WO2023142366A1 - Procédé et appareil d'estimation de la vitesse du vent au niveau du rotor d'une éolienne - Google Patents

Procédé et appareil d'estimation de la vitesse du vent au niveau du rotor d'une éolienne Download PDF

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Publication number
WO2023142366A1
WO2023142366A1 PCT/CN2022/101921 CN2022101921W WO2023142366A1 WO 2023142366 A1 WO2023142366 A1 WO 2023142366A1 CN 2022101921 W CN2022101921 W CN 2022101921W WO 2023142366 A1 WO2023142366 A1 WO 2023142366A1
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Prior art keywords
wind speed
vector
rotor
wind
speed measurement
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PCT/CN2022/101921
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English (en)
Chinese (zh)
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奥德高•彼得•福格
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新疆金风科技股份有限公司
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Priority to AU2022436719A priority Critical patent/AU2022436719A1/en
Publication of WO2023142366A1 publication Critical patent/WO2023142366A1/fr

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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
    • 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
    • F03D7/00—Controlling wind motors 
    • 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
    • F05B2270/50—Control logic embodiment by
    • F05B2270/502—Control logic embodiment by electrical means, e.g. relays or switches
    • 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 disclosure relates to the field of renewable energy, in particular to a method and device for estimating wind speed at a rotor of a wind turbine.
  • the wind speed at a specific location e.g., the location of each wind turbine
  • a wind measuring tower installed in a wind farm it can be measured or estimated by a laser radar (LIDAR) sensor installed on a wind turbine; Estimate the wind speed at a specific location.
  • LIDAR laser radar
  • the purpose of the embodiments of the present disclosure is to provide a method and device for estimating the wind speed at the rotor of a wind turbine, which can accurately estimate the wind speed at the rotor by using lidar sensors and non-wind speed sensors, and improve the control performance of the wind turbine.
  • a method of estimating the wind speed at the rotor of a wind turbine comprising: obtaining a wind speed measurement vector at a predetermined location related to the wind turbine through a lidar sensor of the wind turbine; A wind speed sensor that acquires a non-wind speed measurement vector associated with a rotor of the wind turbine; based on the wind speed measurement vector and the non-wind speed measurement vector, generates an estimated wind speed vector at the rotor using a state space estimation model.
  • an apparatus for estimating a wind speed at a rotor of a wind turbine comprising: a wind speed measurement module configured to acquire a wind speed at a predetermined position related to the wind turbine through a lidar sensor of the wind turbine a measurement vector; a non-wind speed measurement module configured to: obtain a non-wind speed measurement vector related to a rotor of the wind turbine through a non-wind speed sensor of the wind turbine; a wind speed estimation module configured to: based on the wind speed measurement vector and the A non-wind speed measurement vector, using a state space estimation model to generate an estimated wind speed vector at the rotor.
  • a computer readable storage medium storing a computer program which, when executed by a processor, implements the method of estimating wind speed at a rotor of a wind turbine as described above.
  • a computing device includes: a processor; a memory storing a computer program, when the computer program is executed by the processor, the estimation of wind turbine rotor position as described above is realized. method of wind speed.
  • a wind turbine comprising: a lidar sensor configured to measure a wind speed measurement vector at a predetermined location related to the wind turbine; a non-wind speed sensor configured to measure The rotor-related non-wind speed measurement vector; the computing device as described above, said computing device being electrically connected to the lidar sensor and the non-wind speed sensor.
  • At least one of the following technical effects can be achieved: by using a laser radar sensor and a non-wind speed sensor to Accurate estimation of wind speed at the rotor, improving control performance of wind turbines; facilitates estimation of wind speed and wind acceleration at the rotor while updating the tower mode frequency used in the wind speed prediction model of the wind turbine.
  • Fig. 1 is a flowchart of a method of estimating wind speed at a rotor of a wind turbine according to an embodiment of the present disclosure.
  • FIG. 2 is another flowchart of a method of estimating wind speed at a rotor of a wind turbine according to an embodiment of the present disclosure.
  • FIG. 3 is a block diagram of a system for estimating wind speed at a wind turbine rotor according to an embodiment of the disclosure.
  • FIG. 4 is a data flow diagram of a method of estimating wind speed at a wind turbine rotor according to an embodiment of the disclosure.
  • Fig. 5 is another flowchart of a method of estimating wind speed at a rotor of a wind turbine according to an embodiment of the present disclosure.
  • Figure 6 is another data flow diagram of a method of estimating wind speed at a wind turbine rotor according to an embodiment of the disclosure.
  • FIG. 7 is a block diagram of a computing device according to an embodiment of the disclosure.
  • an estimate of the wind speed as input to the controller can be obtained by time-shifting the wind speed measurement from the lidar sensor from the point of measurement to the point in time when the rotor is encountered. It is usually assumed that the travel time from the measurement point to the rotor can be estimated by dividing the travel distance by the mean wind speed.
  • This error in propagation time is critical for accurate estimation of the wind speed at the rotor and control algorithms based on this wind speed (eg, using feed-forward control algorithms).
  • the propagation time error in the traditional method is large, and it is difficult to meet the strict accuracy requirements.
  • the technical solution proposed by the present invention is beneficial to reduce the estimation error in the wind speed estimation result based on the lidar sensor, improve the estimation accuracy of the wind speed at the rotor of the wind turbine, and is beneficial to update the tower mode frequency used in the wind speed prediction model of the wind turbine At the same time, the wind speed and wind acceleration at the rotor are estimated.
  • the technical solution according to the present invention can correct the wind speed measurement data from the lidar measurement device (for example, lidar sensor) by correcting the propagation time delay, and by feeding the wind speed measurement data to the state space estimation model (for example, Kalman filter instrument), to correct the wind speed measurement data.
  • the wind speed measurements from the lidar can be combined with non-wind speed measurements from non-wind speed measurement devices in the turbine using a state space estimation model to adjust the wind speed measurements to match the non-wind speed measurement data. Thereby, the error of wind speed estimation is reduced, and the accuracy of wind speed estimation is improved.
  • first means “first”, “second” and “third” may be used herein to describe various members, components, regions, layers or sections, these members, components, regions, layers or sections should not be referred to as These terms are limited. On the contrary, these terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section.
  • a first member, a first component, a first region, a first layer, or a first portion referred to in examples described herein could also be termed a second member, a second component, or a first portion without departing from the teachings of the examples.
  • Fig. 1 is a flowchart of a method of estimating wind speed at a rotor of a wind turbine according to an embodiment of the present disclosure.
  • a wind speed measurement vector at a predetermined location related to the wind turbine is acquired through a lidar sensor of the wind turbine.
  • a wind speed parameter at a predetermined position (for example, a predetermined position in front of the turbine) may be measured by a lidar sensor disposed on the nacelle of the wind turbine or disposed in the wind field to obtain the wind speed at the predetermined position related to the wind turbine Measure vector.
  • a non-wind speed measurement vector related to the rotor of the wind turbine is obtained by the non-wind speed sensor of the wind turbine.
  • non-wind speed parameters related to the rotor can be measured by non-wind speed sensors such as rotor speed sensors, pitch angle sensors, motor torque sensors, blade load sensors, nacelle acceleration sensors, and elevation angle sensors installed in the wind turbine to obtain A non-wind speed measurement vector associated with the rotor of the wind turbine.
  • the non-wind speed measurement vector may include at least one of the following items: rotational speed of the rotor, pitch angle, motor torque, blade load, nacelle acceleration, elevation angle.
  • a state space estimation model is used to generate a wind speed estimation vector at the rotor.
  • the state space estimation model may be a nonlinear state space estimator.
  • the nonlinear state estimator includes a Kalman filter.
  • the Kalman filter may include an Unscented Kalman Filter (UKF), an Extended Kalman Filter (EKF), a Sigma-Point Kalman Filter.
  • FIG. 2 is another flowchart of a method of estimating wind speed at a rotor of a wind turbine according to an embodiment of the present disclosure.
  • 3 is a block diagram of a system for estimating wind speed at a wind turbine rotor according to an embodiment of the disclosure. Exemplary embodiments according to the present disclosure are described below with reference to FIGS. 2 and 3 , but the present disclosure is not limited thereto.
  • Step S13 in FIG. 1 can be realized through the operations shown in FIG. 2 .
  • step S21 time correction is performed on the wind speed measurement vector to generate a wind speed measurement correction vector.
  • the wind speed measurement vector can be time corrected by the time correction module 321 shown in FIG. 3 .
  • step S22 based on the wind speed measurement correction vector and the non-wind speed measurement vector, a state space estimation model is used to generate a wind speed estimation vector at the rotor.
  • the wind speed estimation vector at the rotor may be generated by the state space estimation module 322 shown in FIG. 3 .
  • the wind speed measurement vector may be time corrected using the propagation time correction vector to generate the wind speed measurement correction vector.
  • the travel time correction vector is updated based on the anemometric correction vector and the non-wind measurement vector.
  • the state-space estimation model may be used to update the travel time correction vector based on the wind velocity measurement correction vector and the non-wind velocity measurement vector.
  • a system for estimating wind speed at a wind turbine rotor may include a lidar sensor 301 , a non-wind speed sensor 302 , a measurement module 31 , a wind speed estimation module 32 and a control module 33 .
  • the measurement module 31 may include a wind speed measurement module 311 and a non-wind speed measurement module 312 .
  • the wind speed estimation module 32 may include a time correction module 321 and a state space estimation module 322 .
  • the wind speed measurement module 311 can obtain a wind speed measurement vector from the lidar sensor 301 .
  • the non-wind measurement module 312 may obtain the non-wind measurement vector from the non-wind sensor 302 .
  • Non-wind speed measurement vectors may include at least one of the following: nacelle acceleration, pitch angle, blade load, rotor rotational speed vector ⁇ r [n], pitch angle vector ⁇ [n], motor torque vector ⁇ g [n].
  • the measurement module 31 may output the wind speed measurement vector to the time correction module 321 .
  • Measurement module 31 may also output non-wind speed measurement vectors (e.g., rotor speed vector ⁇ r [n], pitch angle vector ⁇ [n], motor torque vector ⁇ g [n], blade load) to state space estimation module 322 .
  • the measurement module 31 may also output non-wind speed measurement vectors (eg nacelle acceleration, pitch angle, blade load) to the control module 33 .
  • the non-wind speed measurement vector output to the state space estimation module 322 and the non-wind speed measurement vector output to the control module 33 may be partly the same or different.
  • the time correction module 321 can use the propagation time correction vector ⁇ T t [n] to perform time correction on the wind speed measurement vector to generate the wind speed measurement correction vector v w,L (n).
  • the state space estimation module 322 may update the transit time correction vector ⁇ T t [n] using the state space estimation model based on the wind velocity measurement correction vector v w,L (n) and the non-wind velocity measurement vector.
  • the time correction module 321 can use the updated transit time correction vector ⁇ T t [n] to perform continuous time correction on the wind speed measurement vector.
  • the state space estimation module 322 can generate an estimated wind speed vector at the rotor using a state space estimation model based on the wind speed measurement correction vector v w,L (n) and the non-wind speed measurement vector
  • the control module 33 may generate a motor reference torque ⁇ g,r [n] based on the wind speed estimation vector obtained from the state space estimation module 322 and the non-wind speed measurement vector (eg, nacelle acceleration, elevation angle, blade load) obtained from the measurement module 31 and the reference pitch angle ⁇ r [n] for controlling the operation of the wind turbine. As such, the control module 33 may use the estimated wind speed vector to determine how to control the operation of the wind turbine, for example, may generate turbine control commands based on the estimated wind speed vector.
  • the control module 33 can output the motor reference torque ⁇ g,r [n] and the reference pitch angle ⁇ r [n] to the measurement module 31 .
  • each vector may represent data corresponding to sampling time point n.
  • the wind speed/torque balance model can be represented by a linear state-space model in the discrete time domain.
  • the model is expressed as follows:
  • A, B, C, and D represent the model parameter matrix
  • x[k-1], u[k-1], x[k], y[k] represent the input vector and output vector of the state model, respectively.
  • the rotor speed differential model in the continuous time domain to be discretized can be expressed as follows:
  • J represents the moment of inertia
  • ⁇ represents the friction coefficient of the rotor
  • ⁇ (t) represents the rotor speed
  • ⁇ gen (t) represents the motor torque
  • ⁇ aero (t) is expressed as follows:
  • ⁇ aero (t) f ( ⁇ (t), v w (t), ⁇ , R) (3)
  • ⁇ aero (t) represents the nonlinear function of air density ⁇ , rotor radius R, rotor speed ⁇ (t), and wind speed v w (t).
  • ⁇ aero (t) can be represented by the following mapping expression:
  • Ar represents the circular cross-sectional area swept by the blade
  • ⁇ represents the pitch angle
  • C p represents the power coefficient
  • c 1 , c 2 , and c 3 represent linearized model coefficients, and parameters with a subscript of 0 represent corresponding parameters at time point 0, for example, ⁇ 0 represents the rotor speed at time point 0.
  • the linearized model of wind speed/torque balance can be obtained.
  • means for estimating the wind speed at the rotor of the wind turbine may be included.
  • the apparatus may include the wind speed measurement module 311 , the non-wind speed measurement module 312 , the wind speed estimation module 32 and/or other related modules as described above.
  • a wind turbine according to the present disclosure may include the system shown in FIG. 3 , but the present disclosure is not limited thereto.
  • a wind turbine may include: a lidar sensor 301 configured to measure a wind speed measurement vector at a predetermined location associated with the wind turbine; a non-wind speed sensor 302 configured to measure a non-wind speed measurement vector associated with a rotor of the wind turbine; A device (for example, the computing device 7 shown in Figure 7 below) that interfaces with the lidar sensor and the non-wind sensor.
  • FIG. 4 is a data flow diagram of a method of estimating wind speed at a wind turbine rotor according to an embodiment of the disclosure.
  • the anemometry correction vector may include a plurality of anemometry correction vectors corresponding to different propagation times, for example, N anemometry correction vectors corresponding to N different propagation times. Multiple wind speed measurement correction vectors corresponding to different propagation times may be input to the state space estimation module 41 in parallel.
  • the input a of the state space estimation module 41 can represent the N wind speed measurement correction vectors vw,L (n) based on N different propagation times obtained by the lidar sensor, and the input b can represent the non-wind speed Measured vectors (e.g., rotor speed vector ⁇ r [n], pitch angle vector ⁇ [n], motor torque vector ⁇ g [n]), the output c can represent the estimated wind speed vector at the rotor The output d may represent the travel time correction vector ⁇ T t [n].
  • the input a of the state space estimation module 41 can represent the N wind speed measurement correction vectors vw,L (n) based on N different propagation times obtained by the lidar sensor
  • the input b can represent the non-wind speed Measured vectors (e.g., rotor speed vector ⁇ r [n], pitch angle vector ⁇ [n], motor torque vector ⁇ g [n])
  • the output c can represent the estimated wind speed vector at the rotor
  • the state space estimation module 41 may utilize a state space estimation model to generate an estimated wind speed vector at the rotor based on the wind speed measurement correction vector and the non-wind speed measurement vector, and update the transit time correction vector.
  • Estimation models with augmented states can be formulated, for example, in the continuous time domain and then converted to the discrete time domain before being used in the estimator formulation.
  • This state estimation model can be combined with:
  • ⁇ represents the value of wind speed measured by lidar according to different propagation times (e.g., N-dimensional).
  • the input coefficient vector, ⁇ 1 (t) represents the wind uncertainty model driving the Gaussian noise function, Indicates the differential of the rotor speed ⁇ (t), v un (t) indicates the uncertain elements to be filtered out by the filter, Represents the differential of v un (t), v w,L (t) represents the wind speed measurement data obtained by the lidar sensor, ⁇ gen (t) represents the motor torque.
  • the above model is discretized and the elements in the vector ⁇ are included as augmented states in the estimator model for use in determining the appropriate travel time.
  • the discretized model is represented as follows:
  • a 1 , A 2 , A 3 , B 1 , and B 2 represent the corresponding states and input model parameters after discretization.
  • ⁇ 1 [n], ⁇ 2 [n], . . . , ⁇ N+1 [b] denote the system noise variables for each state applied to the parameter uncertainties driving the model.
  • the output obtained using the state space estimation model is as follows:
  • the travel time correction vector ⁇ T t [n] can be estimated by using ⁇ [n] with the maximum value.
  • the augmented state vector (augmented state vector) x aug [n] can be expressed as ( ⁇ 1 [n], ⁇ 2 [n]... ⁇ N [n]).
  • the initial value of ⁇ [n] or other related variables can be set according to the expected propagation time delay.
  • the input vector of the state space estimation model can be expressed as follows:
  • the Kalman filter is taken as an example for illustration. Time shift from n+1 to n in time units in the model for control and modeling, where n+1 and n correspond to k and k-1 in the Kalman filter, respectively.
  • the dynamic model described above can be updated to predict the state vector corresponding to time k+1.
  • k-1] in the prediction phase of the state estimator can be updated using the estimation feedback part in the update phase of the estimator, and the updated state vector at time k can be expressed as x ag [k
  • the prediction model can be defined based on the dynamic model, which is expressed as follows for brevity:
  • f() represents the function of the state space estimation model.
  • the state space estimation model can be extended to multiple states corresponding to multiple tower modes to apply the state space estimation model to more tower modes.
  • the number of states in the augmented state vector can be calculated based on the number of pylon modes.
  • wind speed estimation is performed using a Kalman filter in wind speed vector prediction, and when the wind speed estimation vector with the lowest estimation error is selected and the corresponding position in the wind speed estimation vector is used as the travel time update, the wind speed estimation vector with the lowest The wind speed estimate vector of the estimation error is taken as output.
  • the application process of the Kalman filter includes a prediction phase and an update phase.
  • An exemplary brief description is given below.
  • the state can be updated based on measured inputs, last state values and a model or predictive model without noisy components.
  • the prediction covariance can be estimated according to:
  • k-1] represents the update covariance calculated in the update stage
  • Q[k] represents the covariance of process noise
  • F[k] represents the state transition matrix, which can be defined as follows:
  • the measurement residual is Among them, ⁇ [k
  • h represents a function given by the following formula.
  • the updated estimated state is obtained as follows:
  • the updated estimated covariance is as follows:
  • Q[k] and R[k] are the model and tuning parameters of the system and are constant during this process.
  • FIGS. 5 and 6 Another exemplary embodiment is described below with reference to FIGS. 5 and 6 .
  • FIG. 5 is another flowchart of a method of estimating wind speed at a rotor of a wind turbine according to an embodiment of the present disclosure.
  • FIG. 6 is another data flow diagram of a method of estimating wind speed at a wind turbine rotor according to an embodiment of the disclosure.
  • step S51 based on the non-wind speed measurement vector and the wind speed time-shift vector corresponding to different propagation times, the wind speed time-shift estimation vector corresponding to different propagation times at the rotor and its corresponding rotor speed estimation error vector are generated.
  • step S52 based on the wind speed measurement correction vector, the wind speed time-shift estimated vector and its corresponding rotor speed estimation error vector, a state space estimation model is used to generate an estimated wind speed vector at the rotor.
  • the wind speed time-shift vector is updated based on the wind speed measurement correction vector, the wind speed time-shift estimation vector and its corresponding rotor speed estimation error vector.
  • step S53 based on the wind speed measurement correction vector, the wind speed time shift estimation vector and its corresponding rotor speed estimation error vector, the wind speed time shift vector is updated using the state space estimation model.
  • Step S53 and step S52 can be executed in parallel.
  • the state space estimation module may include a first estimation module 61 and a second estimation module 62 .
  • the input a of the first estimation module 61 may represent the wind speed measurement correction vector v w,L (n) acquired with the lidar sensor.
  • the wind speed measurement correction vector v w,L (n) may be a single vector.
  • the output c of the first estimation module 61 may represent the estimated wind speed vector at the rotor
  • the output d may represent the travel time correction vector ⁇ T t [n]
  • the output e may represent the wind speed time shift vector v w,L,N [n] corresponding to different travel times (eg, N different travel times).
  • the input b of the second estimation module 62 may represent a non-wind speed measurement vector (for example, the rotational speed vector ⁇ r [n] of the rotor, the pitch angle vector ⁇ [n], the motor torque vector ⁇ g [n]), the second estimation module
  • the output f of 62 can represent the wind speed time-shift estimation vector v' w [n] corresponding to different propagation times at the rotor and its corresponding rotor speed estimation error vector ⁇ error [n].
  • the first estimation module 61 may be configured based on a Kalman filter.
  • FIG. 6 only shows one second estimating module 62 , but the present disclosure is not limited thereto, and multiple parallel second estimating modules 62 can be used to obtain multiple outputs f.
  • the structure of the state space estimation module shown in FIG. 6 is more complicated, but the application of the Kalman filter will be simpler.
  • the state space estimation model is discretized, and the elements in the vector ⁇ are included as augmented states in the estimator model for determining the appropriate travel time.
  • the discretized model can be expressed as follows:
  • a 1 , A 2 , A 3 , B 1 , and B 2 represent the corresponding states and input model parameters after discretization.
  • ⁇ [n] represents the system noise variable for each state applied to drive the model's parameter uncertainties.
  • the output obtained using the state space estimation model is as follows:
  • the input vector of the state space estimation model can be expressed as follows:
  • the predictive model is applied to the wind speed from the lidar sensor based on different travel times.
  • the second estimation module of parallel calculation outputs the wind speed time-shift estimation vector v' w [n] corresponding to different propagation times at the rotor and the corresponding rotor speed estimation error vector ⁇ error [n].
  • the rotor speed estimation error vector ⁇ error [n] may represent the error between the measured and estimated rotor speed for a given wind speed input.
  • the Kalman filter is taken as an example for illustration. Time shift from n+1 to n in time units in the model for control and modeling, where n+1 and n correspond to k and k-1 in the Kalman filter, respectively.
  • the dynamic model described above can be updated to predict the state vector corresponding to time k+1.
  • k-1] in the prediction phase of the state estimator can be updated using the estimation feedback part in the update phase of the estimator, and the updated state vector at time k can be expressed as x[k
  • the prediction model can be defined based on the dynamic model, which is expressed as follows for brevity:
  • f() represents the function of the state space estimation model.
  • the state space estimation model can be extended to various states respectively corresponding to various tower modes, so that the state space estimation model can be extended and applied to more tower modes.
  • the number of states in the augmented state vector can be calculated based on the number of pylon modes.
  • wind speed estimation is performed using a Kalman filter in wind speed vector prediction, and when the wind speed estimation vector with the lowest estimation error is selected and the corresponding position in the wind speed estimation vector is used as the travel time update, the wind speed estimation vector with the lowest The wind speed estimate vector of the estimation error is taken as output.
  • the application process of the Kalman filter includes a prediction phase and an update phase.
  • An exemplary brief description is given below.
  • the state can be updated based on measured inputs, last state values and a model or predictive model without noisy components.
  • the prediction covariance can be estimated according to:
  • k-1] represents the update covariance calculated in the update stage
  • Q[k] represents the covariance of process noise
  • F[k] represents the state transition matrix, which can be defined as follows:
  • the measurement residual is Among them, ⁇ [k
  • h represents a function given by the following formula.
  • the updated estimated state is obtained as follows:
  • the updated estimated covariance is as follows:
  • Q[k] and R[k] are the model and tuning parameters of the system and are constant during this process.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed, the method for estimating a wind speed at a rotor of a wind turbine according to an embodiment of the present disclosure is implemented.
  • the computer-readable storage medium may carry one or more programs, and when the computer programs are executed, the following steps described with reference to FIGS. a radar sensor for obtaining a wind speed measurement vector at a predetermined location associated with the wind turbine; a non-wind speed measurement vector for a rotor of the wind turbine by a non-wind speed sensor of the wind turbine; based on said wind speed measurement vector and said non-wind speed measurement vector , using a state space estimation model to generate an estimated wind speed vector at the rotor.
  • a computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium that contains or stores a computer program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer program embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.
  • a computer-readable storage medium may be included in any device; and may exist independently without being incorporated into the device.
  • FIG. 7 is a block diagram of a computing device 7 according to an embodiment of the disclosure.
  • a computing device 7 may include a memory 71 and a processor 72, and a computer program 73 is stored on the memory 71.
  • the computer program 73 is executed by the processor 72, the implementation according to the present disclosure may be realized.
  • the operation of the method of estimating the wind speed at the rotor of a wind turbine described with reference to FIGS. obtaining a wind speed measurement vector at a predetermined location associated with the wind turbine; obtaining a non-wind speed measurement vector associated with a rotor of the wind turbine through a non-wind speed sensor of the wind turbine; based on said wind speed measurement vector and said non-wind speed measurement vector, utilizing a state A spatial estimation model generates a wind speed estimation vector at the rotor.
  • the computing device shown in FIG. 7 is only an example, and should not limit the functions and scope of use of the embodiments of the present disclosure.
  • control device shown in FIG. 4 and its individual units can be respectively configured as software, hardware, firmware or any combination of the above items to perform specific functions
  • computing device shown in FIG. 5 does not It is limited to include the components shown above, but some components may be added or deleted as needed, and the above components may also be combined.
  • At least one of the following technical effects can be achieved: by using a laser radar sensor and a non-wind speed sensor to Accurate estimation of wind speed at the rotor, improving control performance of wind turbines; facilitates estimation of wind speed and wind acceleration at the rotor while updating the tower mode frequency used in the wind speed prediction model of the wind turbine.
  • Control logic or functions performed by various components or controllers in a control system may be represented by a flowchart or similar diagram in one or more figures. These figures provide representative control strategies and/or logic that may be implemented using one or more processing strategies (such as event-driven, interrupt-driven, multitasking, multithreading, etc.) . As such, various steps or functions illustrated may be performed in the sequence illustrated, in parallel, or in some cases omitted. Although not always explicitly shown, one of ordinary skill in the art will recognize that one or more of the steps or functions shown may be repeatedly performed depending on the particular processing strategy being used.

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  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Optical Radar Systems And Details Thereof (AREA)
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Abstract

L'invention concerne un procédé et un appareil d'estimation de la vitesse du vent au niveau d'un rotor d'une éolienne. Le procédé comprend : l'étape S11, l'acquisition, au moyen d'un capteur radar laser (301) d'une éolienne, d'un vecteur de mesure de vitesse du vent à une position prédéterminée qui est associée à l'éolienne ; l'étape S12, l'acquisition, au moyen d'un capteur de vitesse de non-vent (302) de l'éolienne, d'un vecteur de mesure du vitesse de non-vent associé à un rotor de l'éolienne ; et l'étape S13, la génération d'un vecteur d'estimation de vitesse du vent à l'aide d'un modèle d'estimation d'espace d'état et sur la base du vecteur de mesure du vitesse de vent et du vecteur de mesure du vitesse de non-vent. La présente invention concerne en outre un support de stockage lisible par ordinateur, qui stocke un programme informatique (73), dans lequel lorsque le programme informatique est exécuté par un processeur (72), le procédé est mis en œuvre ; un appareil informatique (7), qui comprend un processeur (72) et une mémoire (71) utilisée pour stocker un programme informatique (73) ; et une éolienne, qui comprend un capteur radar laser (301), un capteur de vitesse non éolienne (302) et l'appareil informatique (7). La présente invention permet l'estimation précise d'une vitesse du vent au niveau d'un rotor, ce qui permet d'améliorer les performances de commande d'une éolienne.
PCT/CN2022/101921 2022-01-29 2022-06-28 Procédé et appareil d'estimation de la vitesse du vent au niveau du rotor d'une éolienne WO2023142366A1 (fr)

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