Viscosity Prediction of Dissolved Gas in Transformer Oil Based on Improved Boxplot and ISMAHHO-KELM

FU Wenlong; ZHAO Yibo; FU Yuchen; WU Chuanfeng

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Viscosity Prediction of Dissolved Gas in Transformer Oil Based on Improved Boxplot and ISMAHHO-KELM

更新时间：2025-06-18

- Viscosity Prediction of Dissolved Gas in Transformer Oil Based on Improved Boxplot and ISMAHHO-KELM
- High Voltage Apparatus Pages: 1-13(2025)
- 作者机构：
  
  1.三峡大学电气与新能源学院，湖北　宜昌　 443000
  2.三峡大学梯级水电站运行与控制湖北省重点实验室，湖北　宜昌　 443000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51741907)
- DOI：
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FU Wenlong, ZHAO Yibo, FU Yuchen, et al. Viscosity Prediction of Dissolved Gas in Transformer Oil Based on Improved Boxplot and ISMAHHO-KELM[J/OL]. High voltage apparatus, 2025, 1-13.
DOI：

FU Wenlong, ZHAO Yibo, FU Yuchen, et al. Viscosity Prediction of Dissolved Gas in Transformer Oil Based on Improved Boxplot and ISMAHHO-KELM[J/OL]. High voltage apparatus, 2025, 1-13. DOI：

摘要

油中溶解气体分析能够有效地揭示变压器内部的运行状况，在评估变压器运行状态和预测潜在故障方面具有重要作用。为此文中提出一种基于改进箱线图和ISMAHHO-KELM的变压器油中溶解气体体积分数预测方法。首先，为提高数据质量，利用基于中位偏差系数的改进箱线图对原始时间序列进行离群值检测和校正；然后采用变分模态分解对校正过的时间序列进行分解，得到多个子序列，以削弱时间序列的非平稳性；其次，通过核极限学习机模型预测子序列，同时提出改进黏菌—哈里斯鹰算法优化其超参数；最后，重构各子序列的预测值，得到最终预测结果。实例分析表明所提方法具有较高的可靠性和稳定性，能够为变压器维修决策提供有力支撑。

Abstract

The analysis of dissolved gases in oil can effectively reveal operational condition inside transformers

which can assess transformer's operational status and predicting potential faults efficiently. Thus

a dissolved gases forecasting framework is proposed based on improved boxplot and ISMAHHO-KELM in this paper. Firstly

to improve data quality

the improved boxplot fused with median deviation coefficient is used to detect and correct outliers. Then

variational modal decomposition is adopted decompose the processed time series into multiple sub-sequences to reduce the non-stationarity of the original time series. Next

kernel extreme learning machine is applied to predict the sub-sequences. Meanwhile

improved slime mold algorithm-harris hawk optimization is proposed to its hyperparameters. Finally

ultimate prediction value is acquired through reconstructing predicted results of each subsequence. Experiments verify the reliability and stability of the proposed framework

thereby providing strong support for maintenance decision-making.

关键词

Keywords

references

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