Title :
Artificial Intelligence Technics Applied to Analisys of Photovoltaic Energy Systems
Author :
Rampinelli, G.A. ; Teyra, M.A.A. ; Krenzinger, A. ; Prieb, C.W.M.
Author_Institution :
Lab. de Energia Solar, Univ. Fed. do Rio Grande do Sul, Porto Alegre, Brazil
Abstract :
This paper presents a procedure to determine the energetic behavior of photovoltaic systems through genetic algorithms and artificial neural networks. The method divides the I-V Curve of the photovoltaic generator in three regions. The best results are achieved for the region around the maximum power point, which can be advantageous for grid-connected systems. The method allows determining the energy delivered by the system and the inverter performance from a few experimental data. The output is a vector comprising the DC power, the maximum power point tracker efficiency, the inverter efficiency and the AC power.
Keywords :
artificial intelligence; electric generators; genetic algorithms; invertors; maximum power point trackers; neural nets; photovoltaic power systems; power engineering computing; power grids; solar power; I-V curve; artificial intelligence technique; artificial neural network; energetic analysis; genetic algorithm; grid connected system; inverter performance; maximum power point tracker; photovoltaic generator; Artificial intelligence; Color; Inverters; Photovoltaic systems; Principal component analysis; Silicon compounds; Wavelet transforms; artificial intelligence; grid-connected photovoltaic system; inverters; solar energy;
Journal_Title :
Latin America Transactions, IEEE (Revista IEEE America Latina)
DOI :
10.1109/TLA.2010.5623503
