ADAPTIVE CONTROL OF RENEWABLE ENERGY RESOURCES FOR IMPROVING THE DISPATCHABILITY AND OPERATIONAL STABILITY OF RENEWABLE ENERGY INTEGRATED POWER SYSTEM

1 online resource (271 pages) : PDF

2018

University of North Carolina at Charlotte

This dissertation presents a measurement based adaptive control framework for renewableenergy resources (RER) in order to improve the dispatch-ability and operationalstability of renewable energy integrated bulk power grid. The proposed techniqueidenties a system model in real-time based on the input and output data which isthen utilized to design a control framework. This approach is utilized as it providescontrol based on a dynamically changing linear model of the system at various operatingpoints as opposed to the conventional control design technique based on a staticmodel at a single operating point. In this work, recursive least squares (RLS) techniqueis utilized for the system identication and minimum variance control (MVC) isused to minimize the variance of the system output from its reference set-point. Thework focuses both on local control of RER as well as utilization of RER to supportthe bulk power grid. Also, it has been demonstrated that the proposed techniqueenhances the reliability of RER, as the dependency of the RER control on multiplesensors is minimized. It has also been demonstrated that proposed identicationbased technique can lead to better damping of system oscillations as it updates thelinear model of the power system based on the changing operating point as opposedto conventional damping control techniques. The design technique is discussed, andthe performance analysis is studied on various test systems using dynamic simulationsin order to validate the merits of the proposed control technique. As the proposedtechnique is dynamically adjusting online, the results show better performance whencompared to the conventional control technique for RER.

doctoral dissertations

Electrical engineering
Renewable energy sources

Ph.D.

Adaptive Control
Adaptive Damping Control
Doubly Fed Induction Generator
Minimum Variance Control
Sensorless Control
System Identification

Electrical Engineering

Kamalasadan, Sukumar

Kakad, Yogendra
Ebong, Abasifreke
Young, David

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2018.

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Bhattarai_uncc_0694D_11661

http://hdl.handle.net/20.500.13093/etd:880