Siemens Gamesa Renewable Energy A/s

Siemens Aktiengesellschaft

Michael J Asheim

Peder Bay Enevoldsen

Stefan Oerlemans

Johannes Gerhardes Wardjan Alberts

Drew Eisenberg

Manjinder J Singh

Steven Buck

Oriol Ferret Gasch

Alex Loeven

Sigmund Wenningsted Torgard

Bodo Richert

Alonso O Zamora Rodriguez

Arni T Steingrimsson

Anders Smaerup Olsen

Justin L Mullings

Kristoffer Ahrens Dickow

Valerio Lorenzoni

Henrik Fredslund Hansen

Peter J Rimmington

Kristian R Dixon

Provided is a noise reduction means for a wind turbine blade comprising an active noise reduction device and a passive noise reduction device, wherein the passive noise reduction device comprises at least one serrated edge profile adapted for fixation to a trailing edge of the wind turbine blade, wherein the active noise reduction device comprises at least one unsteady pressure sensor adapted to produce an output signal corresponding to a turbulent flow condition during operation of the wind turbine blade, at least one actuator and a control unit, wherein the sensor is arranged adjacent to a serrated edge of the serrated edge profile and the control unit is adapted to control the actuator in dependence of the output signal of the sensor to emit an anti-noise signal at least partly reducing the noise generated by the wind turbine blade.

Noise reduction means for a wind turbine blade, wind turbine blade, wind turbine, and method for noise reduction for a wind turbine blade

A method using machine learned, scenario based control heuristics including: providing a simulation model for predicting a system state vector of the dynamical system in time based on a current scenario parameter vector and a control vector; using a Model Predictive Control, MPC, algorithm to provide the control vector during a simulation of the dynamical system using the simulation model for different scenario parameter vectors and initial system state vectors; calculating a scenario parameter vector and initial system state vector a resulting optimal control value by the MPC algorithm; generating machine learned control heuristics approximating the relationship between the corresponding scenario parameter vector and the initial system state vector for the resulting optimal control value using a machine learning algorithm; and using the generated machine learned control heuristics to control the complex dynamical system modelled by the simulation model.

Rotor blade with serrations

A rotor blade for a wind turbine is provided. The rotor blade includes a pressure side, a suction side, a leading edge section with a leading edge, and a trailing edge section with a trailing edge. An airflow flows along the surface of the rotor blade from the leading edge section to the trailing edge section and builds up a boundary layer in close proximity to the surface of the rotor blade. The rotor blade includes a noise reduction device for reducing noise which is generated by interaction of the airflow and the rotor blade. The noise reduction device is located within the boundary layer of the rotor blade. The noise reduction device includes a cover and connection device for connecting the cover to the surface of the rotor blade. The cover spans at least over a part of the surface of the rotor blade.

Rotor blade with noise reduction means

A rotor blade for a wind turbine, wherein the rotor blade includes serrations along at least a portion of the trailing edge section of the rotor blade is provided. The serrations include a first tooth and at least a second tooth, wherein the first tooth is spaced apart from the second tooth. Furthermore, the area between the first tooth and the second tooth is at least partially filled with a plurality of comb elements, wherein the comb elements are arranged substantially parallel to each other and in substantially chordwise direction of the rotor blade such that generation of noise in the trailing edge section of the rotor blade is reduced. The rotor blade is further characterized in that it includes a plurality of ridges including a first ridge and at least a second ridge for manipulating an airflow which is flowing along the ridges.

Rotor blade with a serrated trailing edge

Edge flap for a wind turbine blade

A method is proposed for operating a wind turbine, including the following steps: deriving at least one turbulence characteristic of atmosphere hitting the wind turbine, determining at least one wind turbine specific parameter based on the at least one derived turbulence characteristic, and operating the wind turbine according to the at least one determined turbine specific parameter is provided. Further, a wind turbine and a device as well as a computer program product and a computer readable medium are also provided.

Operating a wind turbine by reducing an acoustic emission during operation

Apparatus for detecting aerodynamic conditions of a rotor blade () of a wind turbine (). An acoustic sensor () may be remotely located from the rotor blade. The sensor may be focused to monitor a portion of a blade path swept by the rotor blade to detect an aerodynamic condition such as flow separation, and may provide input to a controller () for control of a pitch of the blade effective to prevent the flow separation from developing into a full stall condition. One sensor may be focused to monitor blades of more than one wind turbine. A plurality of such sensors in a wind park may be connected to a supervisory controller () to predict the propagation of wind conditions progressing through the park.

Apparatus to detect aerodynamic conditions of blades of wind turbines

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Littleton, CO, United States of America

Michael J Asheim