Method for determining an induction factor between a measurement plane and a wind turbine rotor plane

Abstract

The invention relates to a method of determining an induction factor between a rotor plane (PR) and a measurement plane (PM), involving the steps of measuring the wind speed in at least two measurement planes (PM), determining the wind speed in the rotor plane (PR) from the measurements by means of a Kalman filter, and measuring the induction factor from the measurements and the wind speed in the rotor plane (PR) by means of an adaptive Kalman filter.

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 turbine control (directional, 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] - The gearbox consists of two shafts (the mechanical shaft of the rotor and the mechanical shaft of the motor) 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] Knowing the wind speed at the turbine rotor is crucial for optimizing control. 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 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 technology, 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 a transmitter. This method is primarily used to determine the distance to an object using pulsed laser light. 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.

[0012] 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 12MW). 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 generating energy. This is important for wind turbine projects as it determines their financial reliability.

[0013] 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 the consideration of measurements performed by a LiDAR sensor by precisely estimating the wind speed at the rotor (i.e., in the rotor plane). Such applications are described in particular in patent application FR-3-013777 (US-2015-145253).

[0014] Furthermore, the behavior of wind formed upstream of wind turbines (i.e., the induction zone) has attracted increasing interest over the past decade. In the induction zone, the wind is slowed down due to the presence and operation of the wind turbine (which extracts a portion of the wind's aerodynamic power). A better understanding of the induction zone can help improve control strategies based on LiDAR sensors and wind turbine power assessment. In the first case, the goal is to use upstream wind measurements to predict wind speed in the rotor plane. In the latter case, the aim is to establish the relationship between the power and velocity of the free flow—that is, the wind speed that would be present at a point corresponding to the location of the wind turbine in the absence of the turbine. Therefore, the interest in the induction zone lies in using measurements near the turbine to estimate the effective rotor wind speed.

[0015] In the paper 'Using a cylindrical vortex model to assess the induction zone in front of aligned and yawed rotors' by Emmanuel Simon Pierre Branlard and Alexander Raul Meyer Forsting, presented at the EWEA Offshore 2015 Conference, European Wind Energy Association (EWEA), the analytical formula for the velocity field induced by the cylindrical vortex model was applied to assess the induction zone. These results were compared with actuator disk simulations under different operating conditions.

[0016] In D Medici, Stefan Ivanell, In the paper 'The upstream flow of a wind turbine: blocking effect' by P. Henrik Alfredsson, in Wind Energy 14.5 (2011), pp. 691-697, wind tunnel measurements were used to study the induction zone of various wind turbines. Furthermore, these results were compared with analytical expressions for the induction zone obtained from a linear cylindrical vortex model.

[0017] In Eric Simley, Nikolas Angelou, Torben Mikkelsen, Mikael JakobMann and Lucy Y. Pao's paper, 'Characterization of wind velocities in the upstream induction zone of a wind turbine using scanning continuous-wave lidars', Journal of Renewable and Sustainable Energy 8.1 (2016), p. 013301, studied the induction zone of a wind turbine using a synchronous continuous-wave LiDAR sensor. The results showed that the standard deviation of the longitudinal velocity component remained relatively constant as the wind approached the rotor, while the standard deviations of the vertical and lateral components increased slightly. In Niels Troldborg and Alexander Raul Meyer Forsting's paper 'A simple model of the windturbine induction zone derived from numerical simulations', Wind Energy 20.12 (2017), pp. 2011-2020, the upstream induction zone of various wind turbines was studied by combining steady-state Navier-Stokes simulations with the actuator disk method. The results show that the induced velocity is similar and independent of rotor geometry for distances upstream of the rotor exceeding one rotor radius.

[0018] For all methods in the literature, it should be emphasized that the sensing zone is calculated / estimated offline using simulation or experimental data. Furthermore, once identified, the sensing zone is assumed to be implicitly constant for a given wind speed. Clearly, since this sensing zone is explicitly a function of the blade and yaw angle at a given wind speed, it provides a very average level of information about the wind speed deficit. Therefore, these methods do not allow for online and continuous determination of the physical phenomena involved in the sensing zone.

[0019] Furthermore, patent application FR-3088434 (US-2020 / 0149512) discloses a method for determining the sensing factor online using LiDAR sensors arranged on a wind turbine. This method involves measuring wind speeds in several measurement planes using LiDAR sensors, then using these measurements and a first linear Kalman filter to determine the sensing factor between the measurement planes, and using a second linear Kalman filter to derive the sensing factor between the measurement planes and the rotor plane of the wind turbine. However, this method requires knowledge of the distances between the measurement planes and the rotor plane of the wind turbine. Currently, these distances can be imposed by the LiDAR user, can vary from LiDAR sensor to LiDAR sensor, and can be unknown. Therefore, the method described in this patent application is not applicable in these situations. Summary of the Invention

[0020] The objective of this invention is to determine, in any case and at any measurement distance, the induction factor between the measurement plane and the rotor plane in real time, wherein the measurement is performed by a LiDAR, preferably located on the wind turbine nacelle and preferably oriented in the same direction as the rotor axis, and robust to yaw, pitch, and roll deviations relative to that direction. This invention relates to a method for determining the induction factor between the rotor plane and the measurement plane, involving the steps of measuring wind speeds in at least two measurement planes, determining the wind speeds in the rotor plane based on the measurements using Kalman filters, and measuring the induction factor based on the measurements and the wind speeds in the rotor plane using an adaptive Kalman filter. Two Kalman filters provide continuous determination of the induction factor. This method does not impose a fixed measurement distance, thus allowing unconstrained LiDAR parameterization to select the measurement distance.

[0021] This invention relates to a method for determining a sensing factor between a measurement plane and the rotor plane of a wind turbine equipped with a LiDAR sensor that performs measurements relative to wind speeds in at least two measurement planes remote from the wind turbine. The wind sensing factor represents a wind deceleration coefficient between two distant points upstream of the wind turbine, the deceleration being caused by the operation of the wind turbine in a wind field. The method therefore includes the following steps:

[0022] a) Measure wind speeds in at least two measurement planes remote from the wind turbine using the LiDAR sensor.

[0023] b) Determine the wind speed in the rotor plane using the measurements of the wind speed in at least two measurement planes and a Kalman filter, and

[0024] c) Using the determined wind speed in the rotor plane, the wind speed measurement in the measurement plane, and an adaptive Kalman filter, determine the wind sensing factor between the measurement plane and the rotor plane under consideration.

[0025] According to one embodiment, the wind speed in the rotor plane is determined by using a Kalman filter applied to a parametric function that correlates the wind speed with the distance to the measurement plane.

[0026] Advantageously, the parametric function is a polynomial function, preferably a quadratic polynomial function or a piecewise affine function.

[0027] According to one implementation, the state model used in the Kalman filter is written as: Where k is the discrete time, x is a vector containing the coefficients of the parameter function, y is the wind speed vector in the at least two measurement planes, H is a matrix that depends on the distance between the at least two measurement planes and is defined according to the parameter function, μ is the variation of the coefficients of the parameter function, and ε is the measurement noise vector.

[0028] Advantageously, the parametric function is a polynomial function, which is written as: Where r i Let f be the distance to plane i, f be the parameter function, c0, c1, c2 be the coefficients of the parameter function, and let matrix H be written as: Where n is the number of measurement planes for which measurements have been performed.

[0029] According to one aspect, the sensing factor is determined by applying the adaptive Kalman filter to the following state model: in And k is discrete time, r i It measures the distance r from plane i. j It measures the distance to plane j, a ri,rj It is the inductance factor between measurement plane i and measurement plane j. It is the wind speed in plane i. Let ξ be the wind speed in plane j, and ξ be the change of the induction factor over time. It measures the wind speed in plane i. noise, It measures the wind speed in plane j. The noise.

[0030] Furthermore, the present invention relates to a method for controlling a wind turbine equipped with a LiDAR sensor. The method comprises the following steps:

[0031] a) Using the method described by one of the above features, determine the induction factor between the measurement plane and the rotor plane of the wind turbine, and

[0032] b) Control the wind turbine based on the sensing factor between the considered measurement plane and the rotor plane of the wind turbine.

[0033] This invention also relates to a method for diagnosing and / or monitoring wind turbines equipped with LiDAR sensors. The method comprises the following steps:

[0034] a) Using the method described by one of the above features, determine the wind sensing factor between the measurement plane and the rotor plane of the wind turbine.

[0035] b) Using the wind sensing factor determined between the measuring plane and the rotor plane of the wind turbine, determine the aerodynamic power extracted by the wind turbine from the wind, and

[0036] c) Use the determined aerodynamic power absorbed to diagnose and / or monitor the operation of the wind turbine.

[0037] 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 a unit for processing the LiDAR sensor.

[0038] Furthermore, the present invention relates to a LiDAR sensor for wind turbines, comprising a processing unit that implements a method according to one of the above features.

[0039] Furthermore, the present invention relates to a wind turbine including a LiDAR sensor according to one 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

[0040] Referring to the accompanying drawings, other features and advantages of the method and system according to the invention will become apparent from the following description of embodiments given by way of non-limiting examples, wherein:

[0041] Figure 1 A wind turbine equipped with a LiDAR sensor according to an embodiment of the present invention is shown.

[0042] Figure 2 The steps of a method for determining the wind sensitivity factor according to an embodiment of the present invention are shown.

[0043] Figure 3 The steps of a wind turbine control method according to an embodiment of the present invention are shown.

[0044] Figure 4 The steps of a wind turbine diagnostic method according to an embodiment of the present invention are shown.

[0045] Figure 5 The curve of the sensing factor as a function of time is shown for an example embodiment of the first measurement plane, and

[0046] Figure 6 The curves are for the sensing factor as a function of time in an example embodiment of three measurement planes. Detailed Implementation

[0047] The present invention relates to a method for measuring the resources (i.e., wind) of a wind turbine, particularly with turbine control (orientation, torque and speed regulation) and / or diagnostic and / or monitoring targets, wherein the wind turbine is controlled and / or monitored based on the determination of the wind sensing factor, the turbine being equipped with a LiDAR sensor to perform this estimation.

[0048] The induction factor is the wind deceleration coefficient in the induction zone of a wind turbine. In fact, due to the presence and operation of the wind turbine, the wind slows down in the upstream region of the turbine: in other words, the power extracted by the turbine from the wind causes the wind upstream of the turbine to slow down. Therefore, the induction factor represents a physical phenomenon and provides an indication related to the resources of the wind turbine. The induction factor is calculated between two planes upstream of the wind turbine, and by definition, it corresponds to the speed ratio between these planes. If a represents the induction coefficient, u represents the wind speed, and d1 and d2 represent the corresponding distances of the two planes under consideration from the rotor plane, then the induction factor between the planes located at distances d1 and d2 from the rotor plane can be written as:

[0049]

[0050] When one of the planes under consideration is the rotor plane, d1 = 0, and the induction factor is the induction factor between the measurement plane and the rotor plane. When neither plane is a rotor plane, the induction factor is the induction factor between these measurement planes. In the remainder of this application, the rotor plane is considered as a measurement plane with a distance of zero.

[0051] It can be noted that, in the literature, the sensing factor can be defined as follows:

[0052]

[0053] The method according to the invention is also applicable to the definition of the sensing factor, and the second definition of the sensing factor can be derived by simple subtraction relative to the first definition.

[0054] According to the present invention, the LiDAR sensor allows for the measurement of wind speed in multiple (at least two) measurement planes upstream of a wind turbine. Several types of LiDAR sensors exist, such as scanning LiDAR, continuous wave LiDAR, 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.

[0055] LiDAR sensors allow for rapid measurements. Therefore, using such sensors enables the rapid and continuous determination of wind sensing factors. For example, the sampling rate of LiDAR sensors can range from 1 to 5 Hz (or even higher in the future).

[0056] Figure 1 A 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 in 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 the transverse direction in the horizontal plane (directions x and y form the horizontal plane). Direction z is the upward vertical direction (basically corresponding to the direction of tower 4), and the z-axis is perpendicular to the x-axis and y-axis. The rotor plane is indicated by the 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.

[0057] Traditionally, a wind turbine 1 allows the conversion of wind kinetic energy into electrical or mechanical energy. To convert wind energy into electrical energy, the wind turbine 1 consists of the following components:

[0058] - Tower 4, which allows the rotor (not shown) to be positioned at a sufficient height to enable its movement (necessary for horizontal axis wind turbines), or at a height that allows 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.).

[0059] - 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.

[0060] - A rotor fixed to the nacelle, comprising several blades (usually 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.

[0061] - The gearbox consists of two shafts (the mechanical shaft of the rotor and the mechanical shaft of the motor) connected by a gearbox (gearbox) (not shown).

[0062] As in the example embodiment of a pulsed LiDAR sensor Figure 1 As can be seen, the LiDAR sensor 2 used includes four beams or measurement axes (b1, b2, b3, b4). As a non-limiting example, the method according to the invention can also be operated 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 1 The points are represented by black circles, and they are designated PT1, PT2, PT3, and PT4. Processing the measurements at these points allows for the determination of wind speeds in the measurement plane PM. Therefore, the wind modeling method described in French patent application FR-3,068,139 (US-2020 / 0,124,026) can be applied in particular.

[0063] Preferably, the LiDAR sensor 2 can be installed on the nacelle 3 of the wind turbine 1 or in the hub of the wind turbine 1 (at the upstream end of the rotor).

[0064] According to the present invention, a method for determining the wind sensing factor between a measuring plane and the rotor plane of a wind turbine includes the following steps:

[0065] 1) Measure the wind speed in the various measurement planes.

[0066] 2) Determine the wind speed in the rotor plane, and

[0067] 3) Determine the induction factor between the measuring plane and the rotor plane.

[0068] These steps are performed in real time. They are described in detail in the remainder of the following text specification.

[0069] Figure 2The steps of a method according to an embodiment of the present invention are illustrated by way of non-limiting example. The first step is to measure the wind speed in several measurement planes (MES). The second step uses the measurements and a Kalman filter (KAL1) to determine the wind speed in the rotor plane, using u r0 The third step uses the wind speed u in the rotor plane. r0 The wind sensing factor between the measurement plane and the rotor plane is determined by means of an adaptive Kalman filter (KAL2), and the sensing factor is denoted by a.

[0070] In the remainder of this description, the distance between the measuring plane and the rotor plane is referred to as the distance between the measuring planes. Therefore, it is the longitudinal distance (along...). Figure 1 (x-axis in the diagram).

[0071] 1. Wind speed measurement

[0072] In this step, wind speed is continuously measured in at least two measurement planes located away from the wind turbine using LiDAR sensors. This allows the determination of wind speed upstream of the wind turbine in at least two measurement planes situated at different distances from the turbine. In other words, the wind speed at at least two distances from the rotor plane of the wind turbine can be determined. For this step, three wind components (longitudinal, lateral, and vertical) and variations in wind speed within the measurement planes (e.g., wind speed increasing with altitude) are considered. Since the method according to the invention does not require knowledge of the distances between the measurement planes and the rotor plane, these distances may not be imposed for implementing the method according to the invention.

[0073] According to one embodiment of the invention, wind speeds in at least three measurement planes are measured to improve the accuracy of the knowledge of wind upstream of the wind turbine, and thus improve the accuracy of the estimation of the wind sensing factor in the rotor plane.

[0074] According to one implementation of the invention, the measuring plane can be spaced from the rotor plane by a longitudinal distance between 50 and 400 m (along... Figure 1 (x-axis in the diagram). Therefore, the evolution of wind speed over long distances upstream of a wind turbine can be determined, which also allows for improved estimation accuracy of the wind sensing factor.

[0075] 2. Determine the wind speed in the rotor plane.

[0076] This step involves using wind speed measurements from at least two measurement planes obtained in step 1 and employing a Kalman filter to determine the wind speed in the rotor plane. Applying the Kalman filter allows for the acquisition of a state observer.

[0077] According to one embodiment of the invention, this step can be performed using a Kalman filter applied to a parametric function that correlates wind speed with the distance to the measurement plane. In other words, the parametric function is a function whose variable is the distance to the measurement plane and determines the wind speed at that plane. This function is called a parametric function because it depends on at least one coefficient determined by the Kalman filter in this step. This at least one coefficient varies over time. Once the Kalman filter has determined this at least one coefficient of the parametric function, the function is applied to the rotor plane, i.e., zero distance, to determine the wind speed in the rotor plane.

[0078] According to one implementation of this embodiment, the parameter function can be of any type, such as a polynomial function, preferably a quadratic polynomial function, a piecewise affine function, and so on.

[0079] According to one implementation of this embodiment, the state model used in the Kalman filter for this step can be written as follows: Where k is the discrete time, x is a vector containing the coefficients of the parameter function, y is the wind speed vector in the at least two measurement planes, H is a matrix that depends on the distance between the at least two measurement planes and is defined according to the parameter function, μ is the variation of the coefficients of the parameter function, and ε is the measurement noise vector.

[0080] For the example embodiment where the parameter function is a quadratic polynomial function, this parameter function f can be formed as follows: Where r i It is the distance to plane i (where r is the distance if the rotor plane is considered). i =r0=0), c0, c1, c2 are the coefficients of the parameter function. In this case, matrix H can be written as: Where n is the number of measurement planes for which measurements have been performed.

[0081] In this case, it can be written as:

[0082] Where u ri It measures the wind speed in plane i.

[0083] Considering that wind speed in the measurement plane includes noise ε i (k), the model can be written as:

[0084]

[0085] Assuming the coefficients of the parameter function change very little, it can be written as:

[0086] Where μ represents the change in the coefficient.

[0087] By definition:

[0088]

[0089]

[0090] as well as

[0091] y(k)=[u1(k) u2(k) ... u 10 (k)] T

[0092] ∈(k)=[∈1(k) ∈2(k) ... ∈ 10 (k)] T

[0093] The state model described above was obtained:

[0094]

[0095] For this example, the determination of the state vector x using a Kalman filter is described below. Such filters provide solutions to the following problems:

[0096]

[0097] in

[0098]

[0099] P0, Q, and R are weighted matrices of appropriate dimensions. It is the average value of the initial state x(0). Recall:

[0100] η(k-1)=x(k)-x(k-1) and μ(k)=y(k)-Hx(k)

[0101] The following assumptions can then be made, focusing primarily on the mathematical interpretation of P0, Q, and R:

[0102] x(0) is a random vector that is independent of noise η(k) and μ(k).

[0103] x(0) has a known mean Where P0 is defined as the covariance matrix as follows:

[0104]

[0105] η(k) and μ(k) are white noise with zero mean that is not related to the covariance matrices Q and R, respectively.

[0106]

[0107]

[0108] E[η(k)μ(j) T ] = 0 for all k, j.

[0109] The following notation is also used:

[0110] · It is an estimate of the vector x(k) of the measurements performed up to time k-1.

[0111] · It is an estimate of the vector x(k) of the measurements performed up to time k.

[0112] P(k|k-1) is the covariance matrix of the vector x(k) of the measurements performed up to time k-1.

[0113] P(k|k) is the covariance matrix of the vector x(k) of the measurements performed up to time k.

[0114] The Kalman filter algorithm can then be summarized by two sets of equations as follows:

[0115] • Time update equation

[0116]

[0117] Measurement update equation

[0118]

[0119] Therefore, by performing these steps, an estimate of vector x can be determined. This means that the coefficients of the parametric function can be determined. Therefore, the wind speed u in the rotor plane can be determined by applying the following formula. r0 : (This formula corresponds to the value r) i An example of a quadratic polynomial parametric function with a value of 0, and the formula needs to be adapted depending on the parametric function used.

[0120] 3. Determine the induction factor between the measuring plane and the rotor plane.

[0121] This step involves determining in real-time the wind induction factor between one of the measurement planes for which wind speed has been measured and the rotor plane. Therefore, the evolution of wind at the rotor can be represented by taking into account physical phenomena, particularly wind deceleration. According to the invention, the wind induction factor between the measurement plane and the rotor plane is determined by means of the wind speed in the rotor plane obtained in step 2, the wind speed measurements in at least two measurement planes obtained in step 1, and a Kalman filter (especially an adaptive Kalman filter). Applying a Kalman filter allows for the acquisition of a state observer. The adaptive Kalman filter allows for adaptation of the covariance matrix of the noise based on 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.

[0122] Given that the wind speed is known in at least two measurement planes and the rotor plane, it can be determined directly using the equation defining the induction factor: However, this method has two drawbacks: it does not provide information about the estimated mass, and wind speeds are estimated using their confidence intervals, and numerical stability issues may arise for low wind speeds.

[0123] According to one embodiment of the present invention, the wind sensing factor can be determined by applying an adaptive Kalman filter to the following state model: in And k is discrete time, r i It measures the distance from plane i to the rotor plane (where r is the distance if the rotor plane is considered). i =r0=0), r j It measures the distance from plane j to the rotor plane (where r is the distance if the rotor plane is considered). j =r0=0), a ri，rj It is the induction factor between measurement plane i and measurement plane j (if i = 0 or j = 0, then the plane under consideration is the rotor plane). It measures the wind speed in plane i (where r is the wind speed if the rotor plane is considered). i =r0=0), It measures the wind speed in plane j (where r is the wind speed if the rotor plane is considered). j =r0=0), ξ is the change of the induction factor with time. It measures the wind speed in plane i. noise, It measures the wind speed in plane j. The noise.

[0124] In fact, the adaptive Kalman filter can be used by following the steps described below.

[0125] The induction factor equation can be written as:

[0126]

[0127] Given that wind speed includes noise, a more realistic model can be written as:

[0128] in It measures the wind speed in plane i. noise, It measures the wind speed in plane j. noise

[0129] Then we can define:

[0130]

[0131] This allows it to be written as:

[0132]

[0133] Noise can be assumed and It is irrelevant (in other words, it can be written as: In this case, the variance of v(k) can be:

[0134]

[0135] Note that R v (k) depends on time (k is discrete time).

[0136] Assuming the sensing factor changes very little with time, it can be written as:

[0137] Where ξ is the change of the sensing factor over time.

[0138] Using these equations, we obtain the state model as described above:

[0139]

[0140] Then an adaptive Kalman filter is used because the covariance matrix R of the measurement noise is... v (k) is a function of time. Using this adaptive Kalman filter, it is possible to determine... The estimation of the induction factor between measurement planes i and j is given by the wind speed in measurement planes i and j at time k, and then considering the wind speed in rotor plane r. i =0, and j corresponds to the measurement plane considered in the measurement planes used for measurement in step 1.

[0141] application

[0142] The present invention also relates to a method for controlling a wind turbine equipped with a LiDAR sensor. The method comprises the following steps:

[0143] -The wind sensing factor between the measuring plane and the rotor plane of the wind turbine is determined by using the method of determining the sensing factor according to any of the above-described variations.

[0144] - Control the wind turbine based on the wind sensing factor between the measured plane and the rotor plane.

[0145] Precise, real-time knowledge of wind sensing factors allows for appropriate control of wind turbines, minimizing the impact on their structure and maximizing power recovery. In fact, with this control, LiDAR sensors allow for a reduction of loads on the structure, blades, and towers, amounting to 54% of their cost. Therefore, using LiDAR sensors enables the optimization of wind turbine structures, thereby reducing costs and maintenance.

[0146] According to one implementation of the invention, the blade tilt angle and / or the electrorecovery torque of the wind turbine generator can be controlled depending on the wind speed. Other types of regulating devices may also be used.

[0147] According to one embodiment of the invention, the blade tilt angle and / or the electric recovery torque can be determined using a wind turbine diagram as a function of the wind speed at the rotor. For example, the control method described in patent application FR-2976630A1 (US-2012 / 0321463) can be applied.

[0148] Figure 3 The steps of a method according to an embodiment of the present invention are illustrated schematically by way of non-limiting examples. The first step is the measurement of wind speed in several measurement planes (MES). The second step uses the measurements and a Kalman filter (KAL1) to determine the wind speed in the rotor plane, using u r0 The third step uses the wind speed u in the rotor plane. r0 The wind sensing factor between the measurement plane and the rotor plane is determined using measurements and an adaptive Kalman filter (KAL2), denoted by 'a'. The fourth step (CON) involves wind turbine control based on the wind sensing factor 'a'.

[0149] Furthermore, the present invention relates to a method for diagnosing and / or monitoring a wind turbine equipped with a LiDAR sensor, wherein the following steps are performed:

[0150] -The wind sensing factor between the measurement plane and the rotor plane of the wind turbine is determined by using the sensing factor determination method based on any of the above variations.

[0151] Using the sensing factor determined in the previous step, the aerodynamic power extracted by the wind turbine from the wind is determined, and

[0152] The operation of the wind turbine is diagnosed and / or monitored based on the aerodynamic power determined in the previous step.

[0153] The sensing factor represents the wind deceleration caused by the presence of wind turbines in the wind field, and can be used to determine the aerodynamic power extracted from the wind by the wind turbines. According to one embodiment, the sensing factor a and the free flow velocity V can be used to determine this. inf Air density Ro and surface area A of the wind turbine d To determine the aerodynamic power P extracted aéro ,

[0154]

[0155] The extracted aerodynamic power provides information about the operation of the wind turbine, enabling diagnosis and / or monitoring of its operation. The basic idea is to compare the electrical power generated by the wind turbine with the theoretical electrical power given by the preceding equations by approximating the generator's transmission efficiency and electrical conversion efficiency to 1.

[0156] The ratio of the two power sources allows for the diagnosis and / or monitoring of the wind turbine's operation and effective aerodynamic efficiency.

[0157] Real-time updates to the sensing factor also allow for the quantification of the aerodynamic thrust load applied to the wind turbine, thereby inferring an estimate of cumulative fatigue damage. According to one implementation of the invention, this can be achieved using the thrust coefficient C... T This is accomplished by establishing a correlation with the sensing factor (Burton, Wind Energy Handbook, Chapter 3.2), which can be written as follows: C T =4a(1-a). Furthermore, online estimation of the sensing factor allows for the real-time development and updating of simplified wind turbine wake models. This enables operational diagnostics at the wind farm scale, the identification of risk zones through wake interactions between adjacent wind turbines, or even the diagnosis and control of the wind farm. In this context, the invention may relate to a method for diagnosing and / or monitoring wind farms, wherein the following steps are performed: - For at least one wind turbine in the wind farm, the sensing factor of the wind between the measurement plane and the rotor plane of the wind turbine is determined by means of a sensing factor determination method according to any of the above-described variations.

[0158] -Using the thus determined sensing factor, the thrust coefficient of at least one wind turbine can be determined.

[0159] - Use the thrust coefficient determined thereby to construct a wake model for at least one wind turbine, and

[0160] The operation of at least one wind turbine in the wind farm is diagnosed and / or monitored based on the wake model determined in the previous step.

[0161] According to one embodiment, the wake model can take the form of the Jensen model described in the literature: Wake effect modeling: A review of wind farm layout optimization using Jensen's model, Rabia Shakoor, Mohammad Yusri Hassan, Abdur Raheem, Yuan Kang Wu, Renewable and Sustainable Energy Reviews, Vol. 58, May 2016, pp. 1048-1059.

[0162] The wake provides information about the operation of a wind farm, enabling the diagnosis and / or monitoring of wind turbine operation. The basic idea is to compare the electrical power generated by the wind farm with the theoretical electrical power.

[0163] Precise, real-time wake knowledge allows for appropriate control of wind farms in maximizing the wind energy recovered by the wind farm.

[0164] Figure 4 The steps of a method according to an embodiment of the present invention are illustrated schematically by way of non-limiting examples. The first step is the measurement of wind speed in several measurement planes (MES). The second step uses the measurements and a Kalman filter (KAL1) to determine the wind speed in the rotor plane, using u r0 The third step uses the wind speed u in the rotor plane. r0 The wind induction factor between the measurement plane and the rotor plane is determined using measurements and an adaptive Kalman filter (KAL2), denoted by 'a'. The fourth step (PUI) determines the aerodynamic power P extracted from the wind based on the induction factor 'a' between the measurement plane and the rotor plane, and the wind speed measurements in the measurement plane. aéro The fifth step (DIA) involves calculating the absorbed aerodynamic power P. aéro Diagnose or monitor wind turbines.

[0165] Furthermore, the present invention relates to a computer program product comprising code instructions designed to perform the steps of one of the aforementioned methods (sensitivity factor determination method, control method, diagnostic and / or monitoring method). The program executes on a unit used for processing a LiDAR sensor, or on any similar medium connected to a LiDAR sensor or wind turbine.

[0166] According to one aspect, the present invention also relates to a LiDAR sensor for wind turbines, comprising a processing unit configured to implement one of the above-described methods (sensor factor determination method, control method, diagnostic and / or monitoring method).

[0167] According to one implementation of the present invention, the LiDAR sensor can be a scanning LiDAR, a continuous wave LiDAR, or a pulsed LiDAR sensor. Preferably, the LiDAR sensor is a pulsed LiDAR sensor.

[0168] The present invention also relates to a wind turbine, particularly an offshore or onshore wind turbine equipped with a LiDAR sensor as described above. According to one embodiment of the invention, the LiDAR sensor may be disposed on the nacelle of the wind turbine or in the hub of the wind turbine (i.e., at the end of the wind turbine rotor). The LiDAR sensor is oriented to perform a pulse-jet transmission upstream of the wind turbine (i.e., upstream of the wind turbine and along its longitudinal axis). Figure 1 The x-axis (specified in the diagram) is used to measure wind speed. According to one embodiment, a wind turbine can be similar to... Figure 1 The wind turbine shown.

[0169] In embodiments of the control method, the wind turbine may include control devices, such as those for controlling the pitch angle of the wind turbine blades or for controlling electrical torque, for implementing the method according to the invention.

[0170] For embodiments of diagnostic and / or monitoring methods, the wind turbine may include wind turbine operation diagnostic and / or monitoring devices.

[0171] Application Examples

[0172] Other features and advantages of the method according to the invention will become clear from the following description of examples.

[0173] In this example, experimental measurements using a LiDAR sensor are performed, and the wind sensing factor between the measurement plane and the rotor plane is determined according to an embodiment of the invention. The embodiment of the invention implemented in this example is a quadratic polynomial parameter function. Furthermore, in this example, the LiDAR sensor measures the wind speed in ten measurement planes at distances r1, r2, ..., r10 from the rotor plane.

[0174] Using the method according to an embodiment of the present invention, the following sensing factor is determined:

[0175] - The induction factor between the measuring plane and the rotor plane at a distance r1

[0176] - The induction factor between the measuring plane and the rotor plane at a distance r2, where distance r2 is greater than distance r1, and

[0177] - The induction factor between the measuring plane and the rotor plane at a distance r3 is greater than that at a distance r2.

[0178] Figure 5 It is the curve of the sensing factor a as a function of time T. Figure 5 Only the induction factor between the measuring plane and the rotor plane at a distance r1 is explained, denoted as a. 0,r1 The method according to the invention does indeed allow for the determination of the induction factor between the measuring plane and the rotor plane. Furthermore, it is noted that this induction factor is close to 1 and varies over time.

[0179] Figure 6 It is the curve of the sensing factor a as a function of time T. Figure 6 The induction factor between the measuring plane at a distance r1 and the rotor plane (given by a) was explained. 0,r1 (represented by a), the induction factor between the measuring plane at a distance r2 and the rotor plane (given by a). 0,r2 (represented by), and the induction factor between the measurement plane at a distance r3 and the rotor plane (given by a) 0,r3 (Represented). The method according to the invention does indeed allow for the determination of the induction factors between the measuring plane and the rotor plane. Note that the three induction factors change over time, and the closer the measuring plane is to the rotor plane, the closer the induction factors are to 1, which indeed corresponds to wind-induced phenomena. Determining several induction factors between different measuring planes and the rotor plane allows for the determination of the induction factors varying with the distance from the rotor plane at any given time.

Claims

1. A method for determining a sensing factor between a measuring plane and a rotor plane of a wind turbine (1), the wind turbine (1) being equipped with a LiDAR sensor (2) performing measurements relative to wind speeds in at least two measuring planes remote from the wind turbine (1), the wind sensing factor representing a wind deceleration coefficient between two distant points upstream of the wind turbine, the deceleration being caused by the operation of the wind turbine (1) in a wind field, characterized in that, Perform the following steps: a) Measure wind speeds in at least two measurement planes remote from the wind turbine (1) using the LiDAR sensor (2). b) Determine the wind speed in the rotor plane using the measurements of the wind speed in at least two measurement planes and a Kalman filter, and c) Using the determined wind speed in the rotor plane, the wind speed measurement in the measurement plane, and an adaptive Kalman filter, determine the wind sensing factor between the considered measurement plane and the rotor plane.

2. The method for determining the sensing factor according to claim 1, characterized in that, The wind speed in the rotor plane is determined using a Kalman filter, which is applied to a parameter function that correlates the wind speed with the distance to the measurement plane.

3. The method for determining the sensing factor according to claim 2, characterized in that, The parameter function is a polynomial function or a piecewise affine function.

4. The method for determining the sensing factor according to claim 3, characterized in that, The polynomial function is a quadratic polynomial function.

5. The method for determining the sensing factor according to any one of claims 2 to 4, characterized in that, The state model used in the Kalman filter is written as: , where k is the discrete time, x is a vector containing the coefficients of the parameter function, y is the wind speed vector in the at least two measurement planes, H is a matrix that depends on the distance between the at least two measurement planes and is defined according to the parameter function, µ is the variation of the coefficients of the parameter function, and It is a measurement noise vector.

6. The method for determining the sensing factor according to claim 5, characterized in that, The parameter function is a polynomial function, which is written as: , where r i Let f be the distance to plane i, f be the parameter function, c0, c1, c2 be the coefficients of the parameter function, and let matrix H be written as: , where n is the number of measurement planes for which measurements have been performed.

7. The method for determining the sensing factor according to any one of claims 1 to 4, characterized in that, The sensing factor is determined by applying the adaptive Kalman filter to the following state model: ,in And k is discrete time, r i It measures the distance r from plane i. j It measures the distance to plane j. It is the inductance factor between measurement plane i and measurement plane j. It is the wind speed in plane i. It is the wind speed in plane j. It is the change of the sensing factor over time. It measures the wind speed in plane i. noise, It measures the wind speed in plane j. The noise.

8. A method for controlling a wind turbine (1) equipped with a LiDAR sensor (2), characterized in that, Perform the following steps: a) Determining the induction factor between the measuring plane and the rotor plane of the wind turbine using the method described in any of the preceding claims, and b) Control the wind turbine (1) based on the sensing factor between the measured plane and the rotor plane of the wind turbine under consideration.

9. A method for diagnosing and / or monitoring a wind turbine equipped with a LiDAR sensor, characterized in that, Perform the following steps: a) Determine the induction factor between the measuring plane and the rotor plane of the wind turbine (1) using the method described in any one of claims 1 to 7. b) Using the wind sensing factor determined between the measuring plane and the rotor plane of the wind turbine (1), the aerodynamic power extracted by the wind turbine (1) from the wind is determined, and c) Use the determined aerodynamic power to diagnose and / or monitor the operation of the wind turbine (1).

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

11. A LiDAR sensor (2) for wind turbines, characterized in that, Includes a processing unit that implements the method according to any one of claims 1 to 9.

12. A wind turbine (1), characterized in that, Including the LiDAR sensor (2) according to claim 11.

13. The wind turbine according to claim 12, 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 (1).

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