Estimating wind direction incident on a wind turbine

Systems and methods for estimating a direction of wind incident on a wind turbine, the wind turbine comprising a tower; a rotor-nacelle-assembly (RNA) carried by the tower; a deflection sensor configured to sense a position of the RNA or a deflection of the tower; and a wind direction sensor. One approach includes: obtaining deflection training data from the deflection sensor; obtaining wind direction training data from the wind direction sensor; training a machine learning model on the basis of the deflection training data and the wind direction training data in order to obtain a trained machine learning model; obtaining further deflection data from the deflection sensor; inputting the further deflection data into the trained machine learning model; and operating the machine learning model to output a wind direction estimate on the basis of the further deflection data.

FIELD OF THE INVENTION

The present invention relates to a method of estimating a direction of wind incident on a wind turbine, as well as to a wind turbine and to a computer program product.

BACKGROUND OF THE INVENTION

It is desirable to improve the accuracy and reliability of current wind direction measurements, and to create a backup system in case the primary method of determining wind direction develops a fault.

SUMMARY OF THE INVENTION

A first aspect of the invention provides a method of estimating a direction of wind incident on a wind turbine, the wind turbine comprising a tower; a rotor-nacelle-assembly (RNA) carried by the tower; a deflection sensor configured to sense a position of the RNA or a deflection of the tower; and a wind direction sensor; the method comprising: obtaining deflection training data from the deflection sensor; obtaining wind direction training data from the wind direction sensor; training a machine learning model on the basis of the deflection training data and the wind direction training data in order to obtain a trained machine learning model; obtaining further deflection data from the deflection sensor; inputting the further deflection data into the trained machine learning model; and operating the trained machine learning model to output a wind direction estimate on the basis of the further deflection data.

The method may further comprise controlling the wind turbine on the basis of the wind direction estimate.

Controlling the wind turbine on the basis of the wind direction estimate may comprise changing a yaw angle of the RNA on the basis of the wind direction estimate.

The yaw angle of the RNA may be changed to bring the RNA into the wind.

The method may further comprise: after obtaining the trained machine learning model, obtaining further wind direction data from the wind direction sensor; controlling the wind turbine on the basis of the further wind direction data; detecting a fault in the wind direction sensor; and controlling the wind turbine on the basis of the wind direction estimate in response to the detection of the fault.

The wind direction estimate may be a relative wind direction estimate, in a reference frame of the RNA.

The deflection sensor may be a Global Navigation Satellite System (GNSS) sensor configured to sense a position of the RNA.

The deflection training data and the further deflection data may be obtained by receiving GNSS data from the GNSS sensor, and transforming the GNSS data into a reference frame of the RNA.

The deflection training data and the further deflection data may be obtained by receiving GNSS data from the GNSS sensor, measuring a yaw angle of the RNA with a yaw sensor, determining a natural position of the GNSS sensor on the basis of the yaw angle, and determining a difference between the natural position and the GNSS data.

The method may further comprise: obtaining wind speed training data from a wind speed sensor; and training the machine learning model on the basis of the wind speed training data in order to obtain the trained machine learning model. Optionally the trained machine learning model may be operated to output a wind speed estimate on the basis of the further deflection data.

The method may further comprise: obtaining turbine training data; training the machine learning model on the basis of the turbine training data; obtaining further turbine data; inputting the further turbine data into the trained machine learning model, and operating the trained machine learning model to output the wind direction estimate on the basis of the further turbine data.

The turbine training data and the further turbine data may comprise one or more of: blade load data, yaw data indicating a yaw angle of the RNA, tower top speed data and tower top acceleration data.

A second aspect of the invention provides a wind turbine comprising a tower; a rotor-nacelle-assembly (RNA) carried by the tower; a deflection sensor configured to sense a position of the RNA or a deflection of the tower; a wind direction sensor; and a control system configured to perform the method of the first aspect.

A third aspect of the invention provides a computer program product comprising software code adapted to control a wind turbine when executed on a data processing system, the computer program product being adapted to perform the method of the first aspect.

DETAILED DESCRIPTION OF EMBODIMENT(S)

FIG.1illustrates, in a schematic perspective view, a wind turbine10. The wind turbine10includes a tower12, a nacelle13at the top of the tower, and a rotor14operatively coupled to a generator housed inside the nacelle13. Together, the rotor14and the nacelle13are a rotor-nacelle-assembly (RNA). In addition to the generator, the nacelle houses miscellaneous components required for converting wind energy into electrical energy and various components needed to operate, control, and optimize the performance of the wind turbine10. The rotor14of the wind turbine includes a central hub15and a plurality of blades16that project outwardly from the central hub15. In the illustrated embodiment, the rotor14includes three blades16, but the number may vary.

The RNA13,14can rotate relative to the tower about a vertical yaw axis to change a yaw angle of the RNA. The wind turbine may have a yaw control system (not shown) which rotates the RNA about the vertical yaw axis so that it has a desired yaw angle. For instance the yaw control system may be configured to rotate the RNA to bring the rotor14into the wind. The yaw control system may comprise a yaw sensor configured to measure a yaw angle of the RNA.

The wind turbine10may be included among a collection of other wind turbines belonging to a wind power plant, also referred to as a wind farm or wind park, that serve as a power generating plant connected by transmission lines with a power grid. The power grid generally consists of a network of power stations, transmission circuits, and substations coupled by a network of transmission lines that transmit the power to loads in the form of end users and other customers of electrical utilities.

FIG.2schematically illustrates an embodiment of a wind turbine control system20configured to control the wind turbine10. The control system20may be placed inside the nacelle13and/or distributed at a number of locations inside the turbine. Optionally some, or all, elements of the control system20may be placed in a remote power plant controller (not shown).

The blades16are mechanically connected to an electrical generator22via a gearbox23. In direct drive systems, and other systems, the gearbox23may not be present. The electrical power generated by the generator22is injected into a power grid24via an electrical converter25. The electrical generator22and the converter25may be based on a full scale converter (FSC) architecture or a doubly fed induction generator (DFIG) architecture, but other types may be used.

The control system20comprises a number of elements, including at least one main controller21. In general, the control system20ensures that in operation the wind turbine generates a requested power output level. This is obtained by adjusting the pitch angle of the blades16and/or the power extraction of the converter25. To this end, the control system comprises a pitch system including a pitch controller27using a pitch reference28, and a power system including a power controller29using a power reference26. The rotor blades16can be pitched by a pitch mechanism. The rotor comprises an individual pitch system which is capable of individual pitching of the rotor blades, and may comprise a common pitch system which adjusts all pitch angles on all rotor blades at the same time. The control system20further comprises a wind load block210, configured to determine a direction (and optionally magnitude) of a wind load acting on the wind turbine.

The main controller21comprises a data processing system, and a computer program product comprising software code adapted to control the wind turbine10when executed on the data processing system, the computer program product being adapted to control the wind turbine as described below.

FIG.3shows a flow diagram30showing a method of estimating a direction of wind incident on the wind turbine10. A deflection sensor, in the form of a GNSS sensor31, is configured to sense a position of the RNA13,14.

The GNSS sensor31is a position sensor that uses one or more Global Navigation Satellite Systems (such as GPS, Galileo, GLONASS, BeiDou) to determine its position. The GNSS sensor31can measure its position with cm accuracy to generate position data. The position data may be generated by the GNSS sensor as a set of GNSS coordinates, for example (longitude, latitude, height), (x, y, z) or (r, θ, ϕ).

The GNSS sensor31uses a constellation of satellites to determine its position. Optionally the GNSS sensor may enhance the precision of its position measurement using a terrestrial Real Time Kinematic (RTK) base module. This RTK module may be shared between a number of wind turbines in a wind park.

A GNSS measurement is obtained by the GNSS sensor31. The GNSS sensor31may be mounted on the RNA. The sensed position may comprise GNSS coordinates, e.g. global longitude and latitude coordinates. The sensed RNA position may be indicative of a bend in the tower (if any) due to wind incident on the turbine.

The longitude, latitude and height of the RNA undergo a coordinate transformation32into a reference frame of the RNA. Specifically, the GNSS coordinates are transformed from longitude, latitude and height values to tower deflection values. These values may be linear X, Y coordinates or they may be polar coordinates in the reference frame of the RNA.

By way of example, the GNSS coordinates may be transformed into a rotating Cartesian reference frame with a positive Y-direction which is a horizontal direction pointing in a generally downwind direction as shown inFIGS.1and2. Thus the Y-direction rotates as the yaw angle of the RNA rotates to bring it into the wind. The X-direction of the rotating Cartesian reference frame is a horizontal direction perpendicular to the Y-direction, i.e. a horizontal cross-wind direction. Thrust forces will cause the tower to bend back so the GNSS sensor31moves in the positive Y-direction. If the Y-direction is not precisely aligned with the wind, then the thrust forces will also cause the tower to bend sideways so the GNSS sensor31moves in the positive or negative X-direction (depending on the direction of misalignment).

By way of example, the coordinate transformation32may calculate the movement of the GNSS sensor31(and hence the deflection of the RNA and the top of the tower) with respect to its natural position, in the rotating Cartesian reference frame, to determine the magnitude and sign of tower deflection (Xd) in the crosswind X-direction and the magnitude and sign of tower deflection (Yd) in the downwind Y-direction.

If the GNSS sensor31is not precisely positioned on the yaw axis of the RNA, then the natural position of the GNSS sensor31will move due to the change in the yaw angle of the RNA. To account for this, the yaw control system may be calibrated based on measurements from the GNSS sensor31in low wind and no production. This means for every nacelle yaw angle measured by the yaw sensor, there is a corresponding expected natural position of the GNSS sensor31. For instance if the RNA is pointing directly north and the wind is also coming directly from north, then the RNA will move back and forth in a north-south direction, and the GNSS sensor31will move back and forth in the north-south direction either side of its natural position, which is its average position for that yaw angle. It is then the average difference (Xd) between the expected x position (i.e. the natural x position based on the measured yaw angle) and the measured x position from the GNSS sensor31that is used to determine the transformed coordinates32b.

The transformed coordinates32bare input into a trained machine learning model33. Also input into the trained machine learning model33is turbine data34bobtained by wind turbine sensors34. These parameters may include one or more of: blade load, azimuth angle of the rotor, tower top speed, tower top acceleration, yaw angle of the RNA, and any other sensor signals.

The trained machine learning model33processes these inputs and outputs a wind direction estimate and optionally a wind speed estimate.

The wind direction estimate output by the trained machine learning model33may be a relative wind direction estimate, in the rotating Cartesian reference frame defined above. For instance the wind direction estimate may indicate an angular misalignment of the wind with the positive or negative Y-direction (i.e. the Y-axis).

The trained machine learning model33ofFIG.3is obtained by a training process shown inFIG.4. The machine model shown in its trained state inFIG.3, and in its untrained (or partially trained) state inFIG.4. Therefore the machine model is referred to as a trained machine learning model33in the operating phase ofFIG.3, and a machine learning model33aduring the training phase ofFIG.4.

As shown inFIG.4, the machine learning model33ais trained on the basis of deflection training data32afrom the GNSS sensor31, and wind direction training data35afrom a wind direction sensor35. Optionally the machine learning model33ais also trained on the basis of wind speed training data36afrom a wind speed sensor36, and/or turbine training data34afrom the turbine sensors34.

The machine learning model33ais therefore trained by being arranged to receive inputs that it can expect to receive during normal operation, and associate these inputs with certain wind direction (and optionally wind speed) measurements.

After the machine learning model has been trained on the basis of training data32a,34a,35a,36aas shown inFIG.4, further data is input into the trained machine learning model33in the operating phase ofFIG.3. That is, further deflection data32bis obtained from the GNSS sensor31; the further deflection data32bis input into the trained machine learning model33; and the trained machine learning model33is operated to output a wind direction estimate (and optionally also a wind speed estimate) on the basis of the further deflection data32b.

If the GNSS sensor31is not precisely positioned on the yaw axis of the RNA, then both the deflection training data32aand the further deflection data32bmay be obtained as described above: i.e. by receiving GNSS data from the GNSS sensor31, measuring a yaw angle of the RNA with the yaw sensor, determining a natural position of the GNSS sensor31on the basis of the yaw angle, and determining a difference between the natural position and the GNSS data.

Optionally the operation phase ofFIG.3further comprises obtaining further turbine data34b; inputting the further turbine data34binto the trained machine learning model33, and operating the trained machine learning model33to output the wind direction estimate (and optionally also the wind speed estimate) on the basis of the further turbine data34b.

The training of the machine learning model33amay be based on the type of machine learning model used. In one embodiment, the machine learning model is based on a regression model (e.g. linear regression, quadratic regression, or other type of regression) where, in the training phase, weights are adjusted to minimize the cost function defining the error between the predicted value and the actual value. For example, the weights that minimizes for the linearized prediction expressing the wind direction35aand/or wind speed36abased on of the tower deflection32aand the sensor signals34a. Such weights may be obtained using a training phase implementing a gradient descent.

In another embodiment, the machine learning model is based on a neural network which couple an input layer defined by the tower deflection signal32aand the sensor signals34a, via a number of hidden layers, to an output layer which provides the wind direction35aand/or wind speed36a. The weights used in the neural network may be obtained using a training phase implementing a gradient descent.

Referring now toFIG.5, an algorithm for determining whether to use the wind direction sensor35or the trained machine learning model33(abbreviated to TMLM in the figure) is shown. It can also be seen that the trained machine learning model33receives a number of different sensor inputs, as discussed previously.

A moving average (MA)40between the outputs of the wind direction sensor35and the trained machine learning model33is calculated. This moving average typically covers a time period in the range of 100 to 3600 seconds, for example 1000 seconds. Alternative to a MA, a cumulative sum (CUSUM) may be used—such techniques will be readily understood by those skilled in the art.

After obtaining the trained machine learning model33, further wind direction data35bis obtained from the wind direction sensor35, and a check41is made to see if the wind direction sensor35is working correctly. If it is found that the wind direction sensor35is working correctly, then the turbine operates in step42using the further wind direction data35bfrom the wind direction sensor alone, or the wind direction sensor and the trained machine learning model.

By way of example, the yaw control system of the wind turbine may operate in step42to change the yaw angle of the RNA and bring the RNA in line with the wind. So if the further wind direction sensor data35bindicates a positive misalignment with the wind direction, then the yaw control system may rotate the RNA in a negative direction by the appropriate amount to remove the positive misalignment; and if it indicates a negative misalignment with the wind direction, then the yaw control system may rotate the RNA in a positive direction by the appropriate amount to remove the negative misalignment.

If it is determined at step41that there is a fault in the wind direction sensor35, then a check43is made to see whether the aforementioned moving average 40 is below a threshold.

In other words, the check43determines whether the trained machine learning model33is operating reliably and outputting values that are sufficiently close to the values output by the wind direction sensor35—the use of a moving average 40 (or CUSUM) ensures that this check captures values from the wind direction sensor35that are on average correct, i.e. by incorporating values from before the sensor stops working correctly.

If the check43determines that the moving average (or CUSUM) is below the threshold, then the turbine operates using signals from the trained machine learning model33in step44. As discussed above, the trained machine learning model33will output wind direction values based on a number of other inputs it receives.

Thus, in response to the detection of the fault in step41(and optionally after checking the reliability of the trained machine learning model33in step43), in step44the wind turbine is controlled on the basis of the wind direction estimate from the trained machine learning model33.

The yaw control system of the wind turbine may operate in step44to change the yaw angle of the RNA on the basis of the wind direction estimate from the trained machine learning model33, and bring the RNA in line with the wind. So for example if the trained machine learning model33indicates a positive misalignment with the wind direction, then the yaw control system may rotate the RNA in a negative direction by the appropriate amount to remove the positive misalignment; and if it indicates a negative misalignment with the wind direction, then the yaw control system may rotate the RNA in a positive direction by the appropriate amount to remove the negative misalignment.

If on the other hand the check43determines that the moving average is above the threshold, then it is concluded that the function is not working, and so neither the wind direction sensor35nor the trained machine learning model33provide outputs that are used by the turbine. Instead, an alert may be generated indicating that an engineer needs to visit the turbine and address any issues with the wind direction sensor.

Over the lifetime of the turbine, sensors can suddenly fail, thus leading into a state in which energy production is lost. The wind turbine will remain in such state until the sensor is repaired or replaced, unless a redundancy solution (like the one shown inFIG.5) allows the switching from different signal sources, ensuring production continuity.

The trained machine learning model33may also improve the estimation of wind direction for low wind speed cases, where other methods do not perform as accurately.

FIG.6shows a graph of wind speed versus the amount of tower deflection in the downwind Y-direction. It can be seen that as wind speed increases, the Y-direction tower deflection also increases, with the data following a quadratic approximation.

FIG.7shows a graph of wind direction (i.e. the angle of the wind relative to the Y-direction as measured by the wind direction sensor35) versus the amount of sideways tower deflection in the crosswind X-direction. It can be seen that the X-direction tower deflection changes approximately linearly according to the wind direction. When the wind direction is +2° then the GNSS sensor31moves by about 0.013 m in the positive X-direction, and when the wind direction is −2° then the GNSS sensor31moves in the negative X-direction by about 0.02 m.

Note that the sideways deflection of the tower may not respond predictably or symmetrically. For instance, the amount of sideways deflection caused by a negative angular misalignment may not be the same as the amount of sideways deflection caused by a positive angular misalignment. Also, there may be some sideways deflection even when the wind direction is 0°. These asymmetrical behaviours may be due to the effect of the rotating blades. As shown inFIG.7, the sideways deflection of the tower follows a generally linear trend, but with a significant amount of deviation from the straight line. The trained machine learning model33may enable such asymmetrical and unpredictable tower deflection in the X-direction to be used to estimate the wind direction more accurately than other methods.

In the example above, a Global Navigation Satellite System (GNSS) sensor31is used to sense a position of the RNA, and provides one of the inputs into the machine learning model. In this case, sensing the position of the GNSS sensor31provides an indication of a position of the RNA, which in turn indicates a deflection of the tower. In this example the GNSS sensor31is mounted on the RNA, although in other examples it may be mounted at the top of the tower close to the RNA, or at any other suitable location.

In alternative examples, other types of deflection sensor may be used to directly or indirectly sense a position of the RNA or a deflection of the tower, and provide the inputs into the machine learning model. For example, accelerometers or inclinometers in the nacelle may be used, or strain gauges in the tower.

Where there are a plurality of wind turbines, the aforementioned method is typically applied to each wind turbine individually.