A Maximum Power Point Tracking Algorithm Based on Fuzzy Neural Networks for Grid-Connected Photovoltaic System

Abstract:

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This paper proposes a algorithm of maximum power point tracking using fuzzy Neural Networks for grid-connected photovoltaic systems. The system is composed of a VSI converter, the maximum power point tracking algorithm based on fuzzy Neural Networks outputs a reference voltage as voltage loop import variable. The voltage controller outputs a reference current to control inverter output current in side grid. The fuzzy Neural Networks provide attractive features such as fast response, good performance. Therefore, the system is able to deliver energy to grid. This proposed algorithm is simulated and implemented to evaluate performance. From the simulation and experimental results, the fuzzy Neural Networks can deliver more power than the other algorithm.

Info:

Periodical:

Advanced Materials Research (Volumes 291-294)

Edited by:

Yungang Li, Pengcheng Wang, Liqun Ai, Xiaoming Sang and Jinglong Bu

Pages:

2771-2774

DOI:

10.4028/www.scientific.net/AMR.291-294.2771

Citation:

H. B. Su and Z. C. Cheng, "A Maximum Power Point Tracking Algorithm Based on Fuzzy Neural Networks for Grid-Connected Photovoltaic System", Advanced Materials Research, Vols. 291-294, pp. 2771-2774, 2011

Online since:

July 2011

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Price:

$35.00

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