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Sources of error in substation distribution transformer dynamicthermal modeling

机译:变电站配电变压器动态热模型的误差来源

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When a transformer's windings get too hot, either load has to benreduced (in the short term) or another transformer bay needs to beninstalled (in the long run). To be able to predict when either of thesenremedial schemes must be used, we need to be able to predict thentransformer's temperature accurately. Our experimentation with variousndiscretization, schemes and models, convinced us that the linear andnnonlinear semiphysical models we were using to predict transformerntemperature were near optimal and that other sources of input-data errornwere frustrating our attempts to reduce the prediction error further. Innthis paper we explore some of the sources of error that affect top-oilntemperature prediction. We show that the traditional top-oil rise modelnhas incorrect dynamic behavior and show that another model proposedncorrects this problem. We show that the input error caused by databasenquantization, remote ambient temperature monitoring and low samplingnrate account for about 2/3 of the error experienced with field data. Itnis the opinion of the authors that most of this difference is due to thenabsence of significant driving variables, rather than the approximationnused in constructing a linear semiphysical model
机译:如果变压器的绕组温度过高,则必须减少负载(短期内),或者需要安装另一个变压器托架(从长远来看)。为了能够预测何时必须使用任何一种补救方案,我们需要能够准确地预测变压器的温度。我们对各种离散化,方案和模型的实验使我们确信,用于预测变压器温度的线性和非线性半物理模型接近最佳,而其他输入数据错误源也使我们进一步降低预测误差的努力受挫。在本文中,我们探讨了一些影响顶油温度预测的误差源。我们证明了传统的顶油上升模型具有不正确的动态行为,并表明提出的另一种模型可以纠正这一问题。我们表明,由数据库量化,远程环境温度监控和低采样率引起的输入错误约占现场数据所经历错误的2/3。 Itnis作者认为,这种差异的大部分是由于缺少重要的驱动变量,而不是由于在构建线性半物理模型时采用了近似值

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