--- title: "Optimizing the performance potential of wind turbines: LATODA and Fraunhofer IWES develop AI analysis method" description: "In the \"WindKI\" research project, the Fraunhofer Institute for Wind Energy Systems IWES and the AI company LATODA are developing a new method that detects performance losses in wind turbines at an early stage. The aim of the multi-year project is to implement an AI-supported diagnostic system that will enable objective and data-driven performance optimization of the turbines. The project is funded by the German Federal Ministry of Research, Technology and Space." --- [Press Releases ](https://pes.eu.com/press-releases) # [Optimizing the performance potential of wind turbines: LATODA and Fraunhofer IWES develop AI analysis method](https://pes.eu.com/press-releases/optimizing-the-performance-potential-of-wind-turbines-latoda-and-fraunhofer-iwes-develop-ai-analysis-method) Written by [Negin Hashemi](https://pes.eu.com/press-releases/author/negin-hashemi) | Sep 16, 2025 8:24:34 AM **In the "WindKI" research project, the Fraunhofer Institute for Wind Energy Systems IWES and the AI company LATODA are developing a new method that detects performance losses in wind turbines at an early stage. The aim of the multi-year project is to implement an AI-supported diagnostic system that will enable objective and data-driven performance optimization of the turbines. The project is funded by the German Federal Ministry of Research, Technology and Space.** Wind turbines do not always generate the amount of electricity predicted prior to their construction. There can be many reasons for this, such as unfavorable wind conditions, incorrect rotor blade angle settings, or aerodynamic effects in the wake of other wind turbines. To date, there is no reliable method for automatically detecting the causes of underperformance. **Innovative solution using AI methods** The WindKI project partners are specifically addressing these challenges: By combining measurement data, simulations, and artificial intelligence, they are creating a diagnostic system that uses heuristic algorithms and machine learning models to detect anomalies in the data set. The first models are based on high-resolution SCADA data sets from the 8-megawatt Adwen AD8 research turbine, which Fraunhofer IWES is providing for the project. On this basis, LATODA is developing an AI-based analysis system that automatically determines whether a turbine is operating as expected or is underperforming and suggests relevant parameters for optimization. The AI not only reports anomalies, but also provides clues as to their probable causes – from rotor blade settings to unfavorable operating conditions. This gives operators a tool that helps them identify and rectify problems more quickly. The methodology enables the wind industry to develop customer-oriented AI models based on any time series data. "The results of the project improve our understanding of the overall turbine dynamics," says Philipp Thomas, Group Manager Global Turbine Dynamics at Fraunhofer IWES. “The combination of expert knowledge and AI opens up new possibilities for the wind industry." LATODA Managing Director Daniel Hein also emphasizes the importance of the cooperation: "With our algorithm for analyzing sensor data, we are creating the basis for fast and reliable fault diagnosis. This will significantly reduce failures and reduced performance." The "WindKI" research project offers the opportunity to build a bridge between advanced AI techniques and conventional approaches in the field of wind energy. By linking domain knowledge from wind energy research with state-of-the-art machine learning methods, not only a concrete technical problem is solved, but also a framework for future, interdisciplinary research projects is created. **About Fraunhofer IWES** The Fraunhofer Institute for Wind Energy Systems IWES conducts application-oriented research for a sustainable future. The focus topics of the Fraunhofer IWES are offshore, hydrogen, test infrastructure and digitalization. The research work in these future-oriented key technologies plays a central role in the innovation process and strengthens the business location for the benefit of our society by transferring the research results to industry. More than 400 employees at nine locations are developing innovative methods to accelerate the expansion of the wind energy and hydrogen economy, minimize risks, and increase cost efficiency. **About LATODA** LATODA is a German AI pioneer specializing in the development of complex AI solutions. The company is a spin-off of the Philipps University of Marburg. The WindKI core team combines expertise from the fields of artificial intelligence, business informatics, business administration, wind energy and certification. LATODA has experience in the development of complex AI models, which are used, for example, in the detection and evaluation of damage to wind turbine blades. **Contact persons Fraunhofer Institute for Wind Energy Systems IWES** Dipl.-Ing. Philipp Thomas Group Manager Global Turbine Dynamics Phone +49 471 14290-381 [philipp.thomas@iwes.fraunhofer.de](mailto:philipp.thomas@iwes.fraunhofer.de) Yvonne Schink Manager Science Communications Phone +49 471 14290-189 [yvonne.schink@iwes.fraunhofer.de](mailto:yvonne.schink@iwes.fraunhofer.de) **Contact persons LATODA** Dr. Olaf Mager Press officer LATODA Phone +49 172 4039172 [olaf.mager@latoda.de](mailto:olaf.mager@latoda.de) Daniel Hein CEO LATODA [Daniel.hein@latoda.de](mailto:Daniel.hein@latoda.de) [View full post](https://pes.eu.com/press-releases/optimizing-the-performance-potential-of-wind-turbines-latoda-and-fraunhofer-iwes-develop-ai-analysis-method) ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Negin Hashemi" }, "dateModified" : "2025-09-16T08:24:34.910Z", "datePublished" : "2025-09-16T08:24:34Z", "headline" : "Optimizing the performance potential of wind turbines: LATODA and Fraunhofer IWES develop AI analysis method", "image" : { "@type" : "ImageObject", "height" : 1080, "url" : "https://145893424.fs1.hubspotusercontent-eu1.net/hubfs/145893424/Foto-AD-8-Flugplatz-20201026-281-Fraunhofer-IWES.jpg", "width" : 1440 }, "mainEntityOfPage" : "https://pes.eu.com/press-releases/optimizing-the-performance-potential-of-wind-turbines-latoda-and-fraunhofer-iwes-develop-ai-analysis-method", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "height" : 60, "url" : "/hs/hsstatic/content_shared_assets/static-1.4092/img/default-amp-logo.png", "width" : 60 }, "name" : "Press Releases" } } ```