Provided is a method for adjusting a pitch angle of a rotor blade connected to a rotor of a wind turbine, the method includes: pitching the rotor blade towards a target blade pitch angle, the manner of pitching depending on a load on a pitch bearing and/or an azimuthal position of the rotor.

A method of detecting an adjustment fault related to a wind turbine rotor blade mounted at a wind turbine rotor and including an adaptable flow regulating device, in particular spoiler and/or flap, the method including: estimating a quantity indicative of a change of a driving impact of wind on the wind turbine rotor based on at least two settings of the adaptable flow regulating device; determining another quantity indicative of a desired change of the driving impact on the wind turbine rotor, in order to change a value of a rotor speed to a reference value of the rotor speed; and indicating an adjustment fault based on a comparison of the quantity with the other quantity, is provided.

The invention relates to a method for operating a wind turbine, to a wind turbine designed to carry out the method, and to a computer program product. The method for operating a wind turbine comprising a rotor with rotor blades that can be angularly adjusted via a turbine controller, in which a state variable that reflects the current thrust of the rotor is detected, has the following steps: a) ascertaining a short-term average value of the state variable; b) ascertaining the difference between the short-term average value of the state variable and the detected current state variable; c) ascertaining a first target blade angle correction value from the ascertained difference; and d) taking into consideration the target blade angle correction value while adjusting the blade angle by means of the turbine controller.

A controller structure for a wind turbine having an aerodynamic rotor with at least one rotor blade, wherein the controller structure is designed to control a rotation speed of the rotor of the wind turbine, wherein the controller structure is designed as a cascade control arrangement and has an outer control loop and an inner control loop, wherein the inner control loop receives an input signal which comprises a change in the rotation speed, an acceleration of the rotation speed, a function of the change in the rotation speed and/or a function of the acceleration of the rotation speed.

The invention relates to a method for monitoring a wind farm. The method includes determining a first ice mass on a first wind turbine of the wind farm; determining at least one second ice mass on at least one second wind turbine of the wind farm; comparing a first ice mass with a second ice mass and determining a primary wind turbine, which serves as a reference, and at least one secondary wind turbine from the group of the first wind turbine and the at least one second wind turbine; switching off the at least one secondary wind turbine on the basis of a first ice mass from the primary wind turbine; and switching on the at least one secondary wind turbine on the basis of a second ice mass from the primary wind turbine.

A method for detecting actual slip in a coupling of a rotary shaft, for example, in a wind turbine power system, includes monitoring, via a controller, a plurality of sensor signals relating to the coupling for faults. In response to detecting a fault in the plurality of sensor signals relating to the coupling, the method includes determining, via the controller, whether the fault is indicative of an actual slip or a no-slip event of the coupling using one or more classification parameters. When the fault is indicative of the actual slip, the method includes estimating, via the controller, a magnitude of the actual slip using the plurality of sensor signals and a time duration of the actual slip. Further, the method includes implementing, via the controller, a control action based on the magnitude of the actual slip in the coupling.

A deep learning-based cooling system temperature prediction apparatus has an artificial neural network modeled by connecting a plurality of artificial neural network submodels each including an input layer, a hidden layer, and an output layer is used. A pump flow speed, a cooling water flow rate, a battery inlet cooling water temperature, a motor inlet cooling water temperature, a radiator outlet cooling water temperature, a battery temperature, and a motor temperature are predicted by inputting at least one of a predetermined control variable, an environment variable, or a time variable to the plurality of artificial neural network submodels in accordance with a physical causality. A number of the plurality of artificial neural network submodels and the control variables or environment variables that are sequentially input to each submodel depend on divisional control and integral control of the cooling system.

Systems, methods, and computer program products for determining an inclination of a wind turbine tower based on acceleration measurements by an accelerometer operatively coupled to a nacelle of the wind turbine. Acceleration data is collected from the accelerometer, which is configured to sense acceleration along an accelerometer axis while the nacelle is in each of a plurality of yaw positions. The nacelle is rotated in steps to each yaw position and stopped for a period of time. While the nacelle is stopped, acceleration data is collected, and a static level of acceleration determined along the accelerometer axis due to gravity. Once acceleration data has been collected at each of the positions, the minimum and maximum acceleration levels are identified. The inclination of the tower is then determined based on the minimum and maximum acceleration levels.

Provided is a method of estimation of rotor operational characteristics, in particular rotor speed, rotor azimuth and rotation direction, of a rotating rotor of a wind turbine, the method including: measuring pulse rising edge time and pulse falling edge time of pulses generated by each of multiple proximity sensors originating from multiple detection targets arranged on the rotor; estimating values of parameters associated with the sensors and/or targets, in particular parameters associated with the positioning and/or detection range of at least one sensor and/or the parameters associated with the positioning and/or size of at least one target, based on the measured pulse rising edge times and pulse falling edge times; estimating rotor operational characteristics, in particular a rotor speed and/or a rotor azimuth and/or a rotation direction, based on the measured pulse rising and/or falling edge times and/or the estimated values of parameters associated with the sensors and/or targets.

Provided is a control device for controlling a wind turbine, the wind turbine including a rotor and at least one blade being rotatable mounted to the rotor. The control device includes a detecting device being configured to detect an amount of a bending moment of the blade; and a change element having an input and an output, wherein the input is configured to receive the detected amount of the bending moment of the blade and the output is configured to output a response to a differential of the detected amount of the bending moment of the blade. The control device is configured to control the wind turbine based on the response.