Data-driven estimation of the inertia moment of wind turbines: A new ice-detection algorithm

Abstract

Turbine blades accumulate ice under certain atmospheric conditions, such as low temperature and high humidity. This implies additional loads that might damage the turbine. A reliable ice-detection algorithm is required to shut down the turbine and prevent damages. A simple sensorless technique is proposed in this article for detection of both icing and ice shedding events. The technique is based on the estimation of the turbine inertia moment using generator speed measurements, turbine model and robust and fast convergent data-driven algorithms. Estimated inertia moment can be used for both ice detection and adaptation of the parameters of the control system. Implementation of this technique allows a stable turbine operation during hazardous ice conditions via adjustment of the control system parameters or turbine shut down in the extreme icing conditions.