Online tuned neural networks for fuzzy supervisory control of pv-battery systems

Abstract

The paper deals with a neural network based fuzzy supervisor control to manage power flows in a Photo-Voltaic (PV) - Battery system. An on-line self-learning prediction algorithm is used to forecast, over a determined time horizon, the power mismatch between PV production and electrical consumptions. 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 flows are 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.