The Digital Twin at the Center of Renewable Energy

According to the German Wind Energy Association (BWE), the share of wind energy in German electricity production this year is 27 percent, and in 2020 wind energy even represented the most important energy source in the German electricity mix. In total, more than 31,000 turbines have been installed, saving 89 million tons of CO2 equivalent in 2019. Wind power is thus a mainstay of low-CO2 and sustainable energy generation and makes an important contribution to the energy transition. Further increasing yields while reducing maintenance costs is therefore of great importance.

Increasing the efficiency of wind farms with smart systems

Digital Twins are the central element in exploiting the full potential of wind power and maximizing yields. Driven by the vision of creating a data-based development tool for the wind industry, the WIND IO joint project, funded by the German Federal Ministry for Economic Affairs and Energy, started a year and a half ago.

Under the leadership of the Institute for Integrated Product Development BIK at the University of Bremen, we are working with several consortium partners to build research facilities as cyber-physical systems and retrofit them with sensors, electronics and computers known as IoT gateways. This makes it possible to digitally map all the operating information of the real plant and combine it on a digital twin. The operating behavior can be simulated on the basis of the Digital Twin, which in turn provides insights for further optimization of the wind turbine. The Digital Twin not only provides information about the current energy yield, but also offers a comprehensive overall picture of the condition of each individual turbine.

Improved installation, maintenance and overhaul processes

The information obtained can be used, for example, to optimize maintenance and overhaul processes. For example, the data makes the aging process of components transparent at all times and automatically triggers an alarm if defined limit parameters are exceeded. The Digital Twin also uses the operating, environmental and weather data collected to determine a favorable time for maintenance of the plant. Ideally, this should be carried out when there is little wind, so as not to be at the expense of energy generation.

Both statistical methods and Artificial Intelligence (AI) models are used for the calculations. These methods also help to determine the best time to assemble a wind turbine, since the rotor blades can only be installed under certain conditions. For this purpose, in addition to weather data, additional parameters such as the vibration of the tower are included in the calculations.

Digital Twins for a sustainable industry 

The WIND IO project vividly demonstrates the potential of digitization and especially the concept of the Digital Twin. In addition, companies can use their data to simulate entire production and operating cycles. This makes it possible to minimize resource consumption, reduce energy consumption and at the same time coordinate production steps more effectively and optimize transport routes. Concepts such as the Digital Twin and data-intensive analysis methods are thus essential for a gentle and efficient industry.

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