Dynamic and Performance Analysis of Distributed Hybrid Photovoltaic (PV) and Fuel Cell based Power Generation using Adaptive Neuro Fuzzy Inference System (ANFIS)

Abstract

This paper proposes the dynamic modeling and performance analysis for distributed hybrid power generation. Intelligent modeling of two diode PV/PEMFC systems should provide stable and continuous power to the electrical vehicle. The transformer isolated full bridge converter is proposed to improve the generated Photovoltaic power. The full bridge converter integrates with PV model and achieves Single stage power conversion between sources to dc link. Additionally; high power density, primary switching ZVS (Zero Voltage Switching) and high efficiency have been achieved. The main study of PV two diode modeling is used to reduce computational time and neglecting the series and shunt resistances, also provide more efficient power depends upon irradiance, temperature, shading effects etc. PEMFC is connected to dc-bus via a Cuk converter. In Cuk converter the source and dc link are separated by a dc link capacitor thus energy transferred from the source to load side occurs through this capacitor which has reduce the lode side ripple current. However the additional fuel cell topology promoted to overcome the other energy requirement and reduce the conversion cost. The Adaptive Neuro Fuzzy Inference System (ANFIS) based controller used for the control purpose of DC-DC power conversion. Therefore, the proposed PV/PEMFC models are verified under MATLAB/SIMULINK environment; as the result efficiency increases compared with the conventional system.