Harmonic Distortion Prediction Model of a Grid-Tie Photovoltaic Inverter Using an Artificial Neural Network

• Published in: Energies (Basel) Vol. 12; no. 5; p. 790
• Main Authors: Žnidarec, Matej, Klaić, Zvonimir, Šljivac, Damir, Dumnić, Boris
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 27-02-2019
• Subjects: Activation; Alternative energy sources; Artificial intelligence; artificial neural network; Computer engineering; Computer science; Converters; current harmonics prediction; Distance learning; Electrical engineering; Electronics; Energy resources; Harmonic control; Harmonic distortion; Information technology; Inverters; Measurement techniques; Methods; Mitigation; Neural networks; Operators (mathematics); photovoltaic system; Photovoltaics; Power plants; power quality; Prediction models; Quality; Quality standards; Renewable resources; Sine waves; United States > US; Germany; Croatia
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12050790

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.