Simultaneous fault diagnosis of wind turbine using multichannel convolutional neural networks

Wind turbine technology is pursuing the maturation using advanced multi-megawatt machinery equipped by powerful monitoring systems. In this work, a multichannel convolutional neural network is employed to develop an autonomous databased fault diagnosis algorithm. This algorithm has been evaluated in a 5MW wind turbine benchmark model. Several faults for various wind speeds are simulated in the benchmark model, and output data are recorded. A multichannel convolutional neural network with multiple parallel local heads is utilized in order to consider changes in every measured variable separately to identify subsystem faults. Time-domain signals obtained from the wind turbine are portrayed as images and fed independently to the proposed network. Results show that the multivariable fault diagnosis scheme diagnoses the most common wind turbine faults and achieves high accuracy. (c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

The study developed an autonomous databased fault diagnosis algorithm using a multichannel convolutional neural network, which was successfully evaluated in a 5MW wind turbine benchmark model. Results showed that the algorithm can diagnose common wind turbine faults with high accuracy.