Research on MPPT of PV Systems Based on BP Neural Network

Wen Ying Chen; Yong Jun Lin; Wei Liang Liu; Shuang Sai Liu

doi:10.4028/www.scientific.net/AMR.466-467.930

Paper Titles

Analyses of Coupling Effect of Load Simulator of Aircraft Control Surface on Actuator Simulation Loop
p.912

Low Pass Filters Based on T-Shaped Defected Microstrip Structure
p.916

A Multimode Controller Design for a TBM Autopilot with Optimal/Classical Approach
p.920

Research on Random Drift Modeling and Error Compensation Method for MEMS Gyroscope
p.925

Research on MPPT of PV Systems Based on BP Neural Network
p.930

Dynamic Position Accuracy Analysis of Paper-Converying Grippers in Die-Cutting Machine
p.935

Magnetic Field Analysis of Surface-Mounted Permanent Magnet Motors Using Schwarz-Christoffel Mapping
p.940

The Aircraft Surface Movement Behaviors Modelling and Analysis Using Petri Net
p.946

Design Method of Self-Loading Device of the Side-Crane Used for Container
p.951

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 466-467Research on MPPT of PV Systems Based on BP Neural...

Research on MPPT of PV Systems Based on BP Neural Network

Abstract:

In order to obtain more output power of photovoltaic (PV) array, which depends on solar irradiation and ambient temperature, maximum power point tracking (MPPT) techniques are employed. Among all the MPPT strategies, the Perturb and Observe (P&O) algorithm is more attractive due to the simple control structure. Nevertheless, steady-state oscillations always appear due to the perturbation. In this paper, a new MPPT method based on BP Neural Networks and P&O is proposed for searching maximum power point (MPP) fast and exactly, and its effectiveness is validated by experimental results using hardware platform based on microcomputer.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 466-467)

Pages:

930-934

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.466-467.930

Citation:

Cite this paper

Online since:

February 2012

Authors:

Wen Ying Chen, Yong Jun Lin, Wei Liang Liu, Shuang Sai Liu

Keywords:

BP Neural Network (BPNN), Maximum Power Point Tracking (MPPT), Observe, Perturb

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] V. Salas, E. Olias, A. Barrado, A. Lazaro, Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems, Solar Energy Materials and Solar Cells, vol. 90, no. 11, July (2006).

DOI: 10.1016/j.solmat.2005.10.023

Google Scholar

[2] Tirshan Esram and Patrick L. Chapman, Comparison of photovoltaic array maximum power point tracking techniques, IEEE Transactions on Energy Conversion, vol. 22, no. 2, June (2007).

DOI: 10.1109/tec.2006.874230

Google Scholar

[3] D. P. Hohm and M. E. Ropp, Comparative study of maximum power point tracking algorithms, Proc. of 28th IEEE Photovoltaic Specialists Conference, p.1699–1702, Sept. (2000).

DOI: 10.1109/pvsc.2000.916230

Google Scholar

[4] S. Jain and V. Agarwal, Comparison of the performance of maximum power point tracking schemes applied to single-stage grid-connected photovoltaic systems, IET Electric Power Applications, vol. 1, no. 5, Sept. (2007).

DOI: 10.1049/iet-epa:20060475

Google Scholar

[5] D. Sera, R. Teodorescu, J. Hantschel and M. K. noll, Optimized maximum power point tracker for fast-changing environmental conditions, IEEE Trans. Ind. Electron., vol. 55, no. 7, pp.2629-2637, Jul. (2008).

DOI: 10.1109/tie.2008.924036

Google Scholar

[6] Yeong-Chau Kuo, Tsomg-Juu Liang, Jiann-Fuh Chen, Novel MaximumPower-Point-Tracking controller for photovoItaic energy conversion system, IEEE Transactions on Industrial Electronics, Vol. 48, Issue: 3, June (2001).

DOI: 10.1109/41.925586

Google Scholar

[7] Xin Wang and Aiguo Patrick Hu, An improved maximum power point tracking algorithm for photovoltaic systems, in Proc. Australasian Universities Power Engineering Conference (AUPEC 2004), 26-29 September 2004, Brisbane, Australia.

Google Scholar

[8] L. Fangrui, D. Shanxu, L. Fei, L. Bangyin, and K. Yong, A variable step size INC MPPT method for PV systems, IEEE Trans. Ind. Electron., vol. 55, no. 7, pp.2622-2628, Jul. (2008).

DOI: 10.1109/tie.2008.920550

Google Scholar