Evaluating localized atmospheric conditions for selected cloud seeding to enhance localized electrical power generation from wind turbines by receiving, at a computer, wind farm data related to a plurality of wind turbines for generating electrical power at a location. The wind farm data collected from sensors at the location. An atmospheric condition in the atmosphere at the location is assessed by the computer, using the wind farm data and the data of the atmospheric conditions. The computer generates a prediction of an impact of the atmospheric condition on the atmospheric wind speed resulting in a wind turbine power output reduction. A determination is made when to initiate cloud seeding to generate rain at the location and reduce the atmospheric condition. Generating a communication to a control system which includes a recommendation to initiate the cloud seeding based on the prediction.

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).

The present invention concerns a method for determining the production availability of an offshore wind farm comprising at least one floating wind turbine, the method comprising: obtaining wind farm data, obtaining strategy data relative to operation and maintenance resources to carry out an action on the floating wind turbine(s), obtaining meteorological data relative to an offshore environment for the offshore wind farm over a given period of time, determining motion parameters as a function of the wind farm data and of the meteorological data, and determining the production availability of the offshore wind farm in the offshore environment over the given period of time on the basis of the wind farm data, of the strategy data, of the meteorological data, and of the determined motion parameters.

A system and method for predicting power output of a wind farm are disclosed. The method includes determining first and second parameter values of a power curve for a plurality of wind turbines. A second relationship is determined between the densities associated with the wind turbines and the values of the first parameter. A third relationship is determined between the densities associated with the wind turbines and the values of the second parameter. A value of the first parameter for a specified wind farm density is determined based on the second relationship. A value of the second parameter for the specified wind farm density is determined based on the third relationship. An indication of a power output for the specified wind farm density is generated by applying the determined values of the first and second parameters to the power curve.

A method for handling physical intrusion in a wind turbine (1) is disclosed. An intrusion detection system (3) detects an intrusion event in the wind turbine (1), generates a first intrusion alert signal and provides the first intrusion alert signal to a control system (4) of the wind turbine (1). In response to receiving the first intrusion alert signal, the control system (4) initiates at least one safety action at the wind turbine (1), wherein the at least one safety action comprises stopping power production of the wind turbine (1) and providing a second intrusion alert signal to an intrusion monitoring system (6). The intrusion monitoring system (6) communicatively isolates the wind turbine (1).