The new maximum power point tracking algorithm using ANN based solar PV systems

The new maximum power point tracking algorithmusing ANN-based solar PV systems Lee H.H., Phuong L.M., Dzung P.Q., Dan Vu N.T., Khoa L.D.. School of Electrical Engineering, University of

The new maximum power point tracking algorithm

using ANN-based solar PV systems

Lee H.H., Phuong L.M., Dzung P.Q., Dan Vu N.T., Khoa L.D.

School of Electrical Engineering, University of Ulsan, Ulsan, South Korea; HCMC University of

Technology, Ho Chi Minh City, Viet Nam

Abstract: In grid connected photovoltaic (PV) systems, maximum power point tracking (MPPT) algorithm plays an important role in optimizing the solar energy efficiency In this paper, the new artificial neural network (ANN) based MPPT method has been proposed for searching maximum power point (MPP) fast and exactly For the first time, the combined method is proposed, which is established on the ANN-based

PV model method and incremental conductance (IncCond) method The advantage of ANN-based PV model method is the fast MPP approximation base on the ability of ANN according the parameters of PV array that used The advantage of IncCond method is the ability to search the exactly MPP based on the feedback voltage and current but don't care the characteristic on PV array The effectiveness of proposed algorithm is validated by simulation using Matlab/ Simulink and experimental results using Card DSPACE 1104 © 2010 IEEE

Author Keywords: Artificial neural network (ANN); Incremental conductance (IncCond); Maximum power point tracking (MPPT); Photovoltaic (PV)

Index Keywords: Artificial Neural Network; Combined method; D-space; Feedback voltages; Grid-connected photovoltaic system; Incremental conductance; Incremental conductance (IncCond); Maximum power point; Maximum power point tracking; Maximum Power Point Tracking algorithms; Model method; Photovoltaic (PV); PV arrays; Simulink; Solar PV systems; Algorithms; Energy efficiency; Optical flows; Photovoltaic cells; Photovoltaic effects; Pumps; Solar energy; Solar power generation; Neural networks

Year: 2010

Source title: IEEE Region 10 Annual International Conference, Proceedings/TENCON

Art No.: 5686721

Page : 2179-2184

Link: Scorpus Link

Correspondence Address: Lee, H H.; School of Electrical Engineering, University of Ulsan, Ulsan, South Korea; email: hhlee@mail.ulsan.ac.kr

Sponsors: IEEE Fukuoka Section;IEEE Region 10

Conference name: 2010 IEEE Region 10 Conference, TENCON 2010

Conference date: 21 November 2010 through 24 November 2010

Conference location: Fukuoka

Conference code: 83758

ISBN: 9.78142E+12

CODEN: 85QXA

DOI: 10.1109/TENCON.2010.5686721

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Language of Original Document: English

Abbreviated Source Title: IEEE Region 10 Annual International Conference, Proceedings/TENCON

Document Type: Conference Paper

Source: Scopus

Authors with affiliations:

Lee, H.H., School of Electrical Engineering, University of Ulsan, Ulsan, South Korea

Phuong, L.M., HCMC University of Technology, Ho Chi Minh City, Viet Nam

Dzung, P.Q., HCMC University of Technology, Ho Chi Minh City, Viet Nam

Dan Vu, N.T., HCMC University of Technology, Ho Chi Minh City, Viet Nam

Khoa, L.D., HCMC University of Technology, Ho Chi Minh City, Viet Nam

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