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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/2919

Title: A hardware/software platform for fault detection and identification in electric power distribution systems for testing various detection schemes
Authors: Schegan, Christian M.
Keywords: Electric engineering;Electric power systems;Electric power distribution
Issue Date: 29-Oct-2008
Abstract: Distribution systems are seeing a larger penetration of intelligent devices such as digital protection devices, advanced metering equipment and distributed generation. These devices are generally equipped with processors, which are capable of advanced computations. With advanced measurement and computation, faster and improved fault detection and identification techniques can be investigated. Since actual fault data is rarely released for testing such techniques, research relies heavily on data collected from a simulation tool. This thesis proposes a hardware/software platform for performing fault experimentation in Drexel’s Reconfigurable Distribution Automation and Control (RDAC) laboratory. Specifically, hardware setups have been designed to test a software implementation of a wavelet-based fault detector. This approach to fault studies preserves uncertainty stemming from system parameters and equipment. The effects of load on fault detection are studied with single-, two- and three-phase resistive and series resistive/inductive loads. While a wavelet-based approach was taken in this work, the hardware platform can be used with any detection scheme. The discrete wavelet transform has been recently implemented for power quality analysis and fault detection. For fault detection, most work focuses on balanced power systems using per phase analysis. This thesis proposes a wavelet-based fault detection and identification algorithm capable of detecting and identifying faults within ¼ cycle ofa 60Hz signal in unbalanced radial distribution systems. Fault experiments under a wide range of load distributions and loading levels have been performed in order to design and validate the algorithm’s performance. In addition, studies have been performed on meter placement, sensitivity and detection error with respect to various fault types and locations, in order to further increase the algorithm’s reliability.
URI: http://hdl.handle.net/1860/2919
Appears in Collections:Drexel Theses and Dissertations

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