基于加强灰狼优化VMD-DBN的变压器故障检测

赵一钧; 石雷; 齐笑; 郝成钢; 祝晓宏; 王昕

赵一钧,石雷,齐笑,郝成钢,祝晓宏,王昕.基于加强灰狼优化VMD-DBN的变压器故障检测[J].电测与仪表,2024,61(2):157-163.
ZHAO Yijun,SHI Lei,QI Xiao,HAO Chenggang,ZHU Xiaohong,WANG Xin.Transformer fault detection based on enhanced gray wolf optimization VMD-DBN[J].Electrical Measurement & Instrumentation,2024,61(2):157-163.

基于加强灰狼优化VMD-DBN的变压器故障检测

Transformer fault detection based on enhanced gray wolf optimization VMD-DBN

DOI：10.19753/j.issn1001-1390.2024.02.023

中文关键词: 变压器振动信号加强灰狼 VMD 深度置信网络

英文关键词:transformer, vibration signal, enhanced gray wolf, VMD, deep belief network

基金项目:国家自然科学基金资助项目（ 61673268）

作者中文名	作者英文名	单位
赵一钧	ZHAO Yijun	上海电力大学
石雷	SHI Lei	国网吉林省电力有限公司四平供电公司
齐笑	QI Xiao	国网吉林省电力有限公司四平供电公司
郝成钢	HAO Chenggang	国网吉林省电力有限公司四平供电公司
祝晓宏	ZHU Xiaohong	国网吉林省电力有限公司四平供电公司
王昕	WANG Xin	上海交通大学

摘要点击次数: 1064

中文摘要:

针对当前在线识别变压器运行状态困难、低效的问题，通过提取箱壁的振动信号，提出了基于加强灰狼优化变分模态分解（Variational Mode Decomposition，VMD）深度置信网络（Deep Belief Network，DBN）的检测方法。首先，利用加强灰狼算法以能量误差为适应度函数，优化VMD的重要参数（分解层数k和惩罚因子α），然后分解计算各本征模态分量(Intrinsic Mode Functions, IMF)的能量标值，组成特征数据集，用来表征变压器运行工况。最后使用深度置信网络对特征数据集进行反复学习训练，形成故障诊断模型对变压器状态进行识别。通过实验对比分析VMD能更好地提取信号中有效的特征，提高识别的精准度，同时DBN相较于其他两种经典识别算法，抽象能力更好，学习的能力更强，稳定性更高，能准确识别变压器正常、绕组辐向形变、绕组轴向形变、铁芯故障四种状态。加强灰狼优化VMD-DBN的状态识别率达到了97.45%，均值误差为0.37，相比于其他方法效果最佳。因此，所提方法具有一定的实用价值。

英文摘要:

When diagnosing the online operational status of power transformers, the performance and efficiency can be further improved. In light of the extracted vibration signal of the box wall, this paper proposes a detection method which is based on the enhanced gray wolf optimization variational mode decomposition deep belief network (VMD-DBN). Firstly, the energy error is used as the fitness function of the enhanced gray wolf algorithm, and then, all the necessary parameters of the VMD (the number of decomposition levels k and the penalty factor α) are optimized. Additionally, the intrinsic mode functions (IMF) are decomposed and calculated to form a characteristic data set to characterize the operating conditions of the transformers. Finally, the characteristic data set is trained by the DBN, and a fault diagnosis model is generated to identify the operational status of the transformers. The case study results show that the proposed VMD can extract the effective features in the signal better and improve the accuracy of diagnosis. Meanwhile, compared with the other two recognition algorithms, the DBN is better in feature description, learning, and robustness. It can accurately identify four statuses of the transformers, including the normal, the winding radial deformation, the winding axial deformation, and the iron core failure. The state recognition rate of enhanced gray wolf optimization VMD-DBN reaches 97.45%, and the mean error is 0.37, which is the best compared to other methods. Therefore, the proposed method has certain practical value.

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