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 language | English |
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Title of host publication | 2018 IEEE 16th International Conference on Industrial Informatics (INDIN) |
Publisher | IEEE |
Pages | 647-652 |
Number of pages | 6 |
ISBN (Electronic) | 9781538648292 |
ISBN (Print) | 9781538648308 |
DOIs | |
Publication status | Published - 18 Jul 2018 |
Keywords
- fault detection
- neural network
- wind turbine systems