Harmonic Distortion Prediction Model of a Grid- Tie Photovoltaic Inverter Using an Artificial Neural Network (CROSBI ID 260943)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Žnidarec, Matej ; Klaić, Zvonimir ; Šljivac, Damir ; Dumnić, Boris
engleski
Harmonic Distortion Prediction Model of a Grid- Tie Photovoltaic Inverter Using an Artificial Neural Network
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.
power quality ; photovoltaic system ; current harmonics prediction ; artificial neural network
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano