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

Rahimilarki, Reihane and Gao, Zhiwei (2018) Grey-box Model Identification and Fault Detection of Wind Turbines Using Artificial Neural Networks. In: INDIN 2018 - IEEE 16th International Conference on Industrial Informatics, 18th - 20th July 2018, Porto, Portugal.

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/INDIN.2018.8471943

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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: fault detection, neural network, wind turbine systems
Subjects: G400 Computer Science
H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Paul Burns
Date Deposited: 21 Jan 2019 17:28
Last Modified: 21 Jan 2019 17:28
URI: http://nrl.northumbria.ac.uk/id/eprint/37677

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