Date of Award

August 2014

Degree Type

Thesis

Degree Name

Master of Science

Department

Engineering

First Advisor

David C. Yu

Keywords

Air-Gap Partial Discharge, Cavity Size, Clustering Analysis, Random Forests, Statistical Parameter Extraction

Abstract

Oil-paper insulation system is commonly used for power transformer internal insulation. Partial discharge (PD) is one of the main reasons for aging and disruption of the insulation system. Air-gap PD occurs in gas-filled cavity in transformer oil-paper insulation and is an extremely common and serious defect type. For air-gap PD analysis, most experiments were conducted through the standard air-gap discharge model recommended by CIGRE. Some work has been done to diagnose air-gap PD severity. However, the effect of cavity size on PD activity has not been emphasized yet. My thesis systematically discusses the effect of cavity size on air-gap PD activity through experiments. And pattern recognition classifier is a critical part in PD diagnosis. Artificial neural network and support vector machine are commonly used nowadays and show some good results in site application. To enhance PD diagnosis accuracy is still a main task. In this work, Random Forests is first time introduced in PD diagnosis.

Experiments show that large cavity PD possesses lower inception field, higher charge magnitude, higher inception phase. PD happening in large cavity is more harmful than that happening in small cavity. Besides, during Air-gap PD development process, charge magnitude variation of large and small cavity model both presents concave curve shape with respect to time and discharge phase slowly expends. For small cavity model, when air-gap PD comes to the last stage, positive PD even can expand to the negative half cycle and vice versa. And through clustering, the PD development stage for large and small cavity model are both divided into three stages, initial discharge stage, weak discharge stage and pre-breakdown stage. For air-gap PD development stage identification, total accuracy of random forests classifier is 93.15%, showing a better performance than RBF neural network.

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