Gusts detection in a horizontal wind turbine by monitoring of innovations error of an extended Kalman filter

This paper presents a novel model-based detection scheme capable of detecting and diagnosing gusts. Detection is achieved by monitoring the innovations error (i.e., the difference between the estimated and measured outputs) of an extended discrete Kalman filter. It is designed to trigger a detection confirmation alarm in the presence of wind anomalies. Simulation results are presented to demonstrate that both operating and coherent extreme wind gusts can successfully be detected. The wind anomaly is identified in magnitude and shape through maximum likelihood ratio and goodness of fit, respectively. The detector is capable of isolating extreme wind gusts before the turbine over speeds.