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文章摘要
基于混沌序列优化的BP网络油纸绝缘变压器寿命预测
Life prediction of oil-paper insulated transformer based on chaotic sequence optimization BP neural network
Received:October 11, 2019  Revised:October 11, 2019
DOI:10.19753/j.issn1001-1390.2020.04.020
中文关键词: 寿命预测  油纸绝缘变压器  混沌序列  神经网络
英文关键词: life prediction, oil-paper insulated transformer, chaotic sequence, neural network
基金项目:
Author NameAffiliationE-mail
liuyongxin School of Electrical Engineering and Automation, Wuhan University 124104474@qq.com 
songbin* School of Electrical Engineering and Automation, Wuhan University songbin72@163.com 
wanglinong School of Electrical Engineering and Automation, Wuhan University 1981460448@qq.com 
xurihong School of Electrical Engineering and Automation, Wuhan University 1490026791@qq.com 
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中文摘要:
      变压器在服役期间其各个部分都会发生老化影响使用寿命,通过对其健康程度的刻画有助于电力部门预测变压器在运行期间的故障率以及剩余寿命,对确保变压器的安全运行极为重要。文中利用变压器故障浴盆曲线的思想,对收集变压器故障率进行weibull拟合,得到故障率曲线;考虑变压器运行环境与负荷因素,利用糠醛含量构建健康指数剩余寿命预测模型。通过混沌序列优化BP神经网络权重参数进行数据挖掘,构建起多参数关联的变压器寿命预测模型,并引入交叉验证机制提高网络泛化能力。通过实例训练与测试对比,提出的方法有较高的预测精度,能准确运用于变压器的寿命预测。
英文摘要:
      The aging of every part of transformer will affect its service life. The description of its health will help us to predict the failure rate and residual life of transformer during operation. In this paper, using the idea of transformer fault bathtub curve, Weibull fitting is carried out to collect transformer fault rate, and the fault rate curve is obtained. Considering the operation environment and load factors of transformer, the residual life prediction model of health index is constructed by using the content of furfural. By optimizing the weight parameters of BP neural network through chaotic sequence, a transformer life prediction model with multi-parameter correlation is constructed, and cross-validation mechanism is introduced to improve the generalization ability of the network. By comparing the example training with the test, the method in this paper has higher prediction accuracy and can be applied to the life prediction of transformer accurately.
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