Maximum power point tracking using neural networks for grid-connected photovoltaic system

This paper proposes a method of maximum power point tracking (MPPT) using neural networks for gridconnected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on output from neur...

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Main Authors: Samangkool K., Premrudeepreechacharn S.
Format: Conference or Workshop Item
Language:English
Published: 2014
Online Access:http://www.scopus.com/inward/record.url?eid=2-s2.0-33847203656&partnerID=40&md5=56c0bf47e92da8f0369dc1f5e88f2019
http://cmuir.cmu.ac.th/handle/6653943832/1269
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Institution: Chiang Mai University
Language: English
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spelling th-cmuir.6653943832-12692014-08-29T09:29:02Z Maximum power point tracking using neural networks for grid-connected photovoltaic system Samangkool K. Premrudeepreechacharn S. This paper proposes a method of maximum power point tracking (MPPT) using neural networks for gridconnected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on output from neural networks to control a switch of a boost converter. Back-propagation neural networks is utilized as pattern classifier. Back-propagation neural networks is an example of nonlinear layered feed-forward networks. The single phase inverter uses hysteresis current control which provides current with sinusoidal waveform. Therefore, the system is able to deliver energy with low harmonics and high power factor. MPPT using neural networks are simulated and implemented to evaluate performance. Simulation and experimental results are provided for neural networks and fixed duty ratio under the same atmospheric condition. From the simulation and experimental results, neural networks can deliver more power than the conventional controller. 2014-08-29T09:29:02Z 2014-08-29T09:29:02Z 2005 Conference Paper 9078205024; 9789078205029 69282 http://www.scopus.com/inward/record.url?eid=2-s2.0-33847203656&partnerID=40&md5=56c0bf47e92da8f0369dc1f5e88f2019 http://cmuir.cmu.ac.th/handle/6653943832/1269 English
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
language English
description This paper proposes a method of maximum power point tracking (MPPT) using neural networks for gridconnected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on output from neural networks to control a switch of a boost converter. Back-propagation neural networks is utilized as pattern classifier. Back-propagation neural networks is an example of nonlinear layered feed-forward networks. The single phase inverter uses hysteresis current control which provides current with sinusoidal waveform. Therefore, the system is able to deliver energy with low harmonics and high power factor. MPPT using neural networks are simulated and implemented to evaluate performance. Simulation and experimental results are provided for neural networks and fixed duty ratio under the same atmospheric condition. From the simulation and experimental results, neural networks can deliver more power than the conventional controller.
format Conference or Workshop Item
author Samangkool K.
Premrudeepreechacharn S.
spellingShingle Samangkool K.
Premrudeepreechacharn S.
Maximum power point tracking using neural networks for grid-connected photovoltaic system
author_facet Samangkool K.
Premrudeepreechacharn S.
author_sort Samangkool K.
title Maximum power point tracking using neural networks for grid-connected photovoltaic system
title_short Maximum power point tracking using neural networks for grid-connected photovoltaic system
title_full Maximum power point tracking using neural networks for grid-connected photovoltaic system
title_fullStr Maximum power point tracking using neural networks for grid-connected photovoltaic system
title_full_unstemmed Maximum power point tracking using neural networks for grid-connected photovoltaic system
title_sort maximum power point tracking using neural networks for grid-connected photovoltaic system
publishDate 2014
url http://www.scopus.com/inward/record.url?eid=2-s2.0-33847203656&partnerID=40&md5=56c0bf47e92da8f0369dc1f5e88f2019
http://cmuir.cmu.ac.th/handle/6653943832/1269
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