Protection Against Ungrounded Single Phase Open Circuit Faults in 3-Phase Distribution Transformers

This thesis explores the impacts and behavior of 3-phase distribution transformers when subject to ungrounded single phase open circuit faults. A simple 3-phase system is modeled using MATLAB Simulink and operation under fault conditions are simulated and studied. Simulation results are confirmed via lab experimentation. Finally, a robust detection and protection method using neutral current injection (as proposed in industry literature) is built and demonstrated. Electric utility operating experience has demonstrated that all too often, loads on 3-phase distribution transformers are not adequately protected against an ungrounded single phase open circuit fault (commonly called “single phasing”). This type of fault is amongst the least understood and hence the least protected against. This is especially true at end of transmission system radial feeds where 3-phase transformers can re-create the opened phase voltage due to a variety of effects including magnetic coupling, voltage loops and loading effects. Operating experience in the nuclear power industry has shown that the results can be catastrophic especially considering the impacts to motor loads. Impacts can result in unavailability of emergency loads, tripping of motor protection circuits or even motor damage and failure

Finding Lips in Unconstrained Imagery for Improved Automatic Speech Recognition

Lip movement of a speaker conveys important visual speech information and can be exploited for Automatic Speech Recognition. While previous research demonstrated that visual modality is a viable tool for identifying speech, the visual information has yet to become utilized in mainstream ASR systems. One obstacle is the difficulty in building a robust visual front end that tracks lips accurately in a real-world condition. In this paper we present our current progress in addressing the issue. We examine the use of color information in detecting the lip region and report our results on the statistical analysis and modeling of lip hue images by examining hundreds of manually extracted lip images obtained from several databases. In addition to hue color, we also explore spatial and edge information derived from intensity and saturation images to improve the robustness of the lip detection. Successful application of this algorithm is demonstrated over imagery collected in visually challenging environments