A German company has developed a front wind vector field measurement system to reduce dynamic loads and increase annual wind energy production in wind farms
A German company has developed a front wind vector field measurement system for optimising wind speed, wind direction and turbulence measurements in wind farms including an auto detection system for yaw error misalignment, pitch control and wake. This reduces the dynamic loads on wind turbine components and increases the annual energy production. Licensees, technical or research cooperation with wind turbine manufacturers and operators are sought.
The company is looking for manufacturers of wind turbines or wind farm operators (offshore or onshore) as licensees or for technical or research cooperation.
The offered wind turbine control unit is a new real time method with numerical modeling, process simulation, optimisation and forecast of wind energy measurements. The wind turbine control unit is a front wind vector field measurement system for turbulence, wind speed and direction. It acts like a new kind (factor 100x in precision) of anemometries. The offered wind turbine control unit has an auto detection/correction system of yaw error misalignments included. The offered solution can be combined with LIDAR (light detection and ranging), SODAR (Sound/Sonic Detecting And Ranging) and ultrasonics and other detection modules to optimize wind farms to get the best in benchmarking and to predict extreme short-term wind characteristics. New real-time methods with numerical modeling, process simulation, optimization and prediction offer a wide range of applications for turbine manufacturers and operators. The wind turbine with the surroundings is considered as a whole simulation model in combination with integrated sensor systems e.g. on the rotor blade. Prediction of icing, integrated ice detection, front wind vector field measurement and turbine control for all types of wind turbines are available. SCADA diagnostics (Supervisory Control and Data Acquisition)/ CMS (condition monitoring) with data security detects differences in yaw misalignment, pitch control and wake. The wind turbine is capable to maximize power production and to minimize dynamic loads according to changes in the oncoming wind. Artificial Intelligence (AI)-Driven Business For operation and maintenance of wind farms the offered solution can be integrated in existing workflow processes to get a SCADA data driven workflow diagnostic Business Process Development system like a CMS system with predictive maintenance and to save operation and maintenance costs. To tackle the problem of icing on rotor blades the company has developed a Neural Network Computing System (NNCS) with a University of Applied Sciences in Germany in the year 2009. The NNCS is able to predict icing on rotor blades in different surrounding areas. Licensees, technical or research cooperation with wind turbine manufacturers and operators are sought.
Advantages and innovations
* new real time method with numerical modeling, process simulation, optimisation and prediction for wind energy measurements * combined with an auto detection/correction system for yaw error misalignment, pitch control and wake * auto detection/ correction system for yaw error misalignment, pitch control and wake * can be combined with LIDAR, SODAR and ultrasonics and other detection modules * working with each kind of sensor type for every wind turbine type and location, different implementations are possible * AI - driven business to integrate machine-/ deep learning into your workflow with SCADA diagnostics and predictive maintenance to strategising business processes * highest precision level * increased annual energy production (AEP) > 5% * reduced wind turbine dynamic loads for components and reduced oscillations in the wind turbine control system * easy installation process * products are tested with company partners in Austria in 2012-2017
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