Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Electrical Engineering

Major Professor

Fangxing Li

Committee Members

Yilu Liu, Leon M. Tolbert, Jim Ostrowski


Wind energy as an outstanding and competitive form of renewable energy, has been growing fast worldwide in recent years because of its importance to reduce the pollutant emission generated by conventional thermal power plants and the rising prices and the unstable supplies of fossil-fuel. However, in the development of wind energy, there are still many ongoing challenges.

An important challenge is the need of voltage control to maintain the terminal voltage of a wind plant to make it a PV bus like conventional generators with excitation control. In the literature with PI controllers used, the parameters of PI controllers need to be tuned as a tradeoff or compromise among various operating conditions. In this work, a new voltage control approach is presented. In the proposed approach, the PI control gains are dynamically adjusted based on the dynamic, continuous sensitivity which essentially indicates the dynamic relationship between the change of control gains and the desired output voltage. Hence, this control approach does not require any good estimation or tuning of fixed control gains because it has the self-learning mechanism via the dynamic sensitivity. This also gives the plug-and-play feature of DFIG controllers to make it promising in utility practices.

Another key challenge in power regulation of wind energy is the control design in wind energy conversion system (WECS) to realize the tradeoff between the energy cost and control performance subject to stochastic wind speeds. In this work, the chance constraints are considered to address the control inputs and system outputs, as opposed to deterministic constraints in the literature, where the chance constraints include the stochastic behavior of the wind speed fluctuation. Two different control problems are considered here: The first one assumes the wind speed disturbance’s distribution is Gaussian; the second one assumes the disturbance is norm bounded, and the problem is formulated as a min-max optimization problem which has not been considered in the literature. Both problems are formulated as semi-definite program (SDP) optimization problems that can be solved efficiently with existing software tools. And simulation results are provided to demonstrate the validity of the proposed method.

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