Design and Simulation of Improved Artificial Neural Netwok and Incremental Conductance HybridMPPT for Solar PV System Under Variable Irradiance Condition

Abstract

The increasing concern about preservation of the environment, because of the damaging effects of fossil fuel consumption and the dramatic drop in reserves of this very popular energy source, have led to increased interest in the development of renewable energy sources Solar energy is one of the sources that is gaining more attention. The solar photovoltaic system uses the principle of the photovoltaic effect. Solar PV is regarded as a clean and environmentally friendly energy source. In two types of configurations, standalone and grid connected, PV system are practically used. Standalone PV generating systems are regarded as an attractive source of electricity, particularly in remote areas where it is difficult to use a conventional power plant. The PV system generates different energy conditions depending on the environment. The solar irradiance and temperature are the two major environmental
factors affecting its performance. Due to this dependency, it is not possible to directly connect panels with the load. The Maximum Power Point Tracking (MPPT) technique has been applied to reduce the effects of changing environmental conditions and to improve the power generated by the PV system. It monitors the maximum power from the panel for energy generation improvements. MPPT controllers have certain basic functions, such as simple design, low cost, good performance characteristics with minimal output power fluctuations and the ability to track in changing conditions efficiently and quickly. The MPPT system based on improved neural network has been designed in the present research work. The results show less transient and a steady state response for the proposed system
compared with the current software computing technologies and traditional power point monitoring methods. Comprehensive research has been developed and a prevision model for the analysis of the system is carried out on standalone solar photovoltaic system. In comparison with traditional power point monitoring methods, the results showed less transient and a steady state response for the proposed system