Journal of Ocean Engineering and Technology (한국해양공학회지)

Korean Society of Ocean Engineers (한국해양공학회)
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Fault Classification of a Blade Pitch System in a Floating Wind Turbine Based on a Recurrent Neural Network

  • Cho, Seongpil (Alternative Fuels and Power System Research Center, Korea Research Institute of Ships and Ocean Engineering) ;
  • Park, Jongseo (Division of Naval Architecture and Ocean Systems Engineering, Korea Maritime and Ocean University) ;
  • Choi, Minjoo (Division of Naval Architecture and Ocean Systems Engineering, Korea Maritime and Ocean University)
  • Received : 2021.04.07
  • Accepted : 2021.06.08
  • Published : 2021.08.31
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Abstract

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

Keywords

Acknowledgement

This work was supported by "The development of a fully electrified car ferry and a removable power supply system (Project No. 20200469-01, PMS4700) funded by a national R&D project of the Ministry of Oceans and Fisheries. The authors gratefully would like to express our sincere gratitude for the research fund granted.

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