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文章摘要
一种配电变压器热评估方法
A Thermal Evaluation Method of Distribution Transformers
Received:December 21, 2017  Revised:December 21, 2017
DOI:
中文关键词: 配电变压器  热评估 热传递微分方程 LSTM型循环神经网络
英文关键词: Distribution  transformer, Thermal  evaluation, Thermal  transmission differential  equation, LSTM  Type Recurrent  Neural Network
基金项目:
Author NameAffiliationE-mail
An Yi* State Grid JiangXi Electric Power Research Institute 554049475@qq.com 
Zhu Zhijie State Grid JiangXi Electric Power Research Institute 27962141@qq.com 
Wang Huayun State Grid JiangXi Electric Power Research Institute huayunw@163.com 
Cai Muliang State Grid JiangXi Electric Power Research Institute 675175117@qq.com 
Liu Bei State Grid JiangXi Electric Power Research Institute 154051361@qq.com 
Chen Qiu State Grid JiangXi WuNing Electric Power Supply Company Limtted 710266050@qq.com 
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中文摘要:
      针对配电变压器运行中的负荷特殊性和热传递微分方程参数难以确定的问题,提出了一种配电变压器热评估方法,分析了配电变压器热传递的动态过程,指出了传统神经网络难以适用于配电变压器热评估当中,考虑配电变压器三相负载系数,建立了配电变压器LSTM型循环神经网络热评估模型,对配电变压器顶层油温进行预测,达到配电变压器热评估的目的。算例表明:本文方法的预测结果比热传递微分方程更接近实际情况,验证了该方法的有效性和实用性。
英文摘要:
      With respect to the load speciality of distribution transformers and uncertainty of thermal transmission differential equation parameters, a thermal evaluation of distribution transformers is proposed, in which the dynamic process of thermal transmission in distribution transformers is analyzed, and an idea that traditional neural network is hard to go for thermal evaluation of distribution transformers is raised. When considering three-phase load factors of distribution transformers, the LSTM Type Recurrent Neural Network model of distribution transformers thermal evaluation are established, the top layer oil temperature of distribution transformers is predicted so that the thermal evaluation of distribution transformers are achieved. The calculating example indicates that the predicting outcome method this paper used goes more closer to the practical situation than thermal transmission differential equation, in this way the validity and practicability of the method in the paper is verified.
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