Open AccessPower Conditioning NN Training Analysis of SVPWM Inverter PV System
Author(s) -
Vandana Tiwari,
Rajeev Arya,
Shravan Vishwakarma
Publication year - 2020
Publication title -
smart moves journal ijoscience
Language(s) - English
Resource type - Journals
ISSN - 2582-4600
DOI - 10.24113/ijoscience.v6i12.346
Subject(s) - photovoltaic system , computer science , inverter , renewable energy , power (physics) , controller (irrigation) , grid connected photovoltaic power system , maximum power point tracking , artificial neural network , solar power , electric power system , grid , control theory (sociology) , control engineering , automotive engineering , engineering , control (management) , electrical engineering , voltage , artificial intelligence , mathematics , agronomy , physics , geometry , quantum mechanics , biology
Solar energy is the most abundant and cleanest renewable energy source available in the world. The main objective of the designing of combined control for DC-DC and DC-AC converter algorithm to make the solar system suitable in operation and efficient performance. At last, the system has to be integrated with the utility grid system in order to make it available for driving different types of loads. In this work performance of the neural controller is studied for typical vector control conditions and compared with conventional control methods of the inverter. The work describes how programming methods are employed to train the neural network through a back propagation through time algorithm and then it will be followed by space vector control. The PV system was finally integrated with the power grid. The analysis was carried out for different loads so as to study the effect of power quality on the system.