A novel hybrid mppt method for grid connected photovoltaic systems with partial shading conditions

Energy efficiency and using alternative energy sources issues have become more crucial due to the world energy supply has been subjected to enormous stress. Photovoltaic (PV) systems are one of the mostly used alternative energy generation option. However, PV systems suffer from low system efficiency, high initial cost and erratic atmospheric conditions. The output power of PV panels highly depends on the ambient temperature and the radiation intensity. The modest changes in operating current and voltage of PV panel, which relies on the temperature and radiation, constitutes visible variations in the output power of the panel. In order to mitigate these variations and force the system to study on maximum power point (MPP), several maximum power point tracking (MPPT) techniques are presented in the literature. In this thesis, a grid connected PV system, which consists of an artificial neural network (ANN) based MPPT technique and a novel Hybrid MPPT technique, is analyzed, modeled and simulated. The proposed MPPT is integrated to a two stage grid connected PV system. The performance and efficiency of the proposed system are tested with different simulation cases by PSCAD/EMTDC program. The results obtained from the simulations clearly demonstrates that the presented MPPT algorithm simultaneously performs the tracking of MPP voltage and provide significant efficiency gains under variable atmospheric conditions and partial shading conditions. Furthermore, by employing an interleaved DC-DC boost converter, I2R losses, ripples in the input and output waveform and electromagnetic interference are substantially reduced, and the transient response, which affects the dynamic performance of the entire system, is improved.