Open AccessNeural network model predictive control optimisation for large wind turbines
Author(s) -
Han Bing,
Kong Xiaofang,
Zhang Zhiwen,
Zhou Lawu
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.1989
Subject(s) - turbine , wind power , model predictive control , renewable energy , artificial neural network , computer science , control theory (sociology) , engineering , control engineering , control (management) , artificial intelligence , electrical engineering , mechanical engineering
Energy poverty limits the economy and social development throughout the world. Wind turbine reduces the energy costs and facilitates the development of renewable energy industry, which provides an effective solution to energy crisis and environment pollution and develops rapidly in recently years. In this paper, a radial basis function neural network (RBFNN) optimisation model predictive control (MPC) was proposed for large wind turbines. In accordance with the complexity and uncertainty of wind turbine operation, a linear model based on the blade element momentum theory was established and the influencing factors of the proposed model were evaluated. The MPC taking into full account three degrees of freedom control multivariate was enforced by RBFNN prediction model, which meets the requirements of specified operation region. Additionally, the RBFNN prediction model with the memory of complicated rules and changed trend was trained by a great deal of historical data. The RBFNN in combination with MPC solves global optimisation problems and improves the dynamic performance of system. Simulation results for three‐bladed 5 MW onshore wind turbine verified the effectiveness of the proposed method and confirmed the fact that the fatigue loads were significantly reduced in the turbine tower.