapes. Drives and data backup storage systems, etc.
  • FIG. 3 is only a schematic illustration of a field group control device 50 that can be used to implement various embodiments of the present application. It will be understood by those skilled in the art that the field group control device 50 can be implemented by an existing control device in the current fan control system, or can be implemented by introducing an additional control device, and can also be controlled by an existing control device in the fan control system. Implemented with new add-on devices.
  • the present application also provides a computer readable storage medium storing a computer program, the computer program comprising instructions for performing various operations in the yaw control method of the wind turbine described above.
  • the present application also provides a yaw control device for a wind power generator, including a readable storage medium storing a computer program, the computer program including various operations in a yaw control method for executing the wind power generator described above Instructions.

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Abstract

一种风力发电机组的偏航控制方法，包括以下步骤：获取风力发电机组当前或未来的环境风速值和对风偏差角度值；预估风力发电机组在环境风速值下，基于对风偏差角度值进行偏航对风动作所带来的功率变化量；根据预估的功率变化量，确定风力发电机组的偏航控制策略；发送指令给风力发电机组，以使风力发电机组执行所确定的偏航控制策略。同时，还涉及执行上述控制方法的设备和系统。该偏航控制方法、设备及系统可减小风力发电机组自身的损耗，降低某些极端工况条件下的整机载荷，还可挖掘风力发电机组的发电潜力。

Description

风力发电机组的偏航控制方法、设备及系统 技术领域
本申请总体说来涉及风力发电技术领域，更具体地讲，涉及一种风力发电机组的偏航控制方法、设备及系统。
背景技术
目前，大型风力发电机组一般为上风向风力发电机组，配备有自动偏航系统，通常通过风向检测传感器(例如，风向标)来实时检测风向，并得到风力发电机组的对风偏差角度，当对风偏差角度大于预设的偏航阈值时，向偏航电机发出顺时针转动或逆时针转动的启动指令，偏航电机按照启动指令转动，以通过偏航减速器输出低转速、高扭矩的偏航力矩用于驱动偏航轴承，从而实现风力发电机组的偏航对风动作。在偏航过程中，会实时获取风力发电机组的对风偏差角度，当对风偏差角度小于一定的阈值时，停止偏航对风动作。
发明内容
一方面，本申请提供一种风力发电机组的偏航控制方法，所述偏航控制方法包括：获取风力发电机组当前或未来的环境风速值和对风偏差角度值；预估所述风力发电机组在所述环境风速值下，基于所述对风偏差角度值进行偏航对风动作所带来的功率变化量；根据预估的功率变化量，确定所述风力发电机组的偏航控制策略；发送指令给所述风力发电机组，以使所述风力发电机组执行所确定的偏航控制策略。
另一方面，本申请提供一种风力发电机组的偏航控制设备，所述偏航控制设备包括：数据获取模块，用于获取风力发电机组当前或未来的环境风速值和对风偏差角度值；预估模块，用于预估所述风力发电机组在所述环境风速值下，基于所述对风偏差角度值进行偏航对风动作所带来的功率变化量；策略确定模块，用于根据预估的功率变化量，确定所述风力发电机组的偏航控制策略；发送模块，用于发送指令给所述风力发电机组，以使所述风力发 电机组执行所确定的偏航控制策略。
另一方面，本申请提供一种存储有计算机程序的计算机可读存储介质，当所述计算机程序被处理器执行时实现如上所述的风力发电机组的偏航控制方法。
另一方面，本申请提供一种风力发电机组的偏航控制设备，所述偏航控制设备包括：处理器；存储器，存储有计算机程序，当所述计算机程序被处理器执行时，实现如上所述的风力发电机组的偏航控制方法。
另一方面，本申请提供一种风力发电机组的偏航控制系统，包括多台风力发电机组以及一个场群控制设备，所述场群控制设备执行如上所述的方法，以使所述多台风力发电机组中的至少一台执行相应的偏航控制策略。
附图说明
图1示出根据本申请示例性实施例的风力发电机组的偏航控制方法的流程图；
图2示出根据本申请示例性实施例的风力发电机组的偏航控制设备的框图；
图3示出根据本申请的示例性实施例的风力发电机组的偏航控制系统的框图。
具体实施方式
图1示出根据本申请示例性实施例的风力发电机组的偏航控制方法的流程图。
参照图1，在步骤S10，获取风力发电机组当前或未来的环境风速值和对风偏差角度值。
作为示例，在步骤S10，可获取当前检测到的风力发电机组的环境风速值和对风偏差角度值。例如，可获取当前时刻检测到的风力发电机组的环境风速值和对风偏差角度值，或者，可获取当前一段时间(例如，1秒)内检测到的风力发电机组的环境风速值的均值和对风偏差角度值的均值。
作为另一示例，在步骤S10，可获取预测的风力发电机组未来的环境风速值和对风偏差角度值。例如，可获取预测的风力发电机组在一段时间(例如，7秒)之后的环境风速值和对风偏差角度值，或者，可获取预测的风力 发电机组在未来的一段时间内的环境风速值的均值和对风偏差角度值的均值。
作为示例，可将所述风力发电机组的第一运行数据输入到与所述风力发电机组对应的风速预测模型，以通过所述风速预测模型来预测未来的环境风速值；并将所述风力发电机组的第二运行数据输入到与所述风力发电机组对应的对风偏差角度预测模型，以通过所述对风偏差角度预测模型来预测未来的对风偏差角度值，其中，所述第一运行数据至少包括环境风速值，所述第二运行数据至少包括对风偏差角度值。
作为示例，可将风力发电机组在以当前时刻为终止点的第一预定时间段内的各采样时间点的第一运行数据输入到与所述风力发电机组对应的风速预测模型，以通过所述风速预测模型来得到所述风力发电机组在第二预定时间段之后的环境风速值；并将风力发电机组在以当前时刻为终止点的第一预定时间段内的各采样时间点的第二运行数据输入到与所述风力发电机组对应的对风偏差角度预测模型，以通过所述对风偏差角度预测模型来得到所述风力发电机组在第二预定时间段之后的对风偏差角度值。
关于与风力发电机组对应的风速预测模型，可将以一定的采样周期(例如，20毫秒、1秒或7秒)采集的风力发电机组的第一运行数据(即，基于时间序列的第一运行数据)作为训练集，来训练与风力发电机组对应的风速预测模型，通过训练出来的风速预测模型，能够预测风力发电机组未来的环境风速值。
关于与风力发电机组对应的对风偏差角度预测模型，可将以一定的采样周期(例如，20毫秒、1秒或7秒)采集的风力发电机组的第二运行数据(即，基于时间序列的第二运行数据)作为训练集，来训练与风力发电机组对应的对风偏差角度预测模型，通过训练出来的对风偏差角度预测模型，能够预测风力发电机组未来的对风偏差角度值。
作为示例，第二预定时间段的长度可与训练样本的采样周期相关，训练样本的采样周期越短，第二预定时间段的长度越短。例如，第二预定时间段的长度可与训练样本的采样周期相同，例如，当使用采样周期为7秒的训练样本来训练与风力发电机组对应的风速预测模型时，通过该风速预测模型能够预测风力发电机组7秒之后的环境风速值。
第一运行数据和第二运行数据可以是风力发电机组运行时的风参数和/或风力发电机组自身的运行参数。作为示例，第一运行数据除包括环境风速 值之外，还可包括对风偏差角度值、发电机的转速、发电机的扭矩、输出功率、机舱沿X轴方向和/或Y轴方向的加速度、叶片的桨距角、机舱的位置之中的至少一项。作为示例，第二运行数据除包括对风偏差角度值之外，还可包括环境风速值、发电机的转速、发电机的扭矩、输出功率、机舱沿X轴方向和/或Y轴方向的加速度、叶片的桨距角、机舱的位置之中的至少一项。应该理解，第一运行数据和第二运行数据可完全相同。
此外，应该理解，与风力发电机组对应的风速预测模型/对风偏差角度预测模型除可用于对未来短时间的环境风速值/对风偏差角度值的预测之外，还可用于对未来较长时间段内的依次间隔预定时间间隔的时间点的环境风速值/对风偏差角度值的预测。例如，与风力发电机组对应的风速预测模型/对风偏差角度预测模型可用于对未来5分钟内的依次间隔7秒的时间点的环境风速值/对风偏差角度值的预测，例如，可预测7秒之后的环境风速值/对风偏差角度值、14秒之后的环境风速值/对风偏差角度值、21秒之后的环境风速值/对风偏差角度值，……，5分钟之后的环境风速值/对风偏差角度值。
作为示例，可通过在线的方式训练与风力发电机组对应的风速预测模型和对风偏差角度预测模型。具体说来，可直接使用当前采集到的风力发电机组的第一运行数据来实时训练风速预测模型，可直接使用当前采集到的风力发电机组的第二运行数据来实时训练对风偏差角度预测模型。
作为另一示例，可通过离线的方式训练与风力发电机组对应的风速预测模型和对风偏差角度预测模型。具体说来，可获取风力发电机组在一个历史时段内的第一运行数据来一次性训练风速预测模型，可获取风力发电机组在该历史时段内的第二运行数据来一次性训练对风偏差角度预测模型。
作为示例，在步骤S10，可当确定所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度满足预设条件时，获取预测的所述风力发电机组未来的环境风速值和对风偏差角度值；当确定所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度不满足预设条件时，获取当前检测到的所述风力发电机组的环境风速值和对风偏差角度值。
具体说来，所述风速预测模型的预测准确度可基于通过该风速预测模型得到的一些时刻(即，测试样本)的环境风速值和实际检测到的这些时刻的环境风速值之间的偏差来确定。所述对风偏差角度预测模型的预测准确度可基于通过该对风偏差角度预测模型得到的一些时刻的对风偏差角度值和实际 检测到的这些时刻的对风偏差角度值之间的偏差来确定。作为示例，可将以当前时刻为终止点的一段时间内的各采样时间点作为测试样本。
可通过各种适当的方式来衡量风速预测模型的预测准确度和对风偏差角度预测模型的预测准确度。作为示例，可通过平均绝对误差(MAE，Mean Absolute Error)、平均绝对误差百分比(MAPE，Mean Absolute Percentage Error)、平均绝对误差的标准差(SDMAE)、平均绝对误差百分比的标准差(SDMAPE)等来衡量风速预测模型的预测准确度和对风偏差角度预测模型的预测准确度。
以风速预测模型为例，可利用公式(1)来计算风速预测模型的MAE，
Figure PCTCN2018097914-appb-000001
在公式(1)中，x i为第i个测试样本的风速检测值，
Figure PCTCN2018097914-appb-000002
为第i个测试样本通过风速预测模型得到的风速预测值，1≤i≤n，n为测试集所包括的测试样本的个数。
以对风偏差角度预测模型为例，可利用公式(2)来计算对风偏差角度预测模型的MAPE，
Figure PCTCN2018097914-appb-000003
在公式(2)中，y j为第j个测试样本的对风偏差角度检测值，
Figure PCTCN2018097914-appb-000004
为第j个测试样本通过对风偏差角度预测模型得到的对风偏差角度预测值，1≤j≤m，m为测试集所包括的测试样本的个数。
作为示例，可当风速预测模型的预测准确度高于第一预设阈值，并且对风偏差角度预测模型的预测准确度高于第二预设阈值时，确定所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度满足预设条件。
作为另一示例，可当风速预测模型的预测准确度和对风偏差角度预测模型的预测准确度的加权结果高于第三预设阈值时，确定所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度满足预设条件。例如，可利用公式(3)来计算风速预测模型的预测准确度和对风偏差角度预测模型的预测准确度的加权结果P all，
P all＝w 1*p(f(x 1))+w 2*p(f(x 2))    (3)
在公式(3)中，p(f(x 1))指示风速预测模型f(x 1)的预测准确度(例如， 可以是风速预测模型的MAE)，p(f(x 2))指示对风偏差角度预测模型f(x 2)的预测准确度(例如，可以是对风偏差角度预测模型的MAE)，w 1指示与风速预测模型f(x 1)对应的权重，w 2指示与对风偏差角度预测模型f(x 2)对应的权重，且w 1+w 2＝1。
此外，应该理解，当确定所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度不满足预设条件时，还可继续优化和/或训练风速预测模型和对风偏差角度预测模型，直到所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度能够满足预设条件。
在步骤S20，预估所述风力发电机组在所述环境风速值下，基于所述对风偏差角度值进行偏航对风动作所带来的功率变化量。
作为示例，可预估所述风力发电机组在所述环境风速值下，基于所述对风偏差角度值进行偏航对风动作所带来的输出功率提升量和/或进行所述偏航对风动作所导致的功率损耗量。
可通过各种适当的方法来预估风力发电机组在环境风速值v下，基于对风偏差角度值β进行偏航对风动作使风力发电机组正对风后的输出功率提升量ΔP(v)。
作为示例，可通过公式(4)来计算ΔP(v)：
ΔP(v)＝P e(v)*(1-(cosβ) 2)    (4)
在公式(4)中，P e(v)指示所述风力发电机组在标况下当环境风速值为v时的设计输出功率。
作为另一示例，可确定风力发电机组在环境风速值v下是否处于满发阶段(即，恒功率运行模式)，如果风力发电机组处于满发阶段，则可确定风力发电机组在环境风速值v下，基于对风偏差角度值β进行偏航对风动作使风力发电机组正对风后的输出功率提升量ΔP(v)较小。作为示例，可当P(v)≥P e(v)+P const1时，确定风力发电机组在环境风速值v下处于满发阶段，其中，P(v)指示风力发电机组在环境风速值v下的输出功率，P const1指示预设裕度。作为示例，当在步骤S10获取的是当前的环境风速值和对风偏差角度值时，P(v)的值可为当前检测到的输出功率值；当在步骤S10获取的是未来的环境风速值和对风偏差角度值时，P(v)的值可基于获取的环境风速值v和风力发电机组的运行功率曲线来得到。
可通过各种适当的方法来预估风力发电机组在环境风速值v下，基于对 风偏差角度值β进行偏航对风动作使风力发电机组正对风所导致的功率损耗量P loss。作为示例，可通过公式(5)来简便估计P loss：
P loss＝n*P e′*δ+P const2   (5)
在公式(5)中，n指示风力发电机组中偏航电机的数量，P e′指示每个偏航电机的额定功率，P const2指示用于计算P loss的功率损耗裕度，δ为损耗系数，取值范围为0～1，优选地，可取0～0.3。作为示例，可根据风力发电机组每小时处于偏航状态的时间来设置δ。
在步骤S30，根据预估的功率变化量，确定所述风力发电机组的偏航控制策略。
作为示例，可根据预估的功率变化量，确定所述风力发电机组的偏航控制策略的偏航控制参数。
作为示例，可根据预估的输出功率提升量和/或功率损耗量，确定用于所述风力发电机组的偏航控制的平均对风偏差角度计算时间系数和/或偏航时间延迟常数。换言之，可根据预估的输出功率提升量和/或功率损耗量，确定所述风力发电机组的偏航控制策略的平均对风偏差角度计算时间系数和/或偏航时间延迟常数。
所述平均对风偏差角度计算时间系数指示用于计算平均对风偏差角度值所使用的对风偏差角度值所属的时间段的长度。具体说来，可对一定时间段(例如，30秒或者60秒)内的对风偏差角度值取平均来得到平均对风偏差角度值，以基于该平均对风偏差角度值来确定是否需要进行偏航，例如，当该平均对风偏差角度值大于预设的偏航阈值时，确定需要进行偏航。
所述偏航时间延迟常数指示确定进行偏航对风动作的时刻与实际开始进行偏航对风动作的时刻之间的时间差。作为示例，偏航时间延迟常数可为用于偏航控制的可编程逻辑控制器(PLC)中的延时开启开关的参数，当满足偏航条件(即，确定进行偏航对风动作)时，需要通过该延时开启开关来延迟一段时间(即，偏航时间延迟常数的值)，然后才开始进行偏航对风动作。
应该理解，平均对风偏差角度计算时间系数越大，偏航次数越少；偏航时间延迟常数越大，偏航次数越少。
作为示例，可根据预估的输出功率提升量和/或功率损耗量，将所述风力发电机组的偏航控制策略确定为模式一、模式二和模式三之一，其中，模式一为：平均对风偏差角度计算时间系数为第一预设值，和/或偏航时间延迟常 数为第二预设值；模式二为：平均对风偏差角度计算时间系数为第三预设值，和/或偏航时间延迟常数为第四预设值；模式三为：平均对风偏差角度计算时间系数为第五预设值，和/或偏航时间延迟常数为第六预设值，其中，所述第一预设值＞所述第五预设值＞所述第三预设值；和/或所述第二预设值＞所述第六预设值＞所述第四预设值。
在低风速段(例如，3m/s-5m/s风速段)，风力发电机组的输出功率较小，而此时的风向和风速变化较大，根据现有的偏航控制策略风力发电机组会频繁偏航，而通过偏航对风动作使风力发电机组正对风所带来的发电提升量又可能低于进行该偏航动作所消耗的电量，因此，在低风速段下，可通过增大平均对风偏差角度计算时间系数和/或偏航时间延迟常数，来减少偏航次数，以避免额外损失电量。作为示例，可当ΔP(v)≤P loss时，将所述风力发电机组的偏航控制策略确定为模式一。
在风力发电机组的运行功率曲线的过渡段(即，发电机达到额定转速，但尚未达到额定输出功率)，偏航对风对风力发电机组的功率输出影响较大：针对相同的对风偏差角度值，在该阶段进行偏航对风后获得的发电提升量远远大于在低风速段进行偏航对风后获得的发电提升量；并且，在该阶段，通过偏航对风动作使风力发电机组正对风所带来的发电提升量高于进行该偏航动作所消耗的电量。因此，在过渡段，可通过减小平均对风偏差角度计算时间系数和/或偏航时间延迟常数，来增加偏航次数，使风力发电机组对对风偏差角度更加敏感，尽可能挖掘风力发电机组的发电潜力。作为示例，可当P loss＜ΔP(v)≤P e(v)时，将所述风力发电机组的偏航控制策略确定为模式二。
在高风速段(例如，大于风力发电机组的额定风速的风速段)，风向和风速的变化较小，风力发电机组通过变桨来实现功率的额定输出(也即，风力发电机组已经满发，处于恒功率控制模式)，若此时遇到极端工况，例如，风速突增条件下的风向突变，根据现有的偏航控制策略风力发电机组会频繁偏航，这将导致风力发电机组的电量损耗增大，但风力发电机组的输出功率却保持恒定，即，频繁的偏航无法为风力发电机组带来发电量的提升。此外，偏航过程中风向可能已经发生变化，这可能导致偏航电机无法克服风向突变所带来的额外的施加在叶轮平面上的推力，进而导致偏航电机过载而报故障，同时，风力发电机组的偏航载荷将会增大。因此，在高风速段下，可通过增大平均对风偏差角度计算时间系数和/或偏航时间延迟常数，来减少偏航次数。 作为示例，可当P(v)≥P e(v)+P const1时，将所述风力发电机组的偏航控制策略确定为模式三。
在步骤S40，发送指令给所述风力发电机组，以使所述风力发电机组执行所确定的偏航控制策略。所述指令可包括确定的偏航控制策略所对应的偏航控制参数，例如，所述指令可包括确定的偏航控制策略所对应的平均对风偏差角度计算时间系数和/或偏航时间延迟常数。
根据本申请的示例性实施例，充分考虑了风向在不同风速段时的变化规律，不同风速段(也即，不同工况条件)下风力发电机组进行偏航对风所导致的电量得失情况，来确定偏航控制策略，既能够减小风力发电机组自身的损耗，降低某些极端工况条件下的整机载荷，又能够挖掘风力发电机组的发电潜力。
目前的偏航系统为大时滞、大惯性的随动系统，一般来说，完成一次偏航对风动作需要至少数秒至数分钟，由于风向是时刻变化的，因此现有的偏航系统无法实时跟踪风向并保持风力发电机组时刻正对风，在某些特殊工况条件下，例如，大风速条件下的风向突变，若此时风力发电机组未能及时偏航对风，将加大风力发电机组的载荷。
根据本申请的示例性实施例，当在步骤S10获取的是预测的未来的环境风速值和对风偏差角度值时，在步骤S40，可使风力发电机组执行确定的偏航控制策略，基于预测的未来的对风偏差角度值来对风力发电机组进行提前偏航控制，以使风力发电机组对风更加精准，从而既能够提高风力发电机组的发电量，也能够降低风力发电机组在风向突变下的载荷。具体说来，当预测的未来时刻的对风偏差角度值大于预设的偏航阈值时，可根据风力发电机组的偏航速率计算基于该未来时刻的对风偏差角度值进行偏航对风动作所需要的时间，并基于需要的时间和该未来时刻来确定需要在哪个时间点启动偏航，然后在确定的时间点启动偏航，以使风力发电机组在该未来时刻正对风。
根据本申请的示例性实施例，实现了偏航控制由被动控制向主动控制的转变，使风力发电机组对风更加精准，从而既能够提高风力发电机组的发电量，也能够降低风力发电机组在风向突变下的载荷。
图2示出根据本申请示例性实施例的风力发电机组的偏航控制设备的框图。如图2所示，根据本申请示例性实施例的风力发电机组的偏航控制设备包括：数据获取模块10、预估模块20、策略确定模块30和发送模块40。
数据获取模块10用于获取风力发电机组当前或未来的环境风速值和对风偏差角度值。
作为示例，数据获取模块10可获取当前检测到的所述风力发电机组的环境风速值和对风偏差角度值。
作为另一示例，数据获取模块10可获取预测的所述风力发电机组未来的环境风速值和对风偏差角度值。
作为示例，数据获取模块10可将所述风力发电机组的第一运行数据输入到与所述风力发电机组对应的风速预测模型，以通过所述风速预测模型来预测未来的环境风速值，其中，所述第一运行数据至少包括环境风速值；数据获取模块10可将所述风力发电机组的第二运行数据输入到与所述风力发电机组对应的对风偏差角度预测模型，以通过所述对风偏差角度预测模型来预测未来的对风偏差角度值，其中，所述第二运行数据至少包括对风偏差角度值。
作为示例，第一运行数据除包括环境风速值之外，还可包括对风偏差角度值、发电机的转速、发电机的扭矩、输出功率、机舱的加速度、叶片的桨距角、机舱的位置之中的至少一项。
作为示例，第二运行数据除包括对风偏差角度值之外，还可包括环境风速值、发电机的转速、发电机的扭矩、输出功率、机舱的加速度、叶片的桨距角、机舱的位置之中的至少一项。
作为示例，数据获取模块10可当确定所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度满足预设条件时，获取预测的所述风力发电机组未来的环境风速值和对风偏差角度值；当确定所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度不满足预设条件时，获取当前检测到的所述风力发电机组的环境风速值和对风偏差角度值。
预估模块20用于预估所述风力发电机组在所述环境风速值下，基于所述对风偏差角度值进行偏航对风动作所带来的功率变化量。
作为示例，预估模块20可预估所述风力发电机组在所述环境风速值下，基于所述对风偏差角度值进行偏航对风动作所带来的输出功率提升量和/或进行所述偏航对风动作所导致的功率损耗量。
策略确定模块30用于根据预估的功率变化量，确定所述风力发电机组的偏航控制策略。
作为示例，策略确定模块30可根据预估的输出功率提升量和/或功率损耗量，确定用于所述风力发电机组的偏航控制的平均对风偏差角度计算时间系数和/或偏航时间延迟常数，其中，所述平均对风偏差角度计算时间系数指示用于计算平均对风偏差角度值所使用的对风偏差角度值所属的时间段的长度，所述偏航时间延迟常数指示确定进行偏航对风动作的时刻与实际开始进行偏航对风动作的时刻之间的时间差。
作为示例，策略确定模块30可根据预估的输出功率提升量和/或功率损耗量，将所述风力发电机组的偏航控制策略确定为模式一、模式二和模式三之一，其中，模式一为：平均对风偏差角度计算时间系数为第一预设值，和/或偏航时间延迟常数为第二预设值；模式二为：平均对风偏差角度计算时间系数为第三预设值，和/或偏航时间延迟常数为第四预设值；模式三为：平均对风偏差角度计算时间系数为第五预设值，和/或偏航时间延迟常数为第六预设值，其中，所述第一预设值＞所述第五预设值＞所述第三预设值；和/或所述第二预设值＞所述第六预设值＞所述第四预设值。
作为示例，策略确定模块30可当ΔP(v)≤P loss时，将所述风力发电机组的偏航控制策略确定为模式一。
作为另一示例，策略确定模块30可当P loss＜ΔP(v)≤P e(v)时，将所述风力发电机组的偏航控制策略确定为模式二。
作为另一示例，策略确定模块30可当P(v)≥P e(v)+P const1时，将所述风力发电机组的偏航控制策略确定为模式三。
其中，ΔP(v)指示进行所述偏航对风动作所带来的输出功率提升量，P loss指示进行所述偏航对风动作所导致的功率损耗量，P(v)指示所述风力发电机组在获取的环境风速值下的输出功率，P e(v)指示所述风力发电机组在标况下当环境风速值为获取的环境风速值时的设计输出功率，P const1指示预设裕度。
发送模块40用于发送指令给所述风力发电机组，以使所述风力发电机组执行所确定的偏航控制策略。
应该理解，根据本申请示例性实施例的风力发电机组的偏航控制设备的具体实现方式可参照结合图1描述的相关具体实现方式来实现，在此不再赘述。
图3是实现本申请的示例性实施例的风力发电机组的偏航控制系统的示意图。
如图3所示，风力发电机组的偏航控制系统包括多台风力发电机组(即，风力发电机组1、风力发电机组2、……、风力发电机组n)以及一个场群控制设备50。在本申请的一个示例性实施例中，场群控制设备50的组件可以包括但不限于：一个或更多个处理器或处理单元501、系统存储器502、连接不同系统组件(包括系统存储器502和处理单元501)的总线503。
总线503表示多种总线结构中的一种或多种。举例来说，这些总线结构包括但不限于：工业体系结构(ISA)总线、微通道体系结构(MAC)总线、增强型ISA总线、视频电子标准协会(VESA)局域总线以及外围组件互连(PCI)总线。
在本申请的另一示例性实施例中，场群控制设备50还可包括一种或多种计算机系统可读介质。这些介质可以是任何能够被场群控制设备50访问的可用介质，包括易失性介质和非易失性介质、可移动介质或不可移动介质。
系统存储器502可包括易失性存储器形式的计算机系统可读介质，例如随机存取存储器(RAM)504和/或高速缓存存储器505。系统存储器502可进一步包括其它可移动/不可移动、易失性/非易失性计算机系统存储介质。作为示例，系统存储器502还可包括存储系统506，其中，存储系统506可以用于读写不可移动的、非易失性磁介质(图3中未示出，通常被称为“硬盘驱动器”)。尽管图3中未示出，但系统存储器502还可包括用于对可移动非易失性磁盘(例如软盘)读写的磁盘驱动器、以及对可移动非易失性光盘(例如CD-ROM、DVD-ROM或者其它光介质)读写的光盘驱动器。在这些情况下，每个驱动器可以通过一个或者多个数据介质接口与总线503相连。系统存储器502可以包括至少一个程序产品，其中，程序产品具有被配置为执行本申请各实施例的多个功能的至少一个程序模块507。
具有至少一个程序模块507的程序/实用工具508可被存储在例如系统存储器502中，这样的程序模块507包括但不限于：操作系统、一个或更多个应用程序、其它程序模块以及程序数据，此外，这些示例中的每一个或某种组合中可包括网络环境的实现。程序模块507通常执行本申请所描述的实施例中的功能和/或方法，以使所述多台风力发电机组中的至少一台执行针对其所确定的偏航控制策略。
场群控制设备50也可以与显示器60以及一个或更多个其它外部设备70(例如键盘、指向设备等)通信，还可以与一个或更多个使得用户能够与该 场群控制设备50交互的设备通信和/或与使得该场群控制设备50能与一个或更多个其它计算设备进行通信的任何设备(例如网卡、调制解调器等)通信。这种通信可以通过输入/输出(I/O)接口509进行。此外，场群控制设备50还可通过网络适配器510与一个或更多个网络(例如局域网(LAN)、广域网(WAN)和/或公共网络(例如因特网))进行通信。如图3中所示，网络适配器510可通过总线503与场群控制设备50的其它模块通信。应当明白，尽管图3中未示出，但是可结合计算机系统使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID系统、磁带驱动器以及数据备份存储系统等。
应当注意，图3仅仅示意性地示出了可以用于实现本申请中各个实施方式的场群控制设备50的示意图。本领域技术人员可以理解，该场群控制设备50可以由当前风机控制系统中现有的控制设备来实现，或者可通过引入附加控制设备来实现，还可以由风机控制系统中的现有控制设备和新增的附加设备一起实现。
此外，本申请还提供了一种计算机可读存储介质，存储有计算机程序，所述计算机程序可包括用于执行上述风力发电机组的偏航控制方法中各种操作的指令。
此外，本申请还提供了一种风力发电机组的偏航控制设备，包括存储有计算机程序的可读存储介质，所述计算机程序包括用于执行上述风力发电机组的偏航控制方法中各种操作的指令。
虽然已表示和描述了本申请的一些示例性实施例，但本领域技术人员应该理解，在不脱离由权利要求及其等同物限定其范围的本申请的原理和精神的情况下，可以对这些实施例进行修改。

Claims (21)

  1. 一种风力发电机组的偏航控制方法，其特征在于，所述偏航控制方法包括：
    获取风力发电机组当前或未来的环境风速值和对风偏差角度值；
    预估所述风力发电机组在所述环境风速值下，基于所述对风偏差角度值进行偏航对风动作所带来的功率变化量；
    根据预估的功率变化量，确定所述风力发电机组的偏航控制策略；
    发送指令给所述风力发电机组，以使所述风力发电机组执行所确定的偏航控制策略。
  2. 根据权利要求1所述的偏航控制方法，其特征在于，获取风力发电机组当前或未来的环境风速值和对风偏差角度值的步骤包括：
    获取当前检测到的所述风力发电机组的环境风速值和对风偏差角度值；
    或者，获取预测的所述风力发电机组未来的环境风速值和对风偏差角度值。
  3. 根据权利要求1所述的偏航控制方法，其特征在于，预估所述风力发电机组在所述环境风速值下基于所述对风偏差角度值进行偏航对风动作所带来的功率变化量的步骤包括：预估所述风力发电机组在所述环境风速值下，基于所述对风偏差角度值进行偏航对风动作所带来的输出功率提升量和/或进行所述偏航对风动作所导致的功率损耗量。
  4. 根据权利要求3所述的偏航控制方法，其特征在于，确定所述风力发电机组的偏航控制策略的步骤包括：根据预估的输出功率提升量和/或功率损耗量，确定用于所述风力发电机组的偏航控制的平均对风偏差角度计算时间系数和/或偏航时间延迟常数，
    其中，所述平均对风偏差角度计算时间系数指示用于计算平均对风偏差角度值所使用的对风偏差角度值所属的时间段的长度，
    所述偏航时间延迟常数指示确定进行偏航对风动作的时刻与实际开始进行偏航对风动作的时刻之间的时间差。
  5. 根据权利要求4所述的偏航控制方法，其特征在于，确定所述风力发电机组的偏航控制策略的步骤包括：
    根据预估的输出功率提升量和/或功率损耗量，将所述风力发电机组的偏 航控制策略确定为模式一、模式二和模式三之一，
    其中，模式一为：平均对风偏差角度计算时间系数为第一预设值，和/或偏航时间延迟常数为第二预设值；
    模式二为：平均对风偏差角度计算时间系数为第三预设值，和/或偏航时间延迟常数为第四预设值；
    模式三为：平均对风偏差角度计算时间系数为第五预设值，和/或偏航时间延迟常数为第六预设值，
    其中，所述第一预设值＞所述第五预设值＞所述第三预设值；和/或所述第二预设值＞所述第六预设值＞所述第四预设值。
  6. 根据权利要求5所述的偏航控制方法，其特征在于，确定所述风力发电机组的偏航控制策略的步骤包括：
    当ΔP(v)≤P loss时，将所述风力发电机组的偏航控制策略确定为模式一；
    和/或，当P loss＜ΔP(v)≤P e(v)时，将所述风力发电机组的偏航控制策略确定为模式二；
    和/或，当P(v)≥P e(v)+P const时，将所述风力发电机组的偏航控制策略确定为模式三；
    其中，ΔP(v)指示进行所述偏航对风动作所带来的输出功率提升量，P loss指示进行所述偏航对风动作所导致的功率损耗量，P(v)指示所述风力发电机组在获取的环境风速值下的输出功率，P e(v)指示所述风力发电机组在标况下当环境风速值为获取的环境风速值时的设计输出功率，P const指示预设裕度。
  7. 根据权利要求2所述的偏航控制方法，其特征在于，获取预测的所述风力发电机组未来的环境风速值和对风偏差角度值的步骤包括：
    将所述风力发电机组的第一运行数据输入到与所述风力发电机组对应的风速预测模型，以通过所述风速预测模型来预测未来的环境风速值，其中，所述第一运行数据至少包括环境风速值；
    将所述风力发电机组的第二运行数据输入到与所述风力发电机组对应的对风偏差角度预测模型，以通过所述对风偏差角度预测模型来预测未来的对风偏差角度值，其中，所述第二运行数据至少包括对风偏差角度值。
  8. 根据权利要求7所述的偏航控制方法，其特征在于，第一运行数据还包括对风偏差角度值、发电机的转速、发电机的扭矩、输出功率、机舱的加速度、叶片的桨距角、机舱的位置之中的至少一项；
    和/或，第二运行数据还包括环境风速值、发电机的转速、发电机的扭矩、输出功率、机舱的加速度、叶片的桨距角、机舱的位置之中的至少一项。
  9. 根据权利要求7所述的偏航控制方法，其特征在于，获取风力发电机组当前或未来的环境风速值和对风偏差角度值的步骤包括：
    当确定所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度满足预设条件时，获取预测的所述风力发电机组未来的环境风速值和对风偏差角度值；
    当确定所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度不满足预设条件时，获取当前检测到的所述风力发电机组的环境风速值和对风偏差角度值。
  10. 一种风力发电机组的偏航控制设备，其特征在于，所述偏航控制设备包括：
    数据获取模块，用于获取风力发电机组当前或未来的环境风速值和对风偏差角度值；
    预估模块，用于预估所述风力发电机组在所述环境风速值下，基于所述对风偏差角度值进行偏航对风动作所带来的功率变化量；
    策略确定模块，用于根据预估的功率变化量，确定所述风力发电机组的偏航控制策略；
    发送模块，用于发送指令给所述风力发电机组，以使所述风力发电机组执行所确定的偏航控制策略。
  11. 根据权利要求10所述的偏航控制设备，其特征在于，数据获取模块获取当前检测到的所述风力发电机组的环境风速值和对风偏差角度值；或者，数据获取模块获取预测的所述风力发电机组未来的环境风速值和对风偏差角度值。
  12. 根据权利要求10所述的偏航控制设备，其特征在于，预估模块预估所述风力发电机组在所述环境风速值下，基于所述对风偏差角度值进行偏航对风动作所带来的输出功率提升量和/或进行所述偏航对风动作所导致的功率损耗量。
  13. 根据权利要求12所述的偏航控制设备，其特征在于，策略确定模块根据预估的输出功率提升量和/或功率损耗量，确定用于所述风力发电机组的偏航控制的平均对风偏差角度计算时间系数和/或偏航时间延迟常数，
    其中，所述平均对风偏差角度计算时间系数指示用于计算平均对风偏差角度值所使用的对风偏差角度值所属的时间段的长度，
    所述偏航时间延迟常数指示确定进行偏航对风动作的时刻与实际开始进行偏航对风动作的时刻之间的时间差。
  14. 根据权利要求13所述的偏航控制设备，其特征在于，
    策略确定模块根据预估的输出功率提升量和/或功率损耗量，将所述风力发电机组的偏航控制策略确定为模式一、模式二和模式三之一，
    其中，模式一为：平均对风偏差角度计算时间系数为第一预设值，和/或偏航时间延迟常数为第二预设值；
    模式二为：平均对风偏差角度计算时间系数为第三预设值，和/或偏航时间延迟常数为第四预设值；
    模式三为：平均对风偏差角度计算时间系数为第五预设值，和/或偏航时间延迟常数为第六预设值，
    其中，所述第一预设值＞所述第五预设值＞所述第三预设值；和/或所述第二预设值＞所述第六预设值＞所述第四预设值。
  15. 根据权利要求14所述的偏航控制设备，其特征在于，
    当ΔP(v)≤P loss时，策略确定模块将所述风力发电机组的偏航控制策略确定为模式一；
    和/或，当P loss＜ΔP(v)≤P e(v)时，策略确定模块将所述风力发电机组的偏航控制策略确定为模式二；
    和/或，当P(v)≥P e(v)+P const时，策略确定模块将所述风力发电机组的偏航控制策略确定为模式三；
    其中，ΔP(v)指示进行所述偏航对风动作所带来的输出功率提升量，P loss指示进行所述偏航对风动作所导致的功率损耗量，P(v)指示所述风力发电机组在获取的环境风速值下的输出功率，P e(v)指示所述风力发电机组在标况下当环境风速值为获取的环境风速值时的设计输出功率，P const指示预设裕度。
  16. 根据权利要求11所述的偏航控制设备，其特征在于，数据获取模块将所述风力发电机组的第一运行数据输入到与所述风力发电机组对应的风速预测模型，以通过所述风速预测模型来预测未来的环境风速值，其中，所述第一运行数据至少包括环境风速值；
    数据获取模块将所述风力发电机组的第二运行数据输入到与所述风力发 电机组对应的对风偏差角度预测模型，以通过所述对风偏差角度预测模型来预测未来的对风偏差角度值，其中，所述第二运行数据至少包括对风偏差角度值。
  17. 根据权利要求16所述的偏航控制设备，其特征在于，第一运行数据还包括对风偏差角度值、发电机的转速、发电机的扭矩、输出功率、机舱的加速度、叶片的桨距角、机舱的位置之中的至少一项；
    和/或，第二运行数据还包括环境风速值、发电机的转速、发电机的扭矩、输出功率、机舱的加速度、叶片的桨距角、机舱的位置之中的至少一项。
  18. 根据权利要求16所述的偏航控制设备，其特征在于，数据获取模块当确定所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度满足预设条件时，获取预测的所述风力发电机组未来的环境风速值和对风偏差角度值；当确定所述风速预测模型的预测准确度和所述对风偏差角度预测模型的预测准确度不满足预设条件时，获取当前检测到的所述风力发电机组的环境风速值和对风偏差角度值。
  19. 一种存储有计算机程序的计算机可读存储介质，其特征在于，当所述计算机程序被处理器执行时实现如权利要求1至9中的任意一项所述的风力发电机组的偏航控制方法。
  20. 一种风力发电机组的偏航控制设备，其特征在于，所述偏航控制设备包括：
    处理器；
    存储器，存储有计算机程序，当所述计算机程序被处理器执行时，实现如权利要求1至9中的任意一项所述的风力发电机组的偏航控制方法。
  21. 一种风力发电机组的偏航控制系统，包括多台风力发电机组以及一个场群控制设备，其特征在于，所述场群控制设备执行如权利要求1至9中的任意一项所述的方法，以使所述多台风力发电机组中的至少一台执行相应的偏航控制策略。
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