基于环面积图谱熵特征的变压器局部放电故障诊断

尚海昆; 王志明; 张冉喆; 王明雪; 闫佳雪; 姜岳成

doi:10.19783/j.cnki.pspc.260051

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基于环面积图谱熵特征的变压器局部放电故障诊断

理论分析 | 更新时间：2026-07-27

- 基于环面积图谱熵特征的变压器局部放电故障诊断
- Transformer partial discharge fault diagnosis based on ring area graph entropy features
- 电力系统保护与控制 2026年54卷第12期
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- DOI：10.19783/j.cnki.pspc.260051
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- 纸质出版：2026
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尚海昆, 王志明, 张冉喆, 等. 基于环面积图谱熵特征的变压器局部放电故障诊断[J]. 电力系统保护与控制, 2026,54(12).

SHANG Haikun, WANG Zhiming, ZHANG Ranzhe, et al. Transformer partial discharge fault diagnosis based on ring area graph entropy features[J]. Power System Protection and Control, 2026, 54(12).
尚海昆, 王志明, 张冉喆, 等. 基于环面积图谱熵特征的变压器局部放电故障诊断[J]. 电力系统保护与控制, 2026,54(12). DOI： 10.19783/j.cnki.pspc.260051.

SHANG Haikun, WANG Zhiming, ZHANG Ranzhe, et al. Transformer partial discharge fault diagnosis based on ring area graph entropy features[J]. Power System Protection and Control, 2026, 54(12). DOI： 10.19783/j.cnki.pspc.260051.

摘要

针对传统变压器局部放电(partial discharge

PD)故障诊断方法在特征表达不足和小样本条件下识别性能受限的问题，提出了一种基于环面积图谱熵(ring area graph entropy

RAGE)和双层课程元学习(dual-level curriculum meta-learning

DCML)的变压器PD故障诊断方法。首先，利用改进的完备集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise

ICEEMDAN)对非平稳PD信号进行分解，筛选出与原始信号高度相关的有效固有模态分量(intrinsic mode function

IMF)。随后，将有效IMF的功率谱密度投影至极坐标系，计算其面积加权分布概率得到RAGE，从而定量表征PD信号的不确定性。最后，基于PD特征构建DCML模型，实现小样本条件下变压器PD故障的高效识别。实验结果表明，RAGE特征在多种模型对比中展现出显著优势，能够更为精细描述PD信号特征。

Abstract

To address the limitations of traditional transformer partial discharge (PD) fault diagnosis methods in feature representation and their restricted recognition performance under small-sample conditions

a transformer PD fault diagnosis method based on ring area graph entropy (RAGE) and dual-level curriculum meta-learning (DCML) is proposed. First

the non-stationary PD signals are decomposed using improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)

and the effective intrinsic mode function (IMF) that is highly correlated with the original signals is selected. Subsequently

the power spectral densities of the effective IMFs are projected onto a polar coordinate system

and the area-weighted distribution probabilities are calculated to obtain RAGE

thereby quantitatively characterizing the uncertainty of PD signals. Finally

a DCML model is constructed based on the extracted PD features to achieve efficient identification of transformer PD faults under small-sample conditions. Experimental results show that the proposed RAGE feature presents significant advantages across multiple comparative models and can describe PD signal features more effectively and comprehensively.

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references

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