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We are cooperating with scientists and other partners to create new solutions

We are happy to bring our skills and experience in the area of maintenance to selected research projects, whether in association with universities, research institutes, partner companies or together with some of our customers. We enjoy working on future-oriented projects and promoting solutions that benefit the entire wind industry.

Here is a small selection of the research projects that we are or have been involved:

Predictive Maintenance Wind Turbine

In cooperation and with the support of Wirtschaftsförderung und Technologietransfer Schleswig-Holstein GmbH, WTSH for short, Deutsche Windtechnik's software development department at the Ostenfeld location is working on a project called Predictive Maintenance Wind Turbine.

More about Predictive Maintenance

Goodwind – Good Practice Operation Wind Energy

The main goal of the joint project Goodwind – Good Practice Operation Wind Energy of the Institute for Wind Energy FK-Wind at the Bremerhaven University of Applied Sciences is to improve cooperation between the areas of operations management, service and insurance. We support this project together with wpd windmanager (operator) and Nordwest Assekuranz (insurer). 

By recording the optimization potential and creating synergies between companies, it is possible to reduce the operating costs, increase the resource efficiency and reduce CO2 emissions for wind turbines. As a result, a standard for communication between the three partners is being created. This standard is published as a recommendation for action (Guideline – Good Practice Operation Wind Energy).

Wind Turbine Doctor – Condition monitoring through stochastic analysis of operating data

This project by ForWind University of Oldenburg and Deutsche Windtechnik uses stochastic methods to optimise the monitoring and maintenance of wind turbines. The potential cost reductions that this project is looking to achieve are particularly important in view of the planned revision of the Renewable Energy Sources Act (EEG) because the increased competition created by the tendering process will lead to increasing cost pressure. 

The operation of a wind turbine is determined by turbulent wind fluctuations that are difficult to predict. This leads to stresses and wear for the wind energy system and reduces its service life. The intelligent use of high-resolution data makes it possible to coordinate the control and operation of turbines more effectively. The project is planning to use the Dynamic Power Curve (DPC), which has proven itself to be especially useful for high-frequency and noisy measurement data. In addition, the project will also assess whether the method can be used on a large scale for load case analysis. This would enable the components to be monitored and damage to be detected at an early stage. In turn, this would lead to lower repair costs and fewer yield losses through curtailment (power limitation) and shutdown.

More about this at: Fraunhofer Institute for Wind Energy Systems - Wind Turbine Doctor


WiSA big data

The goal of the joint project WiSA big data, which is being carried out by the Carl-von-Ossietzky University of Oldenburg and numerous partners, is to contribute to the early detection and diagnosis of faults on wind turbines by analyzing high-resolution operating data to support decisions for the planning and implementation of maintenance.

For this purpose, methods that have been used successfully with operating data that is averaged over 10 minutes are developed further and tested for use with high-resolution data. New methods of early error detection are also being transferred to wind energy applications. The methods that are developed and tested in this manner are then subjected to a practical, quantitative comparative evaluation.

As part of the research project, we provide a practical perspective on the capability of using high-resolution operating data, we link this to decision-making processes for maintenance and we show how maintenance could become more efficient in the future.

Carl von Ossietzky University of Oldenburg - WiSA big data

SeeOff – Strategy development for the efficient dismantling of offshore wind farms

The aim of the SeeOff research project at the University of Bremen is to enable all companies involved in dismantling to develop efficient strategies for dismantling wind turbines at sea. Dismantling strategies are deemed efficient if they fulfil all legal requirements, if the level of occupational safety and acceptance is high, if they are inexpensive and if they ensure that the environment is protected. Together with the interdisciplinary network partners, the researchers are identifying the basic conditions for dismantling wind turbines and designing procedures for developing and analyzing dismantling strategies. Areas where there is potential for improvement are identified and finally the results are made available in the form of a manual for the offshore wind energy industry.

The sub-project being carried out by Deutsche Windtechnik is looking to gain knowledge about the necessary and possible disassembly and logistics options for the dismantling of offshore wind farms and to formulate them in terms of potential for improvement. 

Further project information is available at: Strategy development for the efficient dismantling of offshore wind farms

Evoflap – Development of a trailing edge spoiler for rotor blades 

The wind energy start-up evoblade, a spin-off of the Institute of Aerospace Technology (IAT) of the University of Applied Sciences Bremen, has developed a retrofittable spoiler for rotor blades with the help of Deutsche Windtechnik and Wirtschaftsförderung Bremen. The results of the side-by-side study of testing and optimizing the EvoFlap confirm that installing the spoiler can improve the aerodynamic flow in the area of the blade root, resulting in improved efficiency and performance of the turbine.

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