Chin-Chu Tsai

Graduation Semester and Year

2011

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering

Department

Electrical Engineering

First Advisor

Wei-Jen Lee

Abstract

Power systems have become more complex and are found to be consistently operating closer to their stability limits under the deregulated environment. Power system dynamic simulation, which provides significant insight into the dynamic characteristics of system, is one of the most important tools for both planning and operation engineers. Since it heavily relies on the simulation result to make decisions related to operation strategies, grid expansion plain, facility maintenances schedules, an accurate simulation result can avoid unnecessary facility investment and improve the security of the system operation. However, the simulation result is affected by the model and parameters of the equipment. Since most models are provided by the manufacturer and derived through rigorous verification; it is reasonable to assume that model be trustworthy. However, there are many tunable or user definable parameters in the model. Assigning inaccurate parameters become the major source that causes the mismatch between the simulation results and actual system response. However, a large power system is composed of millions of component and many of them are inter-correlated, the parameter identification becomes very difficult. This dissertation proposed a hybrid dynamic simulation strategy for parameter identification based on measurement data of phasor measurement unit (PMU). It efficiently makes a boundary on the measurement point of PMU to overcome the uncertainty of the external system elements which makes the proposed task manageable.To improve the computational efficiency, this dissertation also proposes a key parameter screening process based on trajectory sensitivity to identify the most significant parameters. A high efficiency global optimization algorithm called cooperative simultaneous perturbation stochastic approximation and particle swarm optimization (SPSA-PSO) is proposed to solve the parameter identification problem. The effectiveness and feasibility of the proposed method and process were demonstrated by a new installed generator unit in Electric Reliability Council of Texas (ERCOT) system.

Disciplines

Electrical and Computer Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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