Method for determining average wind speed with lidar sensor

Abstract

The invention relates to a method for determining the mean wind speed in a vertical plane by means of a LiDAR sensor (2), comprising performing measurements (MES), constructing a measurement model (MOD M) and a wind model (MOD V), then determining the wind speed (v) using an adaptive Kalman filter (KAL), and determining the mean wind speed (RAWS) in the vertical plane under consideration.

Description

Technical Field

[0001] This invention relates to the field of renewable energy, and more particularly to measuring the resources (i.e., wind) of wind turbines by means of wind forecasting, turbine control (orientation, torque and speed regulation) and / or diagnostic and / or monitoring targets.

[0002] A wind turbine allows the kinetic energy from wind to be converted into electrical or mechanical energy. To convert wind into electricity, it consists of the following components:

[0003] - A tower, which allows the rotor to be positioned at a sufficient height to enable its movement (necessary for horizontal axis wind turbines) and / or to position the rotor at a height that allows it to be driven by stronger and more regular winds than at ground level. The tower typically houses some of the electrical and electronic components (modulators, control units, multipliers, generators, etc.).

[0004] - The nacelle, mounted atop the tower, houses the mechanical, pneumatic, and some of the electrical and electronic components necessary for operating the turbine. The nacelle can be rotated to orient the machine in the correct direction.

[0005] - A rotor fixed to the nacelle, comprising several blades (typically three) of a wind turbine and a hub. This rotor is driven by wind energy and is connected directly or indirectly (via a gearbox and mechanical shaft system) to a generator that converts recovered energy into electrical energy. The rotor may be equipped with control systems such as variable-angle blades or aerodynamic brakes.

[0006] - Optional gearbox, especially consisting of two shafts (mechanical shaft of the rotor and mechanical shaft of the converter) connected by the gearbox (gearbox).

[0007] Since the early 1990s, there has been renewed interest in wind power, particularly in the European Union, where annual growth rates have reached approximately 20%. This growth is attributed to the inherent potential for carbon-neutral power generation. To sustain this growth, the power output of wind turbines still needs to be further increased. The prospect of increased wind power output necessitates the development of efficient production tools and advanced control systems to improve machine performance. Wind turbines are designed to generate electricity at the lowest possible cost. Therefore, they are typically constructed to achieve their maximum performance at wind speeds of around 15 m / s. It is uncommon for wind turbines not to be designed to maximize their output at higher wind speeds. At wind speeds exceeding 15 m / s, it is necessary to dissipate some of the extra energy contained in the wind to avoid damaging the wind turbine. Therefore, all wind turbines are designed with power regulation systems.

[0008] For this type of power regulation, a controller has been designed for variable-speed wind turbines. The controller aims to maximize the recovered electricity, minimize rotor speed fluctuations, and minimize fatigue and extreme moments in the structure (blades, tower, and platform). Background Technology

[0009] To optimize control, it is essential to know the average wind speed. Various technologies have been developed for this purpose.

[0010] According to the first technique, an anemometer can be used to estimate the wind speed at a certain point, but this inaccurate technique cannot measure the entire wind field or know the three-dimensional components of the wind speed.

[0011] According to the second technique, a LiDAR (Light Detection and Ranging) sensor can be used. LiDAR is a remote sensing or optical measurement technique based on the analysis of the characteristics of a beam of light returning to the transmitter. This method is primarily used to determine the distance to an object using pulsed laser light.

[0012] Unlike radar, which is based on a similar principle, LiDAR sensors use visible or infrared light instead of radio waves. The distance to an object or surface is given by measuring the delay between the pulse and the detected reflected signal.

[0013] In the wind turbine industry, LiDAR sensors have been declared essential for the normal operation of large wind turbines, especially given their increasing size and power (offshore wind turbines are currently 5MW, and will soon reach 15MW). These sensors enable remote wind measurements, primarily allowing for the calibration of wind turbines to deliver maximum power (power curve optimization). For this calibration phase, the sensor can be placed on the ground and vertically oriented (profiler), allowing for measurements of wind speed, direction, and wind speed gradients depending on altitude. This application is particularly critical because it allows for knowledge of the resources used to generate energy. This is important for wind turbine projects as it determines their financial viability.

[0014] A second application involves mounting the sensor on the nacelle of a wind turbine to measure the wind field in front of the turbine when it is oriented nearly horizontally. Firstly, measuring the wind field in front of the turbine allows for advance knowledge of turbulence the turbine will encounter shortly thereafter. However, current wind turbine control and monitoring technologies do not allow for consideration of measurements performed by LiDAR sensors through precise estimation (i.e., in the rotor plane) of the average wind speed. Such applications are described in particular in patent application FR-3-013777 (US-2015-145253).

[0015] Furthermore, a specific characteristic of using LiDAR sensors is that the distances from the measurement plane to the rotor plane of the wind turbine can be imposed by the LiDAR user; these distances can vary from LiDAR sensor to LiDAR sensor and can be unknown. In this case, it is impossible to use wind speed measurement methods such as those described in patent applications FR-3068139 (US-2020 / 0124026) and FR-3088971 (US-2020 / 0166650), which require the forced distance between the measurement plane and the rotor plane of the wind turbine. Summary of the Invention

[0016] The purpose of this invention is to determine the average wind speed in a vertical plane using a LiDAR sensor, where the distance from the measurement plane to the wind turbine rotor plane is not mandatory, allowing users of the LiDAR sensor freedom in parameterizing it. Therefore, this invention relates to a method for determining the average wind speed in a vertical plane using a LiDAR sensor, comprising performing measurements, constructing a measurement model and a wind model, then using an adaptive Kalman filter to determine the wind speed, and determining the average wind speed in the considered vertical plane. These steps do not require prior constraints on the measurement plane of the LiDAR sensor. Therefore, the method according to the invention can be used in any LiDAR sensor configuration. The wind model enables accurate representation of wind speed, independent of distance from the measurement plane of the LiDAR sensor.

[0017] This invention relates to a method for determining the average wind speed in a vertical plane using a LiDAR sensor mounted on a wind turbine. The method comprises the following steps:

[0018] a) Construct a model for the LiDAR measurements;

[0019] b) Construct a wind model considering the spatial and temporal coherence of wind speed;

[0020] c) Using the LiDAR sensor, measure the wind amplitude and direction at at least one measurement plane at a distance from the wind turbine;

[0021] d) Using the LiDAR measurements, the model, the wind model, and the measurements, an adaptive Kalman filter is used to determine the wind speed at each predefined estimation point in the upstream space of the wind turbine, and

[0022] e) The average wind speed in the vertical plane is determined by means of the wind speed determined for each predefined estimation point of the vertical plane under consideration at a certain distance from the wind turbine.

[0023] According to one embodiment, the model for the LiDAR measurement is written as follows: m j，x (k)=a j v j，x (k)+b j v j，y (k)+c j v j，z (k), where m is the measurement, x is the longitudinal direction, j is the measurement beam of the LiDAR sensor, and m j,x It measures the distance of beam j at a distance x, where k is the discrete time, v is the wind speed, and v j,x It refers to the longitudinal component of the wind speed of the measured beam j, v j,y It refers to the lateral component of the wind speed for measuring beam j, v j,z It refers to the vertical component of the wind speed of the measuring beam j, a j b j c j It is a constant measurement coefficient for the measurement beam j.

[0024] According to one implementation, the spatial coherence of the wind model is a function of lateral coherence, vertical coherence, and longitudinal coherence.

[0025] Preferably, the lateral coherence is written as follows: Where x is the vertical component, and y1 and y2 are two lateral positions with the same vertical and longitudinal values. It is the longitudinal component of the wind speed at position y1. It is the longitudinal component of the wind speed at position y2, f t It is a predefined function.

[0026] Advantageously, the vertical coherence is written as follows: Where x is the vertical component, and z1 and z2 are two vertical positions with the same vertical and horizontal values. It is the longitudinal component of the wind speed at position z1. α is the longitudinal component of the wind speed at position z2, and α is the coefficient of the power law.

[0027] Advantageously, the longitudinal coherence is written as follows: Where x is the vertical component, k is the discrete time, and x1 and x2 are two vertical positions with the same horizontal and vertical values. It is the longitudinal component of the wind speed at location x1. It is the longitudinal component of the wind speed at location x2, f l It is a predefined function.

[0028] According to one aspect, the temporal coherence of the wind model is written as follows: ω(k)=A sω(k-1), where k is the discrete time, and ω is a vector that first includes the longitudinal component of the wind speed at n predefined estimation points, and then includes the lateral component of the wind speed at the n predefined estimation points. s It is a constant matrix, which is the wind speed autocorrelation function obtained through the Kaimal spectrum.

[0029] According to one embodiment, the adaptive Kalman filter is applied to the following equation: v x (k)=A S v x (k-1)+η(k) and Where k is the discrete time, v is the wind speed, x is the longitudinal component, y1 and y2 are two lateral positions with the same longitudinal and vertical values, x1 and x2 are two longitudinal positions with the same lateral and vertical values, and z1 and z2 are two vertical positions with the same longitudinal and lateral values. It is the longitudinal component of the wind speed at position y1. It is the longitudinal component of the wind speed at position y2, f t It is a predefined function. It is the longitudinal component of the wind speed at location x1. It is the longitudinal component of the wind speed at location x2, f l It is a predefined function. It is the longitudinal component of the wind speed at position z1. Here, α is the longitudinal component of the wind speed at position z2, α is the coefficient of the power law, j is the measurement beam of the LiDAR sensor, and m is the longitudinal component of the wind speed at position z2. j,x It measures the distance of beam j at a distance x, v j,x It refers to the longitudinal component of the wind speed of the measured beam j, v j,y It refers to the lateral component of the wind speed for measuring beam j, v j,z It refers to the vertical component of the wind speed of the measuring beam j, a j b j c j It is a constant measurement coefficient for the measurement beam j, η is the noise of the state equation, and ε is the constant measurement coefficient for the measurement beam j. t It is transverse noise, ε v It is vertical noise, ε l It is longitudinal noise, ε m It measures noise, A s It is a constant matrix, which is the autocorrelation function of wind speed obtained through the Kaimal spectrum.

[0030] According to one implementation, the wind speed at different points is determined using the following equation: and Where k is the discrete time, and ω is a vector that initially includes the longitudinal components of the wind speed at n predefined estimation points. It is an estimate of the vector ω(k) given the measurements performed up to time k-1. P(k|k-1) is the covariance matrix of the vector ω(k) given the measurements performed up to time k. s It is a constant matrix, which is the autocorrelation function of wind speed obtained from the Kaimal spectrum. Q and R are the covariance matrices of noise ε(k) and η(k), respectively. C a Through in The output equation is obtained by linearizing the surrounding data, where y(k) is the measurement of the LiDAR sensor and I is the identity matrix.

[0031] According to one embodiment option, in the vertical plane at a certain distance from the wind turbine, the wind speed is determined by the average of the longitudinal components of the wind speed at points belonging to the vertical plane. Preferably, the wind speed under consideration includes the wind speed in the projection of the surface swept by the wind turbine rotor in the vertical plane under consideration.

[0032] The present invention also relates to a method for controlling a wind turbine. The method includes the following steps:

[0033] a) Determine the average wind speed using a method based on one of the aforementioned characteristics, and

[0034] b) Control the wind turbine based on the average wind speed.

[0035] Furthermore, the present invention relates to a computer program product comprising code instructions designed to execute steps of a method according to one of the above features when the program is executed on the control and / or diagnostic unit of the wind turbine.

[0036] Furthermore, the present invention relates to a LiDAR sensor comprising a processing unit that implements a method according to one of the above features.

[0037] The present invention also relates to a wind turbine including a LiDAR sensor according to any of the above features, wherein the LiDAR sensor is preferably disposed on the nacelle of the wind turbine or in the hub of the wind turbine. Attached Figure Description

[0038] Referring to the accompanying drawings, other features and advantages of the method according to the invention will become apparent from the following description of embodiments given by way of non-limiting examples, wherein:

[0039] Figure 1The steps of a method for determining average wind speed according to an embodiment of the present invention are shown.

[0040] Figure 2 A wind turbine equipped with a LiDAR sensor according to an embodiment of the present invention is shown.

[0041] Figure 3 The explanation describes, for the first example, a comparison between the average wind speed at a distance of 100m from the rotor of the wind turbine and a reference average wind speed obtained using a method according to an embodiment of the present invention, and...

[0042] Figure 4 The explanation describes, for the second example, a comparison between the average wind speed at a distance of 110m from the rotor of the wind turbine and a reference average wind speed obtained using a method according to an embodiment of the present invention. Detailed Implementation

[0043] The present invention relates to a method for determining the average wind speed in a vertical plane using a LiDAR sensor arranged on a wind turbine.

[0044] According to the present invention, the LiDAR sensor allows for the measurement of wind speed on at least one measurement plane upstream of a wind turbine. Several types of LiDAR sensors exist, such as scanning LiDAR sensors, continuous wave, or pulsed LiDAR sensors. Within the context of the present invention, pulsed LiDAR is preferably used. However, other LiDAR technologies may also be used while remaining within the scope of the present invention.

[0045] LiDAR sensors provide rapid measurements. Therefore, using such sensors enables the rapid and continuous determination of average wind speed. For example, the sampling rate of LiDAR sensors can range between 1 and 5 Hz (or even larger in the future), and it can be as low as 4 Hz. Furthermore, LiDAR sensors allow for the acquisition of information related to the wind upstream of the turbine, which is relevant to the wind blowing towards the turbine. Therefore, LiDAR sensors can be used to predict wind speed in the turbine rotor plane.

[0046] Figure 2A horizontal-axis wind turbine 1 equipped with a LiDAR sensor 2 for use in a method according to an embodiment of the invention is illustrated schematically by way of non-limiting example. The LiDAR sensor 2 is used to measure wind speed at a given distance on multiple measurement planes PM (only two measurement planes are shown). Prior knowledge of the wind speed measurement allows for the provision of a large amount of information. The figure also shows axes x, y, and z. The reference point of this coordinate system is the center of the rotor. Direction x is the longitudinal direction corresponding to the direction of the rotor axis upstream of the wind turbine, which also corresponds to the measurement direction of LiDAR sensor 2. Direction y, perpendicular to direction x, is a lateral or transverse direction located in the horizontal plane (directions x and y form the horizontal plane). Direction z is an upward vertical direction (basically corresponding to the direction of tower 4), with the z-axis perpendicular to the x and y axes. The rotor plane is indicated by a dashed rectangle PR, which is defined by directions y and z (x is zero). The measurement plane PM is the plane formed by directions y and z at a certain distance from the rotor plane PR (for non-zero x). The measurement plane PM is parallel to the rotor plane PR.

[0047] Traditionally, a wind turbine 1 allows the conversion of wind kinetic energy into electrical or mechanical energy. To convert wind energy into electrical energy, it consists of the following components:

[0048] - Tower 4, which allows the rotor (not shown) to be positioned at a sufficient height to enable its movement (necessary for a horizontal axis wind turbine) and / or allows the rotor to be positioned at a height that enables it to be driven by stronger and more regular winds than at ground level 6. Tower 4 typically houses some of the electrical and electronic components (modulators, control units, multipliers, generators, etc.).

[0049] - The nacelle 3, mounted at the top of the tower 4, houses the mechanical, pneumatic, and some electrical and electronic components (not shown) necessary for operating the machine. The nacelle 3 can be rotated to orient the machine in the correct direction.

[0050] - A rotor fixed to the nacelle, comprising several blades (typically three) of a wind turbine and a hub. This rotor is driven by wind energy and is connected directly or indirectly (via a gearbox and mechanical shaft system) to a motor (generator) (not shown), which converts recovered energy into electrical energy. The rotor may be equipped with a control system, such as variable-angle blades or aerodynamic brakes.

[0051] - An optional gearbox, consisting of two shafts (a mechanical shaft for the rotor and a mechanical shaft for the motor) connected by a gearbox (not shown).

[0052] As in the example embodiment of a pulsed LiDAR sensor Figure 2As can be seen, the LiDAR sensor 2 used includes four measurement beams or axes (b1, b2, b3, b4). As a non-limiting example, the method according to the invention can also operate using a LiDAR sensor comprising any number of beams. The LiDAR sensor performs timely measurements at each intersection of the measurement plane PM and the beams (b1, b2, b3, b4). These measurement points are located at... Figure 2 The measurement points are represented by black circles. For the first measurement plane PM, the measurement points are denoted by PT1, PT2, PT3, and PT4. Processing the measurements at these points allows for the determination of wind speed within the measurement plane PM.

[0053] Preferably, the LiDAR sensor 2 can be mounted on the nacelle 3 of the wind turbine 1 or on the hub of the wind turbine 1 (i.e., at the front of the nacelle in the wind direction).

[0054] According to the present invention, the method for determining the average wind speed includes the following steps:

[0055] 1) Construction of LiDAR sensor measurement model

[0056] 2) Construction of wind model

[0057] 3) Wind measurement

[0058] 4) Determining wind speed

[0059] 5) Determination of average wind speed

[0060] Steps 3), 4), and 5) are executed in real time. Steps 1) and 2) can be executed offline and before the real-time steps, and they can be executed in this order, in reverse order, or simultaneously. All steps will be described in detail in the remainder of this specification.

[0061] Figure 1 The steps of a method according to an embodiment of the present invention are illustrated by way of non-limiting example. This method allows for the determination of average wind speed in a vertical plane using LiDAR sensors deployed on a wind turbine. The first step may include constructing an offline wind model MOD V and a measurement model MOD M. Subsequently, the amplitude and direction of the MES wind are measured in real time using LiDAR sensors. Then, the wind speed v at each point is determined in real time using an adaptive Kalman filter KAL, which utilizes the wind model MOD V, the measurement model MOD M, and the measurement MES. Finally, the average wind speed RAWS is determined based on the wind speed v at different points.

[0062] 1) Construction of LiDAR sensor measurement model

[0063] This step involves building a model of the LiDAR sensor measurements. This model correlates the components of wind speed with the measurement signals from the LiDAR sensor.

[0064] According to one embodiment of the present invention, the LiDAR sensor measurement model can be written as follows: m j，x (k)=a j v j，x (k)+b j v j，y (k)+c j v j，z (k), where m is the measurement, x is the longitudinal direction, j is the measurement beam of the LiDAR sensor, and m j,x It measures the distance of beam j at a distance x, where k is the discrete time, v is the wind speed, and v j,x It refers to the longitudinal component of the wind speed of the measured beam j, v j,y It refers to the lateral component of the wind speed for measuring beam j, v j,z It refers to the vertical component of the wind speed of the measuring beam j, a j b j c j This is a constant measurement coefficient for the measurement beam j. Measurement coefficient a j b j c j These measurement coefficients depend only on the beam angle of the LiDAR sensor and are independent of the measurement distance. j b j c j This data could be provided by the LiDAR sensor manufacturer.

[0065] 2) Construction of wind model

[0066] This step involves constructing a wind model that considers spatial and temporal coherence to define the wind speed and its components at any point in space, based on various parameters, particularly temporal and spatial location (and thus, the coordinates of the considered point in the (x,y,z) system). In other words, a wind model is constructed that satisfies both spatial and temporal coherence constraints. These spatial and temporal coherences allow the wind model to represent the wind and provide an accurate determination of wind speed.

[0067] According to one implementation of the invention, the wind model can determine the longitudinal and lateral components of wind speed. Alternatively, the wind model can determine all three components of wind speed.

[0068] According to one embodiment of the invention, the spatial coherence used in the wind model can depend on lateral coherence, longitudinal coherence, and vertical coherence. The representativeness of the wind model is thus improved.

[0069] For this embodiment, the transverse coherence can be expressed by the following equation: Where x is the vertical component, and y1 and y2 are two lateral positions with the same vertical value (x1 = x2 = x) and vertical value (z1 = z2 = z). It is the longitudinal component of the wind speed at position y1. It is the longitudinal component of the wind speed at position y2, f t It is a known predefined function. Therefore, the longitudinal component of the wind speed at point y1 depends on the longitudinal component of the wind speed at point y2 and the distance between points y1 and y2. According to the example embodiment, the predefined function f t It can be an exponential function.

[0070] For this embodiment, vertical coherence can be expressed by the following equation: Where x is the vertical component, and z1 and z2 are two vertical positions with the same vertical value (x1 = x2 = x) and horizontal value (y1 = y2 = y). It is the longitudinal component of the wind speed at position z1. Let z1 be the longitudinal component of the wind speed at position z2, and α be the coefficient of the power law. For this equation, the reference frame for height z is defined relative to the base of the wind turbine tower (not at the LiDAR sensor). Therefore, the longitudinal component of the wind speed at point z1 depends on the longitudinal component of the wind speed at point z2 and the ratio between the heights of points z1 and z2. The coefficient α of the power law can be chosen as a constant, or it can be estimated using LiDAR sensor measurements, for example, according to the method described in patent application number FR-19 / 06569.

[0071] For this embodiment, the longitudinal coherence can be expressed by the following equation: Where x is the vertical component, and x1 and x2 are two vertical positions with the same horizontal value (y1=y2=y) and vertical value (z1=z2=z). It is the longitudinal component of the wind speed at location x1. It is the longitudinal component of the wind speed at location x2, f l It is a known predefined function. Therefore, the longitudinal component of the wind speed at point x1 depends on the longitudinal component of the wind speed at point x2 and the distance between points x1 and x2. According to the example embodiment, the predefined function f l It can be an exponential function.

[0072] Temporal coherence is understood as the variation of wind speed components over time at a single location (i.e., for the same x, y, and z values). In other words, temporal coherence can be expressed as the relationship between wind speed components between two successive discrete time intervals (denoted as k and k-1).

[0073] According to one implementation of the present invention, a known temporal coherence is obtained using the Kaimal spectrum, which can be defined as follows: Where f is the frequency in Hertz, t is the wind speed component (t can therefore correspond to x, y, or z), and S t The Kaimal spectrum is the wind speed component t, where U is the average wind speed at the height of the wind turbine rotor, and L is the wind speed. t It is the integral scale parameter of the wind speed component t, σ t It is the variance determined by the wind turbulence intensity. In fact, the Kaimal spectrum allows for the determination of a discrete transfer function, which correlates the wind value at time k with the wind value at time k-1.

[0074] For an embodiment that determines only the longitudinal and lateral components of wind speed, ω can be let be a vector of dimension 2n, which first includes the longitudinal component of the wind speed at the n points under consideration, and then includes the lateral component of the wind speed at the n points under consideration. To explain this vector ω in a simpler case, consider wind speeds with both longitudinal and lateral components v x1 v y1 The first point is about wind speed components v, which have both longitudinal and lateral speeds. x2 v y2 Secondly, the vector ω is written as follows:

[0075] ω=(v x1 v x2 v y1 v y2 ) T .

[0076] Using this notation and noting that the Kaimal spectrum is a Fourier transform of the autocorrelation function of wind speed, the following equation can be written for time coherence: w(k) = A s w(k-1), A s It is a constant matrix, which is the autocorrelation function of wind speed obtained from the Kaimal spectrum. Matrix A s This can be obtained from the Kaimal spectrum formula as defined above. Therefore, this equation gives the relationship between the wind speed ω at time k and the wind speed ω at time k-1.

[0077] Alternatively, for temporal coherence, von Karman spectra or any similar representation can be used.

[0078] 3) Wind measurement

[0079] In this step, wind amplitude and direction are continuously measured on at least one measurement plane away from the wind turbine using a LiDAR sensor. This measurement corresponds to the signal received by the LiDAR sensor in response to a signal emitted by the LiDAR sensor. In fact, through interferometry and the Doppler effect, part of the laser signal emitted by the LiDAR sensor is reflected by air molecules at the measurement point and also by aerosols (suspended dust and particles).

[0080] According to the implementation of the present invention, the measuring plane can be located at a longitudinal distance from the rotor plane (along... Figure 2 The location (x-axis) is preferably between 50 and 400 m. Therefore, the wind speed evolution over a long distance upstream of the wind turbine can be determined, which also allows for improved accuracy in determining the average wind speed.

[0081] According to one embodiment of the invention, wind speed measurement can be performed in several measurement planes (whose measurement distances are not applied by the method according to the invention) to facilitate wind speed determination, which allows users of LiDAR sensors to freely parameterize the LiDAR sensors.

[0082] For embodiments using pulsed LiDAR, in Figure 2 The measurement points shown are obtained sequentially from beam b1, then beam b2, ..., and finally beam b4. An interesting feature of this coordinate system is that it allows for the simultaneous measurement of wind speed projections at several distances for a given beam. Therefore, 10 consecutive distances between, for example, 50m and 400m can be obtained at the sampling rate of a LiDAR sensor. At each sampling time, only the measurements for the selected current beam are refreshed.

[0083] 4) Determining wind speed

[0084] This step involves using the wind model built in step 2, the LiDAR sensor measurement model built in step 1, and the measurements performed in step 3 to determine the wind speed at various points in the upstream space of the wind turbine using an adaptive Kalman filter. Each wind speed determination point is a predefined estimation point. Applying the Kalman filter allows for the acquisition of a state observer. The adaptive Kalman filter enables the noise covariance matrix to be adapted to the wind speed. Therefore, the filter is effective over a wide range of wind speeds. Furthermore, the adaptive Kalman filter is robust to changes in wind speed.

[0085] It's important to note that in automation and systems theory, state observers or state estimators are extensions of models represented as state representations. When the state of a system is not measurable, an observer is constructed that allows the state to be reconstructed from the model.

[0086] For an embodiment using the equations shown in step 2, the following state model can be written using state equations: v x (k)=A s v x (k-1)+η(k)

[0087] And the output equation:

[0088] Where η is the noise of the state equation, ε t It is transverse noise, ε v It is vertical noise, ε l It is longitudinal noise, and ε m It measures noise.

[0089] Therefore, the problem of estimating the vector ω(k) becomes a state estimation problem, which does not require forcing the position of the LiDAR sensor's measurement plane. One way to estimate the unknown state vector ω(k) (which can take into account information related to the noise η(k) and ε(k)) involves an algorithm that applies an adaptive Kalman filter, using the following notation: In fact, the adaptive Kalman filter provides a solution for the following optimization problem: in

[0090]

[0091] Where P0, Q, and R are adjustment matrices of appropriate dimensions. It is the average value of the initial state ω(0).

[0092] To solve this optimization problem using an adaptive Kalman filter, the following assumptions can be made, especially regarding the mathematical iterations of P0, Q, and R:

[0093] ω(0) is a random vector that is independent of noise η(k) and ε(k).

[0094] ·ω(0) has a known mean Where P0 is the covariance matrix, i.e.:

[0095] η(k) and ε(k) are zero-mean values ​​of the white noise process, respectively, with covariance matrices Q and R, i.e.:

[0096]

[0097]

[0098] E[ε(k)η(j) T ] = 0 for all k,j

[0099] The last assumption implies that Q and R are symmetric positive semi-definite matrices.

[0100] Furthermore, given in the state model, the noise ε l ε v and ε t The covariance matrix R is adapted based on the measured distances x1, x2, y1, y2, z1, z2. According to one embodiment, R can be a polynomial function of the measured distances. Alternatively, R can be obtained from maps, neural networks, etc.

[0101] The following notation can be used:

[0102] · It is an estimate of the vector ω(k) given the measurements performed up to time k-1.

[0103] · It is an estimate of the vector ω(k) given the measurements performed up to time k.

[0104] P(k|k-1) is the covariance matrix of the vector ω(k) given the measurements performed up to time k-1.

[0105] P(k|k) is the covariance matrix of the vector ω(k) given the measurement performed up to time k.

[0106] Then, the adaptive Kalman filter algorithm is used to determine the wind speed at each point using the following equation:

[0107] On the one hand, time updates:

[0108]

[0109] On the other hand, measurement updates:

[0110]

[0111] Where C a Through in The output equation of the surrounding state model is obtained by linearizing it, where y(k) is the measurement of the LiDAR sensor and I is the identity matrix.

[0112] Therefore, these steps allow for the determination of the vector ω, which includes the components of wind speed at several different points. In other words, these steps allow for the determination of the components of wind speed at several different points.

[0113] 5) Determination of average wind speed

[0114] This step involves determining the average wind speed in the vertical plane at a certain distance upstream of the wind turbine (defined by the longitudinal direction) using the wind speed determined in step 4, particularly the wind speed in the vertical plane under consideration.

[0115] According to one embodiment, the average wind speed may be the average of the longitudinal components of the wind speed in the plane under consideration.

[0116] According to a preferred embodiment of the invention, the average wind speed can be the average of the longitudinal components of the wind speed in the plane under consideration, taking into account only the wind speed values ​​in the surface corresponding to the surface swept by the rotor of the wind turbine. In other words, the surface swept by the rotor of the wind turbine (a circle with a radius corresponding to the length of the wind turbine blade at the nacelle height) is projected onto the vertical plane under consideration, and the wind speed is averaged over points belonging to the vertical plane of the projection. This average speed is commonly referred to as RAWS (Rotor Average Wind Speed) and is commonly used for the control and / or diagnosis and / or monitoring of wind turbines.

[0117] The present invention also relates to a method for controlling a wind turbine equipped with a LiDAR sensor. This method performs the following steps:

[0118] -The average wind speed is determined by using the method of determining the average wind speed according to any of the above variations, and

[0119] - The wind turbine is controlled based on the average wind speed determined therefrom.

[0120] Precise, real-time determination of average wind speed allows for appropriate control of wind turbines, minimizing the impact on turbine structure and maximizing power recovery. In fact, through this control, LiDAR sensors allow for reductions in loads on the structure, blades, and towers, which account for 54% of the cost. Therefore, the use of LiDAR sensors allows for optimization of wind turbine structure, thereby reducing costs and maintenance.

[0121] The method may also include an intermediate step involving determining the average wind speed in the rotor plane of the wind turbine based on the average wind speed determined by the method. Therefore, the wind travel time between the vertical plane and the rotor plane can be taken into account (this can be calculated, in particular, by considering the Taylor frozen turbulence assumption), and induced phenomena between the vertical plane and the rotor plane can also be taken into account (e.g., using an induction factor reflecting the wind deceleration upstream of the wind turbine in relation to the presence of the wind turbine blades). The wind turbine is then controlled based on the average wind speed in the rotor plane.

[0122] According to one implementation of the invention, the blade tilt angle and / or electrorecovery torque of a wind turbine generator can be controlled based on wind speed. Other types of regulating devices can also be used.

[0123] According to one embodiment of the present invention, the blade tilt angle and / or regenerative torque can be determined by means of a wind turbine mapping based on the wind speed at the rotor. For example, the control method described in patent application FR-2976630A1 (US2012-0321463) can be applied.

[0124] Furthermore, the present invention relates to a computer program product comprising code instructions designed to perform steps of one of the aforementioned methods (the method for determining wind speed in the rotor plane, the control method). This program can be executed on a processing unit of a LiDAR sensor or on any similar device connected to a LiDAR sensor or a wind turbine.

[0125] According to one aspect, the present invention also relates to a LiDAR sensor for a wind turbine, comprising a processing unit configured to implement one of the above-described methods (methods for determining average wind speed, control methods).

[0126] According to one implementation of the present invention, the LiDAR sensor can be a scanning LiDAR sensor, a continuous wave LiDAR sensor, or a pulsed LiDAR sensor. The LiDAR sensor is preferably a pulsed LiDAR sensor.

[0127] The present invention also relates to a wind turbine, particularly an offshore (overseas) or onshore (land-based) wind turbine equipped with a LiDAR sensor as described above. According to one embodiment of the invention, the LiDAR sensor may be mounted on the nacelle of the wind turbine or on the hub of the turbine (at the end of the nacelle). The LiDAR sensor is oriented to perform measurements of the wind upstream of the turbine (i.e., upstream of the wind turbine and along its longitudinal axis, by...). Figure 2 (The x-axis is specified in the text). According to one embodiment, the wind turbine can be connected to... Figure 2 The wind turbine shown is the same.

[0128] In an embodiment of the control method, the wind turbine may include a control device, such as for controlling the pitch angle or electrical torque of at least one blade of the wind turbine, for implementing the control method according to the invention.

[0129] Obviously, the present invention is not limited to the embodiments of the methods described above by way of example, and it covers any variant embodiments.

[0130] Example

[0131] The features and advantages of the method according to the invention will become clear from the following examples.

[0132] In this example, wind is simulated using a simulator and LiDAR sensor measurements, and the average wind speed is determined using a method according to an embodiment of the invention. This embodiment of the invention uses the described spatial and temporal coherence equations, and it determines the average longitudinal component of the wind speed in the vertical plane.

[0133] According to the first configuration, the measured planar distance is: [50, 70, 90, 100, 120, 140, 160, 180, 190, 200] meters.

[0134] Figure 3 The figure shows a comparison of the average wind speed RAWS (in m / s) over a distance of 100 m between the rotor plane and the vertical plane as a function of time T. In this figure, the dashed curve represents the reference curve REF, and the solid curve represents the average wind speed curve EST obtained by means of the method according to the invention. Note that the two curves almost overlap. Therefore, the method according to the invention makes it possible to accurately determine the average wind speed.

[0135] According to the second configuration, the measured planar distance is: [50, 80, 90, 110, 130, 150, 170, 180, 190, 200] meters.

[0136] Figure 4 The figure shows a comparison of the average wind speed RAWS (in m / s) over a distance of 110 m between the rotor plane and the vertical plane as a function of time T. In this figure, the dashed curve represents the reference curve REF, and the solid curve represents the average wind speed curve EST obtained by means of the method according to the invention. Note that the two curves almost overlap. Therefore, the method according to the invention makes it possible to accurately determine the average wind speed.

[0137] These two curves also show that the method is accurate regardless of the distance considered, and does not require any measurement of planar distances.

Claims

1. A method of determining an average wind speed in a vertical plane by means of a LiDAR sensor (2) arranged on a wind turbine (1), characterized in that, performing the following steps: a) constructing a model (MOD M) of the LiDAR measurements, wherein the model (MOD M) of the LiDAR measurements is written as follows: where m is a measurement, x is a longitudinal direction, j is a measurement beam of the LiDAR sensor, m j,x is a measurement of the measurement beam j at distance x, k is a discrete time, v is a wind speed, v j,x is a longitudinal component of the wind speed for the measurement beam j, v j,y is a transversal component of the wind speed for the measurement beam j, v j,z is a vertical component of the wind speed for the measurement beam j, a j , b j , c j is a constant measurement coefficient for the measurement beam j; b) constructing a wind model (MOD V) taking into account the spatial and temporal coherence of the wind speed, wherein the spatial coherence of the wind model is a function of the lateral coherence, the vertical coherence and the longitudinal coherence, and the temporal coherence of the wind model is written as follows: , k is the discrete time, ω is a vector comprising first the longitudinal component of the wind speed at n predefined estimation points and then the lateral component of the wind speed at the n predefined estimation points, A s is a constant matrix as the autocorrelation function of the wind speed obtained by the Kaimal spectrum, or for the temporal coherence, the von Karman spectrum is performed; c) measuring (MES) wind amplitude and wind direction in at least one measurement plane (PM) away from the wind turbine (1) by means of the LiDAR sensor; d) determining wind speed at each predefined estimation point in the wind turbine upstream space by means of an adaptive Kalman filter (KAL) using the model (MOD M) of the LiDAR measurement, the wind model (MOD V) and the measurement (MES), and e) determining an average wind speed (RAWS) in a vertical plane under consideration by means of the wind speed determined for each predefined estimation point belonging to the vertical plane under consideration at a certain distance from the wind turbine, wherein the wind speed in the vertical plane at a certain distance from the wind turbine is determined by means of the average of the longitudinal components of the wind speed of each point belonging to the vertical plane.

2. The method of claim 1, wherein, The lateral coherence is written as follows: where x is the longitudinal component, y1 and y2 are two lateral positions with the same longitudinal and vertical values, is the longitudinal component of the wind speed at position y1, is the longitudinal component of the wind speed at position y2, f t is a predefined function.

3. The method of claim 1, wherein, The vertical coherence is written as follows: where x is the longitudinal component, z1 and z2 are two vertical positions with the same longitudinal and transversal values, is the longitudinal component of the wind speed at position z1, is the longitudinal component of the wind speed at position z2, is the coefficient of the power law.

4. The method of claim 1, wherein, The longitudinal coherence is written as follows: where x is the longitudinal component, k is the discrete time, x1 and x2 are two longitudinal positions with the same transversal and vertical values, is the longitudinal component of the wind speed at position x1, is the longitudinal component of the wind speed at position x2, f l is a predefined function.

5. The method of claim 1, wherein, The adaptive Kalman filter (KAL) is applied to the following equations: and where k is the discrete time, v is the wind speed, x is the longitudinal component, y1 and y2 are two lateral positions with the same longitudinal and vertical values, x1 and x2 are two longitudinal positions with the same lateral and vertical values, z1 and z2 are two vertical positions with the same longitudinal and lateral values, is the longitudinal component of the wind speed at position y1, is the longitudinal component of the wind speed at position y2, f t is a predefined function, is the longitudinal component of the wind speed at position x1, is the longitudinal component of the wind speed at position x2, f l is a predefined function, is the longitudinal component of the wind speed at position z1, is the longitudinal component of the wind speed at position z2, is the coefficient of the power law, j is the measurement beam of the LiDAR sensor (2), m j,x is the measurement of the measurement beam j at distance x, v j,x is the longitudinal component of the wind speed for the measurement beam j, v j,y is the lateral component of the wind speed for the measurement beam j, v j,z is the vertical component of the wind speed for the measurement beam j, a j , b j , c j is the constant measurement coefficient for the measurement beam j, η is the noise of the state equation, ε t is the lateral noise, ε v is the vertical noise, ε l is the longitudinal noise, ε m is the measurement noise, A s is a constant matrix as the autocorrelation function of the wind speed obtained from the Kaimal spectrum.

6. The method of claim 1, wherein, The wind speed is determined at different points using the following equation: and where k is the discrete time, ω is a vector comprising first the longitudinal component of the wind speed at n predefined estimation points, is an estimate of the vector ω(k) given the measurements performed up to time k-1, is an estimate of the vector ω(k) given the measurements performed up to time k, is the covariance matrix of the vector ω(k) given the measurements performed up to time k-1, is the covariance matrix of the vector ω(k) given the measurements performed up to time k, A s is a constant matrix as the autocorrelation function of the wind speed obtained by the Kaimal spectrum, Q and R are the covariance matrices of the noises ε(k) and η(k), C a is obtained by linearizing the output equation of the surroundings, y(k) is the measurement of the LiDAR sensor (2) and I is the identity matrix.

7. The method of claim 1, wherein, The wind speed under consideration is the wind speed comprised in the projection of the surface swept by the rotor of a wind turbine in the vertical plane under consideration.

8. A method of controlling a wind turbine (1), characterized in that, performing the following steps: a) determining the average wind speed by means of the method according to any one of the preceding claims, and b) controlling the wind turbine (1) as a function of the average wind speed.

9. A computer program product comprising code instructions designed to perform the steps of the method according to any one of the preceding claims when this program is executed on a control and / or diagnostic unit of the wind turbine (1).

10. A LiDAR sensor (2), characterized by comprising a processing unit implementing the method according to any one of claims 1 to 8.

11. A wind turbine (1) characterised in that comprising a LiDAR sensor (2) according to claim 10, the LiDAR sensor (2) being arranged on a nacelle of the wind turbine or in a hub of the wind turbine.

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