Patent Publication Number: US-2021190041-A1

Title: Operating a wind turbine with sensors implemented by a trained machine learning model

Description:
FIELD OF THE INVENTION 
     The present invention relates to a method of operating a wind turbine with an operational parameter where values of the operational parameter are obtained by different sensors and compared to determine the validity of the value. A sensor for determining a value may be implemented by a trained machine learning model. 
     BACKGROUND OF THE INVENTION 
     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&#39;s (I/O&#39;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. 
     SUMMARY OF THE INVENTION 
     A first aspect of the invention provides a method of operating a wind turbine in accordance with an operational parameter, the method comprising:
         obtaining a first value of the operational parameter by a first sensor;   obtaining a second value of the operational parameter by a second sensor different to the first sensor;   comparing 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 the wind turbine using the first value as the operational parameter;   wherein the first sensor and the second sensor are selected from the group of: a trained machine learning model, taking a set of operating parameters as input and outputting the first value or the second value; a reference sensor arranged for measuring the first value or the second value; and a computerized physical model taking a set of operating parameters as input and outputting the first value or the second value.       

     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. 
     In one important embodiment, at least one of the sensors are implemented as a trained machine learning model, taking a set of operating parameters as input and outputting the first value or the second 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 may be different to the first machine learning model. For instance, the second machine learning model may be 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. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the invention will now be described with reference to the accompanying drawings, in which: 
         FIG. 1  shows a wind turbine; 
         FIG. 2  shows a control system for controlling the wind turbine of  FIG. 1 ; 
         FIG. 3  shows a first method of operating the wind turbine; 
         FIG. 4  shows a second method of operating the wind turbine; 
         FIG. 5  shows a third method of operating the wind turbine; and 
         FIG. 6  shows a fourth method of operating the wind turbine. 
     
    
    
     DESCRIPTION OF EMBODIMENT(S) 
       FIG. 1  shows a wind turbine  1 . The wind turbine  1  has a tower  2  and a nacelle  3  at the top of the tower  2 . A wind turbine rotor  4  is connected to the nacelle  3  and arranged to rotate relative to the nacelle  3 . The wind turbine rotor  4  comprises a wind turbine hub  5 , and multiple wind turbines blades  6  extending from the hub  5 . While a wind turbine rotor  4  having three blades  6  is shown, a different number of blades, such as two or four, may be used. 
       FIG. 2  schematically illustrates an embodiment of a control system  20  for controlling the wind turbine  1 . The rotor  4  is mechanically connected to an electrical generator  22  via a gearbox  23 . In direct drive systems, and other systems, the gearbox  23  may not be present. The electrical power generated by the generator  22  is injected into a power grid  24  via an electrical converter  25 . The electrical generator  22  and the converter  25  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  20  comprises a number of elements, including at least one main controller  200  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  20  ensures that in operation the wind turbine generates a requested power output level. This is obtained by adjusting the pitch angle of the blades  6  and/or the power extraction of the converter  25 . To this end, the control system comprises a pitch system including a pitch controller  27  using a pitch reference  28 , and a power system including a power controller  29  using a power reference  26 . The rotor blades  6  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  1  and the control system  20  together constitute a wind turbine system. The control system  20  may be integrated with the wind turbine  1 —for instance housed in the nacelle or tower. Alternatively, the control system  20 , or elements of the control system  20 , may be placed remotely in a power plant controller (not shown) so that the wind turbine  1  may be operated based on externally provided instructions. 
     The control system  20  may receive sensor signals from various sensors, such as the sensors  30 - 41  shown in  FIG. 2 . These sensors comprise a blade load sensor  30 , a rotor azimuth angle sensor  31 , a rotor speed sensor  32 , a wind speed sensor  33 , a hub temperature sensor  34 , a wind direction sensor  35 , a tower top acceleration sensor  36 , a power sensor  37 , a nacelle position sensor  38 , a light intensity sensor  39 , a nacelle temperature sensor  40  and an ambient temperature sensor  41 . This set of sensors is not exhaustive, and less sensors or further sensors may be used if required. 
     The control system  20  may be configured to operate the wind turbine by one or more of the various methods described below with reference to  FIGS. 3 to 6 . More specifically, the control system  20  may store a computer program product comprising software code adapted to operate the wind turbine  1  when executed on a data processing system of the control system  20 . The computer program product is adapted to perform one or more of the various methods described below with reference to  FIGS. 3 to 6 . 
     A first method shown in  FIG. 3  may be used to safeguard the wind turbine against failure or inaccuracy of the wind direction sensor  35 , or any other sensor. The wind direction  35  is in this embodiment the first sensor arranged for obtaining a first value of the operational parameter wind direction. 
     The wind direction sensor  35  outputs a wind direction relative to the heading of the nacelle. For example, the sensor  35  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)  11  shown in  FIG. 3 . The TMM  11  may be stored in the memory of the main controller  200 , or in any other suitable location. 
     Because it is complex to predict the wind direction without a dedicated sensor, the TMM  11  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  30 - 41  of the wind turbine  1 , or other sensors (for instance sensors of another wind turbine). 
     The trained TMM sensor  11  is then able to receive sensor inputs  11   a  (for instance inputs from some or all sensors  30 - 41  of the wind turbine  1 ) during operation of the wind turbine, and produce an output predicting the wind direction based on these sensor inputs  11   a.    
     The TMM sensor  11  may be implemented using a neural network or a support vector machine regression model, for example. The TMM sensor  11  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  12  is also provided. The SPM sensor  12  may be stored in the same memory as the TMM sensor  11 , or in any other suitable place. Unlike the TMM  11 , the SPM  12  is not trained by machine learning but rather generated in some other way. The SPM  12  in this example can produce an output predictive of the wind direction based on input signals  12   a  from a wind speed sensor and a power sensor. 
     The SPM  12  may be stored in the memory of the main controller  200  and receive input signals from the wind speed sensor  33  and the power sensor  37  of the wind turbine  1 . Alternatively, the SPM  12  may be stored on another wind turbine and/or receive equivalent sensor signals from sensors of another wind turbine. 
     If the wind direction sensor  35  is OK, then the wind direction signal from the sensor  35  and/or the wind direction estimate from the TMM  11  is used by the control system  20  to operate the wind turbine at step  50 . 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  50  in response to the change of wind direction. 
     If the wind direction sensor  35  is not OK (for instance if it has failed or is producing inaccurate data) then the TMM sensor  11  is validated at step  15  and only used by the control system  10  as a substitute for the sensor  35  at step  51  if it passes the validation process. 
     More specifically, the control system  20  may feed sensor inputs  11   a  into the TMM sensor  11  to obtain a first value  11   b.  By way of example, these sensor inputs  11   a  may be received from the blade load sensor  30 , rotor azimuth angle sensor  31 , rotor speed sensor  32 , wind speed sensor  33 , tower top acceleration sensor  36 , power sensor  37 , and nacelle position sensor  38 . The TMM  11  is then validated at step  15  by comparing the first value  11  b with a second value  12   b  from the SPM sensor  12 . 
     In response to obtaining valid value of the TMM sensor  11  (that is, if the TMM  11  is validated at step  15 ) then the TMM sensor value  11  may be used to operate the wind turbine at step  51 . Thus for example if the wind direction predicted by the TMM sensor  11  changes, then the yaw direction of the nacelle and/or other operating parameters of the wind turbine may be changed at step  51  in response to the change. 
     The TMM sensor value  11  may be validated by determining a difference or ratio between the first value  11   b  and the second value  12   b;  and comparing the difference or ratio with a threshold. For example, the TMM sensor value  11  may be validated if abs (TMM-SPM) is less than a threshold, where (TMM-SPM) is a difference between the first and second values  11   b  and  12   b  of the sensors. 
     In this case, the TMM  11  is validated at step  15  by directly comparing single instantaneous parameter values (i.e. wind direction values) output by the TMM  11  and the SPM  12 . 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  11 . 
     For example, the control system  20  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  11 , and WD2(Average) is an average of a time series of wind directions generated by the SPM  12 . 
     In another example, the control system  20  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  11 , and SD2 is a standard deviation of a time series of wind directions generated by the SPM  12 . If the ratio SD1/SD2 is outside a range (for instance 0.8 to 1.2), then the TMM  11  is not validated and not used to operate the wind turbine at step  51 . If the ratio SD1/SD2 is within the range, the TMM  11  may be validated and used to operate the wind turbine at step  51 . 
     In other examples, the control system  20  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  11  is not validated, then the process may reach a “function not working” state  52 . When this “function not working” state  52  is reached in response to a non-validation of the TMM  11 , then a number of actions may be taken. For example, the control system  20  may use another substitute system to operate the wind turbine. The substitute system may be a redundant reference sensor, the SPM sensor  12 , a further sensor, or a further model (not shown) for example. Alternatively, the control system  20  may shut down, uprate or de-rate the wind turbine when it reaches state  52 . 
     In summary, upon failure of the wind direction sensor  35 , the process of  FIG. 3  operates as follows. Sensor inputs  11   a  are fed into the TMM sensor  11  to obtain a first value  11   b.  The TMM sensor  11  is validated by comparing the first value  11   b  with a second value  12   b  from the SPM sensor  12 . In response to a validation of the TMM sensor value  11   b,  the TMM  11  sensor is used to operate the wind turbine. The TMM sensor  11  continues to be used, and is continuously validated by feeding further sensor inputs into the TMM sensor  11  to obtain subsequent first values, and validating the TMM sensor  11  by comparing the subsequent first values with subsequent second values from the SPM sensor  12 . This continues until the TMM  11  fails the validation process. In response to a non-validation of the TMM  11 , the process reaches the “function not working” state  52 . 
     The method of  FIG. 3  uses the TMM sensor  11  as a backup solution if the sensor  35  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  11  may give outputs that are wrong. The SPM sensor  12  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  12  and TMM sensor  11  it is possible to check if the TMM  11  is suddenly wrong and thereby inform the control system  20  that the output from the TMM  11  is no longer valid. 
     The method of  FIG. 3  safeguards the wind turbine against failure or inaccuracy of the wind direction sensor  35 , but an equivalent method may be used to safeguard the wind turbine against failure or inaccuracy of any of its other sensors  30 - 41 . 
     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  23 . 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 1° C. higher, 10-12° C. higher and in some cases up to 20° C. higher. So, a simple physical model in this instance could simply be that the oil temperature is assumed to be 10° 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 5° C.). 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 80° C., then the turbine may be stopped. 
     A second method shown in  FIG. 4  uses an ambient temperature prediction from a machine learning model  111  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  111  shown in  FIG. 4 . The TMM1 sensor  111  may be stored in the memory of the main controller  200 , 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  30 - 41  of the wind turbine  1 , or other sensors (for instance sensors of another wind turbine). 
     The trained TMM1 sensor  111  is then during operation able to receive sensor inputs  111   a  (for instance inputs from some or all of the sensors  30 - 41  of the wind turbine  1 ) and produce an output as a first value predicting the ambient temperature based on these sensor inputs. Note that the sensor inputs  111   a  into the TMM  111  may include an input from the ambient temperature sensor  41 , which in this case also acts as a reference sensor so will be referred to below as reference sensor  41 . 
     Like the TMM  11 , the TMM1 sensor  111  may be implementing a neural network or a support vector machine regression model for example. The TMM1 sensor  111  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  111  is validated at step  115  and only used by the control system  20  at step  151  if it passes the validation process. 
     More specifically, the control system  20  may feed sensor inputs  111   a  into the TMM1 sensor  111  to obtain a first value  111   b.  By way of example, these sensor inputs  111   a  may be received from the reference sensor  41 , nacelle temperature sensor  40 , hub temperature sensor  34 , power sensor  37 , wind speed sensor  33 , light intensity sensor  39 , and a time of day sensor. The TMM1  111  is then validated by comparing the first value  111   b  with a second value  41   b  from reference sensor  41 . 
     In response to a validation of the TMM1 sensor value  111   a  (that is, if the TMM  111  is validated at step  115 ) then the TMM1  111  may be used to operate the wind turbine at step  151 . Thus for example if the ambient temperature predicted by the output  111   b  of the TMM1  111  changes, then operating parameters of the wind turbine may be changed at step  151 . For instance, if the ambient temperature becomes very high, then the wind turbine may be de-rated. 
     The TMM1  111  may be validated by determining a difference or ratio between the first value  111   b  from the TMM1 sensor  111  and the second value  41   b  from the second sensor in the form of reference sensor  41 ; and comparing the difference or ratio with a threshold. For example the TMM1  111  may be validated if abs (TMM1-Sensor) is less than a threshold—where (TMM1-Sensor) is a difference between the values  111   b  and  41   b.    
     In this case, the TMM1  111  is validated at step  115  by directly comparing single instantaneous parameter values (i.e. ambient temperature values) output by the TMM1  111  and the reference sensor  41 . 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  111 . 
     For example, the control system  20  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  111 , and T2(Average) is an average of a time series of temperatures generated by the reference sensor  41 . 
     In another example, the control system  20  may calculate a ratio between SD1 and SD2, where SD1 is a standard deviation of a time series of wind directions generated by the TMM1  111 , and SD2 is a standard deviation of a time series of wind directions generated by the reference sensor  41 . 
     If the ratio SD1/SD2 is outside a range (for instance 0.8 to 1.2), then the TMM1  111  is not validated and not used to operate the wind turbine at step  151 . If the ratio SD1/SD2 is within the range, the TMM1  111  may be validated and used to operate the wind turbine at step  151 . 
     In other examples, the control system  20  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 TMM1  111  is not validated at step  115  (i.e. the answer at step  115  is “No”) then the process checks whether the reference sensor  41  is OK. If the reference sensor  41  is OK, then the ambient temperature signal from the reference sensor  41  is used by the control system  20  to operate the wind turbine at step  150 . 
     If the reference sensor  41  is not OK, then the process reaches a “function not working” state  152 . When this “function not working” state  152  is reached, then a number of actions may be taken. For example, the control system  20  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  20  may shut down, uprate or de-rate the wind turbine. 
     In summary, the process of  FIG. 4  operates as follows. Sensor inputs  111   a  are fed into the TMM1 sensor  111  to obtain a first value  111   b.  The TMM1  111  is validated by comparing the first value  111   b  with a second value  41   b  from a reference sensor  41 . In response to a validation of the TMM1 sensor  111 , the TMM1  111  is used to operate the wind turbine. The TMM1  111  continues to be used and continuously validated by feeding subsequent sensor inputs into the TMM1  111  to obtain a subsequent first values, and validating the TMM1  111  by comparing the subsequent first values with subsequent second values from the reference sensor  41 . This continues until the TMM1  111  fails the validation process. In response to a non-validation of the TMM1  111 , the process either uses the reference sensor  41 , or reaches the “function not working” state  152 . 
     The method of  FIG. 4  uses an ambient temperature prediction from the TMM  111  to operate the wind turbine, and uses the ambient temperature sensor  41  as a reference sensor. An equivalent method may be used for other parameters, using one of the other sensors  30 - 41  as a reference sensor. 
     Instead of using a TMM sensor  11  as a backup for a sensor  35  (as in  FIG. 3 ), the method of  FIG. 4  can be used if the TMM1 sensor  111  is much more precise than the sensor  41 . The sensor  41  is only used as backup and to validate the TMM  111 . The sensor  41  can also be used by the TMM  111  to give a more accurate output. 
     A third method shown in  FIG. 5  uses a sensor implementing a trained machine learning model to provide, for example, a wind direction estimate for operation of the wind turbine  1 . 
     The method of  FIG. 5  uses the wind direction sensor  35  and a first sensor implementing a first trained machine learning model (TMM1)  211 . The TMM1  211  may be identical to the TMM  11  of  FIG. 3  except it is trained on a slightly different training data set: in this example the TMM 1   211  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)  121  is also provided. In contrast to the SPM  12  of  FIG. 3 , the TMM2 sensor  121  of  FIG. 5  implements a trained machine learning model similar to the TMM  11 . The TMM2  121  generates an output  121   b  based on sensor inputs  121   a.    
     Like the TMM sensor  11 , the second TMM2 sensor  121  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 TMM 2   121  is slightly different to the training data set for the first TMM1  211 . 
     By way of example, the TMM1  211  may have been trained by feeding a first training data set into the TMM1  211  (for instance blade load, rotor azimuth angle, rotor speed, wind speed and tower top acceleration); and the TMM2  121  may have been trained by feeding a second training data set into the TMM2  121  (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  30 - 41  of the wind turbine  1 , 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  211   a,    121   a  (for instance inputs from some of the sensors  30 - 41  of the wind turbine  1 ) during operation of the wind turbine  1  and produce an output  211   b,    121   b  in the form of sensor values predicting the wind direction based on these sensor inputs. 
     Like the TMM  11  of  FIG. 3 , each sensor  211 ,  121  may comprise a neural network or a support vector machine regression model, for example. The sensors  211 ,  121  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  211  and  121  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  211  and  121  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  211   b  and the sensor TMM2 is the second sensor obtaining the second value  121   b.    
     The values of the sensors  211 ,  121  are validated at step  215  and only used by the control system  20  to operate the wind turbine if they pass the validation process. 
     More specifically, the control system  20  may feed sensor inputs  211   a  into the sensor  211  to obtain a first value  211   b.  By way of example, these sensor inputs  211   a  may be received from the blade load sensor  30 , rotor azimuth angle sensor  31 , rotor speed sensor  32 , wind speed sensor  33  and tower top acceleration sensor  36 . 
     Similarly, the control system  20  may feed sensor inputs  121   a  into the sensor  121  to obtain a second value  121   b.  By way of example, these sensor inputs  121   a  may be received from the blade load sensor  30 , rotor azimuth angle sensor  31 , rotor speed sensor  32 , wind speed sensor  33 , power sensor  37  and nacelle position sensor  38 . 
     As with the training data sets, the sensor inputs  121   a  fed into the sensor  121  may be from a different set of sensors than the sensor inputs  211   a  fed into the sensor  211 . For example, the sensor inputs  211   a  may be from a first set of sensors  30 - 33  and  36 ; and the sensor inputs  121   a  may be from a second set of sensors  30 - 33 ,  37  and  38 . 
     The sensor  211 ,  121  are then validated at step  215  by comparing the first value  211   b  with the second value  121   b.  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  211 ,  121  (that is, if the sensor values are validated at step  215 ) then one or both of the TMMs  211 ,  121  may be used to operate the wind turbine at step  251 . 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  215  by determining a difference or ratio between the first value  211   b  and the second value  121   b ; 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  211   b  and  121   b.    
     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  20  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  211 , and WD2(Average) is a mean average of a time series of wind directions generated by the second TMM2 sensor  121 . 
     In another example, the control system  20  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  211 , and SD2 is a standard deviation of a time series of wind directions generated by the second TMM2 sensor  121 . If the ratio SD1/SD2 is outside a range (for instance 0.8 to 1.2) then the sensor values are not validated and not used to operate the wind turbine at step  251 . 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  251 . 
     In other examples, the control system  20  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 sensors are not validated at step  215 , then the wind direction sensor  35  may be checked. If the wind direction sensor  35  is OK, then the wind direction signal from the sensor  35  may be used by the control system  20  to operate the wind turbine at step  250 . 
     If the wind direction sensor  35  is not OK (for instance if it has failed or is producing inaccurate data) then the process reaches the “function not working” state  52 . 
     The process of  FIG. 5  uses the wind direction sensor  35  as a back-up. Optionally the wind direction sensor  35  may be adjusted or corrected at step  260  on the basis of the wind direction predictions from the validated sensor values  211 ,  121 —for instance by modifying a gain or offset applied to the output of the sensor  35 . This improves the accuracy and reliability of the sensor  35 . In scenarios where the machine learning model implemented sensors fail, the function can switch to the newly calibrated sensor  35  with higher accuracy until the machine learning model implemented sensors produce valid signals. 
     In summary, the process of  FIG. 5  operates as follows. A first sensor is implementing a first machine learning model (TMM1  211 ) 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  121 ) 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  211   a,    121   a  of sensor inputs are fed into the sensors to obtain outputs  211   b,    121   b.  The sets  211   a,    121   a  of sensor inputs are different from each other. The sensor values from sensors  211  and  121  are validated by comparing their outputs  211   b,    121   b.  In response to a validation of the sensors  211 ,  121 , one or both of the sensor values is used to operate the wind turbine. The sensor  211 ,  121  continue to be used and continuously validated by feeding subsequent sensor inputs into the sensors  211 ,  121  to obtain subsequent first and second values, and validating the sensors  211 ,  121  by comparing these subsequent values. This continues until the sensor values fail the validation process. In response to a non-validation of the sensors  211 ,  121 , the sensor  35  is used. If the sensor  35  is not OK then the process reaches the “function not working” state  52 . 
     The method of  FIG. 5  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  35  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  30 - 41  as a back-up. 
     A fourth method shown in  FIG. 6  uses model-based sensors  300 - 302  to provide a parameter value prediction for operation of the wind turbine  1 . 
     The method of  FIG. 6  is similar to  FIG. 5  in that it uses first and second sensors implementing trained machine learning models (sensor TMM1  300  and sensor TMM2  301 ) which have been trained using slightly different training data sets and/or different machine learning methods. The operational parameter for  FIG. 5  is wind direction whereas  FIG. 6  is more generic, not referring to a specific operational parameter. The operational parameter for  FIG. 6  may be wind direction, ambient temperature or any other parameter. The embodiment of  FIG. 6  enables the wind turbine to be operated without any sensor directly measuring the parameter. 
     A third sensor implementing a third model  302  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  300  and the TMM  301 . Alternatively, the third model  302  may be a simple physical model similar to the SPM  12  shown in  FIG. 3 . 
     Different sets  300   a,    301   a,    302   a  of sensor inputs are input into the three models  300 - 302 , which generate three respective outputs  300   b,    301   b,    302   b.  These outputs are compared in validation step  315 . 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  351 . Where the outputs of multiple sensors are used at step  351 , then an average may be used for example. 
     If none of the outputs  300   b,    301   b,    302   b  agree with each other, then the process reaches a “function not working” state  352  similar to the states  52 ,  152  described above. 
     In the description above, a control system  20  is configured to operate the wind turbine by one or more of the various methods described with reference to  FIGS. 3 to 6 . This control system  20  may be part of an initial configuration of the wind turbine. In alternative embodiments of the invention, the methods described with reference to  FIGS. 3 to 6  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  20 ) 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  1  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  FIGS. 3 to 6 . 
     Although the invention has been described above with reference to one or more preferred embodiments, it will be appreciated that various changes or modifications may be made without departing from the scope of the invention as defined in the appended claims.