Title :
ANN Based on IncCond Algorithm for MPP Tracker
Author :
Xu, Jinbang ; Shen, Anwen ; Yang, Cheng ; Rao, Wenpei ; Yang, Xuan
Author_Institution :
Dept. of Control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
Abstract :
In photovoltaic (PV) generation systems, to get the maximum of the solar output power is the essential part to raise the efficiency of the whole system. A new Artificial Neural Network (ANN) based algorithm for Maximum Power Point Tracking (MPPT) has been proposed in this work. By using the duty ratio data generated from the finest results of the traditional Incremental Conductance (IncCond) method as the neural network training data, and building the DC-DC boost tracker to test it in Saber simulation software, the simulation results are shown to clarity the effectiveness of the proposed method.
Keywords :
DC-DC power convertors; maximum power point trackers; neural nets; photovoltaic power systems; power engineering computing; ANN; DC-DC boost tracker; IncCond algorithm; MPPT; PV generation systems; Saber simulation software; artificial neural network; incremental conductance method; maximum power point tracking; neural network training; photovoltaic generation systems; solar output power; Artificial neural networks; Integrated circuit modeling; Mathematical model; Photovoltaic systems; Radiation effects; Training; Artificial Neural Network; DC-DC boost converter; MPPT; Photovoltaic; Saber simulation;
Conference_Titel :
Bio-Inspired Computing: Theories and Applications (BIC-TA), 2011 Sixth International Conference on
Conference_Location :
Penang
Print_ISBN :
978-1-4577-1092-6
DOI :
10.1109/BIC-TA.2011.16
