Semester

Spring

Date of Graduation

2004

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Lane Department of Computer Science and Electrical Engineering

Committee Chair

Ali Feliachi.

Abstract

This dissertation is proposing practical control design techniques for electric power systems load frequency control (LFC) and stability enhancement through tuning of power system stabilizers (PSSs).;For the LFC, two novel design techniques are developed. The first one, called GALMI, is for tuning the gains of the traditional controllers to obtain a robust performance similar to the one that will be obtained using a centralized high order controller based on H-infinity design and Linear matrix Inequalities (LMI). The tuning is performed using genetic algorithms (GA). The second approach is a controller design based on model predictive control (MPC). This design is fully decentralized and requires only local area parameters. Furthermore, due to the ability of MPC to handle constraints on controlled variables this design can coupe with the nonlinearities in the LFC model.;To enhance power system transient stability margins two methodologies for PSS tuning are also proposed. The first methodology for PSS design is useful for the frequently occurring situation where a single PSS needs to be designed to produce maximum impact on the damping of a power system. In this method an identification is first used to derive low order transfer functions of large-scale power systems and then a GA based optimization of a damping index is used for PSS tuning. The second proposed methodology for PSS design is useful for the situations where several PSS controllers can be tuned together. By coordinating these controllers it is possible to achieve better robustness and damping of interarea modes. Multiobjective optimization is utilized to obtain the controller parameters. The first objective is used to enhance the damping performance of the system. To incorporate robustness explicitly additional optimization objective is added which is based on the infinity norm of the sensitivity transfer function of the system.;Effectiveness of the proposed techniques is demonstrated on a number of case studies including benchmark and actual power systems.

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