NN-SMC MPPT Method for PV Generating System

Author

Yong, Zhao ; Hong, Li ; Liqun, Liu

Author_Institution

Electron. & Inf. Eng. Coll., Taiyuan Univ. of Sci. & Technol., Taiyuan, China

fYear

2011

fDate

21-23 Nov. 2011

Firstpage

141

Lastpage

144

Abstract

An efficient Maximum Power Point Tracking (MPPT) method is extremely important to improve the output efficiency and electrical energy quality of a photovoltaic (PV) generating system. The MPPT course is very difficult due to the nonlinear and time-varying output characteristic of a PV system. SMC (sliding mode control) is used to track maximum power point (MPP) of PV system, and the results represent that the SMC have better tracking characteristic as compare with the conventional perturb and observe (PO) method. The RBF neural network is used to improve the SMC in order to increase the electrical energy quality and reduce the output vibration. The simulation results show the reliability of the suggested method, and the output and dynamic characteristics of PV system are significantly improved.

Keywords

maximum power point trackers; photovoltaic power systems; power engineering computing; radial basis function networks; NN-SMC MPPT method; PV generating system; RBF neural network; SMC; electrical energy quality; maximum power point tracking method; photovoltaic generating system; sliding mode control; Artificial neural networks; Biological neural networks; Educational institutions; Photovoltaic systems; Switches; NN-SMC; PV system; maximum power point tracking; neural network; variable structure control;

fLanguage

English

Publisher

ieee

Conference_Titel

Robot, Vision and Signal Processing (RVSP), 2011 First International Conference on

Conference_Location

Kaohsiung

Print_ISBN

978-1-4577-1881-6

Type

conf

DOI

10.1109/RVSP.2011.47

Filename

6114924