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This thesis proposes two methods to estimate transfer function models using System Identification technique that can be used to estimate Control Performance Standard 1 (CPS1) index. The first method is applicable when a load-frequency time domain simulation system of an interconnected power system for estimation of CPS1 is available. This method models an accurate approximate equivalent power system external to a system under consideration. The second method is applicable when a time domain simulation model for estimation of CPS1 is not available. This method uses System Identification technique to model two transfer functions to produce necessary data for the estimation of CPS1. The necessary up-to-date data for System Identification can be obtained from a practical power system dynamic simulation model. The developed models are used to estimate CPS1. The research described in this thesis also shows the applicability of a previously developed method of estimating CPS1 in a practical power system. The techniques, methodology and results presented in this research should provide useful information for operating and planning of power systems.

This thesis examines whether it is possible for a machine to incrementally build a complex model of its environment, and then use this model for control purposes. Given a sequence of noisy observations, the machine forms a piecewise linear approximation to the nonlinear dynamic equations that are assumed to describe the real world. A number of existing online system identification techniques are examined, but it is found that they all either scale poorly with dimensionality, have a number of parameters that make them difficult to apply, or do not learn sufficiently accurate approximations. Therefore a novel framework is developed for learning linear model trees in both batch and online settings. The algorithms are evaluated empirically on a number of commonly used benchmark datasets, a simple test function, and three dynamic domains ranging from a simple pendulum to a complex flight simulator. The new batch algorithm is compared with three state-of-the-art algorithms and is seen to perform favourably overall. The new incremental model tree learner also compares well with a recent online function approximator from the literature. Armed with a tool for effectively constructing piecewise linear models of the environment, a control framework is developed that learns trajectories from a demonstrator and attempts to follow these trajectories within each linear region usinglinear quadratic control. The induced controllers are able to swing up and balance a simple forced pendulum both in simulation and in the real world. They can also swing up and balance a real double pendulum. The induced controllers are empirically shown to perform better than the original demonstrator, and could therefore be used to either replace a human operator or improve upon an existing automatic controller. In addition an ability to generalise the learnt trajectories enables the system to perform novel tasks. This is demonstrated on a flight simulator where, having observed an aircraft flying several times around a circuit, the controller is able to copy the take-off procedure, fly a completely new circuit that includes new manoeuvres, and successfully land the plane.

Thesis (M.S. - Hydrology and Water Resources)--University of Arizona, 1984. / 2 folded leaves in pocket. Includes bibliographical references (leaves 138-139).

Thesis (Ph. D. - Hydrology and Water Resources)--University of Arizona, 1969. / Includes bibliographical references (leaves 105-108).

Thesis (M.S.)--Ohio University, June, 1981. / Title from PDF t.p.

Thesis (M.S.)--Ohio University, August, 1982. / Title from PDF t.p.

Licentiatuppsats Luleå : Luleå tekniska univ., 2004. / Härtill fyra uppsatser.