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Abstract
Power systems are modeled as large-scale systems composed of a set of small-
interconnected subsystems. It is generally impossible to incorporate many feed back
loops into the controller design for large scale interconnected systems and is also too
costly even if they can be implemented. These motivate the development of
decentralized control theory where each subsystem is controlled independently on its
local available information.
On the other hand, the operating conditions of power systems are always varying to
satisfy different load demands. Control systems are therefore required to have the
ability to damp the system oscillations that might threaten the system stability as the
load demand increases. However, as power systems are large-scale nonlinear systems
in nature, the applications of conventional power system stabilizer (PSS) are limited.
There is thus a need for controllers, which are robust to changes in the system
operating condition. Robust controllers based on HeY;) control theory are particularly
suited for this purpose.
This thesis proposes two robust decentralized controllers for multimachine power
system instead of using a complex centralized controller. The first one is based on
H theory, and results in high order controller. The second controller is a
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proportional integral (PI) type, and is tuned by a novel robust performance as the first
one, but it is more appealing from an implementation point of view. In more detail,
the second control design is first cast into the robust H control design in terms of
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linear matrix inequalities (LMI) in order to obtain robustness against system operating
conditions. An additional constraint is that the structure of the controller is predefined
as a PI type, which is ideally practical for industry. In order to obtain the optimal
controller parameters with regards to the H and controller structure constraints,
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genetic algorithms (GAs), a powerful probabilistic search technique is used to find the
control parameters of the PI controller.