Adaptive Control of Renewable Energy Resources for Improving the Dispatchability and Operational Stability of Renewable Energy Integrated Power Syste

This dissertation presents a measurement based adaptive control framework for renewable energy resources (RER) in order to improve the dispatch-ability and operational stability of renewable energy integrated bulk power grid. The proposed technique identies a system model in real-time based on the input and output data which is then utilized to design a control framework. This approach is utilized as it provides control based on a dynamically changing linear model of the system at various operating points as opposed to the conventional control design technique based on a static model at a single operating point. In this work, recursive least squares (RLS) technique is utilized for the system identication and minimum variance control (MVC) is used to minimize the variance of the system output from its reference set-point. The work focuses both on local control of RER as well as utilization of RER to support the bulk power grid. Also, it has been demonstrated that the proposed technique enhances the reliability of RER, as the dependency of the RER control on multiple sensors is minimized. It has also been demonstrated that proposed identication based technique can lead to better damping of system oscillations as it updates the linear model of the power system based on the changing operating point as opposed to conventional damping control techniques. The design technique is discussed, and the performance analysis is studied on various test systems using dynamic simulations in order to validate the merits of the proposed control technique. As the proposed technique is dynamically adjusting online, the results show better performance when compared to the conventional control technique for RER.