Prediction of Dissolved Gas Content in Transformer Oil Based on SMA-VMD-GRU Model
Yang Tongliang1,2, Hu Dong1, Tang Chao1, Fang Yun1, Xie Jufang1,2
1. School of Engineering and Technology Southwest University Chongqing 400715 China; 2. International R&D Center for Smart Grid and New Equipment Technology Southwest University Chongqing 400715 China
Abstract：Dissolved gas analysis (DGA) in transformer oil is the most effective and convenient method for fault diagnosis of oil-immersed transformers. However, DGA only analyzes the real-time content of dissolved gases in transformer oil. Therefore, how to use effective historical data to accurately predict the content of dissolved gas in transformer oil for a period of time in the future is of great significance for transformer early fault diagnosis. The content of dissolved gas in transformer oil is affected by external factors such as temperature and its own content, which will lead to nonlinear and non-stationary characteristics of the gas content sequence, leading to errors in the prediction accuracy. Aiming at the problem that the nonlinear and non-stationary characteristics of dissolved gas concentration series in power transformer oil affect the prediction accuracy, a prediction model of dissolved gas concentration in power transformer oil is proposed based on slime mold algorithm (SMA) to optimize the variated mode decomposition (VMD) and combined with gating cycle unit (GRU). First, the preprocessed original sequence is detrended by the difference method. Secondly, based on the slime mold algorithm and the variational mode decomposition, a variational mode decomposition optimized by the slime mold algorithm is constructed, and the detrending sequence is decomposed into a set of stationary and regular mode components. Thirdly, the GRU with better prediction performance is used to predict the modal components obtained by decomposition. Finally, the final prediction result is obtained by superposition reconstruction. The simulation results of 450 days historical data of an oil-carrying immersed transformer show that the absolute percentage error and root mean square error of the proposed prediction model for the H2 content of dissolved gas in transformer oil in the next 50 days are 0.36% and 1.76μL/L, respectively. Compared with the prediction model composed of empirical mode decomposition (EMD) and long short-term memory neural network (LSTM), the SMA-VMD-GRU prediction model proposed in this study has the smallest error. And the same method was used to predict the dissolved gas CH4, CO and total hydrocarbon content in the same transformer oil. The absolute percentage error of the three gas prediction results was 0.29%, 0.15% and 4.99%, respectively, and the root mean square error was 0.02μL/L, 1.13μL/L and 0.50μL/L, respectively. The effectiveness of the proposed prediction model based on SMA-VMD-GRU was verified. Through simulation analysis, the following conclusions can be drawn: ① Using the difference method to extract the sequence trend term effectively solves the deficiency of VMD that cannot accurately extract the trend term. Then, through VMD decomposition after SMA optimization, the complex dissolved gas sequence in oil can be decomposed into a group of stable and periodic mode components, which effectively solves the problem of the influence of nonlinear and non-stationary characteristics of the original sequence on the prediction accuracy. ② In the prediction of dissolved gas in transformer oil, the GRU network converges faster than the LSTM network. Therefore, GRU network has more advantages than LSTM. On the premise that the differential method and VMD lifting sequence can be predicted in the early stage, the prediction accuracy of dissolved gas concentration in oil is further improved, which is helpful to the early fault diagnosis of transformers. ③ The effectiveness of the prediction model of dissolved gas content in transformer oil based on SMA-VMD-GRU is proved by simulation and prediction experiments of various gas concentrations in dissolved gas in transformer oil.
杨童亮, 胡东, 唐超, 方云, 谢菊芳. 基于SMA-VMD-GRU模型的变压器油中溶解气体含量预测[J]. 电工技术学报, 2023, 38(1): 117-130.
Yang Tongliang, Hu Dong, Tang Chao, Fang Yun, Xie Jufang. Prediction of Dissolved Gas Content in Transformer Oil Based on SMA-VMD-GRU Model. Transactions of China Electrotechnical Society, 2023, 38(1): 117-130.
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2023, Vol. 38 
