Application of Artificial Neural Networks in optimizing MPPT control for standalone solar PV system

Increasing demand of power supply and the limited nature of fossil fuel has resulted for the world to focus on renewable energy resources. Solar photovoltaic (PV) energy source being the most easily available, it is considered to have the potential to meet the ever increasing energy demand. Developing an intelligent system with Artificial Neural Networks (ANN) to track the Maximum Power Point (MPP) of a PV Array is being proposed in this paper. The system adopts Radial Basis Function Network (RBFN) architecture to optimize the control of Maximum Power Point Tracking (MPPT) for PV Systems. A PV array has non-linear output characteristics due to the insolation, temperature variations and the optimum operating point needs to be tracked in order to draw maximum power from the system. The output of the intelligent MPPT controller can be used to control the DC/DC converters to achieve maximum efficiency.