Hot Resistance Estimation for Dry Type Transformer Using Multiple Variable Regression, Multiple Polynomial Regression and Soft Computing Techniques
Hot Resistance Estimation for Dry Type Transformer Using Multiple Variable Regression, Multiple Polynomial Regression and Soft Computing Techniques
复制标题
利用多变量回归、多项式回归和软计算技术估计干式变压器的热阻
DOI：
10.3844/ajassp.2012.231.237
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
A. Krishnan
中科院分区：
文献类型：
--
作者：
M. Srinivasan;A. Krishnan
问题说明：本研究提出了一种确定变压器在预定现场运行条件下平均绕组温升的新方法。根据IEC标准进行的热运行试验中绕组电阻的估计值确定绕组温度的上升。方法：采用多变量回归（MVR）、多元多项式回归（MPR）和人工神经网络（ANN）、自适应神经模糊推理系统（ANFIS）等软计算技术对热阻进行建模。所建立的热电阻模型将有助于在任何负载情况下发现负载损耗，而无需在变压器中使用复杂的测量装置。结果：利用冷阻、环境温度和温升等输入变量，应用于干式变压器热阻估算。结果表明，实测值与计算值吻合较好。结论：根据我们的实验，用55kva干式变压器的温升试验结果验证了所提出的方法。
Problem statement: This study presents a novel method for the determination of average winding temperature rise of transformers under its predetermined field operating conditions. Rise in the winding temperature was determined from the estimated values of winding resistance during the heat run test conducted as per IEC standard. Approach: The estimation of hot resistance was modeled using Multiple Variable Regression (MVR), Multiple Polynomial Regression (MPR) and soft computing techniques such as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The modeled hot resistance will help to find the load losses at any load situation without using complicated measurement set up in transformers. Results: These techniques were applied for the hot resistance estimation for dry type transformer by using the input variables cold resistance, ambient temperature and temperature rise. The results are compared and they show a good agreement between measured and computed values. Conclusion: According to our experiments, the proposed methods are verified using experimental results, which have been obtained from temperature rise test performed on a 55 kVA dry-type transformer.