基于多级特征提取和IHHO-KELM的变压器油中溶解气体体积分数预测

傅雨晨; 陈星; 付文龙; 方念; 张凯; 曹正江

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基于多级特征提取和IHHO-KELM的变压器油中溶解气体体积分数预测

更新时间：2025-06-18

- 基于多级特征提取和IHHO-KELM的变压器油中溶解气体体积分数预测
- Prediction of Dissolved Gas Concentration in Transformer Oil Based on Multistage Feature Extraction and IHHO-KELM
- 高压电器 2025年页码：1-14
- 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
  3.国网浙江省电力有限公司宁波供电公司，浙江　宁波　 315000
- 作者简介：
  
  傅雨晨（1999—），女，硕士研究生，主要研究方向为人工智能应用（E-mail：202108580021063@ctgu.edu.cn）。
  陈星（1988—），女，硕士，讲师，主要研究方向为电力设备状态监测与故障诊断（E-mail：chenxing20230511@163.com）。
  付文龙（1988—），男，博士，副教授，博导，主要研究方向为人工智能应用、电力设备状态监测与故障诊断（E-mail：ctgu_fuwenlong@126.com）。
- 基金信息：
  
  国家自然科学基金资助项目(51741907)
- DOI：
  中图分类号：
- 纸质出版日期：
- 稿件说明：
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傅雨晨, 陈星, 付文龙, 等. 基于多级特征提取和IHHO-KELM的变压器油中溶解气体体积分数预测[J/OL]. 高压电器, 2025,1-14.

FU Yuchen, CHEN Xing, FU Wenlong, et al. Prediction of Dissolved Gas Concentration in Transformer Oil Based on Multistage Feature Extraction and IHHO-KELM[J/OL]. High voltage apparatus, 2025, 1-14.
傅雨晨, 陈星, 付文龙, 等. 基于多级特征提取和IHHO-KELM的变压器油中溶解气体体积分数预测[J/OL]. 高压电器, 2025,1-14. DOI：

FU Yuchen, CHEN Xing, FU Wenlong, et al. Prediction of Dissolved Gas Concentration in Transformer Oil Based on Multistage Feature Extraction and IHHO-KELM[J/OL]. High voltage apparatus, 2025, 1-14. DOI：

摘要

油中溶解气体分析是变压器早期故障诊断的主要方法，准确预测未来特征气体体积分数有助于提前获取变压器的运行状态。为此提出了一种基于多级特征提取和IHHO-KELM的变压器油中溶解气体体积分数预测方法。首先，通过自适应白噪声完全集合经验模态分解将气体体积分数序列分解为多个子序列，利用奇异谱分析对子序列做进一步降噪处理，降低其非平稳性；其次，建立核极限学习机预测模型分别对各子序列进行预测，再将各子序列的预测结果叠加得到油中溶解气体体积分数的最终预测结果，并通过改进哈里斯鹰算法优化其超参数；最后，通过算例验证表明，所提模型具有更优的预测性能，可以更好的追踪油中溶解气体体积分数的变化趋势。

Abstract

Dissolved gas concentration analysis in transformer oil have gradually become a mainstream pattern in early fault discrimination. Accurate prediction of dissolved gas in oil contributes to gain transformer operation status in advance. Therefore

prediction model of dissolved gas concentration in transformer oil based on multistage feature extraction and IHHO-KELM is proposed. Firstly

dissolved gas concentration data is decomposed into multiple subsequences by complete ensemble empirical mode decomposition with adaptive noise

after which the subsequences are further denoised by singular spectrum analysis to reduce its non-stationarity. Then

kernel based extreme learning machine is adopted to forecast each subsequence. Meanwhile

improved harris hawk optimization is applied to optimize the hyper-parameter of kernel based extreme learning machine. Subsequently

the final dissolved gas concentration results are attained by summing the prediction values of all subsequences. Finally

the experimental results are clarified that the proposed model achieves better prediction performance

which can better track the variation trend of dissolved gas concentration in transformer oil.

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references

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