Method for determining wind speed components using a laser remote sensor

Description

Technical field

[0001] The present invention relates to the field of determining the components of wind speed, in particular for the purpose of assessing the advisability of installing a wind turbine on a site.

[0002] Before installing a wind turbine or wind farm, it is necessary to assess the wind potential at that site. Indeed, the size of the wind turbine, its class and its structure depend on wind characteristics, such as the average wind speed, the maximum wind speed, the intensity of wind turbulence (which corresponds to the ratio of the standard deviation of the wind speed to the average wind speed), etc. For example, the size of the wind turbine can be chosen based on the distribution of the average wind value, and the class of the wind turbine can be chosen based on the intensity of turbulence. Since changing from one class of wind turbine to another has a significant cost, it is important to have a good understanding of the wind characteristics before installing a wind turbine.

[0003] Furthermore, determining the components of wind speed is particularly critical because it also allows us to determine the energy-producing resource. This is important for wind projects, as it also determines the financial reliability of the wind turbine installation project. Prior art

[0004] To carry out these measurements, the classic technique is the installation of a measuring mast at the measurement site. Such a measuring mast is equipped with a large number of sensors and requires a specific installation, which involves a significant cost and is not easy to move from one site to another due to its dimensions.

[0005] A second technique is to use a LiDAR sensor (light detection and ranging) (from the English "light amplification by stimulated emission of radiation"). LiDAR is a remote sensing or optical measurement technology based on analyzing the properties of a beam returned to its transmitter. This method is used in particular to determine the distance to an object using a pulsed laser. Unlike radar, which is based on a similar principle, the LiDAR sensor uses visible or infrared light instead of radio waves.

[0006] In the field of wind turbines, the LiDAR sensor is announced as an essential sensor for the proper operation of large wind turbines, especially as their size and power increase (today, 5 MW, soon 12 MW offshore). This sensor allows remote measurement of the wind, initially allowing the calibration of wind turbines so that they can provide maximum power (optimization of the power curve). For this calibration step, the LiDAR sensor can be positioned on the ground and oriented vertically (profiler), which makes it possible to measure the wind speed and its direction, as well as the wind gradient according to altitude. This technique can be called land-based LIDAR.

[0007] This technique is described in particular in patent applications EP3287810 and US2019293836.

[0008] However, it is important to provide processing of the measurement signals, in order to obtain wind speed characteristics in a precise, robust and reliable manner.

[0009] The radial measurement does not provide a complete wind measurement. It is a projection of the wind on the beam's line of sight, filtered by the spatial transfer function inherent in LiDAR measurement technology, and noisy by the measurement chain. To obtain information representative of the wind field passing above the installed LiDAR, it is therefore necessary to combine several radial measurements together, and to associate them with a processing capable of restoring the contribution of the wind in each measurement and deriving a wind field or vector.

[0010] Standard reconstructions are based on the assumption that the radial measurement contains only content consistent with the measured wind, and that the wind field is uniform and homogeneous at a given altitude. Summary of the invention

[0011] The aim of the present invention is to determine the components of wind speed in a precise, robust, reliable and inexpensive manner. For this purpose, the invention relates to a method for determining the components of wind speed using a land-based LiDAR sensor. In this method, the components of wind speed are first approximated using signals from the LiDAR sensor, these approximations are used in a wind signal model, and then in a non-stationary Kalman filter to construct filtered measurement signals. The filtered measurement signals are then used to reconstruct the components of wind speed. The approximation of the components of wind speed makes it possible to construct a reliable and robust model of the wind signal, which allows for a reliable and robust determination of the components of wind speed.Indeed, by filtering the radial measurement to retain only the part actually corresponding to the contribution of the wind to the measurement, the process makes it possible to obtain an estimate of the wind (amplitude and direction) whose mean and standard deviation will be more representative and realistic. This thus makes it possible to use a LiDAR installed as a replacement or in addition to a measurement mast, in order to carry out efficient, precise and potentially less expensive site analyses.

[0012] The invention relates to a method for determining wind speed components using a LiDAR sensor, said LiDAR sensor being oriented substantially vertically to carry out the measurements in at least one substantially horizontal measurement plane. For this method, the following steps are implemented: a) Measurement signals from said LiDAR sensor are acquired in said at least one measurement plane; b) An approximation of the wind speed components in said at least one measurement plane is determined by means of a geometric reconstruction of said wind speed components from said acquired measurement signals; c) An average wind speed and a standard deviation are determined in said at least one measurement plane by means of said approximated wind speed components; d) A model of the wind signal is constructed by the sum of two first-order filters, in this case the sum of an integrator and a low-pass filter, said wind signal model being dependent on two parameters; e) Said two parameters of said wind signal model are determined by means of said determined standard deviation and average wind speed;f) said acquired measurement signals are filtered by means of a non-stationary Kalman filter, said wind signal model and said two determined parameters; and g) the components of the wind speed in said at least one measurement plane are determined by means of a reconstruction of said components of the wind speed from said filtered measurement signals.

[0013] According to one embodiment, the method comprises a step of filtering said measurement signals by a first-order low-pass filter for the step of approximating said components of the wind speed.

[0014] Advantageously, said wind signal model corresponds to a Kaimal model of the wind spectrum.

[0015] According to one aspect, a transfer function H of said wind signal model is written H = a s + b s + τ with a, b the said two parameters, s the Laplace variable, and τ the constant of time.

[0016] Preferably, said parameters a and b are obtained by the equation: a 2 a + b 2 = τ 2 ω 4 + τ 2 ω 2 ω 2 ω 4 + τ 2 ω 2 − 1 . ( L . S ( f , σ k , Vhub ))with f the frequency vector, ω = 2 πf , k the index corresponding to the component concerned, σ k the standard deviation of the approximate wind speed, V hub the approximate wind speed, L the vector of the components of the direction of the measuring beam of the LiDAR sensor, S the vector of the spectral components of the Kaimal model of the wind spectrum.

[0017] According to one implementation, the covariance matrix of said non-stationary Kalman filter is determined by minimizing a cost function taking into account the dispersion and the mean deviation of said measurement signals.

[0018] According to one embodiment, the reconstruction of the wind speed components by means of the measurement signals filtered by said non-stationary Kalman filter is implemented by means of a geometric reconstruction of said wind speed components from said measurement signals filtered by said non-stationary Kalman filter.

[0019] According to one aspect, the method further comprises the following steps: a) segmenting said determined wind speed components by predetermined time interval; and b) determining at least one characteristic of said wind speed for said predetermined time interval.

[0020] Advantageously, said predetermined time interval is between 1 min and 1 hour, and preferably between 5 min and 30 min.

[0021] Advantageously, said at least one characteristic of the wind is chosen from: the average of the wind speed, the standard deviation of the wind speed, the maximum of the wind speed, the average of the wind direction, the intensity of the wind turbulence, the average of the vertical component of the wind speed, and the standard deviation of the vertical component of the wind speed.

[0022] Other characteristics and advantages of the method according to the invention will appear on reading the following description of non-limiting examples of embodiments, with reference to the figures appended and described below. List of figures

[0023] There Figure 1 illustrates a LiDAR sensor installed according to an embodiment of the invention. The Figure 2 illustrates the steps of the method according to a first embodiment of the invention. The Figure 3 illustrates the steps of the method according to a second embodiment of the invention. The Figure 4illustrates the steps of the method according to a third embodiment of the invention. The Figure 5 illustrates the geometric parameterization at a measurement point. Description of the embodiments

[0024] The present invention relates to a method for determining wind speed components using a LiDAR sensor. Wind speed components are defined as projections of wind speed into a reference frame, in particular into an orthonormal reference frame.

[0025] For the invention, the LiDAR sensor is directed substantially vertically, in other words, the measurement is directed along a substantially vertical axis. For example, the LiDAR sensor can be placed on the ground and oriented vertically. According to the invention, the LiDAR sensor makes it possible to measure the wind speed on at least one measurement plane. Given the orientation of the LiDAR sensor, the measurement plane is substantially horizontal. There are several types of LiDAR sensor, for example scanned LiDAR, continuous LiDAR or pulsed LiDAR sensors. In the context of the invention, a pulsed LiDAR is preferably used. However, other LiDAR technologies can be used while remaining within the scope of the invention.

[0026] The LiDAR sensor allows for continuous measurement. Therefore, the use of such a sensor allows for continuous determination of measurement signals. In addition, the LiDAR sensor is easily movable from one site to another. For example, the sampling rate of the LiDAR sensor can be between 0.1 and 5 Hz (or even higher in the future), and can be as low as 1 Hz. In addition, the LiDAR sensor allows for obtaining relative information in several measurement planes at several heights. Therefore, the LiDAR sensor can be used for determining wind speed components at several heights, which can be used, among other things, to determine the variation of wind speed as a function of height.

[0027] There Figure 1represents, in a schematic and non-limiting manner, a LiDAR sensor 1 placed and oriented vertically for the method according to an embodiment of the invention. The LiDAR sensor 1 is used to obtain at least one measurement signal on at least one measurement plane PM (only two measurement planes are represented). In this figure, the x, y and z axes are also represented. The reference point of this reference is the center of the LiDAR sensor. The x direction is a horizontal direction. The y direction, perpendicular to the x direction, is a second horizontal direction (the x, y directions form a horizontal plane). The z direction is the vertical direction (corresponding to the measurement direction of the LiDAR sensor 1) directed upwards, the z axis is perpendicular to the x and y axes. The measurement planes PM are planes formed by the x, y directions at a distance from the LiDAR sensor 1 (for a non-zero value of z). The measurement planes PM are parallel to each other.As seen in the . Figure 1 , which is an exemplary embodiment of a pulsed LiDAR sensor, the LiDAR sensor 1 used comprises four beams or measurement axes 2. The measurement beams 2 are inclined relative to the vertical axis z. In a non-limiting manner, the method according to the invention also works with a LiDAR sensor comprising any number of beams. The LiDAR sensor performs a point measurement at each measurement point (b1, b2, b3, b4) which are points of intersection of a measurement plane PM and a beam 2. These measurement points (b1, b2, b3, b4) are represented by black circles on the Figure 1 .

[0028] In this figure, the wind speed vector W is also represented only at point b1, and these three components Wx, Wy, Wz respectively on the x, y and z axes.

[0029] The method according to the invention is set forth in the attached set of claims and is summarized by the following steps: 1- acquisition of measurement signals 2- approximation of wind speed components 3- determination of mean speed and standard deviation 4- construction of the frequency model of the wind signal 5- determination of model parameters 6- non-stationary Kalman filter 7- determination of wind speed components

[0030] These steps will be detailed in the remainder of the description. Steps 2 to 7 can be implemented by computer means, including a computer. Steps 2 to 7 can be implemented offline after step 1.

[0031] There Figure 2illustrates, schematically and in a non-limiting manner, the steps of the method according to a first embodiment of the invention. First, the measurement signals M acq from the LiDAR sensor are acquired ACQ. Then, the wind speed components W app are approximated APP by a geometric reconstruction of the measurement signals M acq . The wind speed components W app are then used to determine MOY the mean V app and the standard deviation σ app of the wind speed. These data are used with the constructed wind signal model MOD, to determine PAR the parameters a and b of this wind signal model. Then, a non-stationary Kalman filter KAL is applied to obtain filtered measurement signals M kal . These measurement signals M kal allow a reconstruction REC of the wind speed components W est .

[0032] According to a second embodiment of the invention, the method may comprise an additional step of filtering the measurement signals acquired before the approximation of the components of the wind speed, in order to remove the outlier values, which makes it possible to increase the robustness and reliability of the method.

[0033] Thus, the method according to the second embodiment of the invention can comprise the following steps: 1- acquisition of measurement signals 1.2- filtering of measurement signals 2- approximation of wind speed components 3- determination of mean speed and standard deviation 4- construction of the frequency model of the wind signal 5- determination of model parameters 6- non-stationary Kalman filter 7- determination of wind speed components

[0034] These steps will be detailed in the remainder of the description. Steps 1.2 to 7 can be implemented by computer means, in particular a computer.

[0035] There Figure 3illustrates, schematically and in a non-limiting manner, the steps of the method according to the second embodiment of the invention. In a first step, the measurement signals M acq from the LiDAR sensor are acquired ACQ. In a second step, the measurement signals M acq are filtered FIL to obtain the filtered measurement signals M fil . Then, the wind speed components W app are approximated APP by a geometric reconstruction of the filtered measurement signals M fil . The wind speed components W app are then used to determine MOY the mean V app and the standard deviation σ app of the wind speed. These data are used with the wind signal model MOD constructed, to determine PAR the parameters a and b of this wind signal model. Then, a non-stationary Kalman filter KAL is applied to obtain filtered measurement signals M kal . These measurement signals M kal allow a reconstruction REC of the wind speed components W est .

[0036] According to a third embodiment of the invention, the method may comprise additional steps for determining a characteristic of the wind speed. For this third embodiment, the method may comprise the following steps: 1- acquisition of measurement signals 2- approximation of wind speed components 3- determination of mean speed and standard deviation 4- construction of wind signal model 5- determination of model parameters 6- non-stationary Kalman filter 7- determination of wind speed components 8- segmentation of wind speed components 9- determination of a wind speed characteristic

[0037] These steps will be detailed in the rest of the description. Steps 2 to 9 can be implemented by computer means, in particular a computer.

[0038] There Figure 4illustrates, schematically and in a non-limiting manner, the steps of the method according to a third embodiment of the invention. First, the measurement signals M acq from the LiDAR sensor are acquired ACQ. Then, the components of the wind speed W app are approximated APP by a geometric reconstruction of the measurement signals M acq . The components of the wind speed W app are then used to determine MOY the mean V app and the standard deviation σ app of the wind speed. These data are used with the constructed wind signal model MOD, to determine PAR the parameters a and b of this wind signal model. Then, a non-stationary Kalman filter KAL is applied to obtain filtered measurement signals M kal . These measurement signals M kal allow a reconstruction REC of the components of the wind speed W est . A temporal segmentation SEG of these components of the wind speed W est is carried out.Then, a CAR characteristic of the wind speed is determined (for example the average wind speed, or the turbulence intensity).

[0039] The second and third embodiments can be combined. 1- Acquisition of measurement signals

[0040] In this step, the measurement signals from the LiDAR sensor are acquired for at least one measurement plane. In other words, for each measurement point of at least one measurement plane, the measurement signal from the sensor is acquired. Advantageously, these measurement signals can be recorded, in particular on a computer memory so that they can be processed by computer means in the following steps.

[0041] In order to determine the wind speed components at several measurement planes, this step can be performed for several measurement planes.

[0042] Advantageously, the acquisition of the measurement signals can be carried out over a long period of time, for example for a duration which can vary from several days to a year or even more. 1.2- Filtering of measurement signals

[0043] It is important to remember that this is an optional step. During this step, the measurement signals are filtered, in particular to limit outlier values ​​in order to make the process more reliable and robust.

[0044] According to one embodiment, this filtering can be implemented by means of a first-order low-pass filter in order to restore a continuous and realistic representation of the measured wind state. This can be a filter with a variable time constant. The older the last valid value passed to the first-order filter, the more the time constant of the filter decreases (in other words, the weight of the state stored in the filter is increasingly lower compared to the weight of the next valid value t). By this embodiment, an instantaneous, low-frequency, denoised and realistic value of the wind state contained in the radial measurements is derived. 2- Approximation of the components of wind speed

[0045] In this step, the wind speed components are approximated from the LiDAR sensor measurement signals, or if necessary from the filtered LiDAR sensor measurement signals. This approximation is used to adapt the measurement signal model. This is only an approximation for an intermediate step; the values ​​of the wind speed components determined in step 7 of the process are more accurate, reliable, and robust.

[0046] For this step, we reconstruct the components of wind speed using geometric reconstruction.

[0047] According to one embodiment of the invention, the geometric reconstruction of the wind speed components can implement a pseudo-inverse “Moore-Penrose” operation applied to the measurement signals (or where appropriate to the filtered measurement signals).

[0048] There Figure 5illustrates, schematically and in a non-limiting manner, a geometric parameterization of the measurement signals of a LiDAR sensor. In this figure, a single beam 2 of a LiDAR sensor 1 is represented. This beam 2 is oriented along a measurement axis represented by the vector I. This vector I is oriented relative to the x, y, z reference frame (defined in the same way as for the Figure 1 ) by means of the angles θ and Φ. The angle θ is defined in the (x, y) plane relative to the x axis. The angle Φ is defined relative to the z axis. In this figure, the wind speed vector W and its components Wx, Wy, Wz are also represented at the measuring point b1.

[0049] Using geometric projections, we can write the following equations: m 1 ⋮ m i ⋮ m n = sin ϕ 1 cos θ 1 sin ϕ 1 sin θ 1 cos ϕ 1 ⋮ sin ϕ i cos θ 1 sin ϕ i sin θ 1 cos ϕ i ⋮ sin ϕ n cos θ n sin ϕ n sin θ n cos ϕ n ︷ M w x w y w z

[0050] With 1, ..., i, ..., n the measurement points of a measurement plane, m1,..., mi, ..., mn the measurement signals of the measurement plane.

[0051] By means of the pseudo-inverse “Moore-Penrose” operation, we can therefore obtain the components of the wind speed estimated in the measurement plane using the measurement signals: w x w y w z = M T . M − 1 . M T . m 1 ⋮ m n

[0052] According to an implementation of the invention, this step may comprise a step of filtering the estimated speed, in particular to limit outlier values ​​in order to make the method more reliable and robust.

[0053] According to one embodiment, this filtering can be implemented by means of a first-order low-pass filter in order to restore a continuous and realistic representation of the measured wind state. This can be a filter with a variable time constant. The older the last valid value passed to the first-order filter, the more the time constant of the filter decreases (in other words, the weight of the state stored in the filter is increasingly lower compared to the weight of the next valid value). By this embodiment, an instantaneous, low-frequency, denoised and realistic value of the wind state contained in the radial measurements is derived. 3- Determination of the average speed and the standard deviation

[0054] In this step, the average speed and its standard deviation are determined from the approximation of the wind speed components determined in step 2 (possibly filtered). For this step, the classic average and standard deviation calculations can be implemented.

[0055] According to one embodiment of the invention, the average speed and its standard deviation may be determined over a rolling time horizon. For example, the rolling time horizon for this embodiment may be between 10 minutes and several days. 4- Construction of the wind signal model

[0056] In this step, a wind signal model is constructed by the sum of two first-order filters, the wind signal model being dependent on two parameters. The two parameters allow the contributions of each filter to the measurement provided by the LiDAR sensor to be adjusted.

[0057] According to the invention, the wind signal model comprises an integrator and a low-pass filter. The integrator is used to illustrate the average speed and the slow variation of the wind speed. The low-pass filter is used to model the instantaneous turbulence, with a time constant approximating the frequency response of the wind turbulence spectrum, as defined in the IEC61400-1 standard. The sum of the two filters is used to model a power spectral density signal DSP consistent with the Kaimal model defined in the standard. The transfer function H corresponding to the modeling of the DSP can be given by (the function H is also called in the remainder of the description the wind signal model): H = a s + b s + τ = a + b s + a . τ s 2 + τ . s = τ s 2 + τ . s s s 2 + τ . s . a a + b

[0058] With a and b the parameters of the wind signal model, s is the Laplace variable, and τ the time constant.

[0059] Thus, according to one embodiment of the invention, the wind signal model can correspond to the Kaimal model, which can be written: f S k f σ k 2 = 4 f L k / V hub 1 + 6 f L k / V hub 5 3

[0060] With k the index that corresponds to the component considered (k varies between 1 and 3 and represents the z, y and x axes), f the frequency, V hub the average wind speed obtained in step 3, σ the standard deviation component, S k is the spectrum of the speed component in the k direction, L k an integral scale parameter of the speed component.

[0061] Furthermore, we have the relationship: σ k 2 = ∫ 0 ∞ S k f df

[0062] And the parameter σ k is related to the standard deviation determined in the previous step, depending on the component considered. For example, for the z axis, σ 1 can be the standard deviation, for the y axis, σ 2 can be 0.8 times the standard deviation, and for the x axis, σ 3 can be 0.5 times the standard deviation.

[0063] The L parameter can be linked to a scale parameter.

[0064] Alternatively, the method according to the invention is adapted to other wind models, such as the “Von Karman” model. 5- Determination of the model parameters

[0065] In this step, the parameters of the wind signal model are determined using the standard deviation and the mean wind speed determined in step 3. Thus, at the end of this step, the wind signal model is adapted to the measurements made by the LiDAR sensor, which makes the determination of the wind speed components accurate.

[0066] Depending on the embodiment of the transfer function H described above, the parameters a and b of the transfer function are determined. The parameters a and b can be determined using the following equation: H 2 = τ 2 ω 4 + τ 2 ω 2 ω 2 ω 4 + τ 2 ω 2 . a 2 a + b 2 ≈ L → . S f σ k V hub →

[0067] Where the vector L is the vector of the components of the direction of the measurement beam concerned, and S is the vector of the spectral components of the Kaimal model, which depend on known parameters, in particular the mean wind speed and the standard deviation determined in step 3.

[0068] Thus, we can estimate the parameters a and b by the equation: a 2 a + b 2 = τ 2 ω 4 + τ 2 ω 2 ω 2 ω 4 + τ 2 ω 2 − 1 . L → . S f σ k V hub → 6- Non-stationary Kalman filter

[0069] In this step, the measurement signals obtained in step 1 are filtered using a non-stationary Kalman filter, the wind signal model constructed in step 4 and the wind signal model parameters determined in step 5. Thus, filtered measurement signals are obtained that are suitable for the precise determination of the wind speed components in a robust manner. It is recalled that a Kalman filter is an infinite impulse response filter that estimates the states of a dynamic system from a series of incomplete or noisy measurements. The filter is said to be non-stationary because the model embedded in the Kalman filter, explained in particular in steps 4 and 5, describes a signal with a priori variable variance and mean, depending on the measurement conditions and the characteristics of the measured wind.

[0070] For this, we can formulate the transfer function H in the continuous state space as follows: X ˙ = A . X + B . U Y = C . X + D . U

[0071] With A = − τ 0 1 0 , B = 1 0 , C = a + b a . τ et D = 0 0 X contains the states of the transfer function H and Y contains the estimate of the wind contribution to the raw and noisy radial measurement. Y is therefore the filtered radial measurement, in other words the measurement signal filtered by the non-stationary Kalman filter.

[0072] By discretizing this transfer function, we can write: X k + 1 = A . X k + B . U k Y k = C . X k + D . U k

[0073] With A = e − τ . T s 0 1 − e − τ . T s τ 0 , B = 1 − e − τ . T s τ T s − 1 − e − τ . T s τ τ , C = a + b a . τ et D = 0 0

[0074] With Ts the sampling time of the discretization.

[0075] For this step, we can also implement the following recurrence equations: At time k, the prediction of the state at time k+1 is denoted x k +1| k , and the predicted output is denoted y k +1| k with x k +1| k = Ax k | ky k +1| k = CAx k | k .

[0076] The confidence in this prediction, corresponding to the covariance of the error on the state, is as follows: P k +1| k = AP k | k A T< + BQ k BT< with Q the covariance matrix.

[0077] So this prediction gives the most probable value of the state and output.

[0078] At time k+1, we update the prediction with the measurement: x k + 1 k + 1 = x k + 1 k + K k + 1 y k + 1 − y k + 1 k

[0079] The gain K can be given by: K = P k + 1 k ∗ C T ∗ Ck + 1 kC T + DQ k + 1 D T + V k + 1

[0080] It is then possible to update the prediction matrix: P k + 1 k + 1 = I − K k + 1 P k + 1 k C P k + 1 k

[0081] According to one embodiment of the invention, the covariance matrix of the non-stationary Kalman filter can be determined by minimizing a cost function taking into account the dispersion and the mean deviation of the measurement signals. The philosophy of adjusting the covariance matrix can be summarized as follows: The more complex the site, the more uncertain the model, the higher the "process noise" (corresponding to the covariance matrix).

[0082] For example, the following operations can be implemented: The prerequisite: measurement data from the LiDAR sensor and so-called reference data, for example from an anemometric measurement mast, are available. The installed LiDAR sensor can be positioned close enough to the measurement mast so that the respective measurements are correlated. The principle: A relevant cost function is developed, taking into account the dispersion and deviation of the reconstructed turbulence intensity TI from the TI from the "mast" measurements. The cost function is minimized using suitable optimization algorithms, by playing on the parameter "multiplicative gain of the covariance matrix Q".

[0083] Preferably, optimization constraints can be added based on an acceptable error constraint, for example this acceptable error constraint can be defined based on a range of average wind speeds. 7- Determination of the components of wind speed

[0084] In this step, the wind speed components are determined in at least one measurement plane, using the measurement signals filtered by the non-stationary Kalman filter obtained in step 6. For this step, a reconstruction of the wind speed components is implemented from the measurement signals filtered by the non-stationary Kalman filter. Thus, this step makes it possible to determine the wind speed components in a robust and precise manner.

[0085] Several reconstruction techniques, based on more or less strong hypotheses of wind homogeneity, spatial coherence, and / or fixed propagation of turbulence, can be used for this step.

[0086] According to one embodiment, the reconstruction of the wind speed components using the filtered measurement signals can be implemented by means of a geometric reconstruction of said wind speed components from said filtered measurement signals. In other words, for this embodiment, the method of reconstructing the wind speed components implemented in step 2 can be implemented, i.e. the geometric reconstruction of the wind speed components implementing a pseudo-inverse “Moore-Penrose” operation applied to the measurement signals filtered by the non-stationary Kalman filter. 8- Segmentation of wind speed components

[0087] It is recalled that this step is optional. This step can be implemented to determine at least one characteristic of the wind speed from the wind speed components. In this step, the wind speed components obtained in the previous step are temporally segmented by a predetermined time interval. In other words, data sets of the wind speed components are generated for the predetermined time interval. The predetermined time interval is the one over which the wind speed characteristic is to be determined.

[0088] According to one embodiment of the invention, the predetermined time interval may be between 1 min and 1 hour, preferably between 5 min and 30 min, and may be for example 10 min. These time intervals make it possible to have a significant characteristic of the wind speed, in order to determine the possibility of installing a wind turbine at the measurement location. 9- Determination of a characteristic of wind speed

[0089] It is recalled that this step is optional, and follows step 8. During this step, at least one characteristic of the wind speed is determined over the predetermined time interval, for at least one measurement plan.

[0090] According to one embodiment of the invention, a characteristic of the wind is chosen from: the average of the wind speed (or mean wind speed), the standard deviation of the wind speed, the maximum of the wind speed, the average of the wind direction, the intensity of the wind turbulence, the average of the vertical component of the wind speed, and the standard deviation of the vertical component of the wind speed.

[0091] Preferably, in this step, at least the intensity of wind turbulence over the predetermined time interval can be determined, which corresponds to the ratio of the standard deviation of the wind speed to the wind speed. Indeed, this characteristic makes it possible to determine the class of the wind turbine.

[0092] Preferably, at least the intensity of wind turbulence over the predetermined time interval, the average wind speed and the average wind direction can be determined. These characteristics make it possible to determine the class of the wind turbine, its positioning and its dimensions.

[0093] Furthermore, a method of installing a wind turbine, said method not falling within the scope of the claimed invention, in which the following steps are implemented, is described: The components of the wind speed, and / or at least one characteristic of the wind speed are determined by means of the method according to any one of the variants or combinations of variants previously described, at at least one site. A wind turbine is installed on the site according to the components of the wind speed and / or according to said at least one characteristic of the wind speed.

[0094] During the installation stage, the installed wind turbine can be determined in terms of dimensions, class, its structure, its orientation can also be determined, and its control can be determined based on the components of the wind speed and / or based on said at least one characteristic of the wind speed.

[0095] According to said method of installing a wind turbine, said method not falling within the scope of the claimed invention, the first step can be repeated on several sites. Then, the most suitable site for installing a wind turbine is determined based on the components of the wind speed and / or based on said at least one characteristic of the wind speed. This may in particular be the site on which the wind speed is within an operating range suitable for energy recovery by a wind turbine.

Claims

1. A method for determining wind speed components by way of a LiDAR sensor (1), said LiDAR sensor (1) being oriented substantially vertically in order to carry out the measurements in at least one substantially horizontal measurement plane (PM), wherein the following steps are implemented: a) Acquiring measurement signals from said LiDAR sensor (1) in said at least one measurement plane (PM); b) Determining an approximation (APP) of the wind speed components in said at least one measurement plane (PM) by way of a geometric reconstruction of said wind speed components based on said acquired measurement signals; c) Determining an average speed and a standard deviation of the wind (MOY) in said at least one measurement plane (PM) by way of said approximated wind speed components; d) Constructing a wind signal model (MOD) using the sum of two first-order filters, which is the sum of an integrator and of a low-pass filter, said wind signal model being dependent on two parameters (a, b) which are ; e) Determining said two parameters (a, b) of said wind signal model (MOD) by way of said determined standard deviation and the determined average wind speed; f) Filtering said acquired measurement signals by way of a non-stationary Kalman filter (KAL), said wind signal model and said two determined parameters; and g) Determining the wind speed components in said at least one measurement plane, by way of a geometric reconstruction (REC) of said wind speed components based on said filtered measurement signals.

2. The method as claimed in claim 1, wherein the method comprises a step of filtering (FIL) said measurement signals using a first-order low-pass filter for the step of approximating said wind speed components.

3. The method as claimed in either of the preceding claims, wherein said wind signal model (MOD) corresponds to a Kaimal model of the wind spectrum.

4. The method as claimed in one of the preceding claims, wherein a transfer function H of said wind signal model (MOD) is written H = a s + b s + τ where a, b are said two parameters, s is the Laplace variable, and τ is the time constant.

5. The method as claimed in claim 4, wherein said parameters a and b are obtained using the equation: a 2 a + b 2 = τ 2 ω 4 + τ 2 ω 2 ω 2 ω 4 + τ 2 ω 2 − 1 . L → . S f σ k V hub → where f is the frequency vector, ω = 2πf, k is the index corresponding to the component in question, σk is the standard deviation of the approximated wind speed, Vhub is the approximated wind speed, L is the vector of the components of the direction of the measurement beam of the LiDAR sensor, and S is the vector of the spectral components of the Kaimal model of the wind spectrum.

6. The method as claimed in one of the preceding claims, wherein the covariance matrix of said non-stationary Kalman filter (KAL) is determined by minimizing a cost function that takes into account the dispersion and the average deviation of said measurement signals.

7. The method as claimed in one of the preceding claims, wherein the reconstruction (REC) of the wind speed components by way of the measurement signals filtered by said non-stationary Kalman filter (KAL) is implemented by way of a geometric reconstruction of said wind speed components based on said measurement signals filtered by said non-stationary Kalman filter.

8. The method as claimed in one of the preceding claims, furthermore comprising the following steps: a) Segmenting (SEG) said determined wind speed components by predetermined time interval; and b) Determining at least one characteristic (CAR) of said wind speed for said predetermined time interval.

9. The method as claimed in claim 8, wherein said predetermined time interval is between 1 min and 1 h, and preferably between 5 min and 30 min.

10. The method as claimed in claim 8 or 9, wherein said at least one wind characteristic is chosen from among: average wind speed, standard deviation of the wind speed, maximum wind speed, average wind direction, wind turbulence intensity, average of the vertical component of the wind speed, and standard deviation of the vertical component of the wind speed.