research-article

Performance Study of Neural Network and ANFIS Based MPPT Methods For Grid Connected PV System

Authors:

Mohammad Abu Sarhan,

Min Ding,

Xin Chen,

Min WuAuthors Info & Claims

ICNCC '17: Proceedings of the 2017 VI International Conference on Network, Communication and Computing

Pages 227 - 234

https://doi.org/10.1145/3171592.3171623

Published: 08 December 2017 Publication History

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Abstract

The maximum power point tracking (MPPT) methods are applied in PV solar systems to accomplish the desired maximum power from the PV system. Hence, it is important to design the best technique which can reach the maximum power point (MPP) effectively. In this paper, a grid connected PV system is controlled by artificial neural network (ANN) and adaptive neuro-fuzzy system (ANFIS) based MPPT methods. Both of proposed MPPT methods are analyzed related to their performance efficiency and response under the variation of solar irradiation and cell temperature. The obtained results of both methods are compared to experimental results which show that ANFIS has more response and efficiency than ANN in maximum power point tracking. The investigation has been done by using MATLAB/Simulink Environment.

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Cited By

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Moyo RTabakov PMoyo S(2020)Design and Modeling of the ANFIS-Based MPPT Controller for a Solar Photovoltaic SystemJournal of Solar Energy Engineering10.1115/1.4048882143:4Online publication date: 18-Nov-2020
https://doi.org/10.1115/1.4048882
Ibrahim ADing MJin XDai XSarhan MShafik MZhou H(2019)Artificial Neural Network Based Maximum Power Point Tracking for PV System2019 Chinese Control Conference (CCC)10.23919/ChiCC.2019.8865275(6559-6564)Online publication date: Jul-2019
https://doi.org/10.23919/ChiCC.2019.8865275

Index Terms

Performance Study of Neural Network and ANFIS Based MPPT Methods For Grid Connected PV System
1. Hardware
  1. Power and energy
    1. Energy generation and storage
      1. Renewable energy

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Published In

ICNCC '17: Proceedings of the 2017 VI International Conference on Network, Communication and Computing

December 2017

265 pages

ISBN:9781450353663

DOI:10.1145/3171592

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 08 December 2017

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ICNCC 2017

ICNCC 2017: 2017 VI International Conference on Network, Communication and Computing

December 8 - 10, 2017

Kunming, China

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Moyo RTabakov PMoyo S(2020)Design and Modeling of the ANFIS-Based MPPT Controller for a Solar Photovoltaic SystemJournal of Solar Energy Engineering10.1115/1.4048882143:4Online publication date: 18-Nov-2020
https://doi.org/10.1115/1.4048882
Ibrahim ADing MJin XDai XSarhan MShafik MZhou H(2019)Artificial Neural Network Based Maximum Power Point Tracking for PV System2019 Chinese Control Conference (CCC)10.23919/ChiCC.2019.8865275(6559-6564)Online publication date: Jul-2019
https://doi.org/10.23919/ChiCC.2019.8865275

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Recommendations

Hydrogen production from a PV/PEM electrolyzer system using a neural-network-based MPPT algorithm

Energy management in a hybrid PV/wind/battery system using a type-1 fuzzy logic computer algorithm

A comparative study of MPPT techniques for PV systems