Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated ondata from operational wind farms

Abstract

Abstract. Condition monitoring and failure prediction for wind turbines is currently a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on condition monitoring for wind turbines using SCADA data and the Normal Behavior Modelling framework. The first part of the paper consists of an in-depth overview of 5 the current state-of-the-art. In the second part, several techniques from the overview are implemented and compared using data (SCADA and failure data) from five operational wind farms. To this end, 6 demonstration experiments are designed. The first 5 experiments test different techniques for the modeling of the normal behavior. The sixth experiment compares several techniques that can be used for identifying anomalous patterns in the prediction error. The paper concludes with several directions for future work.

Pattern mining based data fusion for wind turbine condition monitoring

“…From the SCADA data, the following signals are used as input: generator front and rear bearing temperatures, nacelle temperature, wind speed, rotor speed, active power, and generator torque. These were selected based on an extensive literature review (see [17]). From the status log data, only the alarms associated with stops and warnings are used.…”