卷积神经网络在电力变压器状态监测中溶解气体分析中的应用

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Shaowei Rao, Shiyou Yang, M. Tucci, S. Barmada
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引用次数: 0

摘要

本文提出了一种基于溶解气体分析(DGA)的卷积神经网络故障诊断方法。介绍了将气体含量(由DGA分析得到)转换成特征图的算法,并将得到的特征图作为CNN的输入。为了考虑到数据集不平衡的事实,将改进的合成少数派过采样技术(SMOTE)与数据清洗技术相结合,保护CNN不受训练偏差的影响。研究了CNN结构对分类性能的影响,确定了最优的CNN参数。对上述所有可能性进行了测试并对其性能进行了研究;此外,在IEC TC 10变压器故障数据库上的最终测试验证了所提出方法的准确性和推广潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convolutional neural networks applied to dissolved gas analysis for power transformers condition monitoring
In this contribution a methodology to diagnose transformer faults based on Dissolved Gas Analysis (DGA) by using a convolutional neural network (CNN) is proposed. The algorithm to transform the gas contents (resulting from the DGA analysis) into feature maps is introduced, and the resulting feature maps are the input of the CNN. In order to take into account the fact that the data set is imbalanced, the improved Synthetic Minority Over-Sampling Technique (SMOTE) is combined with the data cleaning technique to protect the CNN from training bias. The effect of the CNN architecture on the classification performance is also investigated to determine the optimal CNN parameters. All the above mentioned possibilities are tested and their performance investigated; in addition, a final test on the IEC TC 10 transformer fault database validates the accuracy and the generalization potential of the proposed methodology.
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
100
审稿时长
4.6 months
期刊介绍: The aim of the International Journal of Applied Electromagnetics and Mechanics is to contribute to intersciences coupling applied electromagnetics, mechanics and materials. The journal also intends to stimulate the further development of current technology in industry. The main subjects covered by the journal are: Physics and mechanics of electromagnetic materials and devices Computational electromagnetics in materials and devices Applications of electromagnetic fields and materials The three interrelated key subjects – electromagnetics, mechanics and materials - include the following aspects: electromagnetic NDE, electromagnetic machines and devices, electromagnetic materials and structures, electromagnetic fluids, magnetoelastic effects and magnetosolid mechanics, magnetic levitations, electromagnetic propulsion, bioelectromagnetics, and inverse problems in electromagnetics. The editorial policy is to combine information and experience from both the latest high technology fields and as well as the well-established technologies within applied electromagnetics.
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