A method for determining improved control parameters of a number of wind turbines of a wind park is provided. The method considers the impact of individual turbine manufacturing tolerances on the turbine performance, thereby avoiding under-utilization of those wind turbines. The method includes the steps of: receiving, by an interface, one or more actual manufacturing tolerances of characteristic values for each of the number of wind turbines; determining, by a processing unit, for each of the number of wind turbines a power versus wind speed map which is calculated from a given turbine model with the one or more actual manufacturing tolerances of the respective wind turbines as input parameters; and deriving, by the processing unit, the control parameters for each of the number of wind turbines from their associated power versus wind speed map.

A method for computer-implemented controlling of wind turbines in a wind farm is provided. The wind farm includes an upstream first and a downstream second wind turbines, wherein the following steps are performed: i) obtaining environmental data and stress data of the first wind turbine, the environmental data and the stress data being taken; ii) determining a status information indicating whether or not a predetermined event is present at the time of taking the data, wherein the predetermined event requires immediate controlling of the first wind turbine; iii) broadcasting a message which contains environmental data and a timestamp as event information; iv) evaluating the event information whether or not the predetermined event at the first wind turbine will hit the second wind turbine; v) generating a control command for controlling the second wind turbine in case the evaluation holds that the predetermined event will hit the second wind turbine.

Some embodiments are directed to a method for estimating a periodic or substantially periodic force present in a mechanical or electromechanical system, the method comprising: estimating, by a processing device, one or more harmonic frequencies of an acceleration signal representing an acceleration in the system, the substantially periodic force contributing to said acceleration; and estimating, by the processing device, the force on the basis of a dynamic model of the system, the dynamic model being defined by said one or more estimated harmonic frequencies.

A system and method are provided for operating a wind farm. Accordingly, a wind direction affecting the wind farm is determined. Based on the wind direction, a controller identifies a turbine cluster, which is a subset of a plurality of wind turbines of the wind farm. The subset includes at least an upwind turbine and a downwind turbine that is affected by a wake emanating from the upwind turbine. With the turbine cluster identified for the given wind direction, the controller then determines a difference between a freestream maximal cluster power output and a wake-affected cluster power output for the turbine cluster. The controller then determines a mitigation setpoint combination for the subset of wind turbines. The mitigation setpoint combination is configured to establish a mitigated cluster power output. Mitigated cluster power output has a difference from the freestream maximal cluster power output that is less than the difference between the freestream maximal cluster power output in the wake-affected cluster power output for the turbine cluster. Based on the mitigation setpoint combination, an operating state of at least one wind turbine of the turbine cluster is changed.

A method for controlling wind turbines. Incident signal data is obtained from wind turbines and fed to an artificial intelligence (AI) model in order to identify patterns in the incident signals generated by the wind turbines. One or more actions are associated to the identified patterns, based on identified actions performed by the wind turbines in response to the generated incident signals. During operation of the wind turbines, one or more incident signals from one or more wind turbines are detected and compared to patterns identified by the AI model. In the case that the detected incident signal(s) match(es) at least one of the identified patterns, the wind turbine(s) are controlled by performing the action(s) associated with the matching pattern(s).

A method for controlling a wind turbine includes detecting, via a controller, a plurality of analytic outputs of the wind turbine from a plurality of different analytics. The method also includes analyzing, via the controller, the plurality of analytic outputs of the wind turbine. Further, the method includes generating, via the controller, at least one computer-based model of the wind turbine using at least a portion of the analyzed plurality of analytic outputs. Moreover, the method includes training, via the controller, the at least one computer-based model of the wind turbine using annotated analytic outputs of the wind turbine. As such, the method includes checking the plurality of analytic outputs for anomalies using the at least one computer-based model. Accordingly, the method includes implementing a control action when at least one anomaly is detected.

A method predicting and avoiding faults that result in a shutdown of a wind turbine includes receiving operational data of the wind turbine. The method also includes predicting, via a predictive model, current or future behavior of the wind turbine using the operational data. Further, the method includes determining, via a fault detection model, whether the current or future behavior indicates an upcoming short- or long-term fault occurring in the wind turbine. Moreover, the method includes determining, via a prescriptive action model, a corrective action for the wind turbine based on whether the future behavior of the wind turbine indicates the upcoming short- or long-term fault occurring in the wind turbine. Thus, the method also includes implementing the corrective action during operation to prevent the upcoming short- or long-term fault from occurring.

Method for operating a wind turbine, the wind turbine including a wind characteristics sensor for measuring a wind characteristic and at least one wind turbine state sensor for measuring a state of the wind turbine, the method comprising: determining or adjusting (102) one or more wind characteristics relationships; and, performing (104) an operation phase, the operation phase including: measuring the wind characteristics with the wind characteristics sensor, thereby obtaining measured wind characteristics; measuring the state of the wind turbine with the at least one wind turbine state sensor and determining an estimated wind characteristics from the measured state of the wind turbine and parameters of the wind turbine; comparing the estimated wind characteristics to an expected wind characteristics determined from the measured wind characteristics, wherein the expected wind characteristics is determined based on the one or more wind characteristics relationships; and, operating or shutting down the wind turbine based at least in part on the comparison result.

A system for generating power includes an environmental engine operating on one or more computing devices that determines a wind flowing over a blade of a wind turbine, wherein the wind flowing over the blade of the wind turbine varies based on environmental conditions and operating parameters of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between at least two of a rotor speed, a blade pitch, the wind flowing over the blade, a wind speed and a turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric, and simulates execution of the selected model to determine recommended operating parameters.

A method for operating a wind turbine includes determining one or more loading and travel metrics or functions thereof for one or more components of the wind turbine during operation of the wind turbine. The method also includes generating, at least in part, at least one distribution of cumulative loading data for the one or more components using the one or more loading and travel metrics during operation of the wind turbine. Further, the method includes applying a life model of the one or more components to the at least one distribution of cumulative loading data to determine an actual damage accumulation for the one or more components of the wind turbine to date. Moreover, the method includes implementing a corrective action for the wind turbine based on the damage accumulation.