Title :
Dynamic neural control for maximum power point tracking of PV system
Author :
Dounis, A.I. ; Kofinas, P. ; Alafodimos, C. ; Tseles, D.
Author_Institution :
Dept. of Autom., Technol. Educ. Inst. of Piraeus, Egaleo, Greece
Abstract :
Development of an effective maximum power point tracking (MPPT) algorithm is important in order to achieve maximum power point in a photovoltaic system (PV). In this study, a dynamic neural control (DNC) scheme is developed. The adaptation procedure is based on the back propagation learning law and is required only a priori knowledge, that´s, the system output error. The feasibility of the proposed neural control is evaluated by the simulation results and compared to the conventional perturbation and observation (P&O) method.
Keywords :
backpropagation; maximum power point trackers; neurocontrollers; photovoltaic power systems; power generation control; DNC scheme; P&O method; PV system; adaptation procedure; back propagation learning law; dynamic neural control; maximum power point tracking algorithm; perturbation and observation method; photovoltaic system; Current measurement; Heuristic algorithms; Maximum power point tracking; Neural networks; Photovoltaic systems; Voltage measurement; Dynamic neural control; Maximum power point tracking; Perturbation & Observation algorithm; Photovoltaic system; on-line learning algorithm;
Conference_Titel :
Neural Network Applications in Electrical Engineering (NEUREL), 2012 11th Symposium on
Conference_Location :
Belgrade
Print_ISBN :
978-1-4673-1569-2
DOI :
10.1109/NEUREL.2012.6420029
