Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism

The complex and changeable working environment of wind turbine often challenges the condition monitoring and fault detection. In this paper, a new method is proposed for fault detection of wind turbine, in which the convolutional neural network (CNN) cascades to the long and short term memory network (LSTM) based on attention mechanism (AM). Supervisory control and data acquisition (SCADA) data are used from wind turbine as input variables and build CNN architecture to extract dynamic changes of data. AM is applied to strengthen the impact of important information. AM can assign different weighs for concentrating the characteristics of LSTM to increase the learning accuracy through mapping weigh and parameter learning. The proposed model can execute early warning for anomaly state and deduce the faulted component by prediction residuals. Finally, through the cases the early failure of the wind turbine is predicted, which verifies the effectiveness of the proposed method.

Automated summary

A new method for fault detection of wind turbines is proposed in this paper, which combines CNN and LSTM networks trained on SCADA data to enhance accuracy and predictive effectiveness.