期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2018
卷号：28
期号：2
页码：1-22
DOI：10.2478/amcs-2018-0018
出版社：De Gruyter Open
摘要：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. The chosen architectures rely on nonlinear
autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The
developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and
the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based
strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the
proposed solutions against typical parameter uncertainties and disturbances.
