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https://open.uns.ac.rs/handle/123456789/498
Title: | Harmonic distortion prediction model of a grid-tie photovoltaic inverter using an artificial neural network | Authors: | Žnidarec M. Klaić Z. Šljivac D. Dumnić, Boris |
Issue Date: | 27-Feb-2019 | Journal: | Energies | Abstract: | © 2019 by the authors. Expanding the number of photovoltaic (PV) systems integrated into a grid raises many concerns regarding protection, system safety, and power quality. In order to monitor the effects of the current harmonics generated by PV systems, this paper presents long-term current harmonic distortion prediction models. The proposed models use a multilayer perceptron neural network, a type of artificial neural network (ANN), with input parameters that are easy to measure in order to predict current harmonics. The models were trained with one-year worth of measurements of power quality at the point of common coupling of the PV system with the distribution network and the meteorological parameters measured at the test site. A total of six different models were developed, tested, and validated regarding a number of hidden layers and input parameters. The results show that the model with three input parameters and two hidden layers generates the best prediction performance. | URI: | https://open.uns.ac.rs/handle/123456789/498 | DOI: | 10.3390/en12050790 |
Appears in Collections: | FTN Publikacije/Publications |
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