Patent Description:
There are many sensors in a turbine that are required for the turbine to operate in a proper manner. If certain sensors fail, the turbine will stop production and not start up again before the sensor has been fixed or replaced. This means that the turbine will lose production until a service team can come to the turbine.

One way to reduce the downtime is to add a redundant sensor that can be used if the main sensor fails. However, such a redundant sensor may be expensive and require space that is not available. Also, adding new sensors requires new input/output's (I/O's) which may require an upgrade of the control system.

Another way to deal with the problem of a failing sensor is to provide a computerized model of the physical system and use other sensors, together with the model, to estimate or predict the parameter that the main sensor is measuring. This method is normally fine for simple systems where an accurate model can be made. One of the drawbacks is that it normally requires turbine type-specific parameters that need to be updated each time a new turbine type is made. Another drawback is that sometimes the model is so complex that it is very difficult to make it fit all conditions without being inaccurate.

<CIT> discloses use of machine learning models in fault detection in a wind turbine system. Feng Xiaoran et al. discloses in "A model-based predictive control for FTC for wind turbine wind speed sensor fault" in <NUM> Conference on Control and Fault-Tolerant Systems, pages <NUM> to <NUM> use of a least-squares support vector machine in fault tolerant control of a wind turbine. <CIT> discloses training a so-called soft sensor during a training phase for predicting operating data based on input.

A first aspect of the invention provides a method of operating a wind turbine in accordance with claim <NUM>.

The present invention provides a method where the operational parameter is determined by two different sensors, where one of the sensors may be implemented as a model, e.g. a pre-trained machine learning model. The method takes into account possible inaccuracy or unpredictability in the output of a sensor for certain situations and includes a validation of one sensor value with a second sensor value before relying on the value to operate the wind turbine.

In connection with this invention, the term sensor is used in a broad sense, and includes a computing model implemented on a computing unit, interfaced with one or more inputs and an output, the output being the sensor value. In embodiments of the invention, various sensor values from dedicated sensors may be input into a sensor implementing a model, which based on the inputs, provides an output in the form of a sensor value (or signal). A sensor is in one sense a device which based on a physical principle converts a measured value into a sensor value representing a physical quantity, this may be referred to as a physical sensor. A sensor is in another sense implemented on a computing entity which based on calculation provides the sensor value.

A first sensor is implemented as a trained machine learning model, taking a set of operating parameters as input and outputting the first value as the sensor value. For example, an output of the machine learning model may be used as a sensor value such as a yaw direction, a power setting or a blade pitch angle.

In embodiments, reference sensors and/or sensors implementing computerized physical models may be used. The reference sensor may be a sensor of the wind turbine, a sensor of another wind turbine, or a sensor not associated with a particular wind turbine. Likewise, the computerized model may, as an alternative to a machine learning model, be a physical model: including a physical model of the wind turbine, a physical model of another wind turbine, or a physical model not associated with a particular wind turbine.

The operational parameter may be a value, which represents a physical quantity, such as wind speed, wind direction, blade pitch angle, blade load, temperature, rotor azimuth angle, rotor speed, tower acceleration, power, nacelle position, light intensity, or time of day.

Comparing the first value with the second value in accordance with the comparison criterion may be based on comparing a statistical value of the first value and a statistical value of the second value which are obtained by statistically analysing a time series of the first and second values. For instance, the statistical value may be an expected value, an average, a standard deviation, a variance, or an output of a cumulative sequential analysis.

Comparing the first value with the second value in accordance with the comparison criterion may be comparing the first value and the second value to generate an indicator (for instance a difference or a ratio); and comparing the indicator with a validation threshold.

The first sensor and/or second sensor may be one or more of: a temperature sensor, a wind speed sensor, a wind direction sensor, a blade load sensor, a blade pitch angle sensor, a rotor azimuth angle sensor, a rotor speed sensor, a tower top acceleration sensor, a power sensor, a nacelle position sensor, a light intensity sensor, and a time of day sensor.

The machine learning model may be pre-trained in a prior step by feeding a training data set into the machine learning model. The training data set may be from one or more of: a temperature sensor, a wind speed sensor, a wind direction sensor, a blade load sensor, a blade pitch angle sensor, a rotor azimuth angle sensor, a rotor speed sensor, a tower top acceleration sensor, a power sensor, a nacelle position sensor, a light intensity sensor, and a time of day sensor. The sensors used to produce the training data set may be sensors of the wind turbine, a sensor of another wind turbine, or a sensor not associated with a particular wind turbine.

In an embodiment, the first sensor is based on a trained machine learning model and the second sensor is a reference sensor. The machine learning model sensor may be validated by comparing the first output with the second output from the reference sensor. In this case the reference sensor may be a wind direction sensor, a wind direction sensor, a blade pitch angle sensor, a blade load sensor, or an ambient temperature sensor for example,
The reference sensor may provide one of the sensor inputs that are fed into the machine learning model sensor. In this way, the trained machine learning model sensor may be an enhanced version of the reference sensor.

If the first value fails validation then the reference sensor, the computerized model, a further sensor may be used to operate the wind turbine. In an embodiment, in case of a non-validation of the first value, the wind turbine may be shut down, de-rated or a control strategy of the wind turbine may be changed.

In an embodiment, the first sensor is based on a trained machine learning model and the second sensor is based on a computerized model, such as a physical model of the wind turbine.

In an embodiment, the first sensor is based on a first trained machine learning model, and the second sensor is based on a second trained machine learning model.

The second machine learning model is different to the first machine learning model. The second machine learning model is trained on a different training data set to the first machine learning model and/or trained using a different machine learning method to the first machine learning model.

The method may further comprise training the first machine learning model by feeding a first training data set into the first machine learning model; and training the second machine learning model by feeding a second training data set into the second machine learning model, wherein the second training data set is different to the first training data set.

Alternatively, the first and second machine learning models may be trained on the same training data set but using different machine learning methods. For instance, one may be trained by Linear Regression and the other may be trained by Support Vector Machine Regression.

Optionally the sensor inputs are a first set of sensor inputs from a first set of sensors, and the method comprises feeding a second set of sensor inputs from a second set of sensors into the second model to obtain the second output, wherein the second set of sensors is different to the first set of sensors. The first set of sensors may be a single sensor, or multiple sensors. The second set of sensors may be a single sensor, or multiple sensors.

A further aspect of the invention provides a wind turbine system comprising a wind turbine, and a control system configured to operate the wind turbine by the method of the first aspect of the invention. The control system may be part of an initial configuration of the wind turbine system, or it may be retrofit.

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

<FIG> shows a wind turbine <NUM>. The wind turbine <NUM> has a tower <NUM> and a nacelle <NUM> at the top of the tower <NUM>. A wind turbine rotor <NUM> is connected to the nacelle <NUM> and arranged to rotate relative to the nacelle <NUM>. The wind turbine rotor <NUM> comprises a wind turbine hub <NUM>, and multiple wind turbines blades <NUM> extending from the hub <NUM>. While a wind turbine rotor <NUM> having three blades <NUM> is shown, a different number of blades, such as two or four, may be used.

<FIG> schematically illustrates an embodiment of a control system <NUM> for controlling the wind turbine <NUM>. The rotor <NUM> is mechanically connected to an electrical generator <NUM> via a gearbox <NUM>. In direct drive systems, and other systems, the gearbox <NUM> may not be present. The electrical power generated by the generator <NUM> is injected into a power grid <NUM> via an electrical converter <NUM>. The electrical generator <NUM> and the converter <NUM> may 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 system <NUM> comprises a number of elements, including at least one main controller <NUM> with a processor and a memory, so that the processor is capable of executing computing tasks based on instructions stored in the memory.

In general, the control system <NUM> ensures that in operation the wind turbine generates a requested power output level. This is obtained by adjusting the pitch angle of the blades <NUM> and/or the power extraction of the converter <NUM>. To this end, the control system comprises a pitch system including a pitch controller <NUM> using a pitch reference <NUM>, and a power system including a power controller <NUM> using a power reference <NUM>. The rotor blades <NUM> can be pitched by a pitch mechanism. The rotor may comprise a common pitch system which adjusts all pitch angles on all rotor blades at the same time, as well as in addition thereto an individual pitch system which is capable of individual pitching of the rotor blades.

The wind turbine <NUM> and the control system <NUM> together constitute a wind turbine system. The control system <NUM> may be integrated with the wind turbine <NUM> - for instance housed in the nacelle or tower. Alternatively, the control system <NUM>, or elements of the control system <NUM>, may be placed remotely in a power plant controller (not shown) so that the wind turbine <NUM> may be operated based on externally provided instructions.

The control system <NUM> may receive sensor signals from various sensors, such as the sensors <NUM>-<NUM> shown in <FIG>. These sensors comprise a blade load sensor <NUM>, a rotor azimuth angle sensor <NUM>, a rotor speed sensor <NUM>, a wind speed sensor <NUM>, a hub temperature sensor <NUM>, a wind direction sensor <NUM>, a tower top acceleration sensor <NUM>, a power sensor <NUM>, a nacelle position sensor <NUM>, a light intensity sensor <NUM>, a nacelle temperature sensor <NUM> and an ambient temperature sensor <NUM>. This set of sensors is not exhaustive, and less sensors or further sensors may be used if required.

The control system <NUM> may be configured to operate the wind turbine by one or more of the various methods described below with reference to <FIG>. More specifically, the control system <NUM> may store a computer program product comprising software code adapted to operate the wind turbine <NUM> when executed on a data processing system of the control system <NUM>. The computer program product is adapted to perform one or more of the various methods described below with reference to <FIG>.

A first method shown in <FIG> may be used to safeguard the wind turbine against failure or inaccuracy of the wind direction sensor <NUM>, or any other sensor. The wind direction <NUM> is in this embodiment the first sensor arranged for obtaining a first value of the operational parameter wind direction.

The wind direction sensor <NUM> outputs a wind direction relative to the heading of the nacelle. For example, the sensor <NUM> may be a wind vane or ultrasonic wind sensor.

In a prior step, a machine learning model is trained by feeding a training data set into the machine learning model. The trained machine learning model is implemented as the second sensor arranged for obtaining a second value of the operational parameter wind direction. This produces a trained machine learning model sensor (TMM) <NUM> shown in <FIG>. The TMM <NUM> may be stored in the memory of the main controller <NUM>, or in any other suitable location.

Because it is complex to predict the wind direction without a dedicated sensor, the TMM <NUM> may be trained using a lot of different inputs. The training data set may comprise signals indicating various parameter values such as blade load, rotor azimuth angle, rotor speed, wind speed, tower top acceleration, power and/or nacelle position for example, along with wind direction. The training data set may be from some of the sensors <NUM>-<NUM> of the wind turbine <NUM>, or other sensors (for instance sensors of another wind turbine).

The trained TMM sensor <NUM> is then able to receive sensor inputs 11a (for instance inputs from some or all sensors <NUM>-<NUM> of the wind turbine <NUM>) during operation of the wind turbine, and produce an output predicting the wind direction based on these sensor inputs 11a.

The TMM sensor <NUM> may be implemented using a neural network or a support vector machine regression model, for example. The TMM sensor <NUM> may have been trained by supervised learning (developing a predictive model based on both input and output data) or unsupervised learning (grouping and interpreting data based only on input data).

As a further sensor, a computerized model in the form of a simple physical model (SPM) sensor <NUM> is also provided. The SPM sensor <NUM> may be stored in the same memory as the TMM sensor <NUM>, or in any other suitable place. Unlike the TMM <NUM>, the SPM <NUM> is not trained by machine learning but rather generated in some other way. The SPM <NUM> in this example can produce an output predictive of the wind direction based on input signals 12a from a wind speed sensor and a power sensor.

The SPM <NUM> may be stored in the memory of the main controller <NUM> and receive input signals from the wind speed sensor <NUM> and the power sensor <NUM> of the wind turbine <NUM>. Alternatively, the SPM <NUM> may be stored on another wind turbine and/or receive equivalent sensor signals from sensors of another wind turbine.

If the wind direction sensor <NUM> is OK, then the wind direction signal from the sensor <NUM> and/or the wind direction estimate from the TMM <NUM> is used by the control system <NUM> to operate the wind turbine at step <NUM>. Thus, for example if the wind direction changes, then the yaw direction of the nacelle and/or other operating parameters of the wind turbine may be changed at step <NUM> in response to the change of wind direction.

If the wind direction sensor <NUM> is not OK (for instance if it has failed or is producing inaccurate data) then the TMM sensor <NUM> is validated at step <NUM> and only used by the control system <NUM> as a substitute for the sensor <NUM> at step <NUM> if it passes the validation process.

More specifically, the control system <NUM> may feed sensor inputs 11a into the TMM sensor <NUM> to obtain a first value 11b. By way of example, these sensor inputs 11a may be received from the blade load sensor <NUM>, rotor azimuth angle sensor <NUM>, rotor speed sensor <NUM>, wind speed sensor <NUM>, tower top acceleration sensor <NUM>, power sensor <NUM>, and nacelle position sensor <NUM>. The TMM <NUM> is then validated at step <NUM> by comparing the first value 11b with a second value 12b from the SPM sensor <NUM>.

In response to obtaining valid value of the TMM sensor <NUM> (that is, if the TMM <NUM> is validated at step <NUM>) then the TMM sensor value <NUM> may be used to operate the wind turbine at step <NUM>. Thus for example if the wind direction predicted by the TMM sensor <NUM> changes, then the yaw direction of the nacelle and/or other operating parameters of the wind turbine may be changed at step <NUM> in response to the change.

The TMM sensor value <NUM> may be validated by determining a difference or ratio between the first value 11b and the second value 12b; and comparing the difference or ratio with a threshold. For example, the TMM sensor value <NUM> may be validated if abs (TMM-SPM) is less than a threshold, where (TMM-SPM) is a difference between the first and second values 11b and 12b of the sensors.

In this case, the TMM <NUM> is validated at step <NUM> by directly comparing single instantaneous parameter values (i.e. wind direction values) output by the TMM <NUM> and the SPM <NUM>. In other examples, statistical values (i.e. values obtained by statistically analysing a time series of parameter values) may be compared instead of (or in addition to) the parameter values, to validate the TMM <NUM>.

For example, the control system <NUM> may calculate a difference between WD1(Average) and WD2(Average), where WD1(Average) is a mean average of a time series of wind directions generated by the TMM <NUM>, and WD2(Average) is an average of a time series of wind directions generated by the SPM <NUM>.

In another example, the control system <NUM> may calculate a ratio between SD1 and SD2, where SD1 is a standard deviation of a time series of wind directions generated by the TMM <NUM>, and SD2 is a standard deviation of a time series of wind directions generated by the SPM <NUM>. If the ratio SD1/SD2 is outside a range (for instance <NUM> to <NUM>), then the TMM <NUM> is not validated and not used to operate the wind turbine at step <NUM>. If the ratio SD1/SD2 is within the range, the TMM <NUM> may be validated and used to operate the wind turbine at step <NUM>.

In other examples, the control system <NUM> may perform the validation by comparing other statistical measures, for instance a max value, median value, min value, variance or an output of a cumulative sequential analysis such as CUSUM.

If the TMM <NUM> is not validated, then the process may reach a "function not working" state <NUM>. When this "function not working" state <NUM> is reached in response to a non-validation of the TMM <NUM>, then a number of actions may be taken. For example, the control system <NUM> may use another substitute system to operate the wind turbine. The substitute system may be a redundant reference sensor, the SPM sensor <NUM>, a further sensor, or a further model (not shown) for example. Alternatively, the control system <NUM> may shut down, uprate or de-rate the wind turbine when it reaches state <NUM>.

In summary, upon failure of the wind direction sensor <NUM>, the process of <FIG> operates as follows. Sensor inputs 11a are fed into the TMM sensor <NUM> to obtain a first value 11b. The TMM sensor <NUM> is validated by comparing the first value 11b with a second value 12b from the SPM sensor <NUM>. In response to a validation of the TMM sensor value 11b, the TMM <NUM> sensor is used to operate the wind turbine. The TMM sensor <NUM> continues to be used, and is continuously validated by feeding further sensor inputs into the TMM sensor <NUM> to obtain subsequent first values, and validating the TMM sensor <NUM> by comparing the subsequent first values with subsequent second values from the SPM sensor <NUM>. This continues until the TMM <NUM> fails the validation process. In response to a non-validation of the TMM <NUM>, the process reaches the "function not working" state <NUM>.

The method of <FIG> uses the TMM sensor <NUM> as a backup solution if the sensor <NUM> is failing. However, a problem with some trained machine learning models is that they are not always as good if they encounter situations different from the situations they have been trained on. This means that sometimes the TMM <NUM> may give outputs that are wrong. The SPM sensor <NUM> may not be very accurate, but it will follow some simple physical rules that will prevent it from being totally wrong. By comparing the signals from SPM sensor <NUM> and TMM sensor <NUM> it is possible to check if the TMM <NUM> is suddenly wrong and thereby inform the control system <NUM> that the output from the TMM <NUM> is no longer valid.

The method of <FIG> safeguards the wind turbine against failure or inaccuracy of the wind direction sensor <NUM>, but an equivalent method may be used to safeguard the wind turbine against failure or inaccuracy of any of its other sensors <NUM>-<NUM>.

An example of an equivalent method is one which safeguards the wind turbine against failure or inaccuracy of a gearbox oil temperature sensor- i.e. a sensor which measures the temperature of the oil in the gearbox <NUM>. A bearing sensor measure the temperature of the bearing. The oil is heating up because the bearing gets hot and the oil is cooling the bearing down. So it is known that in almost all cases the bearing temperature will be higher than the oil temperature, for instance <NUM> higher, <NUM>-<NUM> higher and in some cases up to <NUM> higher. So, a simple physical model in this instance could simply be that the oil temperature is assumed to be <NUM>° lower than the bearing temperature.

This output of the simple physical model may be compared with the output of a machine learning model which takes as its inputs the inlet water flow, water cooling temperature, power and ambient temperature, for example. This enables the machine learning model to be validated if its output is close to the output of the simple physical model (for instance within <NUM>). The output of the validated machine learning model may then be used to operate the wind turbine: for instance, if the gear oil temperature exceeds <NUM>, then the turbine may be stopped.

A second method shown in <FIG> uses an ambient temperature prediction from a machine learning model <NUM> to operate the wind turbine.

A machine learning model is trained in a prior step by feeding a training data set into the machine learning model. This produces the trained machine learning model (TMM1) sensor <NUM> shown in <FIG>. The TMM1 sensor <NUM> may be stored in the memory of the main controller <NUM>, or in any other suitable location. The training data set may comprise signals indicating ambient temperature, nacelle temperature, hub temperature, power, wind speed, light intensity and time of day. The training data set may be from some of the sensors <NUM>-<NUM> of the wind turbine <NUM>, or other sensors (for instance sensors of another wind turbine).

The trained TMM1 sensor <NUM> is then during operation able to receive sensor inputs 111a (for instance inputs from some or all of the sensors <NUM>-<NUM> of the wind turbine <NUM>) and produce an output as a first value predicting the ambient temperature based on these sensor inputs. Note that the sensor inputs 111a into the TMM <NUM> may include an input from the ambient temperature sensor <NUM>, which in this case also acts as a reference sensor so will be referred to below as reference sensor <NUM>.

Like the TMM <NUM>, the TMM1 sensor <NUM> may be implementing a neural network or a support vector machine regression model for example. The TMM1 sensor <NUM> may be trained by supervised learning (developing a predictive model based on both input and output data) or unsupervised learning (grouping and interpreting data based only on input data).

The TMM1 <NUM> is validated at step <NUM> and only used by the control system <NUM> at step <NUM> if it passes the validation process.

More specifically, the control system <NUM> may feed sensor inputs 111a into the TMM1 sensor <NUM> to obtain a first value 111b. By way of example, these sensor inputs 111a may be received from the reference sensor <NUM>, nacelle temperature sensor <NUM>, hub temperature sensor <NUM>, power sensor <NUM>, wind speed sensor <NUM>, light intensity sensor <NUM>, and a time of day sensor. The TMM1 <NUM> is then validated by comparing the first value 111b with a second value 41b from reference sensor <NUM>.

In response to a validation of the TMM1 sensor value 111a (that is, if the TMM <NUM> is validated at step <NUM>) then the TMM1 <NUM> may be used to operate the wind turbine at step <NUM>. Thus for example if the ambient temperature predicted by the output 111b of the TMM1 <NUM> changes, then operating parameters of the wind turbine may be changed at step <NUM>. For instance, if the ambient temperature becomes very high, then the wind turbine may be de-rated.

The TMM1 <NUM> may be validated by determining a difference or ratio between the first value 111b from the TMM1 sensor <NUM> and the second value 41b from the second sensor in the form of reference sensor <NUM>; and comparing the difference or ratio with a threshold. For example the TMM1 <NUM> may be validated if abs (TMM1-Sensor) is less than a threshold - where (TMM1-Sensor) is a difference between the values 111b and 41b.

In this case, the TMM1 <NUM> is validated at step <NUM> by directly comparing single instantaneous parameter values (i.e. ambient temperature values) output by the TMM1 <NUM> and the reference sensor <NUM>. In other examples, statistical values (i.e. values obtained by statistically analysing a time series of parameter values) may be compared instead, or in addition to the parameter values, to validate the TMM1 <NUM>.

For example, the control system <NUM> may calculate a difference between T1(Average) and T2(Average), where T1(Average) is a mean average of a time series of temperatures generated by the TMM1 <NUM>, and T2(Average) is an average of a time series of temperatures generated by the reference sensor <NUM>.

In another example, the control system <NUM> may calculate a ratio between SD1 and SD2, where SD1 is a standard deviation of a time series of wind directions generated by the TMM1111, and SD2 is a standard deviation of a time series of wind directions generated by the reference sensor <NUM>.

If the ratio SD1/SD2 is outside a range (for instance <NUM> to <NUM>), then the TMM1 <NUM> is not validated and not used to operate the wind turbine at step <NUM>. If the ratio SD1/SD2 is within the range, the TMM1 <NUM> may be validated and used to operate the wind turbine at step <NUM>.

If the TMM1 <NUM> is not validated at step <NUM> (i.e. the answer at step <NUM> is "No") then the process checks whether the reference sensor <NUM> is OK. If the reference sensor <NUM> is OK, then the ambient temperature signal from the reference sensor <NUM> is used by the control system <NUM> to operate the wind turbine at step <NUM>.

If the reference sensor <NUM> is not OK, then the process reaches a "function not working" state <NUM>. When this "function not working" state <NUM> is reached, then a number of actions may be taken. For example, the control system <NUM> may use another substitute system to operate the wind turbine. The substitute system may be a further ambient temperature sensor, or a further model (not shown) for example. Alternatively, the control system <NUM> may shut down, uprate or de-rate the wind turbine.

In summary, the process of <FIG> operates as follows. Sensor inputs 111a are fed into the TMM1 sensor <NUM> to obtain a first value 111b. The TMM1 <NUM> is validated by comparing the first value 111b with a second value 41b from a reference sensor <NUM>. In response to a validation of the TMM1 sensor <NUM>, the TMM1 <NUM> is used to operate the wind turbine. The TMM1 <NUM> continues to be used and continuously validated by feeding subsequent sensor inputs into the TMM1 <NUM> to obtain a subsequent first values, and validating the TMM1 <NUM> by comparing the subsequent first values with subsequent second values from the reference sensor <NUM>. This continues until the TMM1 <NUM> fails the validation process. In response to a non-validation of the TMM1 <NUM>, the process either uses the reference sensor <NUM>, or reaches the "function not working" state <NUM>.

The method of <FIG> uses an ambient temperature prediction from the TMM <NUM> to operate the wind turbine, and uses the ambient temperature sensor <NUM> as a reference sensor. An equivalent method may be used for other parameters, using one of the other sensors <NUM>-<NUM> as a reference sensor.

Instead of using a TMM sensor <NUM> as a backup for a sensor <NUM> (as in <FIG>), the method of <FIG> can be used if the TMM1 sensor <NUM> is much more precise than the sensor <NUM>. The sensor <NUM> is only used as backup and to validate the TMM <NUM>. The sensor <NUM> can also be used by the TMM <NUM> to give a more accurate output.

A third method shown in <FIG> uses a sensor implementing a trained machine learning model to provide, for example, a wind direction estimate for operation of the wind turbine <NUM>.

The method of <FIG> uses the wind direction sensor <NUM> and a first sensor implementing a first trained machine learning model (TMM1) <NUM>. The TMM1 <NUM> may be identical to the TMM <NUM> of <FIG> except it is trained on a slightly different training data set: in this example the TMM1 <NUM> may be trained on a set of signals indicating blade load, rotor azimuth angle, rotor speed, wind speed and tower top acceleration.

A second sensor implementing a second trained machine learning model (TMM2) <NUM> is also provided. In contrast to the SPM <NUM> of <FIG>, the TMM2 sensor <NUM> of <FIG> implements a trained machine learning model similar to the TMM <NUM>. The TMM2 <NUM> generates an output 121b based on sensor inputs 121a.

Like the TMM sensor <NUM>, the second TMM2 sensor <NUM> is trained by feeding a training data set into the machine learning model in a prior step. However, the training data set for the second TMM2 <NUM> is slightly different to the training data set for the first TMM1 <NUM>.

By way of example, the TMM1 <NUM> may have been trained by feeding a first training data set into the TMM1 <NUM> (for instance blade load, rotor azimuth angle, rotor speed, wind speed and tower top acceleration); and the TMM2 <NUM> may have been trained by feeding a second training data set into the TMM2 <NUM> (for instance blade load, rotor azimuth angle, rotor speed, wind speed, power and nacelle position). In this example the second training data set is slightly different to the first training data set. The training data sets may be from some of the sensors <NUM>-<NUM> of the wind turbine <NUM>, or other sensors (for instance sensors of another wind turbine).

Each sensor implementing a trained machine learning model is then able to receive a respective set of sensor inputs 211a, 121a (for instance inputs from some of the sensors <NUM>-<NUM> of the wind turbine <NUM>) during operation of the wind turbine <NUM> and produce an output 211b, 121b in the form of sensor values predicting the wind direction based on these sensor inputs.

Like the TMM <NUM> of <FIG>, each sensor <NUM>, <NUM> may comprise a neural network or a support vector machine regression model, for example. The sensors <NUM>, <NUM> may have been trained by supervised learning (developing a predictive model based on both input and output data) or unsupervised learning (grouping and interpreting data based only on input data).

To reduce the risk that the sensors fail in the same way, the sensors <NUM> and <NUM> may be trained using different machine learning methods such as, but not limited to: Linear and Non-Linear Regressions; Neural Networks (and derived); Support Vector Machine regression; Decision Trees; Gaussian Process Regression; and Tree Ensembles.

Alternatively, the sensor <NUM> and <NUM> may have been trained using the same machine learning methods, but with different sets of training data. For example, the training data sets may be from different sets of sensors, different turbine types or different geographical locations. Any combination of the above-mentioned methods can also be used.

In the illustrated embodiment, the sensor TMM1 is the first sensor obtaining the first value 211b and the sensor TMM2 is the second sensor obtaining the second value 121b.

The values of the sensors <NUM>, <NUM> are validated at step <NUM> and only used by the control system <NUM> to operate the wind turbine if they pass the validation process.

More specifically, the control system <NUM> may feed sensor inputs 211a into the sensor <NUM> to obtain a first value 211b. By way of example, these sensor inputs 211a may be received from the blade load sensor <NUM>, rotor azimuth angle sensor <NUM>, rotor speed sensor <NUM>, wind speed sensor <NUM> and tower top acceleration sensor <NUM>.

Similarly, the control system <NUM> may feed sensor inputs 121a into the sensor <NUM> to obtain a second value 121b. By way of example, these sensor inputs 121a may be received from the blade load sensor <NUM>, rotor azimuth angle sensor <NUM>, rotor speed sensor <NUM>, wind speed sensor <NUM>, power sensor <NUM> and nacelle position sensor <NUM>.

As with the training data sets, the sensor inputs 121a fed into the sensor <NUM> may be from a different set of sensors than the sensor inputs 211a fed into the sensor <NUM>. For example, the sensor inputs 211a may be from a first set of sensors <NUM>-<NUM> and <NUM>; and the sensor inputs 121a may be from a second set of sensors <NUM>-<NUM>, <NUM> and <NUM>.

The sensor <NUM>, <NUM> are then validated at step <NUM> by comparing the first value 211b with the second value 121b. If both machine learning model implemented sensors are behaving as expected, then the outputs should be similar or identical.

In response to a validation of the sensor <NUM>, <NUM> (that is, if the sensor values are validated at step <NUM>) then one or both of the TMMs <NUM>, <NUM> may be used to operate the wind turbine at step <NUM>. For example, an average of the two models may be used, or only one of the models. Thus, if the wind direction predicted by the model(s) changes, then the yaw direction of the nacelle and/or other operating parameters of the wind turbine may be changed in response.

The sensor values may be validated at step <NUM> by determining a difference or ratio between the first value 211b and the second value 121b; and comparing the difference or ratio with a threshold. For example, the sensors may be validated if abs (TMM1-TMM2) is less than a threshold - where (TMM1-TMM2) is a difference between the values 211b and 121b.

In this case, the sensor are validated by directly comparing single instantaneous parameter values (i.e. wind direction values) output by the sensors. In other examples, statistical values (i.e. values obtained by statistically analysing a time series of parameter values) may be compared instead, or in addition to the parameter values, to validate the TMMs.

For example, the control system <NUM> may calculate a difference between WD1(Average) and WD2(Average), where WD1(Average) is a mean average of a time series of wind directions generated by the first TMM1 sensor <NUM>, and WD2(Average) is a mean average of a time series of wind directions generated by the second TMM2 sensor <NUM>.

In another example, the control system <NUM> may calculate a ratio between SD1 and SD2, where SD1 is a standard deviation of a time series of wind directions generated by the first TMM1 sensor <NUM>, and SD2 is a standard deviation of a time series of wind directions generated by the second TMM2 sensor <NUM>. If the ratio SD1/SD2 is outside a range (for instance <NUM> to <NUM>) then the sensor values are not validated and not used to operate the wind turbine at step <NUM>. If the ratio SD1/SD2 is within the range, the sensor values may be validated and one or both may be used to operate the wind turbine at step <NUM>.

If the sensors are not validated at step <NUM>, then the wind direction sensor <NUM> may be checked. If the wind direction sensor <NUM> is OK, then the wind direction signal from the sensor <NUM> may be used by the control system <NUM> to operate the wind turbine at step <NUM>.

If the wind direction sensor <NUM> is not OK (for instance if it has failed or is producing inaccurate data) then the process reaches the "function not working" state <NUM>.

The process of <FIG> uses the wind direction sensor <NUM> as a back-up. Optionally the wind direction sensor <NUM> may be adjusted or corrected at step <NUM> on the basis of the wind direction predictions from the validated sensor values <NUM>, <NUM> - for instance by modifying a gain or offset applied to the output of the sensor <NUM>. This improves the accuracy and reliability of the sensor <NUM>. In scenarios where the machine learning model implemented sensors fail, the function can switch to the newly calibrated sensor <NUM> with higher accuracy until the machine learning model implemented sensors produce valid signals.

In summary, the process of <FIG> operates as follows. A first sensor is implementing a first machine learning model (TMM1 <NUM>) that have been trained by feeding a first training data set into the first machine learning model. A second sensor is implementing a second machine learning model (TMM2 <NUM>) that have been trained by feeding a second training data set into the second machine learning model. The second training data set is different to the first training data set. First and second sets 211a, 121a of sensor inputs are fed into the sensors to obtain outputs 211b, 121b. The sets 211a, 121a of sensor inputs are different from each other. The sensor values from sensors <NUM> and <NUM> are validated by comparing their outputs 211b, 121b. In response to a validation of the sensors <NUM>, <NUM>, one or both of the sensor values is used to operate the wind turbine. The sensor <NUM>, <NUM> continue to be used and continuously validated by feeding subsequent sensor inputs into the sensors <NUM>, <NUM> to obtain subsequent first and second values, and validating the sensors <NUM>, <NUM> by comparing these subsequent values. This continues until the sensor values fail the validation process. In response to a non-validation of the sensors <NUM>, <NUM>, the sensor <NUM> is used. If the sensor <NUM> is not OK then the process reaches the "function not working" state <NUM>.

The method of <FIG> is used to control the wind turbine on the basis of wind direction estimates from a pair of machine learning model implemented sensors, using the wind direction sensor <NUM> as a back-up. An equivalent method may be used to control the wind turbine on the basis of prediction of other parameter values (such as ambient temperature, wind speed etc.) from other machine learning models, using another one of the other sensors <NUM>-<NUM> as a back-up.

A fourth method shown in <FIG> uses model-based sensors <NUM>-<NUM> to provide a parameter value prediction for operation of the wind turbine <NUM>.

The method of <FIG> is similar to <FIG> in that it uses first and second sensors implementing trained machine learning models (sensor TMM1 <NUM> and sensor TMM2 <NUM>) which have been trained using slightly different training data sets and/or different machine learning methods. The operational parameter for <FIG> is wind direction whereas <FIG> is more generic, not referring to a specific operational parameter. The operational parameter for <FIG> may be wind direction, ambient temperature or any other parameter. The embodiment of <FIG> enables the wind turbine to be operated without any sensor directly measuring the parameter.

A third sensor implementing a third model <NUM> is also provided. The third model may be a third trained machine learning model, trained using a different training data set and/or a different machine learning method to the TMM <NUM> and the TMM <NUM>. Alternatively, the third model <NUM> may be a simple physical model similar to the SPM <NUM> shown in <FIG>.

Different sets 300a, 301a, 302a of sensor inputs are input into the three models <NUM>-<NUM>, which generate three respective outputs 300b, 301b, 302b. These outputs are compared in validation step <NUM>. If at least two out of three of the outputs agree, then the two or three models associated with these outputs are validated. The output from one or more of the validated models is then used as the operational parameter of the wind turbine at step <NUM>. Where the outputs of multiple sensors are used at step <NUM>, then an average may be used for example.

If none of the outputs 300b, 301b, 302b agree with each other, then the process reaches a "function not working" state <NUM> similar to the states <NUM>, <NUM> described above.

In the description above, a control system <NUM> is configured to operate the wind turbine by one or more of the various methods described with reference to <FIG>. This control system <NUM> may be part of an initial configuration of the wind turbine. In alternative embodiments of the invention, the methods described with reference to <FIG> may be performed by a retrofit control system which is not part of the initial configuration of the wind turbine, and (like the control system <NUM>) may be remote from the wind turbine. In this case the retrofit control system stores the computer program product comprising software code adapted to operate the wind turbine <NUM> when executed on a data processing system of the retrofit control system. The computer program product stored by the retrofit control system is adapted to perform one or more of the various methods described below with reference to <FIG>.

Claim 1:
A method of operating a wind turbine (<NUM>) in accordance with an operational parameter, the method comprising:
obtaining a first value of the operational parameter by a first sensor, the first sensor being implemented as a trained machine learning model (<NUM>, <NUM>, <NUM>) taking a set of operating parameters as input and outputting the first value (11b, 111b, 211b);
obtaining a second value (41b, 121b) of the operational parameter by a second sensor different to the first sensor;
comparing (<NUM>, <NUM>, <NUM>) the first value with the second value in accordance with a comparison criterion, if the comparing fulfils the comparison criterion, the first value is determined to be valid;
if the first value of the operational parameter is a valid first value, operate (<NUM>, <NUM>, <NUM>) the wind turbine using the first value as the operational parameter;
in response (<NUM>, <NUM>) to a non-validation of the first value: shutting down the wind turbine, or changing a control strategy of the wind turbine;
wherein the second sensor is selected from the group of:
a trained machine learning model (<NUM>), taking a set of operating parameters as input and outputting the second value, wherein the first trained machine learning model is trained by a first training data set; and the second trained machine learning model is trained by feeding a second training data set, wherein the second training data set is different to the first training data set and/or wherein the first machine learning model is trained by a first machine learning method, and the second machine learning model is trained by a second machine learning method, wherein the second machine learning method is different to the first machine learning method;
a reference sensor (<NUM>, <NUM>) arranged for measuring the second value; and
a computerized physical model (<NUM>) taking a set of operating parameters as input and outputting the second value.