Grey-box Model Identification and Fault Detection of Wind Turbines Using Artificial Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

In this paper, a model identification method based on artificial neural networks (ANN) for wind turbine dynamics is studied. Due to the fact that wind turbine has a nonlinear dynamics with partially measured states, ANN cannot be applied directly. To cope with this problem, first a Luenberger observer is designed to estimate the states (both measured and unmeasured ones) and then, for the nonlinear part, a multi-input multi-output (MIMO) back propagation neural-network based observer is proposed. By having an ANN model as the reference, a fault detection method is studied based on the residual of the system. This algorithm is evaluated in simulation on a 4.8 MW wind turbine benchmark and the results approve satisfactory performance of the proposed approach.
Original languageEnglish
Title of host publication2018 IEEE 16th International Conference on Industrial Informatics (INDIN)
PublisherIEEE
Pages647-652
Number of pages6
ISBN (Electronic)9781538648292
ISBN (Print)9781538648308
DOIs
Publication statusPublished - 18 Jul 2018

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