Title :
A Solar-powered Battery Charger with Neural Network Maximum Power Point Tracking Implemented on a Low-Cost PIC-microcontroller
Author :
Petchjatuporn, P. ; Ngamkham, Wannaya ; Khaehintung, Noppadol ; Sirisuk, Phaophak ; Kiranon, Wiwat
Author_Institution :
Dept. of Control & Instrum. Eng., Mahanakorn Univ. of Technol., Bangkok
Abstract :
This paper presents the development of a maximum power point tracking algorithm using an artificial neural network for a solar power system. By applying a three layers neural network and some simple activation functions, the maximum power point of a solar array can be efficiently tracked. The tracking algorithm integrated with a solar-powered battery charging system has been successfully implemented on a low-cost PIC16F876 RISC-microcontroller without external sensor unit requirement. The experimental results with a commercial solar array show that the proposed algorithm outperforms the conventional controller in terms of tracking speed and mitigation of fluctuation output power in steady state operation. The overall system efficiency is well above 91%
Keywords :
battery chargers; microcontrollers; neural nets; photovoltaic power systems; power engineering computing; solar cell arrays; PIC16F876 RISC-microcontroller; artificial neural network; neural network maximum power point tracking; solar array; solar power system; solar-powered battery charging system; Adaptive arrays; Artificial neural networks; Batteries; Logic arrays; Neural networks; Photovoltaic cells; Photovoltaic systems; Power engineering and energy; Solar energy; Solar power generation; RISC-microcontroller; component; maximum power point tracking; neural network; solar power system;
Conference_Titel :
Power Electronics and Drives Systems, 2005. PEDS 2005. International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
0-7803-9296-5
DOI :
10.1109/PEDS.2005.1619739
