Advisor

Younan, Nicholas H.

Committee Member

Du, Jenny Q.

Committee Member

Grzybowski, Stanislaw

Date of Degree

1-1-2011

Document Type

Graduate Thesis - Open Access

Degree Name

Master of Science

Abstract

Underground power cables encounter various problems caused by manufacturing defects and/or environmental contact. In keeping with the Smart Grid vision, researchers must develop diagnostic techniques that can be utilized to facilitate the decision making processes regarding replacement prior to failure can occur, thereby minimizing impact to customers. Due to the impact of the aging infrastructure and in particular underground polymeric cables, various offline and online methods have been developed for the detection of the remaining life of underground cables. The offline methods require power outage, which can lead to further difficulty in their implementation. Signal processing techniques hold promise to provide real time or near real time diagnostics. In this thesis, three different signal processing techniques; fast Fourier transform, short-time Fourier transform, and wavelet transform; are investigated for identifying and classifying various fault types encountered in underground power cables based on cable current and voltage measurements.

URI

https://hdl.handle.net/11668/17840

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