A method for cooperative controlling wind turbines of a wind farm, wherein the wind farm comprises at least one pair of turbines aligned along a common axis approximately parallel to a current wind direction and consisting of an upstream turbine and a downstream turbine , comprising the steps of: a) providing a data driven model trained with a machine learning method and stored in a database , the data driven model providing a correlation between time series data obtained from the pair of turbines in parallel, the time series data being aligned in time to the same wind front, and a ratio of the current power production of the upstream and the downstream turbine related to the aligned time series data; b) determining a decision parameter for controlling at least one of the upstream turbine and the downstream turbine by feeding the data driven model with - the current power production of the upstream turbine which returns, as the decision parameter, a prediction value indicating whether the downstream turbine will be affected by wake, and/or - the temporal evolvement of the current power production of the upstream turbine which returns, as the decision parameter, a prediction of the probable development of the future power production of the downstream turbine ; c) based on the decision parameter, determining control parameters - for the upstream turbine in order to avoid or mitigate wake effects at the downstream turbine ; and/or - for the downstream turbine in order to mitigate expected negative effects of the downstream turbine with respect to fatigue. a) providing a data driven model trained with a machine learning method and stored in a database , the data driven model providing a correlation between time series data obtained from the pair of turbines in parallel, the time series data being aligned in time to the same wind front, and a ratio of the current power production of the upstream and the downstream turbine related to the aligned time series data; b) determining a decision parameter for controlling at least one of the upstream turbine and the downstream turbine by feeding the data driven model with - the current power production of the upstream turbine which returns, as the decision parameter, a prediction value indicating whether the downstream turbine will be affected by wake, and/or - the temporal evolvement of the current power production of the upstream turbine which returns, as the decision parameter, a prediction of the probable development of the future power production of the downstream turbine ; - the current power production of the upstream turbine which returns, as the decision parameter, a prediction value indicating whether the downstream turbine will be affected by wake, and/or - the temporal evolvement of the current power production of the upstream turbine which returns, as the decision parameter, a prediction of the probable development of the future power production of the downstream turbine ; c) based on the decision parameter, determining control parameters - for the upstream turbine in order to avoid or mitigate wake effects at the downstream turbine ; and/or - for the downstream turbine in order to mitigate expected negative effects of the downstream turbine with respect to fatigue. - for the upstream turbine in order to avoid or mitigate wake effects at the downstream turbine ; and/or - for the downstream turbine in order to mitigate expected negative effects of the downstream turbine with respect to fatigue. <EOS>