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Artificial Neural Network-based Maximum Power Point Tracker for the Photovoltaic Application

  • This paper proposes a new artificial neural network-based maximum power point tracker for photovoltaic application. This tracker significantly improves efficiency of the photovoltaic system with series-connection of photovoltaic modules in non-uniform irradiance on photovoltaic array surfaces. The artificial neural network uses irradiance and temperature sensors to generate the maximum power point reference voltage and employ a classical perturb and observe searching algorithm. The structure of the artificial neural network was obtained by numerical modelling using Matlab/Simulink. The artificial neural network was trained using Bayesian regularisation back-propagation algorithms and demonstrated a good prediction of the maximum power point. Relative number of Vmpp prediction errors in range of ±0.2V is 0.05% based on validation data.

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Metadaten
Document Type:Conference Object
Language:English
Parent Title (English):Proceedings of the 2015 1st International Conference on Industrial Netvvorks and Intelligent Systems (INISCOM 2015), Tokyo, Japan, March 2-4, 2015
Pagenumber:6
ISBN:978-1-63190-022-8
DOI:https://doi.org/10.4108/icst.iniscom.2015.258313
Date of first publication:2015/04/09
Tag:artificial neural network; efficiency; maximum power point tracker; partial-shaded photovoltaic; photovoltaic system
Departments, institutes and facilities:Fachbereich Elektrotechnik, Maschinenbau, Technikjournalismus
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Dewey Decimal Classification (DDC):600 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Entry in this database:2016/01/21