Title :
Implementation of the RBF neural network on a SOPC for maximum power point tracking
Author :
Cao, Bo ; Chang, Liuchen ; Li, Howard
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of New Brunswick, Fredericton, NB
Abstract :
In this paper, a radial basis function (RBF) neural network is implemented as a system on a programmable chip (SOPC) to carry out maximum power point tracking (MPPT) for photovoltaic (PV) control systems. The implementation of the SOPC can provide a traditional proportional integral derivative (PID) controller and some additional hardware like a pulse width modulation (PWM) generator by a general purpose field programmable gate array (FPGA) chip, as well as integrate a RBF neural network controller by embedded soft processors. The tracking algorithm changes the duty-cycle of the IGBTs to make the PV converter work at maximum power. As a result, the MPPT unit of the PV system becomes an independent and highly integrated product including peripheral design and a control algorithm.
Keywords :
field programmable gate arrays; photovoltaic power systems; power system control; radial basis function networks; system-on-chip; three-term control; FPGA chip; PID controller; RBF neural network; field programmable gate array; maximum power point tracking; photovoltaic control systems; proportional integral derivative controller; pulse width modulation; radial basis function neural network; system on a programmable chip; Control systems; Field programmable gate arrays; Neural networks; PD control; Photovoltaic systems; Pi control; Proportional control; Pulse width modulation; Solar power generation; Three-term control; FPGA; MPPT; RBF; SOPC; neural network;
Conference_Titel :
Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on
Conference_Location :
Niagara Falls, ON
Print_ISBN :
978-1-4244-1642-4
Electronic_ISBN :
0840-7789
DOI :
10.1109/CCECE.2008.4564682
