Title :
Supervisory control of PV-battery systems by online tuned neural networks
Author :
Ciabattoni, Lucio ; Cimini, Gionata ; Grisostomi, Massimo ; Ippoliti, Gianluca ; Longhi, Sauro ; Mainardi, E.
Author_Institution :
Dipt. di Ing. dell´Inf., Univ. Politec. delle Marche, Ancona, Italy
fDate :
Feb. 27 2013-March 1 2013
Abstract :
The paper deals with a neural network based supervisor control system for a PhotoVoltaic (PV) plant. The aim of the work is to feed the power line with the 24 hours ahead forecast of the PV production. An on-line self-learning prediction algorithm is used to forecast the power production of the PV plant. The learning algorithm is based on a Radial Basis Function (RBF) network and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. The power feeding the electric line is scheduled by a Fuzzy Logic Supervisor (FLS) which controls the charge and discharge of a battery used as an energy buffer. The proposed solution has been experimentally tested on a 14 KWp PV plant and a lithium battery pack.
Keywords :
fuzzy control; hybrid power systems; learning (artificial intelligence); load forecasting; neurocontrollers; photovoltaic power systems; power cables; power generation control; radial basis function networks; resource allocation; secondary cells; FLS; PV plant; PV power production forecasting; PV-battery systems; RBF network; battery charge; battery discharge; electric line feed; energy buffer; fuzzy logic supervisor; growing criterion; lithium battery pack; minimal resource allocating network technique; neural network-based supervisor control system; online self-learning prediction algorithm; online tuned neural networks; photovoltaic plant; power 14 kW; power line; power scheduling; pruning strategy; radial basis function network; Artificial neural networks; Batteries; Fuzzy logic; Inverters; Neurons; Prediction algorithms; Production;
Conference_Titel :
Mechatronics (ICM), 2013 IEEE International Conference on
Conference_Location :
Vicenza
Print_ISBN :
978-1-4673-1386-5
Electronic_ISBN :
978-1-4673-1387-2
DOI :
10.1109/ICMECH.2013.6518518
