Abnormal recognition of wind turbine generator based on SCADA data analysis using CNN and LSTM with nuclear principal component analysis

The diverse and dynamic working environment of a wind turbine (WT) frequently makes it difficult to monitor and identify abnormalities. In this study, a novel approach is proposed for abnormal recognition of WT generator, in which the convolutional neural network (CNN) cascades to the long and short term memory network (LSTM) based on nuclear principal component analysis (KPCA). Firstly, the quartile method is used to preprocess SCADA data to delete abnormal data and improve data effectiveness. Then, by selecting the input variables based on Pearson correlation coefficient, KPCA can eliminate the nonlinearity of process variables and enhance the generalization ability of the algorithm. In this study, CNN and LSTM based on KPCA state recognition model is established by extracting principal com-ponents from KPCA. The model can warn the abnormal state of the generator through the prediction residual. The prediction residual exceeds the threshold for many times, indicating that the operation state is abnormal. Finally, to demonstrate the effectiveness of this approach, the state of WTs generator is forecasted using examples.