Design of Adaptive Robust Controllers for Renewable Energy Sources Integrated Smart Grid System

Abstract

Energy supply and consumption from conventional fossil fuel is seen as a factor to global warming and deterioration of the environment. It is essential to use clean, non-polluting and alternative energy sources. Wind energy conversion technologies have proved attractive and competitive in terms of conventional fossil energy technologies with increased demand for electricity. It may reduce the negative impacts of traditional energy sources on the environment and reducing dependency on fossil fuels. Because of its high efficiency, the wind energy system can be an alternative source of energy for the future. The most frequently used variable-speed wind turbine is to enhance energy capture at distinct wind speeds. Self-excitation, elevated efficiency, power density, a wide variety of velocity, certainty and full isolation of the PMSG from the power grid have rendered it preferable for various wind systems. In addition to the wind power system, photovoltaic (PV) system developments are heightened the need for injecting the PV power in to the grid. PV array is composed of series and parallel PV cell combinations to maintain the required current and voltage levels operate in centralized grid connected inverter. However, substantial power losses have been reported due to the imbalanced generation between PV panels, which is mainly due to partial shading. Fuel cell (FC) act as continuous power source to mitigate the intermittent nature of PV and wind system. FC’s are clean and high efficient independent power generating source with zero emissions. Investigation of the performance of robust and non-linear controllers under varying wind speed scenarios is explored as a preliminary study. It is discovered that automated robust controller design is essential for the renewable power systems applications. Proposed research work intends to address the maximum power tracking issue for the autonomous wind power system and grid connected PMSG based wind energy conversion system, sub-module level PV system, and fuel cell. Genetic algorithm is used to design a new robust Quantitative Feedback Theory (QFT) controller based on automatic loop shaping methodology. The outcome of research work iiiis to extract the maximum power from hybrid renewable energy sources with automated robust QFT control strategy.