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UBC Theses and Dissertations

Diagonostic [sic] of transformer winding movement Jiang, Qiaoshu

Abstract

Power transformers play a vital role in the operation of a power grid. A power transformer converts electricity from one potential level to another, and it is one of the most important and expensive pieces in a power grid. However, transformer failures happen, which lead to power outages, high cost of repair or replacement, personal injuries and environmental damages. Winding movement and/or distortion account for a large percentage of transformer failures. The focus of this thesis is to explore an effective winding movement detection technique. The current diagnostic techniques used for detecting winding movements are discussed and compared in this thesis. The Frequency Response Analysis (FRA) technique is today's most commonly used and effective technique; however, it highly depends on the test frequency range, test set-up, external circuits and different physical structures of different transformers. The FRA's dependence on so many parameters can result in inaccurate data. Based on the wave propagation property and the frequency dependent transmission line model, a new approach for winding movement detection is proposed in this thesis, the Transmission Line Diagnostics (TLD). In this method, by measuring the input and output voltages and the currents, the surge impedance Zc of the winding can be uniquely obtained. Zc is the signature of the winding and it is independent of the external circuits. Any movement of the winding will reflect in a change of Zc. Different from the complex transfer function of numerous resonant frequencies in FRA , Zc obtained by TLD is a simple exponential-like curve, whose vertical shift gives obvious indication to the overall axial or radial winding movements. Furthermore, TLD is very efficient in low frequency range; for example, 2MHz is the maximum frequency used when performing the experiments in this thesis. With all these advantages, TLD can be effectively applied to online transformer winding condition monitoring in the future.

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