Methodology for a Security-Dependability Adaptive Protection Scheme based on Data Mining

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.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2009

Author dc:creator

• Bernabeu, Emanuel

Chairs dc:contributor.committeechair

• Thorp, James S.
• Centeno, Virgilio A.

Committee members dc:contributor.committeemember

• De La Ree, Jaime
• DaSilva, Luiz A.
• Kohler, Werner E.
• Liu, Yilu

• wide area measurements
• data mining
• decision trees
• adaptive protection
• critical locations

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-12172009-201325

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/30131