Article
Version 1
Preserved in Portico This version is not peer-reviewed
Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model
Version 1
: Received: 28 April 2017 / Approved: 1 May 2017 / Online: 1 May 2017 (07:53:08 CEST)
A peer-reviewed article of this Preprint also exists.
Simani, S.; Castaldi, P.; Farsoni, S. Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model. Energies 2017, 10, 866. Simani, S.; Castaldi, P.; Farsoni, S. Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model. Energies 2017, 10, 866.
Abstract
The fault diagnosis of wind farms has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, 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 farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. 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 logic; neural networks; data-driven approaches; nonlinear geometric approach; wind farm benchmark simulator
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment