ThaiScience
ThaiScience
ENGINEERING JOURNAL CHIANG MAI UNIVERSITY
Volume 26, No. 01, Month APRIL, Year 2019, Pages 1 - 9
Power and voltage estimation of photovoltaic system using back-propagation neural network
Dumrongsak Wongta, Chawasak Rakpenthai, Surapol Dumronggittigule and Sermsak Uatrongjit
Abstract Download PDF
This paper applies the artificial neural network to estimate the operating point of photovoltaic (PV) system installed on the building’s roof. Since two PV systems are studied in this work, two backpropagation (BP) neural networks have been developed for estimating their operating points under weather variations. The solar radiation and the ambient temperature are considered as the input of the BP neural network while power and voltage obtained from the PV system are the estimated values. The measured data, i.e. the solar radiation, the ambient temperature, the PV power generation, and the PV voltage, have been recorded and used for training and testing the BP neural networks. Results indicate that the proposed BP neural networks can provide the good estimated values under the test conditions.
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Published by : Faculty of Engineering Chiang Mai University
Contributions welcome at : http://researchs.eng.cmu.ac.th/?name=journal
