Recognition of blade icing state based on vine-Copula model and LSTM-Autoencoder algorithm

Abstract

Blade icing of a wind turbine will affect the startup performance of the blades, resulting in the loss of power generation of the wind turbine, and even affect the safety of production and operation. In order to reflect the blade icing state of wind turbines as truthfully and objectively as possible, this paper proposes a wind turbine blade icing state recognition model based on the combination of vine-Copula network model and Long Short-Term Memory (LSTM)-Autoencoder algorithm. First, the vine-Copula model is used to analyze the correlation between the various parameters in supervisory control and data acquisition (SCADA) system and the blade icing state, thereby constructing a high-dimensional vine-Copula structure. Then, removing the features that are not directly related to the blade icing state, the final vine-Copula model and related features are obtained. The filtered features are input into the LSTM-Autoencoder algorithm, then the “memory” function and non-linear feature extraction capabilities of the LSTM-Autoencoder algorithm are used to obtain the evaluation results of the blade icing state of wind turbines. The experimental results show that the indicators of the wind turbine blade icing state recognition based on this method are overall better than the indicators of the Recurrent Neural Network-Autoencoder algorithm without feature reduction and the LSTM-Autoencoder algorithm without feature reduction and traditional classification algorithms.