A method for detecting a shadow condition from a wind turbine and a system for detecting a shadow condition are provided. An atmospheric condition detected from an atmospheric condition detector is compared to a threshold. From the comparison a determination is made that the atmospheric condition is shadow producing. The shadow condition is detected using the determination.

The object of the invention relates to a method for acquisition and modelling of an incident wind field by a LiDAR sensor. Acquisition and modelling include a step of estimating the wind amplitudes and directions for a set of discretized points, and a step of incident wind field reconstruction in three dimensions and in real time. The invention also relates to a method of controlling and/or monitoring a wind turbine equipped with such a LiDAR sensor from the incident wind field reconstructed in three dimensions and in real time.

A method and system for short term wind power prediction using real time wind speed measurements is disclosed. The method includes receiving at least one real-time characteristic associated with at least one wind turbine, maintaining a database of characteristics associated with the at least one wind turbines, training a machine learning model based on one or both of the database of characteristics and the at least one characteristic, testing the accuracy of the at least one machine learning model and outputting from the machine learning model generated output data based on the training and testing data. Responsive to determining that the accuracy exceeds a predetermined value, one or both of wind speed and energy output of the at least one wind turbine can be calculated.

A method for protecting a wind turbine from an extreme change in wind direction includes receiving a wind direction and/or a wind speed at the wind turbine. When a change in the wind direction or the wind speed exceeds a predetermined threshold, the method includes determining a margin to stall and/or zero lift of the at least one rotor blade of the wind turbine as a function of an angle of attack or change in the angle of attack at a blade span location of at least one rotor blade of the wind turbine. The method also includes implementing a corrective action for the wind turbine (without shutting down the wind turbine) when the margin to stall and/or zero lift exceeds a predetermined value so as to avoid stall and/or negative lift on the at least one rotor blade during operation of the wind turbine.

Methods, apparatus, systems and articles of manufacture are disclosed to provide wind turbine control and compensate for wind induction effects. An example method includes receiving wind speed data from a Light Detecting and Ranging (LIDAR) sensor. The example method includes receiving operating data indicative of wind turbine operation. The example method includes determining an apriori induction correction for wind turbine operating conditions with respect to the LIDAR wind speed data based on the operating data. The example method includes estimating a wind signal from the LIDAR sensor that is adjusted by the correction. The example method includes generating a control signal for a wind turbine based on the adjusted LIDAR estimated wind signal.

The present invention relates to a method of determining the wind speed in the plane of a rotor (PR) of a wind turbine (1), by measuring (MES2) the rotational speed of the rotor, the angle of the blades and the generated power. The method according to the invention uses a wind turbine model (MOD) constructed from wind speed measurements (LID), and by use of measurement clustering (GRO) and regressions (REG).

A laser radar device calculates a wind speed for each of a plurality of divided sections obtained by dividing a trajectory drawn by a laser beam in front of a wind turbine.

A wind turbine includes a wind turbine rotor and rotor blades mounted on the rotor, at least one sensing device disposed on the wind turbine for measuring a first signal representative of a first wind speed at a first distance from the wind turbine rotor and a second signal representative of a second wind speed at a second distance from the wind turbine rotor. The wind turbine system includes a blade pitch actuator for adjusting a pitch of the rotor blades and a generator controller for adjusting a voltage of a wind turbine generator. The wind turbine system also includes a control unit in communication with the blade pitch actuator and the generator controller, the control unit being used for controlling the wind turbine via the blade pitch actuator and the generator controller based on an induction factor derived from the first and second signals.

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.

Turbines, including fluid driven turbines, including wind turbines, do not always operate to their maximum capability due to sub-optimal selection of various possible parameters. Therefore there is industrial advantage in systems which can calculate, adjust or constrain such parameters in order to improve the productivity of turbines. New data also allows for new control methodologies. Such systems may be established through the provision of relevant data. The overall productivity of turbines may be improved, or increased, by extending the lifetime of the turbine, or by increasing the average power output during its lifetime, or reducing maintenance costs. One particular example of turbine under-performance has been observed by the present author for wind turbines operating in hilly terrain such as frequently found on Scottish wind farms but also in many locations around the world. Hilly terrain, or complex terrain, results in complex wind flow and energy production losses when control systems are not best designed to handle such flow. Although complex flow may arise for other reasons, such as complex weather or storms (both onshore and offshore), the complex flow due to complex terrain is always present for many turbines and therefore impacts productivity throughout their operational lifetime. Complex fluid flow data may be measured by instruments including converging beam Doppler LIDAR which is especially advantageous in providing three-dimensional fluid velocity data. Therefore the provision of data allows for control parameter adjustment to account for operational variables including fluid characteristics. Therefore the control parameters may be adjusted in order to better control a turbine for its local conditions. This allows for greater generation of renewable energy. Derivations thereof may also be applied to improve operational parameters of vehicles, including vehicles incorporating a rotor, as well as aircraft and spacecraft launching or operating within a fluid. This offers better vehicle control and improved safety.