Control of a multi-rotor wind turbine system. A local controller is arranged for each wind turbine module and implementing a local model predictive control (MPC) routine. A central controller is arranged to determine a set of operational constraints of the wind turbine modules. Based on a current operational state of the wind turbine module and the set of operational constraints, one or more predicted operational trajectories are calculated and used for controlling the wind turbine module.

Provided is a method of determining a control setting of at least one wind turbine of a wind park, the method including: determining a free-stream wind turbulence and deriving the control setting based on the free-stream wind turbulence, wherein the control setting includes a yawing offset, and wherein the yawing offset is derived to be the smaller, the higher the free-stream wind turbulence is.

The present invention relates to non-thermal renewable energy turbines (20,24,34, 38,40), in particular to the monitoring of turbine performance to identify a loss of performance indicative of faults or component degradation. The method involves comparison of measured power from a target turbine (20) with a predicted value for same turbine. The predicted value is calculated using the output from a plurality of other turbines (24,34,38,40) from an array and a predictive model including weightings for the other turbines (24, 34,38,40) based on the strength of correlation of their historical with historical data from the target turbine (20).

A system and method are provided for operating a power generating asset coupled to an electrical grid. Accordingly, a controller receives an environmental data set indicative of at least one environmental variable projected to affect the power generating asset over a plurality of potential modeling intervals. The controller then determines the variability of the environmental data set and a corresponding modeling-confidence level at each of the potential modeling intervals based on the variability. A modeling interval is thus selected corresponding to a desired modeling-confidence level. A computer-implemented model is employed to predict a future power profile for the power generating asset over the selected modeling interval. The future power profile is indicative of a power-delivery capacity of the power generating asset at each of a plurality of time intervals of the modeling interval. Based, at least in part, on the future power profile, the controller determines an obligated-power-production schedule for the power generating asset over the modeling interval. The obligated-power-production schedule corresponds to a power production agreement with the electrical grid. In accordance with the obligated-power-production schedule, the controller modifies at least one setpoint of the power generating asset to deliver electrical power to the electrical grid.

A method for operating a wind turbine for generating electrical power from wind, wherein the wind turbine has an aerodynamic rotor with a rotor hub and rotor blades of which the blade angle is adjustable, and the aerodynamic rotor can be operated with a variable rotation speed, and the wind turbine has a generator, which is coupled to the aerodynamic rotor, for the purpose of generating a generator power, wherein the generator can be operated with a variable generator torque, comprising the steps of: determining a loading variable which indicates a loading on the wind turbine by the wind, and reducing the rotation speed and/or the generator power in a loading mode depending on the loading variable, wherein at least one force variable that acts on the wind turbine is used for determining the loading variable or as the loading variable.

A system and method are provided for controlling a wind turbine of a wind farm. Accordingly, a controller implements a first model to determine a modeled performance parameter for the first wind turbine. The modeled performance parameter is based, at least in part, on an operation of a designated grouping of wind turbines of the plurality of wind turbines, which is exclusive of the first wind turbine. The controller then determines a performance parameter differential for the first wind turbine at multiple sampling intervals. The performance parameter differential is indicative of a difference between the modeled performance parameter and a monitored performance parameter for the first wind turbine. A second model is implemented to determine a predicted performance parameter of the first wind turbine at each of a plurality of setpoint combinations based, at least in part, on the performance parameter differential the first wind turbine. A setpoint combination is then selected based on the predicted performance parameter and an operating state of the first wind turbine is changed based on the setpoint combination.

A method of controlling pitch of individual blades in a wind turbine is described, together with a suitable controller. Wind speed is determined as a function of azimuthal angle. Wind speed is then predicted for individual blades over a prediction horizon using this determination of wind speed as a function of azimuthal angle. The predicted wind speed for each individual blade is used in a performance function, which is optimized to control individual blade pitches.

A power control method and apparatus for a wind power generator. The power control method comprises: predicting, according to historical wind resource data, wind resource data within a predetermined future time period (S10); estimating, according to the remaining design lifetime of a wind power generator, the maximum design lifetime allowed to be consumed within the predetermined future time period (S20); determining, according to the predicted wind resource data and the estimated maximum design lifetime, optimal output powers of the wind power generator in respective wind velocity ranges within the predetermined future time period (S30); and controlling operation of the wind power generator according to the determined optimal output powers of the wind power generator in the respective wind velocity ranges within the predetermined future time period (S40).

A computing system is configured to detect irregular yawing at wind turbines. To this end, the computing system (i) for each respective turbine of an identified cluster of wind turbines: (a) obtains yaw-activity data indicative of the respective turbine's yaw activity during a window of time, and (b) based on obtained yaw-activity data, derives a yaw-activity-measure dataset having measures of the respective turbine's yaw activity during time intervals within the window of time, (ii) based on the respective yaw-activity-measure datasets for the turbines in the cluster, derives a cluster-level yaw-activity-measure dataset, (iii) evaluates the respective yaw-activity-measure dataset for one or more turbines in the cluster as compared to the cluster-level yaw-activity-measure dataset, (iv) based on the evaluation, identifies at least one turbine of the cluster that exhibited irregular yaw activity, and (v) transmits, to an output device, a notification of the irregular yaw activity at the at least one turbine.

A wind turbine control apparatus, method and non-transitory computer-readable medium are disclosed. The wind turbine control apparatus comprises a generator connected to a wind turbine with a drive train. The drive train comprises a rotor, a low speed shaft, a gear box, a high speed shaft, and a controller module. The controller module is configured to obtain a maximum power within a large range of varying wind velocities by operating the rotor at a neural network determined optimal angular speed for the current wind velocity.