Title :
Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System
Author :
Lin, Whei-Min ; Hong, Chih-Ming ; Chen, Chiung-Hsing
Author_Institution :
Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan
Abstract :
A stand-alone hybrid power system is proposed in this paper. The system consists of solar power, wind power, diesel engine, and an intelligent power controller. MATLAB/Simulink was used to build the dynamic model and simulate the system. To achieve a fast and stable response for the real power control, the intelligent controller consists of a radial basis function network (RBFN) and an improved Elman neural network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by the ENN, and the solar system uses RBFN, where the output signal is used to control the dc/dc boost converters to achieve the MPPT.
Keywords :
DC-DC power convertors; diesel-electric power stations; hybrid power systems; intelligent control; maximum power point trackers; neurocontrollers; power control; power generation control; solar power stations; wind power plants; wind turbines; DC-DC boost converters; ENN; MPPT; Matlab-Simulink; RBFN; diesel engine; improved Elman neural network; intelligent power controller; maximum power point tracking; neural-network-based MPPT control; radial basis function network; solar power; solar system; stand-alone hybrid power generation system; wind power; wind turbine; Aerodynamics; Arrays; Artificial neural networks; Generators; Wind power generation; Wind turbines; Diesel engine; improved Elman neural network (ENN); maximum power point tracking (MPPT); photovoltaic (PV) power system; radial basis function network (RBFN); wind power system;
Journal_Title :
Power Electronics, IEEE Transactions on
DOI :
10.1109/TPEL.2011.2161775
