A yaw auto-calibration method configured to calibrate at least one anemometer of a yaw control system to correct for yaw misalignment. The yaw auto-calibration method includes collecting wind turbine data over a plurality of time periods with respect to the at least one anemometer. The wind turbine data including one or more of mechanical speed, wind speed, turbine power, and wind direction. The method includes determining from the collected data a wind direction compensation signal associated with a plurality of operational parameter ranges and the wind direction compensation signals correspond to the effects on the at least one anemometer due to yaw misalignment. The method further includes providing the wind compensation signals to the yaw control system to adjust the wind direction data of the at least one anemometer for each of the associated operational parameter ranges.

A method of correcting a pitch angle deviation of a blade of a wind turbine is provided. First blade load measurements measured when the wind turbine was operating in a first operational mode are provided, and used to determine calibration parameters. Second blade load measurements measured when the wind turbine was operating in a second operational mode are provided, and the calibration parameters applied to determine calibrated blade load measurements. A pitch angle deviation of at least one blade of the turbine is estimated based on an identified difference between the calibrated blade load measurements, and a pitch angle is adjusted to correct for the pitch angle deviation.

Method of controlling a wind turbine. A data set is obtained that includes a direction of the wind relative to the wind turbine and a pitch angle parameter representing a pitch angle of at least one of the wind turbine blades. Based on the obtained data sets, a statistical representation of the pitch angle parameter as a function of the relative wind direction is determined, which is then used in estimating a wind direction offset corresponding to the relative wind direction where the pitch angle parameter attains a maximum. The relative wind direction of the wind turbine is then adjusted as a function of the wind direction offset.

This invention relates to system for determining the wind yaw misalignment of a horizontal axis on-shore wind turbine comprising the wind turbine, a lidar, a topographical station, an external computing unit and a telecommunication network connecting them. Said wind turbine further comprises two target points made of reflective materials placed on the external surface of the nacelle on its side facing the ground, such that be detected by the topographic station. The lidar is configured to determine wind direction angle in respect to the north and wind speed, the topographic station is configured to determine the geographical position and orientation of the pair of target points. The lidar and the topographical station communicate the values determined to the external computing unit.

The present invention relates to a system and a method for optimal yaw control of a wind turbine, comprising a tower carrying a rotatable nacelle rotated by a yaw motor, which nacelle comprises at least one generator connected by a shaft to a rotor, comprising one or more wings, which nacelle further comprises means for detecting wind direction and wind velocity, which system performs measurement and storing data related to power production, wind velocity and wind direction. The object of this invention is to optimize the yaw position of a nacelle to the wind direction. The object can be fulfilled by power production measured in a positive direction to actual yaw position is accumulated in a first storages related to measured wind direction and that power production measured in a negative direction to actual yaw position is accumulated in a second storages related to measured wind direction. By this system the power production of the wind turbine is optimized by self-calibrating yaw control.

The invention relates to a method by means of which a system that is capable of oscillating can be monitored. The method comprises detection of natural oscillation modes of the system that can oscillate as a function of at least one operating parameter and/or as a function of at least one environmental parameter of the system that is capable of oscillating, creation of a frequency distribution of the detected natural oscillating modes, division of the natural oscillating modes into frequency classes and, in at least one frequency class, determination of a mode profile over the operating parameter and/or over the environmental parameter.

A wind turbine includes a wind direction sensor, a yawing system, and a control system for yawing the wind turbine rotor relative to the wind. The control system measures a wind direction parameter by the wind direction sensor Over time a group of data sets is obtained and a wind direction offset is determined from the group of data sets which is used to adjust the wind direction parameter. The adjusted wind direction parameter is then used in the controlling of the wind turbine.

The present disclosure is directed to systems and methods for automatically calibrating a load sensor system of a wind turbine and determining health of same. In one embodiment, the method includes receiving a plurality of sensor signals generated by the plurality of load sensors from the load sensor system. The method also includes determining, via a computer model, a load estimation of the wind turbine based on the sensor signals, turbine geometry, and one or more additional input parameters (e.g. rotor azimuth angle, pitch angle, rotor position, etc.). Another step includes comparing the load estimation to a load measurement to determine one or more correlation coefficients. Thus, the method also includes calibrating the plurality of sensors in the load sensor system based on the correlation coefficients.

A method of controlling a wind turbine includes: accumulating a cumulative damage degree Du of each evaluation point of the wind turbine in a unit period over an entire evaluation period, to calculate a total cumulative damage degree Dt of the entire evaluation period at each evaluation point; comparing the Dt at each of the evaluation points with a first threshold value (P*Q) and comparing an increase rate dDt/dt of Dt at each evaluation point with a second threshold value to evaluate fatigue of a part to which each evaluation point belongs; and determining an operation mode based on an evaluation result into a normal operation mode or a low-load operation mode in which an output is suppressed compared with the normal operation mode.

A method for determining a yaw position offset of a wind turbine is provided. A neighbouring wind turbine of the wind farm is identified, said neighbouring wind turbine being arranged in the vicinity of the wind turbine. Produced power data and/or wind speed data from the wind turbine and from the neighbouring wind turbine, is obtained during a period of time, and a yaw position offset of the wind turbine is derived, based on the obtained produced power data and/or wind speed data, and based on the geographical positions of the wind turbine and the neighbouring wind turbine. A local maximum and a local minimum being separated by an angular difference in yaw position being substantially equal to 180°.