The present invention relates to a method of determining an induction factor between the rotor plane (PR) and a measurement plane (PM), involving measuring the wind speed in at least two measurement planes (PM), determining the wind speed in rotor plane (PR) by use of a Kalman filter from the measurements, and measuring the induction factor by use of an adaptive Kalman filter from the measurements and the wind speed in rotor plane (PR).

A method for constrained control of a wind turbine includes receiving a plurality of operating parameters corresponding to the wind turbine. The plurality of operating parameters includes a wind preview parameter and a plurality of constraint parameters. The method further includes generating a constraint parameter estimate corresponding to a future time instant for at least one constraint parameter of the plurality of constraint parameters based on the plurality of operating parameters and a wind preview model. The method also includes predicting an extreme event corresponding to the at least one constraint parameter based on the constraint parameter estimate. The method includes determining a control parameter value corresponding to a wind turbine control parameter among a plurality of wind turbine control parameters. The method also includes operating the wind turbine using a feedforward control technique based on the control parameter value to circumvent the extreme event.

The invention is a method for detecting aberrant values of an incident wind field in a space located upstream of a lidar sensor. The method comprises acquiring and modelling a measurement rws(k) with the lidar sensor of an incident wind field, by estimating a median mr(k) and a mean absolute deviation dr(k) in real time of measurements of the incident wind field and detecting aberrant values in real time using the estimated median mr(k) and the mean absolute deviation dr(k).

A method of determining a corrected wind speed in the region of a wind turbine including the steps of measuring a wind speed in the region of a wind turbine, determining a force exerted on at least one rotor blade by the wind, determining a wind speed difference value which is dependent on the determined force and determining a corrected wind speed by correcting the measured wind speed in dependence on the wind speed difference value.

A wind turbine for carrying out the method.

An acoustic monitoring system and method for the health status of an offshore wind turbine and an ocean wave are provided. The acoustic monitoring system includes a first laser transmitter, a second laser transmitter, a telephoto camera provided at a hub, a vibration detection sensor provided on a tower of a wind turbine, and four acoustic detection sensors arranged at an interval of 90° along the circumference of the tower. The first and second laser transmitters are arranged at the bottom of a nacelle of the wind turbine and emit laser lights vertically downward. The first laser transmitter, the second laser transmitter, the telephoto camera, the vibration detection sensor, and the acoustic detection sensors are connected to a data acquisition and conversion module through a transmission module. The acoustic monitoring system combines laser light detection with acoustic signal feature detection to improve stability and safety of the offshore wind turbine.

Disclosed is a method of visually inspecting a wind turbine generator (WTG) and parts thereof during operation. The method comprising acts of pointing a visual inspection system with a field of view about a line of sight in a plane of rotor blade of the wind turbine generator (WTG); of capturing multiple images of the field of view using the visual inspection system; of selecting at least one reference image amongst the captured images or from elsewhere; of comparing the at least one other captured image with the at least one reference image; and thus inspecting structural aspects of the wind turbine generator (WTG) as a function of the result of the act of comparing.

A wind turbine has a Lidar device to sense wind conditions upstream of the wind turbine. Signals from the wind turbine are processed to detect an extreme change in wind direction. The detection is performed by differentiating the rate of change of wind direction and filtering for a period of time. On detection of extreme change the system controller takes the necessary evasive action which may include shutting down the turbine, commencing an immediate yawing action, and de-rating the turbine until the yawing action is complete.

Model-based predictive control method (MPC) for the reduction of structural load in wind turbines comprising: exclusively proposing a single internal linear model for the MPC for the entire operating range of the turbine; obtaining the adjustable parameters of the linear internal model from the experimental data previously measured in the turbine; choosing the discrete time values for the control and prediction horizons; adjusting the MPC controller and performing a practical implementation test.

The invention relates to a wind turbine comprising a rotor hub (7) rotating about a rotor axis (8), rotor blades (9, 10, 11) extending radially with respect to the rotor axis (8) at the same angular spacing relative to one another, and a remote wind gauge (19) that is fastened externally on the surface of the hub (7) in a mounting (20) and oriented such that wind characteristics at a distance in front of the hub (7) can be ascertained or measured, wherein the remote wind gauge (19) is arranged between two neighbouring rotor blades (9, 10) and in the radial direction of the rotor axis (8) and the mounting (20) is fastened to a respective blade bearing flange (21, 22) in the region of the connection of the rotor hub (7) to the rotor blades (9, 10), such that the remote wind gauge (19) can be retrofitted to the wind turbine (1).

The present invention is a method of determining an induction factor of the wind for a wind turbine (1) equipped with a LiDAR sensor (2). For this method, wind speed measurements are performed in measurement planes (PM) by use of LiDAR sensor (2), then induction factors between measurement planes (PM) are determined by use of the measurements and of a first linear Kalman filter, and the induction factor between a measurement plane (PM) and the rotor plane (PR) of wind turbine (1) is determined by a second linear Kalman filter.