Large volumes of data have been generated in technical operations management for many years. Analyzing the data and gaining insights from them offers enormous potential for optimization.
"We as technical operations managers use - beyond the usual monitoring and reporting - machine learning techniques to analyze operational data" ~ Gerrit Schmidt, MD, VSB Services
Machine learning can predict trends or anomalies from the complex systems of wind turbines, which are subject to a high degree of interdependencies. The analysis and forecasting functions implemented in collaboration with Turbit Systems enable VSB to move from traditional reactive operational management to proactive control of wind farms.
"Our algorithms learn the performance behavior of each plant. As a result, underperformance can be detected regardless of manufacturer and per site. With data from over 6000 turbine years, we are able to analyze and classify detected performance deviations" Michael Tegtmeier, Co-CEO & Founder Turbit Systems
Turbit Systems uses machine learning algorithms to investigate the performance behavior of the wind farms. The following relevant questions were highlighted during the pilot phase: What will be the impact of performance upgrades implemented by the turbine manufacturer, for example? Will additional yields actually be achieved? Or are there performance reductions or shutdowns that are missed by the monitoring software in use?
Big Data and Machine Learning enable VSB to understand and control plant performance better than ever before - while optimizing operations management processes. These technologies are essential components of modern technical operations management. Because this is how assumptions become tangible knowledge and the right and economically optimal decisions are made with foresight.
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