首页|基于相关变分模态分解和CNN-LSTM的变压器油中溶解气体体积分数预测

基于相关变分模态分解和CNN-LSTM的变压器油中溶解气体体积分数预测

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为解决变压器油中溶解气体实际监测数据中噪声信号对模型预测性能的影响以及单一长短期记忆神经网络(long short-term memory,LSTM)无法对数据间的深层特征进行有效提取的问题,提出了一种融合了相关变分模态分解(correlation variational mode decomposition,CVMD)、1维卷积神经网络(one dimensional convolutional neural network,1D-CNN)和LSTM的组合预测模型.首先,利用CVMD去除原始气体序列中的噪声信号,并将去噪序列分解为 1 组相对平稳的子序列分量;然后,针对各子序列分量分别构建CNN-LSTM预测模型,利用 1D-CNN挖掘数据间的深层特征形成特征向量,并将其输入到 LSTM中进行预测;最后,对各子序列预测结果叠加重构,得到最终的气体预测值.并通过 4 组对比实验对所提模型进行了全方位、多角度的验证.算例研究结果表明,所提模型单步和多步预测的平均绝对百分比误差分别为 1.53%和 2.09%.相较于现有模型,该文所提模型在单步和多步预测性能上均有明显提升,为变压器在线监测和故障预警提供了重要技术支撑.
Prediction of Dissolved Gas Volume Fraction in Transformer Oil Based on Correlation Varia-tional Mode Decomposition and CNN-LSTM
To address the issues of noise interference in actual monitoring data of dissolved gases in transformer oil and the ineffective extraction of deep features between data by a single long short-term memory(LSTM)neural network,a combined prediction model combining correlation variational mode decomposition(CVMD),1D convolutional neural network(1D-CNN)and LSTM is proposed.Firstly,CVMD is used to remove the noise signal from the original gas se-quence,and the denoising sequence is decomposed into a group of relatively stable subsequence components.Then,CNN-LSTM prediction models are constructed for each subsequence component,and 1D-CNN is used to mine deep fea-tures between data to form feature vectors,which is input into LSTM for prediction.Finally,the prediction results of each sub-sequence is superimposed and reconstructed to obtain the final gas predicted value.Four groups of comparison ex-periments were carried out to verify the proposed model in a comprehensive and multi-angle way.The results of the example study show that the average absolute percentage errors of single-step and multi-step prediction of the proposed model are 1.53%and 2.09%,respectively.Compared with the existing model,the proposed model in this paper has sig-nificantly improved the performance of the single-step and multi-step prediction.The study provides important technical support for transformer on-line monitoring and fault warning.

dissolved gas in oilcorrelation variational mode decompositionone dimensional convolutional neural networklong short-term memorygas volume fraction prediction

范志远、杜江

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河北省电磁场与电器可靠性重点实验室(河北工业大学),天津 300130

省部共建电工装备可靠性与智能化国家重点实验室(河北工业大学),天津 300130

油中溶解气体 相关变分模态分解 1维卷积神经网络 长短期记忆神经网络 气体体积分数预测

国家自然科学基金天津市自然科学基金重点项目河北省自然科学基金

5200704719JCZDJC32100E2018202282

2024

10.13336/j.1003-6520.hve.20221805

高电压技术
中国电力科学研究院　中国电机工程学会

高电压技术

CSTPCD北大核心
影响因子：2.32
ISSN：1003-6520
年,卷(期)：2024.50(1)
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