Open AccessPitch control of wind turbine based on deep neural network
Author(s) -
Wei Jie,
Jingchun Chu,
Lin Yuan,
Wenliang Wang,
Jian Dong
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/619/1/012034
Subject(s) - extreme learning machine , turbine , artificial neural network , computer science , wind power , control theory (sociology) , controller (irrigation) , representation (politics) , feature extraction , feature (linguistics) , coding (social sciences) , artificial intelligence , control engineering , engineering , control (management) , mathematics , mechanical engineering , agronomy , linguistics , philosophy , statistics , electrical engineering , politics , law , political science , biology
This paper analyzed the input and output data of wind farm based on deep neural network, developed intelligent model, and realized the predictive modeling of important parameter variables and control of wind turbine. By establishing the Deep Extreme Learning Machine(DELM), the higher-order nonlinear model is simplified. In this structure, unsupervised hierarchical ELM is conducted for feature extraction, and the features of the lower layer are transferred to the higher layer through layer by layer coding to form a relatively complete feature representation. Finally, the Extreme Learning Machine (ELM) is used to complete the mapping of feature representation to target output to minimize the loss of information in the transmission process. The target output is used as reference data for Pitch control of wind turbine, which is proposed by using a radial basis function (REF) neutral network. Simulation results from GH-Bladed show that proposed control algorithm can mitigate the loads effectively. The algorithm provides a practical reference for the design of wind turbine controller.