Title page for ETD etd-12172009-201325


Type of Document Dissertation
Author Bernabeu, Emanuel
URN etd-12172009-201325
Title Methodology for a Security-Dependability Adaptive Protection Scheme based on Data Mining
Degree PhD
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Centeno, Virgilio A. Committee Co-Chair
Thorp, James S. Committee Co-Chair
DaSilva, Luiz A. Committee Member
De La Ree Lopez, Jaime Committee Member
Kohler, Werner E. Committee Member
Liu, Yilu Committee Member
Keywords
  • wide area measurements
  • data mining
  • decision trees
  • adaptive protection
  • critical locations
Date of Defense 2009-12-09
Availability unrestricted
Abstract
The power industry is currently in the process of re-inventing itself. The unbundling of the traditional monopolistic structure that gave birth to a deregulated electricity market, the mass tendency towards a greener use of energy, the new emphasis on distributed generation and alternative renewable resources, and new emerging technologies have revolutionized the century old industry.

Recent blackouts offer testimonies of the crucial role played by protection relays in a reliable power system. It is argued that embracing the paradigm shift of adaptive protection is a fundamental step towards a reliable power grid. The adaptive philosophy of protection systems acknowledges that relays may change their characteristics in order to tailor their operation to prevailing system conditions. The purpose of this dissertation is to present methodology to implement a security/dependability adaptive protection scheme. It is argued that the likelihood of hidden failures and potential cascading events can be significantly reduced by adjusting the security/dependability balance of protection systems to better suit prevailing system conditions.

The proposed methodology is based on Wide Area Measurements (WAMs) obtained with the aid of Phasor Measurement Units (PMUs). A Data Mining algorithm known as Decision Trees is used to classify the power system state and to predict the optimal security/dependability bias of a critical protection scheme.

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