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, the first sensor being implemented as a trained machine learning model (11, 111, 211) 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 (15, 115, 215) 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 (51, 151, 250) the wind turbine using the first value as the operational parameter; in response (52, 152) 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, 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 (35, 41) arranged for measuring the second value; and a computerized physical model taking a set of operating parameters as input and outputting the second value. obtaining a first value of the operational parameter by a first sensor, the first sensor being implemented as a trained machine learning model (11, 111, 211) 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 (15, 115, 215) 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 (51, 151, 250) the wind turbine using the first value as the operational parameter; in response (52, 152) 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, 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 (35, 41) arranged for measuring the second value; and a computerized physical model taking a set of operating parameters as input and outputting the second value. a trained machine learning model, 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 (35, 41) arranged for measuring the second value; and a computerized physical model taking a set of operating parameters as input and outputting the second value.