University of Illinois Urbana-Champaign

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

Liu, Shanshan

Content Files
1_Liu_Shanshan.pdf

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

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
Date of Ingest
2009-06-01T16:05:27Z
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
Permalink
http://hdl.handle.net/2142/11988
Copyright and License Information
Copyright 2009 Shanshan Liu

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