Development of new parameter extraction schemes and maximum power point controllers for photovoltaic power systems

In the recent years, in every parts of the world, focus is on supplementing the conventional fossil fuel based power generation with power generated from renewable sources such as photovoltaic (PV) and wind systems. PV technology is one of the fastest growing energy technologies in the world owing to its abundant availability. But unfortunately, the cost of PV energy is higher than that of other electrical energy from other conventional sources.Therefore, a great deal of research opportunities lie in applying power electronics and control technologies for harvesting PV power at higher efficiencies and efficient utilization. Simulation and control studies of a PV system require an accurate PV panel model. Further, for efficient utilization of the available PV energy, a PV system should operate at its maximum power point (MPP). A maximum power point tracker (MPPT) is needed in the PV system to enable it to operate at the MPP.The output characteristic of a PV system is non-linear and its output power fluctuates to a large extent in accordance with the variation of solar irradiance and temperature. A lot of research is being pursued on this area and several MPPT techniques have been proposed and implemented. But, still there is a lot of scope on designing new parameter extraction algorithms to achieve fast and accurate extraction of PV panel parameters. Further, there is need of development of efficient MPPT algorithms that can be adapted to different weather conditions with minimal fluctuations in input PV current and voltage.The work described in the thesis involves development of some new parameter extraction and robust adaptive MPPT algorithms. Two parameter extraction algorithms have been proposed namely a hybrid Newton Raphson method (hybrid NRM) and an evolutionary computational technique called Bacterial Foraging Optimization (BFO). These two parameter extraction techniques are found to be extracting parameters of a PV panel accurately in all weather conditions with less computational overhead. Further, these two parameter extraction techniques do not suffer from singularity problem during convergence. BFO technique being a global optimization technique provides accurate PV panel parameters