Fatigue Damage Monitoring of Wind Turbine Blades by Using the Kalman Filter

Abstract

Fatigue damage accumulation needs to be monitored for extending the lifetime of horizontal axis wind turbines. To monitor accumulation of fatigue damage, this paper proposes a blade load and fatigue damage monitoring system consisting of physics models coupled with the Kalman filter (KF). The proposed monitoring system estimates bending moment distribution along the blade as a time series from 1 Hz data collected by a supervisory control and data acquisition (SCADA) system and from 20 Hz strain sensor data collected at the blade root. Static load is computed on the basis of blade element and momentum theory from SCADA data, whereas dynamic response of the blade is computed by assuming a mass–spring–damper system. The KF corrects the estimated moment distribution with moment measured at the blade root. The monitoring system is demonstrated and validated by installing it on a 2 MW floating downwind turbine and estimating strain and fatigue damage at the medium span position of the blades, where a strain sensor is installed. The validation results show that strain wave forms and fatigue damage estimated by the monitoring system with the KF are consistent with those computed from the strain measurement with sufficient accuracy for structural health monitoring.