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

doi:10.11930/j.issn.1004-9649.202210089

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202210089

https://www.electricpower.com.cn/EN/Y2023/V56/I1/132

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