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| 基于MODWT的变压器绕组轻微故障检测及分类研究 |
| Study on the detection and classification transformer winding slight fault based on MODWT |
| 投稿时间：2018-05-07 修订日期：2018-05-07 |
| DOI： |
| 中文关键词: 变压器 绕组轻微故障 匝间电弧放电 等效瞬时励磁电感 MODWT |
| 英文关键词:transformer, winding slight failure, partial arc discharge fault, equivalent instantaneous magnetizing inductance, MODWT |
| 基金项目:国家自然科学基金项目（ 面上项目） |
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| 摘要点击次数: 80 |
| 中文摘要: |
| 本文基于变压器在不同运行工况下的等效瞬时励磁电感的差异，利用最大重叠离散小波变换 (MODWT) 提取有效故障特征参数，实现对变压器绕组轻微匝间故障以及匝间电弧放电故障的检测。首先提取变压器在各种工况下的电气量，求取等效瞬时励磁电感，选取基于db4小波函数的最大重叠离散小波变换进行分析，提取特征量。将故障特征量作为决策树的训练集和测试集，从而实现变压器绕组轻微故障的识别以及分类。最后通过仿真证明，所提出的算法能够准确检测以及区分励磁涌流、轻微匝间短路故障以及匝间电弧放电故障。 |
| 英文摘要: |
| Based on the difference of the equivalent instantaneous magnetizing inductance of transformer under different operating conditions of transformer, this paper proposes to used the maximal overlapping discrete wavelet transform (MODWT) to extract effective fault characteristic parameters, and realize the detection of transformer windings with slight inter-turn short circuit faults and partial arc discharge faults. Firstly, the electric quantity of the transformer under various working conditions is extracted to obtain the equivalent instantaneous magnetizing inductance. The maximal overlapping discrete wavelet transform based on db4 wavelet function is selected for analysis, and feature quantities are extracted. The fault feature quantity is used as the training set and test set of the decision tree to realize the identification and classification of slight transformer winding faults. Finally, the simulation results show that the proposed algorithm can accurately detect and distinguish between magnetizing inrush currents, slight inter-turn short circuit faults and partial arc discharge faults. |
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