In this thesis the eigenstructure (eigenvalues and eigenvectors) assignment technique based algorithm has been developed for the design of controllers for power system applications. The application of the algorithm is demonstrated by designing power system stabilizers (PSSs) that are extensively used to address the small-signal rotor angle stability problems in power systems. In the eigenstructure assignment technique, the critical eigenvalues can be relocated as well as their associated eigenvectors can be modified. This method is superior and yield better dynamical performance compared to the widely used frequency domain design method, in which only the critical eigenvalues are relocated and no attempt is made to modify the eigenvectors. The reviewed published research has demonstrated successful application of the eigenstructure assignment technique in the design of controllers for small control systems. However, the application of this technique in the design of controllers for power systems has not been investigated rigorously. In contrast to a small system, a power system has a very large number state variables compared to the combined number of system inputs and outputs. Therefore, the eigenstructure assignment technique that has been successfully applied in the design of controllers for small systems could not be applied as is in the design of power system controllers. This thesis proposes a novel approach to the application of the eigenstructure assignment technique in the design of power system controllers. In this new approach, a multi-objective nonlinear optimization problem (MONLOP) is formulated by quantifying different design objectives as a function of free parametric vectors. Then the MONLOP is solved for the free parametric vectors using a nonlinear optimization technique. Finally, the solution of the controller parameters is obtained using the solved free parametric vectors. The superiority of the proposed method over the conventional frequency domain method is demonstrated by designing controllers for three different systems and validating the controllers through nonlinear transient simulations. One of the cases includes design of a PSS for the Manitoba Hydro system having about 29,000 states variables, which demonstrates the applicability of the proposed algorithm for a practical real-world system.

In this thesis the eigenstructure (eigenvalues and eigenvectors) assignment technique based algorithm has been developed for the design of controllers for power system applications. The application of the algorithm is demonstrated by designing power system stabilizers (PSSs) that are extensively used to address the small-signal rotor angle stability problems in power systems. In the eigenstructure assignment technique, the critical eigenvalues can be relocated as well as their associated eigenvectors can be modified. This method is superior and yield better dynamical performance compared to the widely used frequency domain design method, in which only the critical eigenvalues are relocated and no attempt is made to modify the eigenvectors. The reviewed published research has demonstrated successful application of the eigenstructure assignment technique in the design of controllers for small control systems. However, the application of this technique in the design of controllers for power systems has not been investigated rigorously. In contrast to a small system, a power system has a very large number state variables compared to the combined number of system inputs and outputs. Therefore, the eigenstructure assignment technique that has been successfully applied in the design of controllers for small systems could not be applied as is in the design of power system controllers. This thesis proposes a novel approach to the application of the eigenstructure assignment technique in the design of power system controllers. In this new approach, a multi-objective nonlinear optimization problem (MONLOP) is formulated by quantifying different design objectives as a function of free parametric vectors. Then the MONLOP is solved for the free parametric vectors using a nonlinear optimization technique. Finally, the solution of the controller parameters is obtained using the solved free parametric vectors. The superiority of the proposed method over the conventional frequency domain method is demonstrated by designing controllers for three different systems and validating the controllers through nonlinear transient simulations. One of the cases includes design of a PSS for the Manitoba Hydro system having about 29,000 states variables, which demonstrates the applicability of the proposed algorithm for a practical real-world system.

Characteristic modes (CMs) provide deep insight into the electromagnetic behaviour of any arbitrarily shaped conducting structure because the CMs are unique to the geometry of the object. We exploit this very fact by predicting a perhaps surprising number of important antenna metrics such as resonance frequency, radiation efficiency and antenna Q (bandwidth) without needing to specify a feeding location. In doing so, it is possible to define a collection of objective functions that can be used in an optimizer to shape-synthesize antennas without needing to define a feed location a priori. We denote this novel form of optimization “feedless” or “excitation-free” antenna shape synthesis. Fundamentally, we are allowing the electromagnetics to dictate how the antenna synthesis should proceed and are in no way imposing the physical constraints enforced by fixed feeding structures. This optimization technique is broadly applied to three major areas of antenna research: electrically small antennas, multi-band antennas and reflectarrays. Thus, the scope of applicability ranges from small antennas, to intermediate sizes and concludes with electrically large antenna designs, which is a testament to the broad applicability of characteristic mode theory. Another advantage of feedless electromagnetic shape synthesis is the ability to synthesize antennas whose desirable properties approach the fundamental limits imposed by electromagnetics. As an additional benefit, the feedless optimization technique is shown to have greater computational efficiency than traditional antenna optimization techniques.

Characteristic modes (CMs) provide deep insight into the electromagnetic behaviour of any arbitrarily shaped conducting structure because the CMs are unique to the geometry of the object. We exploit this very fact by predicting a perhaps surprising number of important antenna metrics such as resonance frequency, radiation efficiency and antenna Q (bandwidth) without needing to specify a feeding location. In doing so, it is possible to define a collection of objective functions that can be used in an optimizer to shape-synthesize antennas without needing to define a feed location a priori. We denote this novel form of optimization “feedless” or “excitation-free” antenna shape synthesis. Fundamentally, we are allowing the electromagnetics to dictate how the antenna synthesis should proceed and are in no way imposing the physical constraints enforced by fixed feeding structures. This optimization technique is broadly applied to three major areas of antenna research: electrically small antennas, multi-band antennas and reflectarrays. Thus, the scope of applicability ranges from small antennas, to intermediate sizes and concludes with electrically large antenna designs, which is a testament to the broad applicability of characteristic mode theory. Another advantage of feedless electromagnetic shape synthesis is the ability to synthesize antennas whose desirable properties approach the fundamental limits imposed by electromagnetics. As an additional benefit, the feedless optimization technique is shown to have greater computational efficiency than traditional antenna optimization techniques.

Characteristic modes (CMs) provide deep insight into the electromagnetic behaviour of any arbitrarily shaped conducting structure because the CMs are unique to the geometry of the object. We exploit this very fact by predicting a perhaps surprising number of important antenna metrics such as resonance frequency, radiation efficiency and antenna Q (bandwidth) without needing to specify a feeding location. In doing so, it is possible to define a collection of objective functions that can be used in an optimizer to shape-synthesize antennas without needing to define a feed location a priori. We denote this novel form of optimization “feedless” or “excitation-free” antenna shape synthesis. Fundamentally, we are allowing the electromagnetics to dictate how the antenna synthesis should proceed and are in no way imposing the physical constraints enforced by fixed feeding structures. This optimization technique is broadly applied to three major areas of antenna research: electrically small antennas, multi-band antennas and reflectarrays. Thus, the scope of applicability ranges from small antennas, to intermediate sizes and concludes with electrically large antenna designs, which is a testament to the broad applicability of characteristic mode theory. Another advantage of feedless electromagnetic shape synthesis is the ability to synthesize antennas whose desirable properties approach the fundamental limits imposed by electromagnetics. As an additional benefit, the feedless optimization technique is shown to have greater computational efficiency than traditional antenna optimization techniques.