Title :
Simulation of wind power system involving flywheel energy storage unit based on wind speed forecasting by RBF neural network
Author :
Yi Feng ; Heyun Lin ; Jianhu Yan ; Xiaoquan Lu ; Hui Yang
Author_Institution :
Eng. Res. Center for Motion Control of MOE, Southeast Univ., Nanjing, China
Abstract :
A wind power system (WPS) involving a flywheel energy storage (FES) unit is proposed to reduce the fluctuation of output power due to the intermittent nature of wind power. The control of the FES unit is based on a wind speed forecasting technique by the RBF neural network. A new control strategy adopting different reference DC bus voltages under different forecasting wind speeds is presented to improve the output power regulation capability of the FES unit. The wind speeds in the next time array can be accurately forecasted by the radical basis function (RBF) neural network. The mathematical model of a stand-alone wind power generation system including a FES unit is established and simulated by Matlab/Simulink. The simulation results verify the correctness of the system model and the effectiveness of the control method.
Keywords :
flywheels; power control; power engineering computing; power generation control; radial basis function networks; wind power plants; FES unit control; Matlab-Simulink; RBF neural network; flywheel energy storage unit; output power fluctuation reduction; output power regulation capability; radical basis function neural network; reference DC bus voltage; stand-alone wind power generation system; wind power system; wind speed forecasting; Flywheels; Forecasting; Mathematical model; Neural networks; Neurons; Voltage control; Wind speed; flywheel energy storage (FES); radial basis function (RBF) neural network; wind speed forecasting;
Conference_Titel :
Renewable Energy Research and Applications (ICRERA), 2013 International Conference on
Conference_Location :
Madrid
DOI :
10.1109/ICRERA.2013.6749855
