Convolutional neural network fault classification based on time-series analysis for benchmark wind turbine machine

Rahimilarki, Reihane, Gao, Zhiwei, Jin, Nanlin and Zhang, Aihua (2022) Convolutional neural network fault classification based on time-series analysis for benchmark wind turbine machine. Renewable Energy, 185. pp. 916-931. ISSN 0960-1481

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Abstract

Fault detection and classification are considered as one of the most mandatory techniques in nowadays industrial monitoring. The necessity of fault monitoring is due to the fact that early detection can restrain high-cost maintenance. Due to the complexity of the wind turbines and the considerable amount of data available via SCADA systems, machine learning methods and specifically deep learning approaches seem to be powerful means to solve the problem of fault detection in wind turbines. In this article, a novel deep learning fault detection and classification method is presented based on the time-series analysis technique and convolutional neural networks (CNN) in order to deal with some classes of faults in wind turbine machines. To validate this approach, challenging scenarios, which consists of less than 5% performance reduction (which is hard to identify) in the two actuators or four sensors of the wind turbine along with sensors noise are investigated, and the appropriate structures of CNN are suggested. Finally, these algorithms are evaluated in simulation based on the data of a 4.8 MW wind turbine benchmark and their accuracy approves the convincing performance of the proposed methods. The proposed algorithm are applicable to both on-shore and off-shore wind turbine machines.

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