title: Modeling, control, and simulation of grid connected intelligent hybrid battery/photovoltaic system using new hybrid fuzzy-neural method.
abstract: Nowadays, photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is its dependence on weather conditions. Therefore, battery energy storage (BES) can be considered to assist for a stable and reliable output from PV generation system for loads and improve the dynamic performance of the whole generation system in grid connected mode. In this paper, a novel topology of intelligent hybrid generation systems with PV and BES in a DC-coupled structure is presented. Each photovoltaic cell has a specific point named maximum power point on its operational curve (i.e. current-voltage or power-voltage curve) in which it can generate maximum power. Irradiance and temperature changes affect these operational curves. Therefore, the nonlinear characteristic of maximum power point to environment has caused to development of different maximum power point tracking techniques. In order to capture the maximum power point (MPP), a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. Obtained results represent the effectiveness and superiority of the proposed method, and the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison to the conventional methods. It has the advantages of robustness, fast response and good performance. A detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink.
authors.affiliation: Young Researchers and Elite Club, Saveh Branch, Islamic Azad University, Saveh, Iran. Electronic address: alireza.rezvani.saveh@gmail.com.
authors.forename: Alireza
authors.initials: A
authors.lastname: Rezvani
authors.affiliation: Department of Engineering, Shahrekord University, Iran.
authors.forename: Abbas
authors.initials: A
authors.lastname: Khalili
authors.affiliation: Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran.
authors.forename: Alireza
authors.initials: A
authors.lastname: Mazareie
authors.affiliation: Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran.
authors.forename: Majid
authors.initials: M
authors.lastname: Gandomkar
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D011211:Electric Power Supplies; D017143:Fuzzy Logic; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D058117:Neural Prostheses; D015203:Reproducibility of Results; D013696:Temperature
keywords: Artificial neural network; Fuzzy logic controller; Genetic algorithm; P/Q control; Photovoltaic