Cost effective assessment of transformers using machine learning approach

机译：使用机器学习方法对变压器进行经济有效的评估

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Furan content in transformer oil is highly correlated with the transformer insulation paper aging. In this paper, the ranges of furan content in power transformer is predicted using measurements of transformer oil tests like breakdown voltage, acidity and water content. Machine learning approach is adopted, and maintenance data collected from 90 transformers are used. A maximum of 67% recognition rate was achieved using Decision Tree classifier. The major challenge of the used data is the relatively low number of available samples in certain furan intervals. Two solutions have been proposed to overcome this imbalanced classification problem, namely, using an over-sampling technique and balancing data distributions by reducing the number of intervals to be predicted to three instead of five intervals. The recognition rate has improved to reach 80%.

机译：变压器油中的呋喃含量与变压器绝缘纸的老化高度相关。在本文中，使用变压器油测试（如击穿电压，酸度和水含量）的测量结果来预测电力变压器中呋喃的含量范围。采用机器学习方法，并使用从90个变压器收集的维护数据。使用决策树分类器最大可达到67％的识别率。使用数据的主要挑战是在某些呋喃间隔中可用样品的数量相对较少。已经提出了两种解决方案来克服该不平衡分类问题，即，使用过采样技术和通过将要预测的间隔数减少为三个而不是五个间隔来平衡数据分布。识别率提高到80％。

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来源
《2014 IEEE Innovative Smart Grid Technologies - Asia》|2014年|328-332|共5页
会议地点 Kuala Lumpur(MY)
作者
Benhmed Kamel; Shaban Khaled Bashir; El-Hag Ayman;
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作者单位

Research Unit of Modeling, Analysis and Control of Systems (MACS), National Engineering School of Gabes, Gabes, Tunisia;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Artificial neural networks; Asia; Data models; Decision trees; Oil insulation; Power transformer insulation; Furan content; Power transformer; machine learning; transformer oil;

机译：人工神经网络;亚洲;数据模型；决策树；绝缘油；电力变压器绝缘；呋喃含量；电源变压器;机器学习变压器油；;

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Cost effective assessment of transformers using machine learning approach

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