With Turbit Systems, VSB banks on big data analyses for proactive operational management

Technical management has generated large amounts of data for many years now. Analysing this data and extracting the right insights offers an enormous potential for optimisation.

“In addition to the usual monitoring and reporting, as technical managers we use machine learning techniques to analyse operating data,” said Gerrit Schmidt, Managing Director in the area of operational management at VSB.

Machine learning can draw conclusions about trends and anomalies from the complex systems of wind turbines, which are subject to a whole host of interactions. Thanks to the analysis and forecasting functions implemented in cooperation with Turbit Systems, VSB is able to move from conventional reactive operational management to forward-looking and proactive approaches. 

“Our algorithms learn the performance characteristics of each turbine. This makes it possible to detect dips in performance – regardless of the manufacturer, and per site. With the data from over 6000 turbine years, we are able to detect and classify deviations in performance,” said Michael Tegtmeier, founder and Managing Director of Turbit Systems GmbH.  

Turbit Systems uses machine learning algorithms to investigate the performance characteristics of wind farms. The pilot phase focused on questions such as the impact of performance upgrades implemented by the turbine manufacturer. Does this really result in increased yields? Or are there performance reductions or periods of downtime that are overlooked by the monitoring software? 

Big data and machine learning have the potential both to understand and control turbine performance better than before and to optimise operational management processes. These technologies are therefore essential components of modern technical management in order to derive tangible insights from assumptions and thus make the right decisions.