Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Liu, Shanshan

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https://hdl.handle.net/2142/11988 Copy

Description

Title

Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Author(s)

Liu, Shanshan

Issue Date

2009-06-01T16:05:27Z

Director of Research (if dissertation) or Advisor (if thesis)

Sauer, Peter W.

Doctoral Committee Chair(s)

Sauer, Peter W.

Committee Member(s)

• Namachchivaya, N. Sri
• Overbye, Thomas J.
• Pai, M.A.

Department of Study

Electrical and Computer Engineering

Discipline

Electrical and Computer Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Load modeling
• Nonlinear filtering
• State estimation
• Order reduction
• Balanced truncation

Abstract

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.

Graduation Semester

2009-5

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http://hdl.handle.net/2142/11988

Copyright and License Information

Copyright 2009 Shanshan Liu

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