Comparative Analysis of NN based MPPT Algorithms for PV-Boost Converter

The output across solar is based upon the solar irradiance falling on the PV-plate. The irradiances are fluctuating in nature, so does the output power of the solar. Maximum Power Point Tracking (MPPT) is used for tracking the maximum point of voltage and current generation at any instant from PV-array. This helps in obtaining the maximum power output from solar at any value of irradiance. The efficiency of the MPPT can be improved several times by optimizing its parameters of variations using any intelligent technique. This paper considers Perturb and Observe (P&O) as well as Incremental and Conductance (I&C) MPPT whose output is optimized using Neural Network (NN) based AI technique. DC-DC converter is designed to regulate the output voltage of the PV module in buck/boost mode as per the need. A single switch unidirectional topology is opted for designing the DC-converter whose gate pulses are controlled via NN based MPPT algorithm. A comparative analysis is presented for P&O and I&C algorithm with and without NN technique. The results are obtained in MATLAB software for simulink to validate the topology proposed.