Open AccessShort-Term Reliability Prediction of Key Components of Wind Turbine Based on SCADA Data
Author(s) -
Ketian Liu,
Jun Zhang,
Feng Shi
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/6/062047
Subject(s) - scada , turbine , wind power , reliability (semiconductor) , principal component analysis , artificial neural network , term (time) , reliability engineering , computer science , key (lock) , engineering , wind speed , data mining , artificial intelligence , power (physics) , meteorology , mechanical engineering , physics , computer security , quantum mechanics , electrical engineering
In this paper, the Principal Component Analysis (PCA) method combined with the Radial Basis Function (RBF) neural network is used to establish a short-term reliability prediction model for wind turbines based on the SCADA data. The PCA method is used to reduce the dimensionality of the SCADA data and extract the principal components as the input data of the RBF neural network. The RBF neural network is used to predict the running state of key components of the wind turbine. Finally, a short-term reliability prediction model of wind turbines based on PCA-RBF is established. With the real wind farm SCADA data, the short-term reliability of wind turbine gearbox is predicted. The result shows that the short-term reliability prediction model can better reflect the reliability of key components and provide reference for the operation and maintenance of wind turbines.