Short-Term Fault Prediction of Wind Turbines Based on Integrated RNN-LSTM

Abstract

This paper presents a data-driven approach to short-term wind turbine fault prediction and condition monitoring based on a hybrid architecture of recurrent neural network and long short-term memory. The proposed architecture is established by utilizing time series data from the supervisory control and data acquisition system and a Bladed model of a 5 MW wind turbine to predict faults occurring to the wind generator. The recurrent neural network-long short-term memory training procedure is enhanced with self-organizing maps and long short-term memory auto encoder so as to describe the complex interaction between the mechanical system and unpredictable wind speed. To verify the performance of the proposed scheme, we conduct in-depth numerical experiments by applying the hybrid architecture to the Bladed 5 MW wind turbine model with rated wind speed of 11.8 m/s. Experimental results confirm that the proposed scheme has superior accuracy and practicality of fault prediction compared with eminent existing machine learning algorithms such as extreme gradient boost and random forest regressor.

Keywords

• Extreme gradient boosting
• fault prediction
• long short-term memory autoencoder
• random forest regressor
• self-organizing maps
• wind turbine