Making Wind Energy More Efficient With Data At Turbit Systems

Summary Wind energy is an important component of an ecologically friendly power system, but there are a number of variables that can affect the overall efficiency of the turbines. Michael Tegtmeier founded Turbit Systems to help operators of wind farms identify and correct problems that contribute to suboptimal power outputs. In this episode he shares the story of how he got started working with wind energy, the system that he has built to collect data from the individual turbines, and how he is using machine learning to provide valuable insights to produce higher energy outputs. This was a great conversation about using data to improve the way the world works. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? 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Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host is Tobias Macey and today I’m interviewing Michael Tegtmeier about Turbit, a machine learning powered platform for performance monitoring of wind farms Interview Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Turbit and your motivation for creating the business? What are the most problematic factors that contribute to low performance in power generation with wind turbines? What is the current state of the art for accessing and analyzing data for wind farms? What information are you able to gather from the SCADA systems in the turbine? How uniform is the availability and formatting of data from different manufacturers? How are you handling data collection for the individual turbines? How much information are you processing at the point of collection vs. sending to a centralized data store? Can you describe the system architecture of Turbit and the lifecycle of turbine data as it propagates from collection to analysis? How do you incorporate domain knowledge into the identification of useful data and how it is used in the resultant models? What are some of the most challenging aspects of building an analytics product for the wind energy sector? What have you found to be the most interesting, unexpected, or challenging aspects of building and growing Turbit? What do you have planned for the future of the technology and business? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Turbit Systems LIDAR Pulse Shaping Wind Turbine SCADA Genetic Algorithm Bremen Germany Pitch Yaw Nacelle Anemometer Neural Network Swarm64 Podcast Episode Tensorflow The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast