Machine Learning Based Fault Detection for Wind Turbines
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Nispera is a third-party energy forecasting and performance monitoring solution provider for renewable energy assets. In this project we develop a new software module for condition-based and predictive maintenance for the main components of wind turbines and integrate it in the existing platform of Nispera. For this purpose we develop state of the art machine learning algorithms for early fault detection and isolation in critical turbine components. Early detection enables wind farm owners to schedule maintenance in a planned manner before a complete stoppage of the turbine, thus avoiding long downtimes and the related high costs. The cost effectiveness of this software solution is due to the fact that the required data is recorded and stored by the Supervisory Control And Data Acquisition (SCADA) system which is already present on all wind farms and thus does not require any additional investment by the Owner. We develop a framework to combine time series data together with error log data in order to enhance the precision and robustness of the fault detection algorithms and allow for their generalization to various operating conditions and equipment manufacturers.