Condition Monitoring of Large Scale Offshore Wind Turbine Systems by Using Model Based Robust Fault Detection and Estimation Techniques

摘要

Since the maintenance of offshore wind turbines is much more difficult and expensive than that of onshore wind turbines, the reliability needs to be improved to reduce the maintenance cost and to increase the availability. In this paper, we consider sensor and actuator fault detection and estimation issues for large scale wind turbine systems where individual pitch control is used for loads reduction. The faults considered in the paper are mainly focused on the blade root bending moment sensors and blade pitch actuators. For the considered components, we use fault detection observer developed recently based on the generalized KYP lemma to monitor their health condition. The observer handles not only the unknown inputs such as the wind disturbances, but the model uncertainties as well. It is capable to detect the sensors, actuator and other components fault with the aid of the dynamical model. When there is a detectable fault, the observer will send an alarm signal if the output of the residual evaluation functions is larger than a preset threshold. In order to estimate the fault magnitude, a dynamical filter is applied to identify the fault, which is also based on the generalized KYP lemma. Simulation results for several fault scenarios show that the methods used in this paper achieve sound performance.