Harmonic Distortion Prediction Model of a Grid-Tie Photovoltaic Inverter Using an Artificial Neural Network
1
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, J.J. Strossmayer University of Osijek, Osijek 31000, Croatia
2
Faculty of Technical Sciences, University of Novi Sad, Novi Sad 21000, Serbia
*
Author to whom correspondence should be addressed.
Energies 2019, 12(5), 790; https://doi.org/10.3390/en12050790
Received: 4 February 2019 / Revised: 22 February 2019 / Accepted: 23 February 2019 / Published: 27 February 2019
(This article belongs to the Section Electrical Power and Energy System)
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.
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Keywords:
power quality; photovoltaic system; current harmonics prediction; artificial neural network
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MDPI and ACS Style
Žnidarec, M.; Klaić, Z.; Šljivac, D.; Dumnić, B. Harmonic Distortion Prediction Model of a Grid-Tie Photovoltaic Inverter Using an Artificial Neural Network. Energies 2019, 12, 790.
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