Investigation of anomaly detection technique for wind turbine pitch systems

Abstract

Anomaly detection for Wind Turbine pitch system fault detection is an active area of research within the Wind Energy community. In this paper a novel condition monitoring technique was presented with the aim to reduce data storage requirements and computational intensity. The technique utilised an Isolation Forest Anomaly Detection model. This new technique was trained on a period of data per turbine to then test on each subsequent month of data individually. The anomaly count is then plotted to observe a trend before failure. This was done as a tool to assist in planning maintenance actions. The number of training months were compared for each turbine to find the minimum appropriate. It was found that one month was appropriate in most cases, with others requiring 3 or 4 months. The technique was able to detect failure up to 3 months before, and abnormal activity roughly 10 to 12 months before failure. Therefore this could be very beneficial to wind farm operators for scheduling maintenance activity around weather windows and vessel rate fluctuations.