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Number 2 - June 2018
Volume 28 - 2018
Data-driven techniques for the fault diagnosis of a wind turbine benchmark
Silvio Simani, Saverio Farsoni, Paolo Castaldi
Abstract
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.
Keywords
fault diagnosis, analytical redundancy, fuzzy systems, neural networks, residual generators, fault estimation, wind turbine benchmark