The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	An optic fiber sensor for partial discharge acoustic detection
Author:	Wang, Xiaodong
View Online:	njit-etd2005-040 (xvi, 104 pages ~ 8.0 MB pdf)
Department:	Federated Physics Department of NJIT and Rutgers-Newark
Degree:	Doctor of Philosophy
Program:	Applied Physics
Document Type:	Dissertation
Advisory Committee:	Russo, Onofrio L. (Committee co-chair) Chin, Ken K. (Committee co-chair) Schaden, Martin (Committee member) Roman, Harry T. (Committee member) Li, Baoqing (Committee member)
Date:	2005-01
Keywords:	Partial discharge Fiber optic Transformer Sensor MEMS
Availability:	Unrestricted
Abstract:	Partial discharge (PD) is a very common problem in operating power transformers and is one of the factors that could lead to failure of power transformers, leading to power outage and expensive repairs. The acoustic wave induced by PD can be measured and used for monitoring, diagnosing, and locating potential failures in power transformers. The effects of the temperature of the transformer and transformer oil are one of the very important parameters in PD and these effects are investigated in detail. The Fast Fourier Transform (FF1') is used to synthesize the measured data and results show that for periodic PD events, the dominant components of the energy of the PD shift to higher frequencies as the temperature increases. The experimental results are consistent with theoretical expectations. Fiber optic-based sensors have been shown to be attractive devices for PD detection because of a number of inherent advantages including small size, high sensitivity, electrical nonconductivity, and immunity to electromagnetic interference (EMI). A fiber optic sensor based on a Fabry-Perot interferometry is constructed by a simple micromachining process compatible with MEMS (Microelectromechanical system) technology. The sensor is used in a transformer to measure PD acoustic waves. The experimental results show that the sensor not only has an inherent high signal to noise capability, but is able to accurately localize the PD sources inside the transformer.