Case Study: a German energy company reduces wind turbine downtime with SumatoSoft's predictive maintenance platform

German Energy Company cuts unplanned turbine downtime by 38% with SumatoSoft

A german energy company operating a 28-turbine wind farm faced challenges with unplanned gearbox and generator failures, which caused significant downtime and high emergency repair costs. Their existing SCADA system was unable to detect early signs of wear, leading to reactive maintenance. To address this, they employed SumatoSoft to develop an IoT- and ML-based predictive maintenance solution.

SumatoSoft implemented a system that layered on top of the existing infrastructure, using data from current sensors to run machine learning models that flagged component wear weeks in advance. This solution reduced unplanned downtime by 38%, increased fleet availability to 97.7%, and cut annual OPEX by €504,000. The project delivered a full payback on its €640,000 investment in just 9.8 months.