CN116557222A - Method and device for estimating wind speed at wind turbine rotor - Google Patents
Method and device for estimating wind speed at wind turbine rotor Download PDFInfo
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- CN116557222A CN116557222A CN202210110380.9A CN202210110380A CN116557222A CN 116557222 A CN116557222 A CN 116557222A CN 202210110380 A CN202210110380 A CN 202210110380A CN 116557222 A CN116557222 A CN 116557222A
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Classifications
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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
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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
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- 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
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- 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
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- 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
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- 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
Abstract
The present disclosure provides a method and apparatus for estimating wind speed at a wind turbine rotor. The method comprises the following steps: acquiring, by a lidar sensor of the wind turbine, a wind speed measurement vector at a predetermined location associated with the wind turbine; acquiring, by a non-wind speed sensor of the wind turbine, 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, a state space estimation model is utilized to generate a wind speed estimation vector at the rotor.
Description
Technical Field
The present disclosure relates to the field of renewable energy, and in particular to a method and apparatus for estimating wind speed at a wind turbine rotor.
Background
For wind power generation, accurate measurement or estimation of wind speed distribution around the wind turbine and at the rotor is critical to controlling the operation of the wind turbine. For example, the wind speed at a particular location (e.g., a machine location of each wind turbine) may be measured or estimated by a anemometer tower disposed in the wind farm; wind speed at a particular location may be measured or estimated by a laser radar (LIDAR) sensor disposed on the wind turbine.
Currently, wind speed estimation techniques for wind turbines are still evolving, and it is desirable to significantly improve the accuracy and efficiency of wind speed measurements.
Disclosure of Invention
It is an object of embodiments of the present disclosure to provide a method and apparatus for estimating wind speed at a rotor of a wind turbine, which can accurately estimate wind speed at the rotor using a lidar sensor and a non-wind speed sensor, improving the control performance of the wind turbine.
According to an embodiment of the present disclosure, there is provided a method of estimating wind speed at a wind turbine rotor, the method comprising: acquiring, by a lidar sensor of the wind turbine, a wind speed measurement vector at a predetermined location associated with the wind turbine; acquiring, by a non-wind speed sensor of the wind turbine, 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, a state space estimation model is utilized to generate a wind speed estimation vector at the rotor.
According to an embodiment of the present disclosure, there is provided an apparatus for estimating wind speed at a wind turbine rotor, the apparatus comprising: a wind speed measurement module configured to: acquiring, by a lidar sensor of the wind turbine, a wind speed measurement vector at a predetermined location associated with the wind turbine; a non-wind speed measurement module configured to: acquiring, by a non-wind speed sensor of the wind turbine, a non-wind speed measurement vector associated with a rotor of the wind turbine; a wind speed estimation module configured to: based on the wind speed measurement vector and the non-wind speed measurement vector, a state space estimation model is utilized to generate a wind speed estimation vector at the rotor.
According to an embodiment of the present disclosure, a computer readable storage medium storing a computer program is provided, which when executed by a processor implements a method of estimating wind speed at a wind turbine rotor as described above.
According to an embodiment of the present disclosure, there is provided a computing device including: a processor; a memory storing a computer program which, when executed by a processor, implements a method of estimating a wind speed at a wind turbine rotor as described above.
According to an embodiment of the present disclosure, there is provided a wind turbine comprising: a lidar sensor configured to measure a wind speed measurement vector at a predetermined location associated with the wind turbine; a non-wind speed sensor configured to measure a non-wind speed measurement vector associated with a rotor of the wind turbine; a computer device as described above, said computing device being electrically connected to a lidar sensor and a non-wind speed sensor.
With a method and apparatus for estimating wind speed at a wind turbine rotor, a computer readable storage medium, a computing device and a wind turbine according to embodiments of the present disclosure, at least one of the following technical effects may be achieved: the control performance of the wind turbine is improved by accurately estimating the wind speed at the rotor by using a lidar sensor and a non-wind speed sensor; it is advantageous to estimate the wind speed and wind acceleration at the rotor while updating the tower pattern frequency used in the wind speed prediction model of the wind turbine.
Drawings
The foregoing and other objects and features of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings.
FIG. 1 is a flowchart of a method of estimating wind speed at a wind turbine rotor according to an embodiment of the present disclosure.
FIG. 2 is another flowchart of a method of estimating wind speed at a wind turbine rotor 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 present 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 present disclosure.
FIG. 5 is another flowchart of a method of estimating wind speed at a wind turbine rotor 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 present disclosure.
Fig. 7 is a block diagram of a computing device according to an embodiment of the present disclosure.
Detailed Description
Currently, the wind speed measurement data of a lidar sensor is mostly directly used as reference wind speed data, and is input to a controller of a wind turbine to be controlled, and even if the influence of the propagation time is considered, it is difficult to accurately estimate an error caused by the propagation time, and thus, a large error occurs in a wind speed estimation result based on the lidar sensor. For example, a wind speed estimate as an input to the controller may be obtained by moving a wind speed measurement of the lidar sensor from a measurement point to a point in time when the rotor is encountered. It will generally be assumed that the propagation time from the measurement point to the rotor can be estimated by dividing the propagation distance by the average wind speed, the error of which is critical for an accurate estimation of the wind speed at the rotor and a control algorithm based on the wind speed (e.g. using a feed forward control algorithm). However, the propagation time error in the conventional method is large, and it is difficult to satisfy strict accuracy requirements.
The technical scheme provided by the invention is beneficial to reducing the estimation error in the wind speed estimation result based on the laser radar sensor, improving the estimation accuracy of the wind speed at the rotor of the wind turbine, and estimating the wind speed and the wind acceleration at the rotor while updating the tower mode frequency used in the wind speed prediction model of the wind turbine. According to the technical scheme, the wind speed measurement data from a laser radar measurement device (for example, a laser radar sensor) can be corrected by correcting propagation time delay, and the wind speed measurement data is corrected by feeding the wind speed measurement data to a state space estimation model (for example, a Kalman filter), and the wind speed measurement data from the laser radar can be combined with non-wind speed measurement data from a non-wind speed measurement device in a turbine by using the state space estimation model to adjust the wind speed measurement data to be matched with the non-wind speed measurement data, so that wind speed estimation errors are reduced, and wind speed estimation accuracy is improved.
The following description of specific embodiments is provided in connection with the accompanying drawings to assist the reader in a comprehensive understanding of the methods, apparatus, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, devices, and/or systems described herein will be apparent after an understanding of the present disclosure. For example, the order of operations described herein is merely an example and is not limited to those set forth herein, but may be altered as will be apparent after an understanding of the disclosure of the present application, except for operations that must occur in a particular order. Furthermore, descriptions of features known in the art may be omitted for clarity and conciseness.
The features described herein may be embodied in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein have been provided to illustrate only some of the many possible ways to implement the methods, devices, and/or systems described herein, which many possible ways will be apparent after an understanding of the present disclosure.
As used herein, the term "and/or" includes any one of the listed items associated as well as any combination of any two or more.
Although terms such as "first," "second," and "third" may be used herein to describe various elements, components, regions, layers or sections, these elements, components, regions, layers or sections should not be limited by these terms. Rather, these terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first member, first component, first region, first layer, or first portion referred to in the examples described herein may also be referred to as a second member, second component, second region, second layer, or second portion without departing from the teachings of the examples.
The terminology used herein is for the purpose of describing various examples only and is not intended to be limiting of the disclosure. Singular forms also are intended to include plural forms unless the context clearly indicates otherwise. The terms "comprises," "comprising," and "having" specify the presence of stated features, amounts, operations, components, elements, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, amounts, operations, components, elements, and/or combinations thereof.
Unless defined otherwise, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs after understanding this disclosure. Unless explicitly so defined herein, terms (such as those defined in a general dictionary) should be construed to have meanings consistent with their meanings in the context of the relevant art and the present disclosure, and should not be interpreted idealized or overly formal.
In addition, in the description of the examples, when it is considered that detailed descriptions of well-known related structures or functions will cause a ambiguous explanation of the present disclosure, such detailed descriptions will be omitted.
FIG. 1 is a flowchart of a method of estimating wind speed at a wind turbine rotor according to an embodiment of the present disclosure.
As shown in fig. 1, at step S11, a wind speed measurement vector is acquired at a predetermined location in relation to the wind turbine by a lidar sensor of the wind turbine. For example, a wind speed parameter at a predetermined location (e.g., a predetermined location in front of the turbine) may be measured by a lidar sensor provided on the nacelle of the wind turbine or provided in the wind farm to obtain a wind speed measurement vector at the predetermined location in relation to the wind turbine.
In step S12, a non-wind speed measurement vector associated with the rotor of the wind turbine is acquired by a non-wind speed sensor of the wind turbine. For example, non-wind speed parameters related to the rotor may be measured by non-wind speed sensors provided in the wind turbine, such as rotor speed sensors, pitch angle sensors, motor torque sensors, blade load sensors, nacelle acceleration sensors, elevation angle sensors, etc., to obtain non-wind speed measurement vectors related to the rotor of the wind turbine. In embodiments of the present disclosure, the non-wind speed measurement vector may include at least one of: rotor speed, pitch angle, motor torque, blade load, nacelle acceleration, and elevation.
In step S13, a wind speed estimation vector at the rotor is generated using a state space estimation model based on the wind speed measurement vector and the non-wind speed measurement vector. In embodiments of the present disclosure, the state space estimation model may be a nonlinear state space estimator. For example, the nonlinear state estimator comprises a kalman filter. For example, 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 wind turbine rotor 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 present disclosure. Exemplary embodiments according to the present disclosure are described below in connection with fig. 2 and 3, but the present disclosure is not limited thereto.
Step S13 in fig. 1 may be implemented by the operation shown in fig. 2.
As shown in fig. 2, in step S21, the wind speed measurement vector is time-corrected to generate a wind speed measurement correction vector. For example, the wind speed measurement vector may be time corrected by the time correction module 321 shown in FIG. 3. In step S22, a wind speed estimation vector at the rotor is generated using the state space estimation model based on the wind speed measurement correction vector and the non-wind speed measurement vector. For example, a wind speed estimation vector at the rotor may be generated by the state space estimation module 322 shown in FIG. 3.
According to embodiments of the present disclosure, the wind speed measurement vector may be time corrected using the propagation time correction vector to generate a wind speed measurement correction vector. In one embodiment of the present disclosure, the propagation time correction vector is updated based on the wind speed measurement correction vector and the non-wind speed measurement vector. For example, the propagation time correction vector may be updated with the state space estimation model based on the wind speed measurement correction vector and the non-wind speed measurement vector.
Referring to FIG. 3, 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. Wind speed measuring dieBlock 311 may obtain a wind speed measurement vector from lidar sensor 301. The non-wind speed measurement module 312 may obtain a non-wind speed measurement vector from the non-wind speed sensor 302. The non-wind speed measurement vector may include at least one of: nacelle acceleration, elevation angle, blade load, and rotor speed vector ω r [n]Pitch angle vector beta [ n ]]Motor torque vector τ g [n]。
The measurement module 31 may output a wind speed measurement vector to the time correction module 321. The measurement module 31 may also output a non-wind speed measurement vector (e.g., a rotational speed vector ω of the rotor) to the state space estimation module 322 r [n]Pitch angle vector beta [ n ]]Motor torque vector τ g [n]Blade load). The measurement module 31 may also output non-wind speed measurement vectors (e.g., nacelle acceleration, elevation, blade load) to the control module 33. Wherein the non-wind speed measurement vector output to the state space estimation module 322 may be partially the same or different from the non-wind speed measurement vector output to the control module 33.
The time correction module 321 may utilize the propagation time correction vector Δt t [n]Time correcting the wind speed measurement vector to generate a wind speed measurement correction vector v w,L (n). The state space estimation module 322 may correct the vector v based on the wind speed measurement w,L (n) and non-wind speed measurement vectors to update propagation time correction vector DeltaT using state space estimation model t [n]. Further, the time correction module 321 can correct the vector ΔT using the updated propagation time t [n]And continuous time correction is carried out on the wind speed measurement vector. The state space estimation module 322 may correct the vector v based on the wind speed measurement w,L (n) and non-wind speed measurement vectors, generating a wind speed estimation vector at the rotor using a state space estimation model
The control module 33 may generate the motor reference torque τ based on the wind speed estimation vector obtained from the state space estimation module 322 and the non-wind speed measurement vector obtained from the measurement module 31 (e.g., nacelle acceleration, elevation angle, blade load) g,r [n]And a reference pitch angle beta r [n]For controlling the operation of the wind turbine. As such, control module 33 may utilize the wind speed estimation vector to determine how to control operation of the wind turbine, e.g., turbine control instructions may be generated from the wind speed estimation vector. The control module 33 may reference the motor torque τ g,r [n]And a reference pitch angle beta r [n]Output to the measurement module 31. In the above example, each vector may represent data corresponding to the sampling time point n.
For wind turbines, the wind speed/torque balance model may be represented by a linear state space model in the discrete time domain, with the model parameters known, the model is represented as follows:
where A, B, C, D represents the model parameter matrix, x [ k-1], u [ k-1], x [ k ], y [ k ] represent the input vector and the output vector of the state model, respectively.
The rotor speed differential model of the continuous time domain to be discretized can be expressed as follows:
wherein J represents moment of inertia, phi represents the friction coefficient of the rotor, omega (t) represents the rotor speed, τ gen (t) represents motor torque, τ aero (t) is represented as follows:
τ aero (t)＝f(ω(t),v w (t),ρ,R) (3)
τ aero (t) represents the air density ρ, the rotor radius R, the rotor rotational speed ω (t), the wind speed v w A nonlinear function of (t).
The above state estimation model may be built using a variety of algorithms to correlate or match wind speed data with non-wind speed data at the turbine rotor.
For example, τ may be represented using the following mapping expression aero (t)：
Wherein A is r Representing the circular cross-sectional area swept by the blade, β representing the pitch angle, C p Representing the power coefficient.
Whatever nonlinear model is used, it can be linearized into a model with parameters according to the operating point. For example, the expression of the linear model is as follows:
wherein c 1 、c 2 、c 3 The parameter representing the linearization model coefficient, subscript 0 representing the corresponding parameter at time 0, e.g., ω 0 The rotor speed at time point 0 is indicated.
A linearized model of wind speed/torque balance may be obtained in a similar manner to the examples described above.
In the system for estimating wind speed at a wind turbine rotor shown in fig. 3, means for estimating wind speed at a wind turbine rotor may be included. The apparatus may include a wind speed measurement module 311, a non-wind speed measurement module 312, a wind speed estimation module 32, and/or other related modules as described above. Wind turbines according to the present disclosure may include the system shown in FIG. 3, but the present disclosure is not limited thereto. For example, a wind turbine may include: a lidar sensor 301 configured to measure a wind speed measurement vector at a predetermined location in relation to 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 computing device (e.g., computing device 7 shown below in fig. 7) coupled to the lidar sensor and the non-wind speed sensor.
Exemplary embodiments will be further described below with reference to the accompanying drawings.
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 present disclosure. In the embodiment illustrated in FIG. 4, the wind speed measurement correction vector may include a plurality of wind speed measurement correction vectors corresponding to different propagation times, for example, N wind speed measurement correction vectors corresponding to N different propagation times. A plurality of wind speed measurement correction vectors corresponding to different propagation times may be input in parallel to the state space estimation module 41.
As shown in FIG. 4, the input a of the state space estimation module 41 may represent N wind speed measurement correction vectors v based on N different propagation times acquired with a lidar sensor w,L (n), the input b may represent a non-wind speed measurement vector (e.g., a rotational speed vector ω of the rotor r [n]Pitch angle vector beta [ n ]]Motor torque vector τ g [n]) The output c may represent a wind speed estimation vector at the rotorThe output d may represent the propagation time correction vector DeltaT t [n]。
The state space estimation module 41 may generate a wind speed estimation vector at the rotor using the state space estimation model based on the wind speed measurement correction vector and the non-wind speed measurement vector, and update the propagation time correction vector. The estimation model with the augmented states may be formulated, for example, in the continuous time domain and then converted to the discrete time domain before being used in the estimator formula. The state estimation model may be combined with the following:
wherein J represents moment of inertia, β (t) represents pitch angle, c 1 、c 2 、c 3 Representing the linearization model coefficients, α represents the input coefficient vector, σ, of the wind speed measured by lidar of different propagation times (e.g., N dimensions) 1 (t) represents a wind uncertainty model driving a gaussian noise function,representing the differentiation of the rotor speed ω (t), v un (t) represents an uncertain element to be filtered out by the filter, ">Representing v un Differentiation of (t), v w,L (t) represents wind speed measurement data obtained by a lidar sensor, τ gen And (t) represents motor torque.
The above model is discretized and the elements in the vector α are included as augmented states within the estimator model for determining the appropriate propagation times.
The discretized model is represented as follows:
wherein A is 1 、A 2 、A 3 、B 1 、B 2 Representing the discretized corresponding states and the entered model parameters. (sigma) 1 [n]，σ 2 [n]，...，σ N+1 [n]) A system noise variable representing each state of parameter uncertainty applied to the driving model.
The output obtained using the state space estimation model is as follows:
by using alpha n having a maximum value]To estimate the propagation time correction vector DeltaT t [n]. Augmentation state vector (augmented state vector) x aug [n]Can be expressed as (alpha) 1 [n],α 2 [n]……α N [n]). Alpha n can be set according to an expected propagation time delay]Or other initial values of related variables.
The input vector of the state space estimation model can be expressed as follows:
a kalman filter is described herein as an example. Time shifted from n+1 to n in the time cell in the model for control and modeling, n+1 and n correspond to k and k-1 in the Kalman filter, respectively.
The dynamic model described above may be updated to the predictions of the state vector corresponding to time k+1 for a given estimated state corresponding to k. Current state x in the prediction phase of a state estimator ag [k|k-1]The state vector updated at time k may be represented as x ag [k|k]. The predictive model may then be defined based on a dynamic model, expressed for simplicity as follows:
x ag [k|k-1]＝f(x ag [k-1|k-1],u[k],σ[k]) (11)
where f () represents a function of the state space estimation model.
According to embodiments of the present disclosure, the state space estimation model extension may be applied to more tower modes by extending the state space estimation model to a plurality of states corresponding to a plurality of tower modes, respectively. The number of states in the augmented state vector may be calculated based on the number of tower modes.
According to an embodiment of the present disclosure, wind speed estimation is performed using a kalman filter in wind speed vector prediction, and when a wind speed estimation vector having the lowest estimation error is selected and a corresponding position in the wind speed estimation vector is used as a propagation time update, the wind speed estimation vector having the lowest estimation error is used as an output.
The application process of the Kalman filter comprises a prediction stage and an updating stage. An exemplary brief description is provided below.
For the prediction phase of the method,
the state may be updated based on measured inputs, final state values, and the model or predictive model without noise parts.
The prediction covariance may be estimated according to the following equation:
P[k|k-1]＝F[k]P[k-1|k-1]F T [k]+Q[k] (13)
where P [ k-1|k-1] represents the update covariance calculated during the update phase, Q [ k ] represents the covariance of the process noise, and F [ k ] represents the state transition matrix, which can be defined as follows:
for the update phase of the time period,
the measurement residual (measurement residual) is Wherein ω [ k|k-1]Representing the rotor speed estimated by the model. Residual mean square error is S [ k ]]＝H[k]P[k|k-1]H T [k]+R[k]Wherein R < k >]Representing covariance of measured noise, H [ k ]]The observation matrix calculated as follows is represented.
Where h represents a function given by the following formula.
The approximate optimal Kalman gain is:
K[k]＝P[k|k-1]H T [k]S -1 [k] (17)
the updated estimated states are obtained as follows:
the updated estimated covariance is as follows:
P[k|k]＝(I-K[k]H[k])P[k|k-1] (19)
q [ k ] and R [ k ] are model parameters and tuning parameters of the system and are unchanged during the process.
Another exemplary embodiment is described below in connection with fig. 5 and 6.
FIG. 5 is another flowchart of a method of estimating wind speed at a wind turbine rotor 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 present disclosure.
As shown in fig. 5, in step S51, based on the non-wind speed measurement vector and the wind speed shift vector corresponding to the different propagation times, a wind speed shift estimation vector corresponding to the different propagation times at the rotor and a rotor speed estimation error vector corresponding thereto are generated.
In step S52, a wind speed estimation vector at the rotor is generated using the state space estimation model based on the wind speed measurement correction vector, the wind speed shift estimation vector, and the corresponding rotor speed estimation error vector.
In one embodiment of the present disclosure, the wind speed shift vector is updated based on the wind speed measurement correction vector, the wind speed shift estimation vector, and their corresponding rotor speed estimation error vectors.
Optionally, in step S53, the wind speed shift vector is updated with the state space estimation model based on the wind speed measurement correction vector, the wind speed shift estimation vector and its corresponding rotor speed estimation error vector. Step S53 and step S52 may be performed in parallel.
As shown in fig. 6, 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 a wind speed measurement correction vector v obtained with a lidar sensor w,L (n). In the embodiment shown in FIG. 6, the wind speed measurement correction vector v w,L (n) may be a single vector. First, theThe output c of an estimation module 61 may represent an estimated vector of wind speed at the rotorThe output d may represent the propagation time correction vector DeltaT t [n]The output e may represent a wind speed time shift vector v corresponding to different propagation times (e.g., N different propagation times) w,L,N [n]。
The input b of the second estimation module 62 may represent a non-wind speed measurement vector (e.g., a rotational speed vector ω of the rotor r [n]Pitch angle vector beta [ n ]]Motor torque vector τ g [n]) The output f of the second estimation module 62 may represent a wind speed time shift estimation vector v 'corresponding to different propagation times at the rotor' w [n]Rotor speed estimation error vector omega corresponding to the same error [n]。
The first estimation module 61 may be configured based on a kalman filter. Fig. 6 shows only one second estimation module 62, but the present disclosure is not limited thereto, and a plurality of outputs f may be obtained using a plurality of parallel second estimation modules 62. The structure of the state space estimation module shown in fig. 6 is more complex than the state space estimation module 41 shown in fig. 4, but the application of the kalman filter will be simpler.
In the example shown in fig. 6, the state space estimation model is discretized, and the elements in vector α are included as augmented states within the estimator model for determining the appropriate propagation times.
The discretized model may be represented as follows:
wherein A is 1 、A 2 、A 3 、B 1 、B 2 Representing the discretized corresponding states and the entered model parameters. Sigma [ n ]]A system noise variable representing each state of parameter uncertainty applied to the driving model.
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 wind speeds from lidar sensors based on different propagation times. The second estimation module of the parallel calculation outputs a wind speed time shift estimation vector v 'corresponding to different propagation times at the rotor' w [n]Rotor speed estimation error vector omega corresponding to the same error [n]. Rotor speed estimation error vector omega error [n]The error between the measured rotor speed and the estimated rotor speed for a given wind speed input may be represented.
A kalman filter is described herein as an example. Time shifted from n+1 to n in the time cell in the model for control and modeling, n+1 and n correspond to k and k-1 in the Kalman filter, respectively.
The dynamic model described above may be updated to the predictions of the state vector corresponding to time k+1 for a given estimated state corresponding to k. The current state x k 1 in the prediction phase of the state estimator may be updated using the estimation feedback part in the update phase of the estimator, and the updated state vector at time k may be expressed as x k. The predictive model may then be defined based on a dynamic model, expressed for simplicity as follows:
x[k|k-1]＝f(x[k-1|k-1],u[k],σ[k]) (23)
where f () represents a function of the state space estimation model.
According to embodiments of the present disclosure, the state space estimation model extension may be applied to more tower modes by extending the state space estimation model to a plurality of states corresponding to a plurality of tower modes, respectively. The number of states in the augmented state vector may be calculated based on the number of tower modes.
According to an embodiment of the present disclosure, wind speed estimation is performed using a kalman filter in wind speed vector prediction, and when a wind speed estimation vector having the lowest estimation error is selected and a corresponding position in the wind speed estimation vector is used as a propagation time update, the wind speed estimation vector having the lowest estimation error is used as an output.
The application process of the Kalman filter comprises a prediction stage and an updating stage. An exemplary brief description is provided below.
For the prediction phase of the method,
the state may be updated based on measured inputs, final state values, and the model or predictive model without noise parts.
The prediction covariance may be estimated according to the following equation:
P[k|k-1]＝F[k]P[k-1|k-1]F T [k]+Q[k] (25)
where P [ k-1|k-1] represents the update covariance calculated during the update phase, Q [ k ] represents the covariance of the process noise, and F [ k ] represents the state transition matrix, which can be defined as follows:
for the update phase of the time period,
the measurement residual (measurement residual) is Wherein ω [ k|k-1]Representing the rotor speed estimated by the model. Residual mean square error is S [ k ]]＝H[k]P[k|k-1]H T [k]+R[k]Wherein R < k >]Representing covariance of measured noise, H [ k ]]The observation matrix calculated as follows is represented.
Where h represents a function given by the following formula.
The approximate optimal Kalman gain is:
K[k]＝P[k|k-1]H T [k]S -1 [k] (29)
the updated estimated states are obtained as follows:
the updated estimated covariance is as follows:
P[k|k]＝(I-K[k]H[k])P[k|k-1] (31)
q [ k ] and R [ k ] are model parameters and tuning parameters of the system and are unchanged during the process.
According to an embodiment of the present disclosure, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed, implements a method of estimating wind speed at a wind turbine rotor according to an embodiment of the present disclosure.
In an embodiment of the present disclosure, the computer-readable storage medium may carry one or more programs, which when executed, may implement the following steps described with reference to fig. 1 to 6: acquiring, by a lidar sensor of the wind turbine, a wind speed measurement vector at a predetermined location associated with the wind turbine; acquiring, by a non-wind speed sensor of the wind turbine, 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, a state space estimation model is utilized to generate a wind speed estimation vector at the rotor.
The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a computer program for use by or in connection 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: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing. The computer readable storage medium may be embodied in any device; or may exist alone without being assembled into the device.
Fig. 7 is a block diagram of a computing device 7 according to an embodiment of the present disclosure.
Referring to fig. 7, a computing device 7 according to an embodiment of the present disclosure may comprise a memory 71 and a processor 72, with a computer program 73 stored on the memory 71, which when executed by the processor 72, implements a method of estimating wind speed at a wind turbine rotor according to an embodiment of the present disclosure.
In an embodiment of the present disclosure, the computer program 73, when executed by the processor 72, may implement the operations of the method of estimating wind speed at a wind turbine rotor described with reference to fig. 1-6: acquiring, by a lidar sensor of the wind turbine, a wind speed measurement vector at a predetermined location associated with the wind turbine; acquiring, by a non-wind speed sensor of the wind turbine, 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, a state space estimation model is utilized to generate a wind speed estimation vector at the rotor.
The computing device illustrated in fig. 7 is merely an example and should not be taken as limiting the functionality and scope of use of embodiments of the present disclosure.
Methods, apparatus, computer readable storage media, computing devices for estimating wind speed at a wind turbine rotor according to embodiments of the present disclosure have been described above with reference to fig. 1-7. However, it should be understood that: the control device and its various units shown in fig. 4 may be configured as software, hardware, firmware, or any combination thereof, respectively, that performs a specific function, the computing device shown in fig. 5 is not 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.
With a method and apparatus for estimating wind speed at a wind turbine rotor, a computer readable storage medium, a computing device and a wind turbine according to embodiments of the present disclosure, at least one of the following technical effects may be achieved: the control performance of the wind turbine is improved by accurately estimating the wind speed at the rotor by using a lidar sensor and a non-wind speed sensor; it is advantageous to estimate the wind speed and wind acceleration at the rotor while updating the tower pattern frequency used in the wind speed prediction model of the wind turbine.
The control logic or functions performed by the various components or controllers in the control system may be represented by flow diagrams or similar illustrations 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, multi-tasking, multi-threading, and the like. Accordingly, various steps or functions illustrated may be performed in the order 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 illustrated may be repeatedly performed depending on the particular processing strategy being used.
Although the present disclosure has been shown and described with respect to the preferred embodiments, it will be understood by those skilled in the art that various modifications and changes may be made to these embodiments without departing from the spirit and scope of the disclosure as defined by the appended claims.
Claims (15)
1. A method of estimating wind speed at a wind turbine rotor, the method comprising:
acquiring, by a lidar sensor of the wind turbine, a wind speed measurement vector at a predetermined location associated with the wind turbine;
acquiring, by a non-wind speed sensor of the wind turbine, 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, a state space estimation model is utilized to generate a wind speed estimation vector at the rotor.
2. The method of claim 1, wherein the generating a wind speed estimation vector at the rotor using a state space estimation model based on the wind speed measurement vector and the non-wind speed measurement vector comprises:
performing time correction on the wind speed measurement vector to generate a wind speed measurement correction vector;
based on the wind speed measurement correction vector and the non-wind speed measurement vector, a state space estimation model is utilized to generate a wind speed estimation vector at the rotor.
3. The method of claim 2, wherein the time correcting the wind speed measurement vector to generate a wind speed measurement correction vector comprises:
time correcting the wind speed measurement vector with a propagation time correction vector to generate a wind speed measurement correction vector,
wherein the travel time correction vector is updated based on the wind speed measurement correction vector and the non-wind speed measurement vector.
4. A method according to claim 3, wherein the propagation time correction vector is updated with the state space estimation model based on a wind speed measurement correction vector and the non-wind speed measurement vector.
5. A method according to any one of claims 2 to 4, wherein the wind speed measurement correction vector comprises a plurality of wind speed measurement correction vectors corresponding to different propagation times.
6. The method according to any one of claims 2 to 4, wherein the generating a wind speed estimation vector at the rotor using a state space estimation model based on a wind speed measurement correction vector and the non-wind speed measurement vector comprises:
generating a wind speed time shift estimation vector corresponding to different propagation time at the rotor and a rotor rotating speed estimation error vector corresponding to the wind speed time shift estimation vector based on the non-wind speed measurement vector and the wind speed time shift vector corresponding to different propagation time;
based on the wind speed measurement correction vector, the wind speed time shift estimation vector and the corresponding rotor speed estimation error vector, generating a wind speed estimation vector at the rotor by using a state space estimation model,
wherein the wind speed time shift vector is updated based on the wind speed measurement correction vector, the wind speed time shift estimation vector, and a corresponding rotor speed estimation error vector.
7. The method of claim 6, wherein the wind speed time shift vector is updated with the state space estimation model based on the wind speed measurement correction vector, the wind speed time shift estimation vector, and their corresponding rotor speed estimation error vectors.
8. A method according to claim 3, characterized in that the method further comprises:
generating a wind speed time shift estimation vector corresponding to different propagation time at the rotor and a rotor rotating speed estimation error vector corresponding to the wind speed time shift estimation vector based on the non-wind speed measurement vector and the wind speed time shift vector corresponding to different propagation time;
and updating the propagation time correction vector by using a state space estimation model based on the wind speed measurement correction vector, the wind speed time shift estimation vector and the corresponding rotor speed estimation error vector.
9. The method according to any one of claims 1 to 4, wherein the non-wind speed measurement vector comprises at least one of: the rotational speed, pitch angle, motor torque, blade load, nacelle acceleration, and elevation of the rotor.
10. The method according to any one of claims 1 to 4, wherein the state space estimation model is a nonlinear state space estimator comprising a kalman filter.
11. An apparatus for estimating wind speed at a wind turbine rotor, the apparatus comprising:
a wind speed measurement module configured to: acquiring, by a lidar sensor of the wind turbine, a wind speed measurement vector at a predetermined location associated with the wind turbine;
a non-wind speed measurement module configured to: acquiring, by a non-wind speed sensor of the wind turbine, a non-wind speed measurement vector associated with a rotor of the wind turbine;
a wind speed estimation module configured to: based on the wind speed measurement vector and the non-wind speed measurement vector, a state space estimation model is utilized to generate a wind speed estimation vector at the rotor.
12. A computer readable storage medium storing a computer program, characterized in that the method of estimating wind speed at a wind turbine rotor according to any of claims 1-10 is implemented when the computer program is executed by a processor.
13. A computing device, the computing device comprising:
a processor;
memory storing a computer program which, when executed by a processor, implements a method of estimating a wind speed at a wind turbine rotor according to any of claims 1 to 10.
14. A wind turbine, the wind turbine comprising:
a lidar sensor configured to measure a wind speed measurement vector at a predetermined location associated with the wind turbine;
a non-wind speed sensor configured to measure a non-wind speed measurement vector associated with a rotor of the wind turbine; and
the computing device of claim 13, wherein the computing device is electrically connected to a lidar sensor and a non-wind speed sensor.
15. Wind turbine according to claim 14, wherein the computing means is provided in a control device of the wind turbine.
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| AU2022436719A AU2022436719A1 (en) | 2022-01-29 | 2022-06-28 | Method and apparatus for estimating wind speed at rotor of wind turbine |
| PCT/CN2022/101921 WO2023142366A1 (en) | 2022-01-29 | 2022-06-28 | Method and apparatus for estimating wind speed at rotor of wind turbine |
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