4.5 Article

Reliable Assessment of Oil-Paper Insulation Used in Power Transformer Using Concise Dielectric Response Measurement

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TDEI.2023.3261824

关键词

Mathematical models; Power transformer insulation; Current measurement; Dielectric measurement; Oil insulation; Dielectrics; Conductivity; Conductivity measurement; dielectric measurement; dielectrics; insulation; moisture content; power transformers; time-domain analysis

向作者/读者索取更多资源

Oil-impregnated paper is the preferred insulation material for power transformers. Estimating insulation condition using polarization current profiles is time-consuming, so a proposed technique based on short-duration polarization current data is introduced to predict performance parameters related to paper insulation. The method is tested on measured data from various transformers to demonstrate its effectiveness.
Oil-impregnated paper is usually the preferred choice of insulation in power transformers. The working life of any transformer depends on its insulation condition. The paper conductivity, moisture content, and dissipation factor of solid insulation are a few crucial parameters used to determine the insulation condition. The reliable estimation of such parameters for the diagnosis of solid insulation requires a complete profile of polarization current, which is time-consuming. Hence, utilities always prefer noninvasive and rapid techniques to predict insulation conditions and minimize shutdown time. The proposed technique is based on short-duration polarization current data. The present manuscript suggested a method that uses the initial decay rate (IDR) of recorded polarization current data to predict various performance parameters related to paper insulation. In order to show the effectiveness of the proposed method, the analysis is performed on the data measured from various in situ transformers.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据