Data-driven techniques for the fault diagnosis of a wind turbine benchmark - Simani, Silvio; Farsoni, Saverio; Castaldi, Paolo (Zielona Góra: Uniwersytet Zielonogórski)
This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults.
Keywords: fault diagnosis; analytical redundancy; fuzzy systems; neural networks; residual generators; fault estimation; wind turbine benchmark