基于混合模态分解和LSTM-CNN的变压器油中溶解气体浓度预测

doi:10.11930/j.issn.1004-9649.202210089

中国电力 ›› 2023, Vol. 56 ›› Issue (1): 132-141.DOI: 10.11930/j.issn.1004-9649.202210089

基于混合模态分解和LSTM-CNN的变压器油中溶解气体浓度预测

陈铁^1,2, 张治藩^1,2, 李咸善^1,2, 陈一夫^1,2, 李鸿鑫^1,2

1. 梯级水电站运行与控制湖北省重点实验室（三峡大学），湖北宜昌 443002;
2. 三峡大学电气与新能源学院，湖北宜昌 443002

收稿日期:2022-10-21 修回日期:2022-11-10 出版日期:2023-01-28 发布日期:2023-01-14
作者简介:陈铁(1975-),男,副教授,从事电力变压器运行状态预测与故障诊断、人工智能等方向的研究,E-mail:chent@ctgu.edu.cn;张治藩(1999-),男,通信作者,硕士研究生,从事电力变压器运行状态预测研究,E-mail:512070331@qq.com
基金资助:
国家自然科学基金资助项目（51741907）；梯级水电站运行与控制湖北省重点实验室开放基金（2019KJX08）

Prediction of Dissolved Gas Concentration in Transformer Oil Based on Hybrid Mode Decomposition and LSTM-CNN

CHEN Tie^1,2, ZHANG Zhifan^1,2, LI Xianshan^1,2, CHEN Yifu^1,2, LI Hongxin^1,2

1. Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station (China Three Gorges University), Yichang 443002, China;
2. School of Electrical Engineering and New Energy (China Three Gorges University), Yichang 443002, China

Received:2022-10-21 Revised:2022-11-10 Online:2023-01-28 Published:2023-01-14
Supported by:
This work is supported by National Natural Science Foundation of China (No.51741907), Open Fund of Key Laboratory for Operation and Control of Cascaded Hydropower Station in Hubei Province (No.2019KJX08)

摘要/Abstract

摘要： 对油中溶解气体浓度进行预测，可提前掌握变压器运行趋势。提出一种基于混合模态分解和LSTM-CNN（long short-term memory network-convolution network）网络的预测方法，实现精准的气体浓度预测。首先，为消除分解中模态混叠和残余白噪声的影响，对气体序列进行ICEEMDAN分解，以削弱序列的非平稳性；然后，使用VMD对聚合重构后的高频分量进行二次分解，降低高频分量的复杂度；最后，为了增强模型对序列时间特征和空间特征的拟合，采用结合时间注意力机制的LSTM-CNN网络对分解分量分别进行预测并重构气体浓度数据。算例验证表明，所提出的模型相比其他模型具有更强的预测性能，为后续故障预测提供有力支撑。

关键词: 变压器, 油中溶解气体, 混合模态分解, 长短期记忆网络, 注意力机制, 卷积网络

Abstract: Predicting the concentration of dissolved gas in oil can help to know in advance the operation trend of transformers. A prediction method is thus proposed based on hybrid mode decomposition and LSTM-CNN network to achieve accurate gas concentration prediction. Firstly, in order to eliminate the influence of mode aliasing and residual white noise in the decomposition, the gas sequence is decomposed with ICEEMDAN to weaken the non-stationarity of the sequence. Then, the VMD is used to decompose the high frequency components after aggregation reconstruction to reduce the complexity of the high frequency components. Finally, in order to enhance the fitting of the model to the temporal and spatial features of the sequence, the TA-LSTM-CNN is used to predict the decomposition components and reconstruct the gas concentration data. Case study shows that the proposed model has stronger prediction performance than other models, which can provide strong support for subsequent fault prediction.

Key words: transformer, dissolved gas, hybrid mode decomposition, long short-term memory network, attention mechanism, convolutional network

陈铁, 张治藩, 李咸善, 陈一夫, 李鸿鑫. 基于混合模态分解和LSTM-CNN的变压器油中溶解气体浓度预测[J]. 中国电力, 2023, 56(1): 132-141.

CHEN Tie, ZHANG Zhifan, LI Xianshan, CHEN Yifu, LI Hongxin. Prediction of Dissolved Gas Concentration in Transformer Oil Based on Hybrid Mode Decomposition and LSTM-CNN[J]. Electric Power, 2023, 56(1): 132-141.

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基于混合模态分解和LSTM-CNN的变压器油中溶解气体浓度预测

Prediction of Dissolved Gas Concentration in Transformer Oil Based on Hybrid Mode Decomposition and LSTM-CNN

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基于混合模态分解和LSTM-CNN的变压器油中溶解气体浓度预测

Prediction of Dissolved Gas Concentration in Transformer Oil Based on Hybrid Mode Decomposition and LSTM-CNN

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