Digital twins are fast taking over as a crucial component of wind farm operations. Digital twins can help spot potential problems before they occur, provide insights into how the wind farm is performing, and provide critical data for planning and forecasting by utilizing data and AI.  This can help wind farms maximize efficiency and reduce downtime. One can use Digital Twins to monitor and adjust turbine performance in real-time, ensuring that the turbines operate at peak efficiency. Wind farm operators may maintain efficient and cost-effective operations by deploying digital twins. 

Optimized Modeling and Design

Digital twin technology makes it possible to predict a wind farm’s performance before it is built. This technology uses data gathered from current wind turbines to create models of potential designs and match various turbine combinations. The Digital Wind Farm is a system developed by GE Renewable Energy which consists of a 2MW wind turbine and software to monitor and optimize the turbine to increase energy production. The Digital Wind Farm utilizes digital twin technology, allowing up to twenty different turbine configurations to be combined and matched, to guarantee the most effective wind turbine for the actual location of the farm.

After the actual wind turbine is installed, the digital twin model gathers and examines data from the real-world counterpart and makes recommendations for improving the wind turbine’s efficiency. Integrating the software and hardware of wind energy systems can increase energy production by up to 20%, potentially resulting in an extra $100 million in revenue over the lifetime of the wind turbine. The Digital Wind Farm is a lot more productive and efficient than traditional wind turbines because of this hardware and software setup. 

Siemens Gamesa and NVIDIA have partnered to create AI-powered digital twins of Siemens’ turbines to optimize their function and protect them in case of storms. Siemens can respond appropriately with less expensive solutions thanks to digital twins, which help them better grasp the potential and constraints of their machinery.  With AI frameworks and chips, NVIDIA is accelerating simulation modeling up to 4,000X faster than traditional methods and using NVIDIA Omniverse and NVIDIA Modulus to build high-fidelity, parameterized surrogate models with near-real-time latency. By accomplishing this, Siemens can lower the level of uncertainty and produce realistic models of its turbines that can be used to assess future use cases, novel control techniques, and impending weather occurrences. 

Failure Diagnoses and Prediction – Digital Twins

Digital Twins can provide insights into how the wind farm is operating, helping identify potential issues before they arise. To learn more about how wind turbines affect animals and ecosystems, SSE Renewables, Microsoft, and Avanade are working together to digitally recreate offshore wind farms and the surrounding area. Azure IoT enables energy companies to automate data collection, thereby allowing them to gain a better understanding of the impact of their developments before construction. The implementation of the project will take place in the Dutch North Sea, and the project team will gather data to guide future wind farm construction and other activities that may impact wildlife. Microsoft and SSE Renewables have previously used Microsoft Azure to explore the effects of wind farms on Scottish puffins. That project aimed to gain insight into the potential impacts of wind farms on the environment.

For wind turbines, DNV created WindGEMINI, a digital twin analytics tool that offers real-time insights into individual turbine performance. The system’s algorithms produce a range of insights, from predictive maintenance to forecasting of long-term energy production. It enable turbine owners to improve the operation of their wind farms. An example of WindGEMINI’s effectiveness is a wind farm in the US. After implementing a control system upgrade, WindGEMINI detected a 2% decrease in production from one turbine. Further investigation found an incorrect pitch setting in the turbine, resulting in cost savings of USD 80,000.

Reduced LCoE – Digital Twins

Aker Offshore Wind is the leader of the NextWind project, a collaboration that researches emerging wind technologies with the potential to lower the Levelized Cost of Energy (LCoE) for floating offshore wind.Aker Offshore Wind and its partners developed a digital twin simulation as part of the NextWind project, enabling real-time monitoring of the offshore floating wind farm’s condition and its impact on the environment. It furnishes valuable data that can enhance the performance, reliability, and competitiveness of offshore wind generation. The Aker project is striving to reduce the LCOE by focusing on around 30-40 percent of the cost components that vary, such as substructures, mooring, dynamic cables, marine operations, and installation processes.Unasys, Transmission Dynamics, ORE Catapult, and universities are collaborating on a digital twin project for offshore wind. This digital twin uses sensors on wind turbines to monitor their entire lifecycle, collecting, analyzing, and interpreting data.

The goal of this project is to determine the best location for installing digital twinning technology to make maintenance and operations planning more effective and reduce the need for unnecessary inspections and interventions in wind farms. The project’s ultimate goal is to lower the levelized cost of energy by transforming future turbine operations and maintenance, boosting safety, and prolonging turbine life. 

Digital twins will drive wind farms of the future

Digital twins transform wind energy by allowing remote access to real-time data and turbine status, revolutionizing the industry. Advances in computing power, cloud tech, IoT, and ML algorithms make it easier to access digital twin technology. Wind farms are adopting interconnected digital technology to address a long-standing need for greater flexibility in renewable wind power generation and integration. Digital twins enable proactive maintenance for wind turbines, reducing costs, labor hours, and increasing uptime, replacing traditional maintenance strategies. As a result, digital twins will certainly have significant market penetration in the wind sector in the coming years.