基于SMA-VMD-GRU模型的变压器油中溶解气体含量预测

Prediction of Dissolved Gas Content in Transformer Oil Based on SMA-VMD-GRU Model

针对电力变压器油中溶解气体浓度序列非线性、非平稳特性影响预测精度问题,该文基于黏菌算法(SMA)和变分模态分解(VMD)构成黏菌算法优化的变分模态分解(SMA-VMD),结合门控循环单元(GRU)组成分解-预测-重构的变压器油中溶解气体含量预测模型。首先,采用差分法提取原始序列趋势项;然后利用SMA-VMD对剩余序列进行分解,得到一组平稳的模态分量;之后通过GRU对分解所得各模态分量分别进行预测;最后对预测结果进行重构。该研究通过对变压器油中溶解气体H_(2)进行仿真实验,并与另外五种预测模型对比,得出SMA-VMD-GRU模型预测结果平均绝对百分比误差为0.36%,方均根误差为1.76μL/L,有效地提高了变压器油中溶解气体含量含量预测精度。通过对变压器油中溶解气体成分CH_(4)、CO、总烃进行仿真实验,证明了该研究所提预测模型的有效性。

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 H_(2) 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 CH_(4),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:①Using 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.②In 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.③The 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.