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Artificial neural network and non-linear models for prediction of transformer oil residual operating time

机译：人工神经网络和非线性模型预测变压器油的剩余运行时间

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摘要

This paper presents two modeling techniques for the prediction and monitoring of the characteristics of transformer oil. The first employs artificial neural network (ANN) and the second employs non-linear modeling (nlm). The proposed techniques are implemented for predicting the transformer oil residual operating time (t_(rot)) which is defined as the service period after which the breakdown voltage (BDV) violates the limits given in the standard specifications.rnThe selection of the most influential characteristics on residual operating time (t_(rot)) in the proposed techniques is obtained by statistical analysis. The non-linear model depends on linear combination of non-linear functions for each characteristic. The ANN technique for modeling these characteristics preserves the non-linear relationship between these characteristics and (t_(rot)). The results are compared with previously published modeling techniques namely multiple linear regression and polynomial regression models. Different evaluation indices have been used to justify the superiority of the proposed modeling techniques for predicting (t_(rot)).
机译：本文提出了两种用于预测和监测变压器油特性的建模技术。第一种采用人工神经网络（ANN），第二种采用非线性建模（nlm）。所提出的技术用于预测变压器油的剩余工作时间（t_（rot）），该时间定义为工作时间，在此之后的击穿电压（BDV）违反标准规范中给出的限制。rn选择最具影响力的特性通过统计分析获得了所提出技术中的剩余工作时间（t_（rot））。非线性模型取决于每个特性的非线性函数的线性组合。用于建模这些特征的ANN技术保留了这些特征与（t_（rot））之间的非线性关系。将结果与以前发布的建模技术（即多元线性回归和多项式回归模型）进行比较。已经使用不同的评估指标来证明所提出的建模技术用于预测（t_（rot））的优越性。

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  • 来源
    《Electric power systems research》 |2011年第1期|p.219-227|共9页
  • 作者

    Mohamed A.A. Wahab; Mohamed. M. Hamada; Ahmed Mohamed;

  • 作者单位

    Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Minia 61111. Egypt;

    rnElectrical Engineering Department, Faculty of Engineering, Minia University, Minia, Minia 61111. Egypt;

    rnElectrical and Computer Engineering Department, College of Electrical and Computer Engineering Energy Systems Research Laboratory, Florida International University, 10555 W Flagler Street, Room EC-3810, Miami, FL33174, United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    artificial neural networks; non-linear modeling; residual operating time; transformer oil;

    机译：人工神经网络;非线性建模;剩余工作时间;变压器油;

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