Method for determining wind direction using LiDAR sensors

Abstract

The present invention relates to a method for determining wind direction by using a LiDAR sensor (2). The method comprises: performing measurement by using the LiDAR sensor (2); deriving Gaussian distributions of a longitudinal component (u) and a transverse component (v) of wind speed; and determining wind direction (θ) by using a spherical volume approximation method and the Gaussian distributions of the longitudinal component and the transverse component of wind speed.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy and more particularly to wind turbines, wind resource measurement with wind forecasting, turbine control (orientation, torque and speed regulation) and / or diagnostics and / or monitoring objectives.

[0002] Wind turbines convert kinetic energy from wind into electrical or mechanical energy. For wind energy conversion, they consist of the following components:

[0003] - a tower that positions the rotor at a sufficient height to enable its movement (required for horizontal axis wind turbines) or at a height that allows it to be driven by stronger and more frequent winds than at ground level. The tower usually houses part of the electrical and electronic components (modulators, controllers, multipliers, generators, etc.),

[0004] - The nacelle, which is mounted at the top of the tower and houses the mechanical, pneumatic, and some electrical and electronic components needed to run the turbine. The nacelle can be rotated to orient the rotor in the correct direction.

[0005] - A rotor fastened to the nacelle, comprising several blades (usually three) and the hub of the wind turbine. The rotor is driven by wind energy and is connected by a mechanical shaft directly or indirectly (via a gearbox and a mechanical shaft system) to an electric motor (generator) or to any other type of converter that converts the recovered energy into electrical energy or any other type of energy. The rotor may be provided with control systems such as variable angle blades or aerodynamic brakes,

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

[0007] Since the beginning of the 1990s, there has been a renewed interest in wind power, particularly in the European Union, where annual growth rates are around 20%. This growth is attributed to the inherent possibility of generating electricity without carbon emissions. In order to sustain this growth, there remains a need to further increase the energy output of wind turbines. The prospect of increased wind power production requires the development of efficient production tools and advanced control tools to improve machine performance. Wind turbines are designed to generate power at the lowest possible cost. Therefore, they are usually built to reach their maximum performance at wind speeds of around 15 m / s. It is not common to design wind turbines that maximize their output at higher wind speeds. At wind speeds above 15 m / s, some of the extra energy contained in the wind must be lost to avoid damage to the wind turbine. Therefore, all wind turbines are designed with a power regulation system.

[0008] For this power regulation, controllers have been designed for variable speed wind turbines. The controllers aim to maximize the recovered power, minimize rotor speed fluctuations, and minimize fatigue and extreme moments in the structure (blades, tower, and platform). Background Art

[0009] For optimal control, it is important to know the wind speed at the rotor of a wind turbine. Various techniques have been developed for this purpose.

[0010] According to a first approach, the use of anemometers enables the wind speed at one point to be estimated, but this imprecise technique does not allow the measurement of the entire wind field or the knowledge of the three-dimensional components of the wind speed or the wind direction.

[0011] According to the second approach, LiDAR (Light Detection and Ranging) sensors can be used. LiDAR is a remote sensing or optical measurement technology based on analyzing the properties of a beam returning from a transmitter. This method is specifically used to determine the distance to an object using pulsed laser light. Unlike radar, which is based on similar principles, LiDAR sensors use visible or infrared light rather than radio waves. The distance to an object or surface is determined by measuring the delay between the pulse and the detection of the reflected signal.

[0012] In the field of wind turbines, LiDAR sensors have been declared essential for the proper operation of large wind turbines, especially as their size and power are increasing (currently 5 MW for offshore turbines, soon to be 12 MW). This sensor enables remote wind measurement, first allowing the wind turbines to be calibrated so that they can deliver maximum power (power curve optimization). For this calibration phase, the sensor can be positioned on the ground and oriented vertically (profiler), which allows measuring wind speed and direction, as well as wind gradients depending on altitude. This application is particularly critical because it allows knowing the source of energy generation. This is important for wind turbine projects because it determines the economic feasibility of the project. However, this approach can be expensive because, in addition to the LiDAR sensor provided on the wind turbine for the application scenarios described below, it also requires a LiDAR sensor fixedly mounted on the ground or in the water and oriented vertically.

[0013] A second application scenario involves placing the sensor on the nacelle of a wind turbine to measure the wind field in front of the turbine when oriented nearly horizontally. Measuring the wind field in front of the turbine provides a priori knowledge of the turbulence the wind turbine will soon encounter. However, current wind turbine control and monitoring technologies are unable to take into account the measurements performed by the LiDAR sensor by accurately estimating the wind speed at the rotor, i.e., in the rotor plane. This application scenario is notably described in patent application FR-3-013,777 (US-2015-145,253).

[0014] Because LiDAR sensors are relatively recent, it's still difficult to understand how to utilize wind field characteristics such as wind speed, wind direction, wind shear, turbulence, and induction factors by converting the raw data from LiDAR sensors. In particular, determining wind direction is crucial for controlling and diagnosing wind turbines. This wind direction determination must be reliable, robust, and real-time. Summary of the Invention

[0015] The purpose of the method according to the invention is to determine the wind direction in a reliable and robust manner and in real time. Therefore, the present invention relates to a method for determining the wind direction by means of a LiDAR sensor. The method comprises: performing measurements by means of a LiDAR sensor; deriving therefrom a Gaussian distribution of the longitudinal and transverse components of the wind speed; and determining the wind direction by means of a spherical volume approximation method and the Gaussian distribution of the longitudinal and transverse components of the wind speed. The spherical volume approximation method enables the wind direction to be determined in real time because it is fast: it does not require many calculations and it does not involve complex calculations, unlike the Monte Carlo method which is not suitable for real-time estimation problems.

[0016] The present invention relates to a method for determining wind direction by means of a LiDAR sensor arranged on a wind turbine, wherein the following steps are performed:

[0017] a) performing wind measurements by means of the LiDAR sensor in at least one measurement plane upstream of the wind turbine, the measurement plane being perpendicular to the measurement direction of the LiDAR sensor,

[0018] b) determining, by means of the measurements, a Gaussian distribution of a longitudinal component and a transverse component of the wind speed, the longitudinal component corresponding to a measurement direction of the LiDAR sensor and the transverse component corresponding to a direction perpendicular to the measurement direction of the LiDAR sensor, and

[0019] c) determining the wind direction in real time by means of a spherical volume approximation method using the determined Gaussian distributions of the longitudinal and transverse components of the wind speed.

[0020] According to an embodiment of the present invention, the Gaussian distribution of the longitudinal component and the transverse component of the wind speed is determined by a wind field estimator.

[0021] Advantageously, the method also determines the standard deviation of the wind direction.

[0022] According to an embodiment, the spherical volume approximation involves five random realizations of the Gaussian distribution according to the longitudinal and transverse components of the wind speed.

[0023] According to one aspect, the wind direction is determined by a spherical volume approximation method by performing the following steps:

[0024] i) Determine the longitudinal component u of the wind speed j and the transverse component v j A random realization of the Gaussian distribution, where j ranges from -2 to 2, such that:

[0025]

[0026] in,

[0027] P(k)=S∑S T ,

[0028] and are the estimated values ​​of u and v, P(k) is the covariance matrix of the Gaussian distribution, S and Σ are matrices obtained from the singular value decomposition of the covariance matrix P(k), S1 and S2 are the columns of the matrix S,

[0029] ii) For each random realization j, determine the wind direction θ by the following equation j : as well as

[0030] iii) Determine the wind direction using the following equation:

[0031] Among them, ω j is the weight of the random realization.

[0032] Preferably, the wind direction Standard deviation Determined by the following equation:

[0033]

[0034] Advantageously, the weight ω j The definition is as follows:

[0035]

[0036] The present invention also relates to a method for controlling a wind turbine equipped with a LiDAR sensor. The method comprises the following steps:

[0037] a) determining said wind direction upstream of the wind turbine by a method according to one of the above features, and

[0038] b) controlling the wind turbine according to the wind direction upstream of the wind turbine.

[0039] Furthermore, the invention relates to a computer program product comprising code instructions designed to carry out the steps of the method according to one of the above-mentioned features when the program is executed on a processing unit of the LiDAR sensor.

[0040] Furthermore, the invention relates to a LiDAR sensor for a wind turbine, comprising a processing unit for performing the method according to one of the above features.

[0041] The invention also relates to a wind turbine comprising a LiDAR sensor according to any one of the above features, said LiDAR sensor being preferably arranged on a nacelle of said wind turbine or in a hub of said wind turbine. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Other characteristics and advantages of the method according to the invention will become apparent from the following description of an embodiment given by way of non-limiting example, with reference to the accompanying drawings, in which:

[0043] - Figure 1 FIG. 2 shows a wind turbine equipped with a LiDAR sensor according to an embodiment of the present invention.

[0044] - Figure 2 The steps of the method for determining wind direction according to an embodiment of the present invention are shown.

[0045] - Figure 3 For an example embodiment, the longitudinal component of wind speed as a function of time is shown,

[0046] - Figure 4 for Figure 3 The example shown shows the lateral component of wind speed as a function of time, and

[0047] - Figure 5 By means of an embodiment of the method according to the invention and by means of the Monte Carlo method it is shown that Figure 3 and Figure 4 Wind direction according to time for the example shown. DETAILED DESCRIPTION

[0048] The present invention relates to a method for determining wind direction using a LiDAR sensor. Wind direction is understood to be the angle formed by the wind direction relative to the measurement direction of the LiDAR sensor. The measurement direction of the LiDAR sensor is also referred to as the longitudinal direction.

[0049] According to the present invention, a LiDAR sensor measures wind speed relative to the wind circulation above at least one measurement plane upstream of a wind turbine. There are various types of LiDAR sensors, such as scanning LiDAR sensors, continuous wave LiDAR sensors, or pulsed LiDAR sensors. In 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.

[0050] LiDAR sensors provide rapid measurements. Therefore, using such sensors enables rapid, continuous, and real-time determination of wind direction. For example, the sampling rate of a LiDAR sensor can range from 1 to 5 Hz (or higher in the future), and can be as low as 4 Hz. Furthermore, LiDAR sensors provide information about the wind upstream of the turbine, i.e., wind approaching the turbine. Therefore, LiDAR sensors can be used to determine wind direction.

[0051] As a non-limiting example, Figure 1 A schematic diagram illustrates a horizontal-axis wind turbine 1 equipped with a LiDAR sensor 2 for use in a method according to an embodiment of the present invention. The LiDAR sensor 2 is used to measure wind speed at given distances above multiple measurement planes PM (only two are shown). Knowing the wind measurements a priori can provide a wealth 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 the LiDAR sensor 2. Direction y, perpendicular to direction x, is a transverse direction lying in the horizontal plane (directions x and y form the horizontal plane). Direction z is an upward vertical direction (essentially corresponding to the direction of tower 4), with axis z perpendicular to axes x and y. The rotor plane is represented by a rectangle shown by a dashed line PR; for zero values ​​of x, it is defined by directions y and z. Measurement plane PM is the plane formed by the y and z directions at a certain distance from rotor plane PR (for non-zero values ​​of x). Measurement plane PM is parallel to rotor plane PR.

[0052] Generally, a wind turbine 1 can convert the kinetic energy of wind into electrical energy or mechanical energy. For wind energy conversion, it consists of the following facilities:

[0053] a tower 4 which positions the rotor (not shown) at a sufficient height to enable its movement (required for horizontal axis wind turbines) and / or at a height that allows it to be driven by stronger and more frequent winds than at ground level 6. The tower 4 generally houses part of the electrical and electronic components (modulators, controllers, multipliers, generators, etc.),

[0054] - a nacelle 3, mounted at the top of a tower 4, housing the mechanical components, pneumatic components and some electrical and electronic components (not shown) necessary to operate the machine. The nacelle 3 can be rotated to orient the machine (rotor) in the correct direction,

[0055] - A rotor fastened to the nacelle, comprising a plurality of blades 7 (typically three) and the hub of the wind turbine. The rotor is driven by wind energy and is connected by a mechanical shaft directly or indirectly (via a gearbox and a mechanical shaft system) to a motor (generator) (not shown), which converts the recovered energy into electrical energy. The rotor may be provided with control systems such as variable angle blades or aerodynamic brakes.

[0056] - a transmission consisting of two shafts (the mechanical shaft of the rotor and the mechanical shaft of the electric motor) connected by a transmission (gearbox) (not shown).

[0057] As in the example embodiment of a pulsed LiDAR sensor Figure 1 As can be seen in FIG, the LiDAR sensor 2 used comprises four beams or measurement axes (b1, b2, b3, b4). As a non-limiting example, the method according to the invention also works with LiDAR sensors comprising any number of beams. The LiDAR sensor performs a point measurement at each measurement point (PT1, PT2, PT3, PT4), which is the intersection of the measurement plane PM with the beams (b1, b2, b3, b4). These measurement points (PT1, PT2, PT3, PT4) are located at the intersection of the measurement plane PM and the beams (b1, b2, b3, b4). Figure 1 The measurements at these measuring points (PT1, PT2, PT3, PT4) allow the wind speed to be determined in the measuring plane PM at various heights: the heights of the measuring points PT1 and PT2 are greater than the heights of the measuring points PT3 and PT4.

[0058] Preferably, the LiDAR sensor 2 may be mounted on the nacelle 3 of the wind turbine 1 , in the hub of the wind turbine 1 or directly in the blade 7 .

[0059] According to the present invention, the method for determining wind direction using a LiDAR sensor comprises at least the following steps:

[0060] 1) Wind speed measurement,

[0061] 2) Determine the longitudinal and transverse components of wind speed,

[0062] 3) Determine wind direction.

[0063] These steps are performed in real time and are described in detail below.

[0064] As a non-limiting example, Figure 2 The following diagram schematically illustrates the steps of a method for determining wind direction according to one embodiment of the present invention. The first step involves measuring wind speed using a LiDAR sensor (MES). The wind field (REC) is then reconstructed to determine the Gaussian distribution of the longitudinal and transverse components of wind speed, u, and v. Finally, the wind direction θ is determined from the Gaussian distributions of the longitudinal and transverse components of wind speed, u, and v, using the spherical volume approximation method (ACS).

[0065] 1. Wind speed measurement

[0066] In this step, the wind speed is continuously measured in at least two measurement points in at least one measurement plane away from the wind turbine by means of a LiDAR sensor. The LiDAR sensor is capable of measuring radial velocity: along each measurement beam of the LiDAR sensor (corresponding to Figure 1 The wind speed upstream of the wind turbine can thus be known in at least one measuring plane.

[0067] According to one embodiment of the present invention, each measuring plane may be at a longitudinal distance (along the Figure 1 At the axis x in the figure, the longitudinal distance range is preferably between 50 and 400 m. Therefore, the wind speed variation over a longer distance upstream of the wind turbine can be determined, which also improves the accuracy of wind direction determination.

[0068] Alternatively, the measurement plane may be closer or further away than the preferred range.

[0069] According to a non-limiting example embodiment, the LiDAR sensor may perform measurements on ten measurement planes, which may in particular be located at distances of 50, 70, 90, 100, 110, 120, 140, 160, 180 and 200 m respectively from the rotor plane.

[0070] 2. Determine the longitudinal and transverse components of wind speed

[0071] This step involves using the measurements of step 1) to determine the Gaussian distribution of the longitudinal and transverse components of the wind speed. In other words, the radial wind speed measurements performed by the LiDAR sensor are converted into longitudinal and transverse components. The longitudinal component corresponds to the measurement direction of the LiDAR sensor ( Figure 1The x-direction in the image), while the lateral component corresponds to the direction perpendicular to the measurement direction of the LiDAR sensor, i.e. Figure 1 The direction parallel to the axis y.

[0072] According to an embodiment of the invention, any known method may be used to reconstruct the wind field, in particular by projecting the radial velocity onto the longitudinal axis, or, as a non-limiting example, a wind field estimator may be applied, which may in particular correspond to the method for modeling wind described in French patent application FR-3,068,139 (WO-2018 / 234,409), the main steps of which are as follows:

[0073] Gridding the space upstream of the LiDAR sensor, the grid including both estimated and measured points,

[0074] Measure the amplitude and direction of the wind at various measurement points,

[0075] Estimate the wind amplitude and direction for all estimation points at all times using a recursive least squares cost function, and

[0076] Reconstruct the incident wind field in three dimensions in real time at all discrete points.

[0077] The longitudinal and transverse components of the estimated wind speed obtained by any known method can be represented by u(k) and v(k), respectively. The vector [u(k)v(k)] T Follows the mean is a Gaussian distributed random variable with a positive definite covariance matrix P(k) (the covariance matrix P(k) characterizes the amount of noise in the estimated wind speed). Then, it can be written as:

[0078]

[0079] in, is a Gaussian distribution. The mean and covariance matrix can be used at any time, as they are output from wind field reconstruction, in particular for the method described in patent application FR-3,068,139 (WO-2018 / 234,409).

[0080] 3. Determine wind direction

[0081] This step involves determining the wind direction in real time by means of a spherical volume approximation method using a Gaussian distribution applied to the longitudinal and transverse components of the wind speed obtained in step 2. The spherical volume approximation method is a method that approximates the distribution of a random variable using a finite number of points (i.e., a finite number of random realizations). Such a method is described in particular in the following document: I. Arasaratnam, "Cubature Kalman filtering theory & applications", PhD thesis, 2009. The spherical volume approximation method enables the real-time determination of the wind direction because it does not require many calculations and does not involve complex calculations, unlike the Monte Carlo method, which is not suitable for real-time estimation problems due to the large amount of computing time required due to the number of calculations and the computational complexity.

[0082] Additionally, this step uses the following equation that defines the angle θ of the wind direction: Here, u represents the longitudinal component of wind speed, and v represents the transverse component of wind speed.

[0083] According to an embodiment of the present invention, the standard deviation of the wind direction may also be determined in this step, so that the robustness of the wind direction determination can be determined.

[0084] According to one embodiment of the present invention, the spherical volume approximation method may be implemented for five random Gaussian distributions according to the longitudinal and transverse components of the wind speed.

[0085] Therefore, the number of calculations is limited, which allows this step to be performed in real time. Moreover, this number of random realizations provides reliability in wind direction determination via the spherical volume approximation method.

[0086] According to one embodiment of the present invention, the wind direction may be determined by a spherical volume approximation method by performing the following steps:

[0087] i) Determine the longitudinal component of wind speed u j and the transverse component v j Gaussian distribution of random realizations (e.g., five random realizations), where j ranges from -2 to 2, such that:

[0088]

[0089] in,

[0090] P(k)=S∑S T ,

[0091] as well as are the estimated values ​​of u and v, P(k) is the covariance matrix of the Gaussian distribution of the longitudinal and transverse components of the wind speed, S and Σ are matrices obtained from the singular value decomposition of the covariance matrix P(k), S1 and S2 are the columns of the matrix S,

[0092] ii) For each random realization j (j ranges from -2 to 2), determine the wind direction θ by the following equation j : as well as

[0093] iii) Determine the wind direction using the following equation: Among them, ω j is the weight of the random realization (in other words, the wind direction is determined by taking the weighted average of the wind directions obtained by each random realization).

[0094] This embodiment enables quick and easy determination of wind direction.

[0095] For which the wind direction is also determined Standard deviation For example, the following equation can be used:

[0096]

[0097] According to a non-limiting example embodiment, the weight ω may be determined by the following equation: j : These weights may provide a robust determination of wind direction and possibly a determination of the wind direction standard deviation.

[0098] Alternatively, other weightings may be implemented.

[0099] The present invention also relates to a method for controlling a wind turbine equipped with a LiDAR sensor. The following steps may be performed for this method:

[0100] - determining the wind direction upstream of the wind turbine by a method for determining wind direction according to any one of the above variants or combinations of variants, and

[0101] - Control wind turbines based on wind direction upstream of the turbines.

[0102] Accurate, real-time predictions of the wind turbine's upstream direction enable appropriate wind turbine control to minimize impacts on the turbine structure and maximize recovered power. This control effectively predicts the direction of the wind approaching the turbine using these predictions, enabling the turbine equipment to adapt with phase advance, ensuring that the turbine is optimally configured for the wind when its arrival is estimated. Furthermore, LiDAR sensors reduce the burden on the structure, blades, and tower, which account for approximately 54% of the cost. Thus, the use of LiDAR optimizes the wind turbine's structure, thereby reducing costs and reducing maintenance.

[0103] According to one embodiment of the present invention, the pitch angle of the blades and / or the electrical recovery torque of the wind turbine generator and / or the orientation of the nacelle can be controlled based on wind speed and direction and / or the orientation of the nacelle. Preferably, the pitch angle of each blade can be controlled individually. Other types of adjustment devices can also be used. Blade pitch is controlled so that energy recovery is optimized based on the incident wind on the blades.

[0104] According to one embodiment of the present invention, the blade pitch angle and / or the electrical recovery torque can be determined by using a wind turbine rotor-dependent wind speed map. For example, the control method described in patent application FR-2,976,630A1 (US2012-0,321,463) can be applied.

[0105] The present invention also relates to a method for monitoring and / or diagnosing a wind turbine equipped with a LiDAR sensor. The following steps may be performed for this method:

[0106] - determining the wind direction upstream of the wind turbine by a method for determining wind direction according to any one of the above variants or combinations of variants, and

[0107] - Monitor and / or diagnose wind turbine operation based on wind direction upstream of the turbine.

[0108] The monitoring and / or diagnosis may for example correspond to the mechanical strains experienced by the structure of the wind turbine as a function of the wind direction.

[0109] Furthermore, the present invention relates to a computer program product comprising code instructions designed to perform the steps of one of the above-described methods (method for determining wind direction, control method). This program is executed on a processing unit of a LiDAR sensor or any similar device associated with a LiDAR sensor or a wind turbine.

[0110] 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-mentioned methods (method for determining wind direction, control method).

[0111] According to one embodiment of the present invention, the LiDAR sensor may be a scanning LiDAR sensor, a continuous wave LiDAR sensor, or a pulsed LiDAR sensor. The LiDAR sensor is preferably a pulsed LiDAR sensor.

[0112] The present invention also relates to a wind turbine, in particular an offshore wind turbine or an onshore wind turbine, equipped with a LiDAR sensor as described above. According to one embodiment of the present invention, the LiDAR sensor may be arranged on the nacelle of the wind turbine or in the hub of the turbine. The LiDAR sensor is oriented so as to perform a wind survey upstream of the turbine (i.e. in front of the wind turbine and along its longitudinal axis, in the Figure 1 According to one embodiment, the wind turbine can be connected to Figure 1 The same wind turbine as shown in .

[0113] For an embodiment of the control method, the wind turbine may include a control device, for example for controlling the tilt angle (or pitch angle) of at least one blade of the wind turbine or for controlling the electric torque, for implementing the control method according to the present invention.

[0114] It is clear that the present invention is not limited to the embodiments of the methods given above as examples, but it includes any variant embodiments.

[0115] Example

[0116] The characteristics and advantages of the method according to the invention will become more apparent from the following examples of application scenarios.

[0117] This example uses a four-beam pulsed LiDAR sensor mounted on the nacelle of a wind turbine with a hub height of 83 meters above ground and a rotor diameter of 80 meters. The LiDAR sensor measures radial wind speed upstream of the turbine, denoted by RWS. Radial wind speeds are measured in measurement planes at 50, 70, 90, 100, 120, 140, 150, 170, 190, and 200 meters upstream of the wind turbine.

[0118] These measurements are fed into a wind field estimator as described in French patent application FR-3,068,139 (WO-2018 / 234,409). Thus, the longitudinal and transverse components of the wind speed of the three-dimensional field can be obtained using their covariance matrix, which characterizes the amount of noise in the estimated wind speed.

[0119] Figure 3 and Figure 4 The longitudinal component u and the transverse component v (m / s) of a beam of the LiDAR sensor in a measurement plane located at 200 m are shown as a function of time T (s). Figure 3and Figure 4 Corresponds to one day's measurement.

[0120] For this example, a method of determining wind direction in real time is compared to a prior art method of determining wind direction based on a Monte Carlo method, which cannot be implemented in real time due to the large number of calculations required and the large amount of computing time involved due to the complexity of these calculations.

[0121] Figure 5 By way of example, the relationship between wind direction θ (degrees) and time T (seconds) is shown for an embodiment according to the present invention, denoted by INV, and for a prior art embodiment based on a Monte Carlo method, denoted by MCA. Note that the two curves overlap. Thus, although the method according to the present invention involves fewer calculations than the prior art method, it provides results that are as accurate as those of a complete and complex method.

Claims

1. A method for determining wind direction by means of a LiDAR sensor (2) arranged on a wind turbine (1), wherein: Perform the following steps: a) performing wind measurements (MES) by means of the LiDAR sensor (2) in at least one measurement plane (PM) upstream of the wind turbine, the measurement plane (PM) being perpendicular to the measurement direction (x) of the LiDAR sensor (2), b) determining, by means of the measurements, a Gaussian distribution of a longitudinal component (u) and a transverse component (v) of the wind speed, wherein the longitudinal component (u) corresponds to the measurement direction of the LiDAR sensor (2) and the transverse component (v) corresponds to a direction (y) perpendicular to the measurement direction of the LiDAR sensor (2), and c) determining the wind direction (θ) in real time by means of the determined Gaussian distribution of the longitudinal component (u) and the transverse component (v) of the wind speed through the spherical volume approximation method (ACS).

2. The method for determining wind direction according to claim 1, wherein: The Gaussian distribution of the longitudinal component (u) and the transverse component (v) of the wind speed is determined by a wind field estimator.

3. The method for determining wind direction according to claim 1, wherein: The method also includes determining a standard deviation of the wind direction.

4. The method for determining wind direction according to claim 1, wherein: The spherical volume approximation (ACS) involves five random realizations of the Gaussian distributions of the longitudinal and transverse components of wind speed.

5. The method for determining wind direction according to claim 1, wherein: The (θ) is determined by the spherical volume approximation method by performing the following steps: i) Determine the longitudinal component u of the wind speed j and the transverse component v j A random realization of the Gaussian distribution, where j ranges from -2 to 2, such that: in, as well as are the estimated values ​​of u and v, P(k) is the covariance matrix of the Gaussian distribution, S and Σ are matrices obtained from the singular value decomposition of the covariance matrix P(k), S1 and S2 are the columns of the matrix S, ii) For each random realization j, determine the wind direction θ by the following equation j : as well as iii) Determine the wind direction using the following equation: Among them, ω j is the weight of the random realization.

6. The method for determining wind direction according to claim 3, wherein: The wind direction The standard deviation of Determined by the following equation:

7. The method for determining wind direction according to any one of claims 5 or 6, characterized in that: The right j Redefine as follows:

8. A method for controlling a wind turbine (1) equipped with a LiDAR sensor (2), characterized in that: The method comprises the following steps: a) determining the wind direction upstream of the wind turbine (1) by means of a method according to any one of the preceding claims, and b) controlling the wind turbine (1) according to the wind direction upstream of the wind turbine (1).

9. A computer program product, characterized in that The computer program product comprises code instructions designed to perform the steps of the method according to any one of the preceding claims when the program is executed on a processing unit of a LiDAR sensor (2).

10. A LiDAR sensor (2) for a wind turbine, characterized in that: The LiDAR sensor comprises a processing unit implementing the method according to any one of claims 1 to 8.

11. A wind turbine (1), characterized in that: The wind turbine comprises the LiDAR sensor (2) according to claim 10.

12. The wind turbine (1) according to claim 11, characterized in that The LiDAR sensor (2) is arranged on the nacelle of the wind turbine (1) or in the hub of the wind turbine.

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