WO2022188392A1 - 用于风力发电机组的控制方法及控制装置 - Google Patents

用于风力发电机组的控制方法及控制装置 Download PDF

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Publication number
WO2022188392A1
WO2022188392A1 PCT/CN2021/119597 CN2021119597W WO2022188392A1 WO 2022188392 A1 WO2022188392 A1 WO 2022188392A1 CN 2021119597 W CN2021119597 W CN 2021119597W WO 2022188392 A1 WO2022188392 A1 WO 2022188392A1
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Prior art keywords
wind
condition
complex
incoming
wind turbine
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PCT/CN2021/119597
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English (en)
French (fr)
Inventor
卞凤娇
杨优生
刘磊
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新疆金风科技股份有限公司
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Priority to EP21929857.7A priority Critical patent/EP4293216A1/en
Priority to US18/549,500 priority patent/US20240151209A1/en
Priority to AU2021432887A priority patent/AU2021432887A1/en
Publication of WO2022188392A1 publication Critical patent/WO2022188392A1/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
    • F03D7/043—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
    • 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
    • F03D7/028—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power
    • F03D7/0292—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power to reduce fatigue
    • 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
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • 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
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F03D17/001—Inspection
    • F03D17/003—Inspection characterised by using optical devices, e.g. lidar or cameras
    • 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
    • F03D7/022—Adjusting aerodynamic properties of the blades
    • F03D7/0224—Adjusting blade pitch
    • 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
    • F03D7/022—Adjusting aerodynamic properties of the blades
    • F03D7/0236—Adjusting aerodynamic properties of the blades by changing the active surface of the wind engaging parts, e.g. reefing or furling
    • 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
    • F03D7/0276—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling rotor speed, e.g. variable speed
    • 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
    • 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
    • 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
    • F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
    • 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
    • F05B2260/84—Modelling or simulation
    • 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
    • F05B2270/321—Wind directions
    • 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
    • F05B2270/322—Control parameters, e.g. input parameters the detection or prediction of a wind gust
    • 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
    • F05B2270/40—Type of control system
    • F05B2270/404—Type of control system active, predictive, or anticipative
    • 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
    • F05B2270/80—Devices generating input signals, e.g. transducers, sensors, cameras or strain gauges
    • F05B2270/804—Optical devices
    • F05B2270/8042—Lidar systems
    • 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 to the technical field of wind power generation, and in particular, to a control method and a control device for a wind power generating set.
  • the unit control strategies provided for these complex wind conditions have poor adaptability and frequent unit failures, making it difficult to achieve refined energy optimization management and optimal power generation under the premise of ensuring unit safety.
  • the purpose of the present application is to provide a control method and a control device for a wind turbine.
  • a control method for a wind power generating set comprising: acquiring inflow wind information of the wind power generating set; Whether there is a sector with complex wind conditions; in response to the existence of a sector with complex wind conditions around the wind generator set, perform feedforward load shedding control on the wind generator set according to the complex wind conditions.
  • a control device for a wind turbine comprising: a wind condition prediction unit configured to: acquire incoming wind information of the wind turbine; a sector identification unit , is configured to: determine whether there is a sector with complex wind conditions around the wind turbine according to the acquired inflow wind information; the load shedding control unit is configured to: in response to the presence of complex wind around the wind turbine According to the complex wind conditions, feedforward load shedding control is performed on the wind turbine generator set.
  • a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned control method for a wind turbine.
  • a computer device comprising: a processor; a memory storing a computer program, when the computer program is executed by the processor, the aforementioned computer program for wind power is implemented.
  • the control method of the generator set is provided.
  • the control method and control device for a wind power generating set can enable the wind power generating set to perform automatic self-control for various complex wind conditions without adding new investment (such as additional hardware equipment).
  • FIG. 1 shows a schematic diagram of sector-based wind condition distribution for a wind turbine according to an exemplary embodiment of the present application
  • FIG. 2 shows a schematic diagram of a lidar wind measurement system for a wind turbine according to an exemplary embodiment of the present application
  • FIG. 3 shows a flowchart of a control method for a wind turbine according to an exemplary embodiment of the present application
  • FIG. 4 shows a schematic diagram of the wind speed change speed of the incoming wind in a duration period according to an exemplary embodiment of the present application
  • Figure 5 shows a schematic diagram of the wind shear factor of incoming wind over a duration period according to an exemplary embodiment of the present application
  • FIG. 6 shows a structural block diagram of a control device for a wind turbine according to an exemplary embodiment of the present application.
  • FIG. 7 shows a schematic diagram of a system architecture for a wind turbine according to an exemplary embodiment of the present application
  • Figure 8 shows a schematic diagram of a wind turbine according to an exemplary embodiment of the present application.
  • the concept of the present application is that the wind turbine will bear loads caused by various complex wind conditions (such as excessive turbulence intensity, sudden change in wind speed, sudden change in wind direction, etc.) while capturing wind energy.
  • the induced loads are also different. Therefore, before the complex wind conditions reach the wind turbines, different feedforward load reduction strategies can be implemented for the running wind turbines for different complex wind conditions to ensure that the wind turbines are in It can also operate safely and stably in complex terrain areas, and ensure the overall power generation of the unit to the greatest extent.
  • FIG. 1 shows a schematic diagram 100 of sector-based distribution of wind conditions for a wind turbine according to an exemplary embodiment of the present application.
  • FIG. 2 shows a schematic diagram 200 of a lidar wind measurement system for a wind turbine according to an exemplary embodiment of the present application.
  • a lidar wind measurement system 201 for a wind turbine can be arranged on the top of the nacelle of the wind turbine, and the front and surrounding areas of the wind turbine can be accurately measured by the lidar wind measurement system
  • the incoming wind information including, but not limited to, wind speed, wind direction, turbulence and other information.
  • the characteristic wind condition recognition algorithm can be used to capture complex wind conditions in advance, so that the wind turbine can respond positively, thereby effectively reducing the negative impact of various complex wind conditions on the wind turbine, and further improving the safety of the wind turbine. and stability.
  • FIG. 3 shows a flowchart 300 of a control method for a wind turbine according to an exemplary embodiment of the present application.
  • control method shown in FIG. 3 may include the following steps:
  • step 310 the incoming wind information of the wind turbine may be obtained.
  • the forward wind condition information around the wind turbine can be detected by a lidar wind measurement system (as shown in FIG. 2 ), and the incoming wind information of the wind turbine can be derived from the detected forward wind information.
  • the flow wind information may include, but is not limited to, various wind parameter information such as wind speed, wind direction, and wind speed status. In this way, the incoming wind information of the wind turbine in all directions can be predicted before the incoming wind blows to the impeller of the wind turbine, so as to provide accurate and reliable data basis for the subsequent feedforward load shedding control. .
  • step 320 it may be determined whether there is a sector with complex wind conditions around the wind turbine according to the acquired inflow wind information.
  • the value of the wind condition characteristic of the incoming wind when the wind turbine is subjected to the maximum load can be selected as the value of the wind condition characteristic of the incoming wind.
  • the value of the wind condition characteristic corresponding to the vertex moment of the envelope of the nacelle acceleration or the relative position load can be selected.
  • the value that is the wind condition characteristic of the incoming wind is not limited to this, for example, the value of the wind condition characteristic corresponding to the mean value moment of the wind turbine under load may also be selected as the value of the wind condition characteristic of the incoming wind.
  • the wind condition characteristics may include, but are not limited to, one or a combination of the following characteristics: the turbulence intensity of the incoming wind in the duration period, the wind speed change speed of the incoming wind in the duration period, the incoming wind in the duration period The wind direction twist angle in the segment, the wind shear factor of the incoming wind in the duration segment, the wind direction change speed of the incoming wind in the duration segment, and the wind direction fluctuation amplitude of the incoming wind in the duration segment.
  • wind condition characteristics are only exemplary, and the present application is not limited thereto, and other wind condition characteristics, such as leeward, etc., may also be used as required.
  • a correlation coefficient between each of the plurality of wind condition characteristics and the load ie, The influence weight of each wind condition feature on the load
  • the wind condition feature whose correlation coefficient among the multiple wind condition features is greater than a predetermined threshold is identified as a complex wind condition (that is, the correlation coefficient among the multiple wind condition features is identified as a complex wind condition
  • the larger several wind conditions are characterized as complex wind conditions).
  • the wind condition feature with the largest correlation coefficient among the multiple wind condition characteristics can also be identified as a complex wind condition. In this regard, this application has no limitation.
  • Wind turbines are prone to various failures under these complex wind conditions, such as increased nacelle acceleration, overspeed of the unit, and blade sweeping.
  • the wind turbine After identifying the complex wind conditions, the wind turbine can be divided into several sectors along the surrounding 360° direction (as shown in Figure 1), and then based on the inflow direction of the complex wind conditions, it is determined whether there are complex wind turbines around the wind turbine. Sector of wind conditions.
  • FIG. 4 shows a schematic diagram 400 of the speed of change of the wind speed of the incoming wind in a duration period according to an exemplary embodiment of the present application.
  • the wind speed change speed of the incoming wind in the duration period t 0 -t 1 to t 0 (ie, the change amount of the sudden change in wind speed) can be determined by the duration period t 0 -t 1 to t 0 shown in FIG. 4 . It is characterized by the integral area 401 between the minimum and maximum wind speed within. When the integrated area 401 exceeds the threshold, the sudden change of wind speed shown in FIG. 4 can be regarded as a complex wind condition.
  • FIG. 5 shows a schematic diagram 500 of the wind shear factor of incoming wind over a duration period according to an exemplary embodiment of the present application.
  • the wind shear factor of the incoming wind in the duration t 0 -t 1 to t 0 can be simulated by two wind speeds at different heights in the duration t 0 -t 1 to t 0 shown in FIG. 5 It is characterized by the integrated area 501 between the contour lines (ie, the change in wind shear mutation). When the integrated area 501 exceeds a threshold (or is a negative value), the sudden change in wind shear shown in FIG. 5 can be regarded as a complex wind condition.
  • the turbulent intensity of the incoming wind over a duration can be characterized by the ratio of the standard deviation of the wind speed to the mean wind speed over the duration (ie, the amount of change in the turbulent intensity abrupt change).
  • the ratio exceeds a threshold, the sudden change in turbulence intensity can be regarded as a complex wind condition.
  • the wind direction reversal angle of the incoming wind in the duration period can be determined by the extreme value difference or standard deviation between the maximum and minimum value of the wind direction time series in the duration period (ie, the change of the wind direction abrupt change quantity) to characterize.
  • the extreme value difference or standard deviation exceeds the threshold, the sudden change in wind direction can be regarded as a complex wind condition.
  • feedforward load shedding control may be performed on the wind turbine according to the complex wind conditions in response to the existence of a sector with complex wind conditions around the wind turbine.
  • the feedforward load shedding control may include, but is not limited to, one or a combination of the following operations: increasing the pitch angle of the wind turbine, increasing the pitch speed of the wind turbine, decreasing the wind turbine's pitch angle generator speed and reduce the generator torque of the wind turbine.
  • These load reduction control operations can effectively reduce the increased unit load due to the above-mentioned complex wind conditions.
  • feedforward load shedding operation manners are merely exemplary, and the present application is not limited thereto, and other feedforward load shedding operation manners may also be adopted as required.
  • complex wind response parameters such as, but not limited to, pitch angle, pitch speed, generator speed, and generator torque, etc.
  • the optimal load shedding control strategy included in the wind condition shedding model and set for the response parameters of each complex wind condition can be defined according to historical load shedding operation data, or can be defined by using neural network training.
  • the data of the historical load reduction operation of the wind turbine and the data of each load reduction operation are obtained by training.
  • the wind load shedding model constructed by neural network training can make the load shedding control of wind turbines more accurate and intelligent, thereby effectively reducing unit failures caused by various complex wind conditions (even extreme wind conditions). and shutdown, reduce the resulting loss of power generation, and improve the adaptability of wind turbines to the natural environment (especially, complex terrain).
  • an enhanced dynamic fuzzy neural network model can be used, and its implementation process is as follows:
  • Step 1 confirm the fuzzy set.
  • the concept of fuzzy set is generalized on the basis of general set, and it is also composed of some elements, but these elements are described by fuzzy language, which can be described by three fuzzy language values.
  • a membership function for example, a Gaussian membership function
  • the membership function is to measure the degree of membership of a certain exact quantity to a certain fuzzy language value.
  • the output value of the membership function can be in the interval [0,1], and the degree of uncertainty can be converted into a mathematical expression through the membership function.
  • Step III the fuzzy rules can be confirmed.
  • the TSK model can be used here, the output of which is an exact quantity and is determined by all inputs to the system (eg, radar wind direction, radar wind speed, turbulence intensity, wind shear, wind twist, pitch angle, acceleration in the X direction and acceleration in the Y direction, etc. information), and its coefficients are equivalent to different weight coefficients.
  • Step IV the dynamic fuzzy neural network based on ellipse can be used.
  • the increase or decrease of the fuzzy rules can be judged by predicting the systematic deviation between the control signal and the actual signal and the mapping range of the Gaussian accommodating boundary, and normalizing through the ellipse basis.
  • the fuzzy rules are pruned by the least squares method that minimizes the systematic deviation.
  • step V the optimal load shedding control function can be output.
  • the parameter (function) of the optimal load shedding control is the feedforward pitch rate under complex wind conditions.
  • the enhanced dynamic fuzzy neural network model adopted above is only exemplary, and the present application is not limited thereto, and other enhanced neural network models or other different types of neural network models may also be used as required.
  • the upper limit of the output power of the wind turbine may also be limited in response to the absence of a response parameter of the complex wind condition matching the complex wind condition in the wind condition derating model.
  • the upper limit of power can be defined by parameters, and can also be set as the power corrected according to different wind speeds.
  • the wind turbine can be controlled to perform the feedforward load shedding control operation described above based on the output power upper limit, such as, but not limited to, increasing the pitch angle of the wind turbine.
  • the feedforward load shedding control (also called power limiting operation) performed by limiting the upper limit of the output power of the wind turbine can also be used as the optimal load shedding control set for the response parameters of complex wind conditions
  • the strategy is recorded into the wind derating model.
  • the time series data before and after the feedforward load shedding control performed by limiting the upper limit of the output power of the wind turbine can be used as a sample for learning the optimal load shedding control strategy for complex wind conditions, and the accumulated samples are learned through neural network training, and the It is recorded into the wind derating model along with the response parameters matched to the complex wind conditions.
  • the initial state of the optimal load shedding control parameter flag can be defaulted to False (that is, the wind turbine has not obtained the parameters of the optimal load shedding control), and the complex wind turbine before the power limiting operation is executed
  • the response parameters of the wind condition are output to the wind load reduction model for training, and then the wind load reduction model is divided into a test set and a validation set according to certain rules to select a multi-level neural network for training, and the hidden layer information is normalized. Processing to eliminate the problem of different scales between different information dimensions.
  • the parameters of the optimal load shedding control can be obtained through training. At this time, the optimal load shedding control parameter flag bit can be output as True to end the training.
  • FIG. 6 shows a structural block diagram 600 of a control device for a wind turbine according to an exemplary embodiment of the present application.
  • a control apparatus for a wind turbine may include a wind condition prediction unit 610 , a sector identification unit 620 and a load reduction control unit 630 , wherein the wind condition prediction unit 610 may be is configured to acquire the incoming wind information of the wind turbine; the sector identification unit 620 may be configured to determine whether there is a sector with complex wind conditions around the wind turbine according to the acquired incoming wind information; the load reduction control unit 630 may be configured to Feedforward load shedding control is performed on the wind turbine according to the complex wind conditions in response to the existence of a sector having complex wind conditions around the wind turbine.
  • the wind condition prediction unit 610 may include a radar detection unit and a wind condition acquisition unit (neither are shown), wherein the radar detection unit may be configured to detect the wind turbine through a lidar wind measurement system surrounding forward wind condition information; the wind condition acquisition unit may be configured to derive the incoming wind information of the wind turbine from the detected forward wind condition information.
  • the sector identification unit 620 may include a load determination unit, a feature determination unit, a wind condition identification unit and a sector identification unit (none of which are shown), wherein the load determination unit may be configured as determining whether the load on the wind turbine park due to the incoming wind exceeds a set threshold; the characteristic determination unit may be configured to determine the wind condition of the incoming wind when the wind turbine is subjected to the load in response to the load exceeding the set threshold Whether the value of the characteristic exceeds the characteristic threshold; the wind condition identification unit may be configured to identify the wind condition of the future flow wind as a complex wind condition in response to the value of the wind condition characteristic exceeding the characteristic threshold; the sector identification unit may be configured according to the complex wind condition The direction of the inflow determines whether there are sectors with complex wind conditions around the wind turbine.
  • the wind condition characteristics may include, but are not limited to, at least one of the following characteristics: the turbulence intensity of the incoming wind in the duration period, the wind speed change speed of the incoming wind in the duration period , the wind direction twist angle of the incoming wind in the duration period, the wind shear factor of the incoming wind in the duration period, the wind direction change speed of the incoming wind in the duration period and the wind direction fluctuation amplitude of the incoming wind in the duration period.
  • the wind condition identification unit may be further configured to determine a correlation between each wind condition characteristic of the plurality of wind condition characteristics and the load. coefficient (ie, the influence weight of each wind condition feature on the load), and a wind condition feature whose correlation coefficient among the plurality of wind condition features is greater than a predetermined threshold is identified as a complex wind condition.
  • a wind condition selection unit (not shown) may also be included, and the wind condition selection unit may be configured to select the wind condition characteristic of the incoming wind when the wind turbine is subjected to the maximum value of the load The value of is the value of the wind condition characteristics of the incoming wind when the wind turbine is under load.
  • the load shedding control unit 630 may include a wind condition matching unit, a load shedding acquisition unit and a first control unit (none of which are shown), wherein the wind condition matching unit may be configured to determine the wind condition Whether there are response parameters of complex wind conditions matching the complex wind conditions in the load reduction model, wherein the wind load reduction model includes the optimal load reduction control strategy set for the response parameters of each complex wind condition of the wind turbine;
  • the derating acquisition unit may be configured to acquire, in response to the presence of a response parameter of the complex wind condition matching the complex wind condition in the wind condition derating model, obtain the response parameter for the matched complex wind condition from the wind condition derating model and set
  • the optimal load shedding control strategy is obtained;
  • the first control unit may be configured to perform feedforward load shedding control on the wind turbine according to the obtained optimal load shedding control strategy.
  • the load shedding control unit 630 may further include a second control unit (not shown), and the second control unit may be configured to respond to the absence of and complex wind conditions in the wind condition shedding model.
  • the maximum output power of the wind turbine is limited by matching the response parameters of complex wind conditions.
  • the control device shown in FIG. 6 may further include a load reduction recording unit (not shown), and the load reduction recording unit may be configured to use the feedforward load reduction control performed to limit the upper limit of the output power of the wind turbine as a target
  • the optimal load shedding control strategy set in response to the complex wind conditions is recorded in the wind condition shedding model.
  • the feedforward load shedding control may include, but is not limited to, the following operations: increasing the pitch angle of the wind turbine, increasing the pitch speed of the wind turbine, and reducing the wind turbine increase the generator speed and reduce the generator torque of the wind turbine.
  • FIG. 7 is a schematic diagram 700 of a system architecture for a wind turbine according to an exemplary embodiment of the present application.
  • a system architecture for a wind turbine may include a control device 710, a wind turbine 720, and a wind turbine controller 730 shown in FIG. 6 (such as, but not limited to, The main control PLC system or pitch control system in the wind turbine, etc.).
  • the control method for the wind turbine according to the exemplary embodiment of the present application may be run as an algorithm in the computing unit of the control device 710, and the control device 710 may include, but not limited to, the wind condition prediction shown in FIG. 6 . unit 610 , sector identification unit 620 and load shedding control unit 630 .
  • the control device 710 can obtain the sensed inflow wind information A of the wind turbine from the lidar wind measurement system (as shown in FIG. 2 ) disposed on the top of the nacelle of the wind turbine. , and then determine whether there is a sector with complex wind conditions around the wind turbine according to the incoming wind information A, and feed forward the wind turbine according to the complex wind condition in response to the existence of a sector with complex wind conditions around the wind turbine Load drop control.
  • FIG. 7 shows a system architecture for a wind turbine according to an exemplary embodiment of the present application
  • the present application is not limited thereto, for example, the control device 710 may also be provided in the wind turbine controller 730 and the wind turbine 720.
  • the control device 710 may be integrated in the wind turbine controller 730 or a backend controller in the wind farm for dispatching wind turbines or other controllers in addition to being integrated in a separate controller.
  • this application has no limitation.
  • the control device 710 (not shown in the figure) is integrated into the main control PLC system 731 of the wind turbine, and the lidar wind measurement system arranged on the top of the nacelle of the wind turbine can obtain the sensed The incoming wind information of the received wind turbine, and send the incoming wind information to the wind turbine main control PLC system 731; the wind turbine main control PLC system 731 can determine whether there is a complex wind condition around the wind turbine 720 according to the incoming wind information and in response to the existence of a sector with complex wind conditions around the wind generator set 720, the feedforward load shedding control is performed on the wind generator set according to the complex wind conditions.
  • the control method and control device for a wind power generating set can enable the wind power generating set to perform automatic self-control for various complex wind conditions without adding new investment (such as additional hardware equipment).
  • Exemplary embodiments according to the present application may also provide a computer-readable storage medium storing a computer program.
  • the computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to execute the control method for a wind turbine according to the present application.
  • the computer-readable recording medium is any data storage device that can store data read by a computer system. Examples of the computer-readable recording medium include read-only memory, random-access memory, optical disks, magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission through the Internet via wired or wireless transmission paths).
  • Exemplary embodiments according to the present application may also provide a computer apparatus.
  • the computer device includes a processor and memory. Memory is used to store computer programs.
  • the computer program is executed by the processor so that the processor executes the computer program for the control method for a wind turbine according to the present application.

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Abstract

用于风力发电机组（720）的控制方法及控制装置（710），控制方法包括：获取风力发电机组（720）的来流风信息；根据获取的来流风信息，确定风力发电机组（720）周围是否存在具有复杂风况的扇区；响应于风力发电机组（720）周围存在具有复杂风况的扇区，根据复杂风况对风力发电机组（720）进行前馈降载控制。

Description

用于风力发电机组的控制方法及控制装置 技术领域
本申请涉及风力发电技术领域，尤其涉及一种用于风力发电机组的控制方法及控制装置。
背景技术
随着对风能资源富集、地形地域开阔的优质风区的不断开发，人们开始将风电场部署在复杂地形的山地区域。然而，这些区域地形起伏大、变化多，风能资源分布不均匀，且气候条件比较复杂、植被繁多，风况变化多端，使得风力发电机组的应用环境变得格外复杂。在实际应用中，因复杂地形而导致的复杂风况往往超出了在设计时针对风力发电机组所考虑的标准和极限范围，这给风力发电机组的强度、寿命以及发电性能带来很大的危害。
在相关技术中，针对这些复杂风况提供的机组控制策略的适应性欠佳，机组故障多发，难以在保证机组安全的前提下实现精细化的能量优化管理和发电量最优。
发明内容
本申请的目的在于提供用于风力发电机组的控制方法及控制装置。
根据本申请的一方面，提供一种用于风力发电机组的控制方法，所述控制方法包括：获取所述风力发电机组的来流风信息；根据获取的来流风信息，确定所述风力发电机组周围是否存在具有复杂风况的扇区；响应于所述风力发电机组周围存在具有复杂风况的扇区，根据所述复杂风况对所述风力发电机组进行前馈降载控制。
根据本申请的另一方面，提供一种用于风力发电机组的控制装置，所述控制装置包括：风况预测单元，被配置为：获取所述风力发电机组的来流风信息；扇区识别单元，被配置为：根据获取的来流风信息，确定所述风力发电机组周围是否存在具有复杂风况的扇区；降载控制单元，被配置为：响应于所述风力发电机组周围存在具有复杂风况的扇区，根据所述复杂风况对所 述风力发电机组进行前馈降载控制。
根据本申请的另一方面，提供一种存储有计算机程序的计算机可读存储介质，当所述计算机程序在被处理器执行时，实现如前面所述的用于风力发电机组的控制方法。
根据本申请的另一方面，提供一种计算机设备，所述计算机设备包括：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，实现如前面所述的用于风力发电机组的控制方法。
根据本申请的示例性实施例的用于风力发电机组的控制方法及控制装置能够在不增加新投入(诸如，额外的硬件设备)的前提下使得风力发电机组能够针对各种复杂风况进行自适应降载，从而有效地降低因各种复杂风况而引起的机组载荷，提高机组的安全性以及对自然环境的适应能力。
附图说明
通过下面结合附图进行的描述，本申请的上述目的和特点将会变得更加清楚，其中：
图1示出了根据本申请的示例性实施例的用于风力发电机组的基于扇区的风况分布的示意图；
图2示出了根据本申请的示例性实施例的用于风力发电机组的激光雷达测风系统的示意图；
图3示出了根据本申请的示例性实施例的用于风力发电机组的控制方法的流程图；
图4示出了根据本申请的示例性实施例的来流风在持续时间段内的风速变化速度的示意图；
图5示出了根据本申请的示例性实施例的来流风在持续时间段内的风剪切因子的示意图；
图6示出了根据本申请的示例性实施例的用于风力发电机组的控制装置的结构框图；以及
图7示出了根据本申请的示例性实施例的用于风力发电机组的系统架构的示意图；
图8示出根据本申请的示例性实施例的风力发电机组的示意图。
具体实施方式
本申请的构思在于：风力发电机组在捕获风能的同时会承受因各种复杂风况(诸如，湍流强度过大、风速突变、风向突变等)而引起的载荷，由于因不同的复杂风况而引起的载荷也是不同的，因此，可在复杂风况还未到达风力发电机组之前，针对不同的复杂风况对运行中的风力发电机组执行不同的前馈降载策略，以确保风力发电机组在复杂地形区域中也能安全稳定运行，并在最大程度上保证机组的整体发电量。
下面，将参照附图来详细说明本申请的实施例。
图1示出了根据本申请的示例性实施例的用于风力发电机组的基于扇区的风况分布的示意图100。
参照图1，可以看出，当风力发电机组运行在复杂地形条件下，风力发电机组的各个扇区的地形存在很大的差异，导致风力发电机组的各个扇区中的风况差异也很大，其中颜色最深的扇区为具有复杂风况的扇区。针对具有复杂风况的扇区，应采取相应且有效的降载控制策略，以确保风力发电机组能够在复杂风况下安全、稳定地运行，并在最大程度上保证各个机组的整体发电量，同时降低现场维护成本并增加项目整体投资收益率的效果。
图2示出了根据本申请的示例性实施例的用于风力发电机组的激光雷达测风系统的示意图200。
参照图2，根据本申请的示例性实施例的用于风力发电机组的激光雷达测风系统201可设置在风力发电机组的机舱顶部，通过激光雷达测风系统可精准地测量风力发电机组周围前方的来流风信息，包括，但不限于，风速、风向、湍流等信息。在此基础上，可使用特征风况识别算法来提前捕获复杂风况，以使风力发电机组做出积极响应，从而有效地降低各种复杂风况对机组的负面影响，进一步提高机组的安全性和稳定性。
图3示出了根据本申请的示例性实施例的用于风力发电机组的控制方法的流程图300。
参照图3，图3所示的控制方法可包括如下步骤：
在步骤310，可获取风力发电机组的来流风信息。
在一个示例中，可通过激光雷达测风系统(如图2所示)探测风力发电机组周围的前方风况信息，并从探测的前方风况信息中导出风力发电机组的来流风信息，该来流风信息可包括，但不限于，风速风向、风速状态等各种 风参信息。以这种方式，可在来流风还未吹到风力发电机组的叶轮之前，预判出风力发电机组在所有方向上的来流风信息，以便为后续前馈降载控制提供精准、可靠的数据依据。
应当理解，尽管上面描述了通过激光雷达测风系统获取来流风信息的示例，但是该示例仅仅是示例性的，本申请并不限于此，例如，也可从气象预报或其他遥感测风设备中获取来流风信息。
在步骤320，可根据获取的来流风信息确定风力发电机组周围是否存在具有复杂风况的扇区。
在一个示例中，可确定风力发电机组因来流风而承受的载荷是否超过设定阈值，响应于所述载荷超过设定阈值而确定风力发电机组在承受所述载荷时的来流风的风况特征的值是否超过特征阈值，响应于风况特征的值超过特征阈值而将来流风的风况识别为复杂风况，根据复杂风况的入流方向确定风力发电机组周围是否存在具有复杂风况的扇区。作为可行的实施方式，可选择风力发电机组在承受载荷中的最大值时的来流风的风况特征的值作为来流风的风况特征的值。例如，当使用风力发电机组的机舱加速度或相关位置载荷来表征风力发电机组因来流风而承受的载荷时，可选择机舱加速度或相关位置载荷的包络的顶点时刻所对应的风况特征的值作为来流风的风况特征的值。然而，本申请并不限于此，例如，还可选择风力发电机组在承受载荷中的均值时刻所对应的风况特征的值作为来流风的风况特征的值。
这里，风况特征可包括，但不限于，以下特征中的一者或其组合：来流风在持续时间段内的湍流强度，来流风在持续时间段内的风速变化速度，来流风在持续时间段内的风向扭转角度，来流风在持续时间段内的风剪切因子，来流风在持续时间段内的风向变化速度以及来流风在持续时间段内的风向波动幅度。
应当理解，以上风况特征仅仅是示例性的，本申请并不限于此，根据需要，也可使用其他风况特征，例如，背风等。
另外，在该示例中，在来流风的多个风况特征超过相应特征阈值的情况下，可确定多个风况特征中的每个风况特征与所述载荷之间的相关系数(即，每个风况特征对所述载荷的影响权重)，并且将多个风况特征中的相关系数大于预定阈值的风况特征识别为复杂风况(即，将多个风况特征中的相关系数较大的若干风况特征识别为复杂风况)。此外，也可将多个风况特征中的相关 系数最大的风况特征识别为复杂风况。对此，本申请没有限制。
风力发电机组在这些复杂风况下容易地发生各种故障，诸如，机舱加速度增大、机组过速、叶片扫塔等问题。
在识别出复杂风况之后，可将风力发电机组沿周围360°方向上拆分成若干扇区(如图1所示)，然后基于复杂风况的入流方向确定风力发电机组周围是否存在具有复杂风况的扇区。
下面，将参照图4和图5来具体地描述上述复杂风况中的一部分。
图4示出了根据本申请的示例性实施例的来流风在持续时间段内的风速变化速度的示意图400。
参照图4，来流风在持续时间段t 0-t 1到t 0内的风速变化速度(即，风速突变的变化量)可由图4中所示的持续时间段t 0-t 1到t 0内的风速的最小值与最大值之间的积分面积401来表征。当该积分面积401超过阈值时，可将图4所示的风速突变视为复杂风况。
图5示出了根据本申请的示例性实施例的来流风在持续时间段内的风剪切因子的示意图500。
参照图5，来流风在持续时间段t 0-t 1到t 0内的风剪切因子可由图5中所示的持续时间段t 0-t 1到t 0内的两条不同高度风速拟合轮廓线之间的积分面积501(即，风剪切突变的变化量)来表征。当该积分面积501超过阈值(或为负值)时，可将图5所示的风剪切突变视为复杂风况。
尽管未在图中示出，但是来流风在持续时间段内的湍流强度可由持续时间段内的风速标准差与风速均值的比值(即，湍流强度突变的变化量)来表征。当该比值超过阈值时，可将该湍流强度突变视为复杂风况。
尽管未在图中示出，但是来流风在持续时间段内的风向扭转角度可由持续时间段内的风向时序的最大值与最小值之间的极值差或标准差(即，风向突变的变化量)来表征。当该极值差或标准差超过阈值时，可将该风向突变视为复杂风况。
再次返回图3，在步骤330，可响应于风力发电机组周围存在具有复杂风况的扇区而根据复杂风况对风力发电机组进行前馈降载控制。
这里，前馈降载控制可包括，但不限于，以下操作中的一者或其组合：增大风力发电机组的桨距角度，增大风力发电机组的变桨速度，减小风力发电机组的发电机转速以及减小风力发电机组的发电机扭矩。这些降载控制操 作可有效降低因上述复杂风况而增大的机组载荷。
应当理解，以上前馈降载操作方式仅仅是示例性的，本申请并不限于此，根据需要，也可采用其他前馈降载操作方式。
在一个示例中，可确定风况降载模型中是否存在与复杂风况匹配的复杂风况的响应参数(例如，但不限于，桨距角度、变桨速度、发电机转速和发电机扭矩等控制参数)，其中，风况降载模型包括针对各个复杂风况的响应参数而设置的最优降载控制策略；响应于风况降载模型中存在与复杂风况匹配的复杂风况的响应参数而从风况降载模型中获取针对所匹配的复杂风况的响应参数而设置的最优降载控制策略；并且根据获取的最优降载控制策略对风力发电机组进行前馈降载控制。作为可行的实施方式，风况降载模型中所包括的针对各个复杂风况的响应参数而设置的最优降载控制策略可根据历史降载操作数据来定义，也可通过采用神经网络训练的方式对风力发电机组的历史降载操作数据以及每次降载操作数据进行训练来获得。采用神经网络训练的方式构建的风况降载模型能够使得对风力发电机组的降载控制更精准、更智能，从而有效地减少因各种复杂风况(甚至极端风况)而引起的机组故障和停机，降低由此而产生的发电量损失，提高风力发电机组对自然环境(特别是，复杂地形)的适应性。
在该示例中，可采用增强型的动态模糊神经网络模型，其实施过程如下：
步骤Ⅰ，可确认模糊集，模糊集的概念是在一般集合的基础上推广而来的，也是由一些元素组成，只是这些元素是由模糊语言来描述的，可使用三个模糊语言值来描述风况复杂度：低、一般、高，例如，如果分别使用字母将其表示为NS、ZO和PS，则模糊集合可表示为T＝{NS,ZO,PS}。
步骤Ⅱ，可确认隶属函数(例如，高斯型隶属函数)，隶属函数是为了衡量某确切量隶属某一模糊语言值的隶属程度。隶属函数的输出值可在区间[0,1]之间，通过隶属函数可使得不确定的程度转化成数学表达式。
步骤Ⅲ，可确认模糊规则。这里可采用TSK模型，其输出是一个确切量并且是由系统的所有输入(例如，雷达风向、雷达风速、湍流强度、风剪切、风扭转、桨距角、X方向加速度和Y方向加速度等信息)的线性组合而得到的，其系数等价于不同的权重系数。
步骤Ⅳ，可采用椭圆基的动态模糊神经网络，具体地，可通过预测控制信号与实际信号的系统偏差和高斯可容纳边界的映射范围来判别模糊规则的 增减，通过椭圆基来进行归一化处理，并且通过使得系统偏差达到最小的最小二乘法来对模糊规则进行修剪。
步骤Ⅴ，可输出最优降载控制函数，这里，最优降载控制的参数(函数)为复杂风况下前馈变桨速率。
应当理解，以上所采用的增强型的动态模糊神经网络模型仅仅是示例性的，本申请并不限于此，根据需要，也可采用其他增强型神经网络模型或其他不同类型的神经网络模型。
另外，在该示例中，还可响应于风况降载模型中不存在与复杂风况匹配的复杂风况的响应参数而限制风力发电机组的输出功率上限。该功率上限可通过参数来限定，也可设置为根据不同风速修正的功率。作为可行的实施方式，可控制风力发电机组基于该输出功率上限而执行前面所描述的前馈降载控制操作，例如，但不限于，增大风力发电机组的桨距角度等。
另外，在该示例中，还可将限制风力发电机组的输出功率上限而进行的前馈降载控制(也称为限功率操作)作为针对复杂风况的响应参数而设置的最优降载控制策略记录到风况降载模型中。例如，可将限制风力发电机组的输出功率上限而进行的前馈降载控制前后时序数据作为复杂风况学习最优降载控制策略的一个样本，对累计的样本通过神经网络训练学习，并将其与复杂风况匹配的响应参数一起记录到风况降载模型中。这样可为风力发电机组或其他风力发电机组在未来运行过程中遇到相同或相似的复杂风况而提供相应的最优降载控制策略。作为可行的实施方式，可将最优降载控制参数标志位的初始状态默输出为False(即，风力发电机组尚未获取最优降载控制的参数)，并将限功率操作执行之前的复杂风况的响应参数输出到风况降载模型中进行训练，然后将风况降载模型按照一定规则划分为测试集和验证集以选择多层级神经网络进行训练，并对隐藏层信息进行归一化处理以消除不同信息维度间尺度不同的问题。对于不同的信息可采用不同的权重值，最终以预测控制信号与实际信号之间的偏差作为优化目标。通过训练可获取最优降载控制的参数，此时可将最优降载控制参数标志位输出为True以结束训练。
图6示出了根据本申请的示例性实施例的用于风力发电机组的控制装置的结构框图600。
参照图6，根据本申请的示例性实施例的用于风力发电机组的控制装置可包括风况预测单元610、扇区识别单元620和降载控制单元630，其中，风 况预测单元610可被配置为获取风力发电机组的来流风信息；扇区识别单元620可被配置为根据获取的来流风信息确定风力发电机组周围是否存在具有复杂风况的扇区；降载控制单元630可被配置为响应于风力发电机组周围存在具有复杂风况的扇区而根据所述复杂风况对风力发电机组进行前馈降载控制。
在图6所示的控制装置中，风况预测单元610可包括雷达探测单元和风况获取单元(均未示出)，其中，雷达探测单元可被配置为通过激光雷达测风系统探测风力发电机组周围的前方风况信息；风况获取单元可被配置为从探测的前方风况信息中导出风力发电机组的来流风信息。
在图6所示的控制装置中，扇区识别单元620可包括载荷确定单元、特征确定单元、风况识别单元和扇区识别单元(均未示出)，其中，载荷确定单元可被配置为确定风力发电机组因来流风而承受的载荷是否超过设定阈值；特征确定单元可被配置为响应于所述载荷超过设定阈值而确定风力发电机组在承受所述载荷时的来流风的风况特征的值是否超过特征阈值；风况识别单元可被配置为响应于风况特征的值超过特征阈值而将来流风的风况识别为复杂风况；扇区识别单元可被配置为根据复杂风况的入流方向确定风力发电机组周围是否存在具有复杂风况的扇区。
在图6所示的控制装置中，风况特征可包括，但不限于，以下特征中的至少一者：来流风在持续时间段内的湍流强度，来流风在持续时间段内的风速变化速度，来流风在持续时间段内的风向扭转角度，来流风在持续时间段内的风剪切因子，来流风在持续时间段内的风向变化速度以及来流风在持续时间段内的风向波动幅度。相应地，在来流风的多个风况特征超过相应特征阈值的情况下，风况识别单元还可被配置为确定多个风况特征中的每个风况特征与所述载荷之间的相关系数(即，每个风况特征对所述载荷的影响权重)，并且将多个风况特征中的相关系数大于预定阈值的风况特征识别为复杂风况。
在图6所示的控制装置中，还可包括风况选择单元(未示出)，风况选择单元可被配置为选择风力发电机组在承受载荷中的最大值时的来流风的风况特征的值作为风力发电机组在承受载荷时的来流风的风况特征的值。
在图6所示的控制装置中，降载控制单元630可包括风况匹配单元、降载获取单元和第一控制单元(均未示出)，其中，风况匹配单元可被配置为确定风况降载模型中是否存在与复杂风况匹配的复杂风况的响应参数，其中， 风况降载模型包括针对风力发电机组的各个复杂风况的响应参数而设置的最优降载控制策略；降载获取单元可被配置为响应于风况降载模型中存在与复杂风况匹配的复杂风况的响应参数而从风况降载模型中获取针对所匹配的复杂风况的响应参数而设置的最优降载控制策略；第一控制单元可被配置为根据获取的最优降载控制策略对风力发电机组进行前馈降载控制。
在图6所示的控制装置中，降载控制单元630还可包括第二控制单元(未示出)，第二控制单元可被配置为响应于风况降载模型中不存在与复杂风况匹配的复杂风况的响应参数而限制风力发电机组的输出功率上限。
在图6所示的控制装置中，还可包括降载记录单元(未示出)，降载记录单元可被配置为将限制风力发电机组的输出功率上限而进行的前馈降载控制作为针对复杂风况的响应参数而设置的最优降载控制策略记录到风况降载模型。
在图6所示的控制装置中，前馈降载控制可包括，但不限于，以下操作：增大风力发电机组的桨距角度，增大风力发电机组的变桨速度，减小风力发电机组的发电机转速以及减小风力发电机组的发电机扭矩。
图7根据本申请的示例性实施例的用于风力发电机组的系统架构的示意图700。
参照图7，根据本申请的示例性实施例的用于风力发电机组的系统架构可包括图6所示的控制装置710、风力发电机组720和风力发电机组控制器730(诸如，但不限于，风力发电机组中的主控PLC系统或变桨控制系统等)。根据本申请的示例性实施例的用于风力发电机组的控制方法可作为算法运行在控制装置710的计算单元中，并且该控制装置710可包括，但不限于，图6所示的风况预测单元610、扇区识别单元620和降载控制单元630。
在图7所示的系统架构中，控制装置710可从设置在风力发电机组的机舱顶部的激光雷达测风系统(如图2所示)获取其所感测到的风力发电机组的来流风信息A，然后根据来流风信息A确定风力发电机组周围是否存在具有复杂风况的扇区，并且响应于风力发电机组周围存在具有复杂风况的扇区而根据该复杂风况对风力发电机组进行前馈降载控制。
应当理解，尽管图7示出了根据本申请的示例性实施例的用于风力发电机组的系统架构，但是本申请并不限于此，例如，控制装置710还可被设置在风力发电机组控制器730与风力发电机组720之间。另外，控制装置710 除了可被集成在单独的控制器中之外，还可被集成在风力发电机组控制器730中或风电场中的用于调度风力发电机组的后台控制器或其他可连接至风力发电机组控制器730或风力发电机组720的控制设备中。对此，本申请没有限制。
示例性地，如图8所示，控制装置710(图中未示出)集成在风力发电机组主控PLC系统731，设置在风力发电机组的机舱顶部的激光雷达测风系统可以获取其所感测到的风力发电机组的来流风信息，并将来流风信息发送给风力发电机组主控PLC系统731；风力发电机组主控PLC系统731可以根据来流风信息确定风力发电机组720周围是否存在具有复杂风况的扇区，并且响应于风力发电机组720周围存在具有复杂风况的扇区而根据该复杂风况对风力发电机组进行前馈降载控制。
根据本申请的示例性实施例的用于风力发电机组的控制方法及控制装置能够在不增加新投入(诸如，额外的硬件设备)的前提下使得风力发电机组能够针对各种复杂风况进行自适应降载，从而有效地降低因各种复杂风况而引起的机组载荷，提高机组的安全性以及对自然环境的适应能力。
根据本申请的示例性实施例还可提供一种存储有计算机程序的计算机可读存储介质。该计算机可读存储介质存储有当被处理器执行时使得处理器执行根据本申请的用于风力发电机组的控制方法的计算机程序。该计算机可读记录介质是可存储由计算机系统读出的数据的任意数据存储装置。计算机可读记录介质的示例包括：只读存储器、随机存取存储器、只读光盘、磁带、软盘、光数据存储装置和载波(诸如经有线或无线传输路径通过互联网的数据传输)。
根据本申请的示例性实施例还可提供一种计算机设备。该计算机设备包括处理器和存储器。存储器用于存储计算机程序。所述计算机程序被处理器执行使得处理器执行根据本申请的用于风力发电机组的控制方法的计算机程序。
尽管已参照优选实施例表示和描述了本申请，但本领域技术人员应该理解，在不脱离由权利要求限定的本申请的精神和范围的情况下，可以对这些实施例进行各种修改和变换。

Claims (22)

  1. 一种用于风力发电机组的控制方法，其特征在于，所述控制方法包括：
    获取所述风力发电机组的来流风信息；
    根据获取的来流风信息，确定所述风力发电机组周围是否存在具有复杂风况的扇区；
    响应于所述风力发电机组周围存在具有复杂风况的扇区，根据所述复杂风况对所述风力发电机组进行前馈降载控制。
  2. 根据权利要求1所述的控制方法，其特征在于，所述获取所述风力发电机组的来流风信息，包括：
    通过激光雷达测风系统探测所述风力发电机组周围的前方风况信息；
    从探测的前方风况信息中导出所述风力发电机组的来流风信息。
  3. 根据权利要求1所述的控制方法，其特征在于，所述确定所述风力发电机组周围是否存在具有复杂风况的扇区，包括：
    确定所述风力发电机组因来流风而承受的载荷是否超过设定阈值；
    响应于所述载荷超过设定阈值，确定所述风力发电机组在承受所述载荷时的来流风的风况特征的值是否超过特征阈值；
    响应于所述风况特征的值超过特征阈值，将来流风的风况识别为复杂风况；
    根据复杂风况的入流方向，确定所述风力发电机组周围是否存在具有复杂风况的扇区。
  4. 根据权利要求3所述的控制方法，其特征在于，所述风况特征包括以下特征中的至少一者：
    来流风在持续时间段内的湍流强度；
    来流风在持续时间段内的风速变化速度；
    来流风在持续时间段内的风向扭转角度；
    来流风在持续时间段内的风剪切因子；
    来流风在持续时间段内的风向变化速度；以及
    来流风在持续时间段内的风向波动幅度。
  5. 根据权利要求4所述的控制方法，其特征在于，在来流风的多个风况特征超过相应特征阈值的情况下，所述将来流风的风况识别为复杂风况，包 括：
    确定所述多个风况特征中的每个风况特征与所述载荷之间的相关系数；
    将所述多个风况特征中的相关系数大于预定阈值的风况特征识别为复杂风况。
  6. 根据权利要求3所述的控制方法，其特征在于，所述控制方法还包括：
    选择所述风力发电机组在承受所述载荷中的最大值时的来流风的风况特征的值作为所述风力发电机组在承受所述载荷时的来流风的风况特征的值。
  7. 根据权利要求1所述的控制方法，其特征在于，所述根据所述复杂风况对所述风力发电机组进行前馈降载控制，包括：
    确定风况降载模型中是否存在与所述复杂风况匹配的复杂风况的响应参数，其中，所述风况降载模型包括针对各个复杂风况的响应参数而设置的最优降载控制策略；
    响应于风况降载模型中存在与所述复杂风况匹配的复杂风况的响应参数，从风况降载模型中获取针对所匹配的复杂风况的响应参数而设置的最优降载控制策略；
    根据获取的最优降载控制策略，对所述风力发电机组进行前馈降载控制。
  8. 根据权利要求4所述的控制方法，其特征在于，所述根据所述复杂风况对所述风力发电机组进行前馈降载控制，还包括：
    响应于风况降载模型中不存在与所述复杂风况匹配的复杂风况的响应参数，限制所述风力发电机组的输出功率上限。
  9. 根据权利要求5所述的控制方法，其特征在于，所述控制方法还包括：
    将限制所述风力发电机组的输出功率上限而进行的前馈降载控制作为针对所述复杂风况的响应参数而设置的最优降载控制策略记录到所述风况降载模型中。
  10. 根据权利要求1至9中任意一项所述的控制方法，其特征在于，所述降载控制包括以下操作中的至少一者：
    增大所述风力发电机组的桨距角度；
    增大所述风力发电机组的变桨速度；
    减小所述风力发电机组的发电机转速；以及
    减小所述风力发电机组的发电机扭矩。
  11. 一种用于风力发电机组的控制装置，其特征在于，所述控制装置包 括：
    风况预测单元，被配置为：获取所述风力发电机组的来流风信息；
    扇区识别单元，被配置为：根据获取的来流风信息，确定所述风力发电机组周围是否存在具有复杂风况的扇区；
    降载控制单元，被配置为：响应于所述风力发电机组周围存在具有复杂风况的扇区，根据所述复杂风况对所述风力发电机组进行前馈降载控制。
  12. 根据权利要求11所述的控制装置，其特征在于，所述风况预测单元包括：
    雷达探测单元，被配置为：通过激光雷达测风系统探测所述风力发电机组周围的前方风况信息；
    风况获取单元，被配置为：从探测的前方风况信息中导出所述风力发电机组的来流风信息。
  13. 根据权利要求11所述的控制装置，其特征在于，所述扇区识别单元包括：
    载荷确定单元，被配置为：确定所述风力发电机组因来流风而承受的载荷是否超过设定阈值；
    特征确定单元，被配置为：响应于所述载荷超过设定阈值，确定所述风力发电机组在承受所述载荷时的来流风的风况特征的值是否超过特征阈值；
    风况识别单元，被配置为：响应于所述风况特征的值超过特征阈值，将来流风的风况识别为复杂风况；
    扇区识别单元，被配置为：根据复杂风况的入流方向，确定所述风力发电机组周围是否存在具有复杂风况的扇区。
  14. 根据权利要求13所述的控制装置，其特征在于，所述风况特征包括以下特征中的至少一者：
    来流风在持续时间段内的湍流强度；
    来流风在持续时间段内的风速变化速度；
    来流风在持续时间段内的风向扭转角度；
    来流风在持续时间段内的风剪切因子；
    来流风在持续时间段内的风向变化速度；以及
    来流风在持续时间段内的风向波动幅度。
  15. 根据权利要求14所述的控制装置，其特征在于，在来流风的多个风 况特征超过相应特征阈值的情况下，所述风况识别单元还被配置为：
    确定所述多个风况特征中的每个风况特征与所述载荷之间的相关系数；
    将所述多个风况特征中的相关系数大于预定阈值的风况特征识别为复杂风况。
  16. 根据权利要求13所述的控制装置，其特征在于，所述控制装置还包括：
    风况选择单元，被配置为：选择所述风力发电机组在承受所述载荷中的最大值时的来流风的风况特征的值作为所述风力发电机组在承受所述载荷时的来流风的风况特征的值。
  17. 根据权利要求11所述的控制装置，其特征在于，所述降载控制单元包括：
    风况匹配单元，被配置为：确定风况降载模型中是否存在与所述复杂风况匹配的复杂风况的响应参数，其中，所述风况降载模型包括针对各个复杂风况的响应参数而设置的最优降载控制策略；
    降载获取单元，被配置为：响应于风况降载模型中存在与所述复杂风况匹配的复杂风况的响应参数，从风况降载模型中获取针对所匹配的复杂风况的响应参数而设置的最优降载控制策略；
    第一控制单元，被配置为：根据获取的最优降载控制策略，对所述风力发电机组进行前馈降载控制。
  18. 根据权利要求14所述的控制装置，其特征在于，所述降载控制单元还包括：
    第二控制单元，被配置为：响应于风况降载模型中不存在与所述复杂风况匹配的复杂风况的响应参数，限制所述风力发电机组的输出功率上限。
  19. 根据权利要求15所述的控制装置，其特征在于，所述控制装置还包括：
    降载记录单元，被配置为：将限制所述风力发电机组的输出功率上限而进行的前馈降载控制作为针对所述复杂风况的响应参数而设置的最优降载控制策略记录到所述风况降载模型中。
  20. 根据权利要求11至19中任意一项所述的控制装置，其特征在于，所述降载控制包括以下操作中的至少一者：
    增大所述风力发电机组的桨距角度；
    增大所述风力发电机组的变桨速度；
    减小所述风力发电机组的发电机转速；以及
    减小所述风力发电机组的发电机扭矩。
  21. 一种存储有计算机程序的计算机可读存储介质，其中，当所述计算机程序被处理器执行时，实现权利要求1至10中任意一项所述的用于风力发电机组的控制方法。
  22. 一种计算装置，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，实现权利要求1至10中任意一项所述的用于风力发电机组的控制方法。
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