MAXIMUM POWER POINT TRACKING BASED ARTIFICIAL NEURAL NETWORK APPROACH FOR SOLAR PHOTOVOLTAIC SYSTEM

In a photovoltaic system, tracking the maximum power point (MPP) is a difficult task due to changes in climatic conditions. In addition, due to several peaks in the power supply voltage characteristics, the tracking algorithm becomes more complicated under partial shadow conditions. This paper introduces a new method for tracking the global maximum power point under partial shadow conditions. This method combines the maximum power point tracking The method combines an artificial neural network controller. This paper discusses one of the most important algorithms to extract maximum power from the PV panel implemented with DC-to-DC converters and based Artificial Neural Networks based Maximum power point tracking used to provide maximum power from the photovoltaic module to the load. Therefore, this new ANN method shows the main ability to extract the maximum power. A new MPPT search method for the maximum power point based on artificial neural networks has been used in this work. Solar radiation changes sharply. It is possible to determine precisely the extract power of MPP, which can decide that the system will operate in a stable mode. An artificial neural network can predict solar radiation level and battery temperature according to different operating conditions under changing environmental conditions to optimize energy production and optimize solar power tracking from solar cell systems. Therefore, this new ANN method demonstrates its most important ability to extract maximum power from the solar panel MPPT algorithms are typically used in photovoltaic systems to optimize solar power. When solar radiation changes sharply, MPP benefits as higher fault tolerance and a simpler implementation, making the system work in stable conditions. The simulation of this proposed model has performed on MATLAB software, and 85%. accuracy obtained in this proposed system.