Aiming at the problems of internal complexity and too hard predicting the dissolved gas concentration of transformer oil,a method combining VMD with CS-SVR was proposed for decomposing,predicting,and reconstructing gas concentration.In this paper,firstly,VMD is utilized to decompose the original dissolved gas concentration into a set of stationary modal components.Subsequently,SVR,which has relatively good predictive performance,was used to predict each modal component separately.Finally,CS is utilized for global search to optimize and select SVR parameters,and the predicted dissolved gas concentration results are overlaid and reconstructed.Through simulation experiments on the H2 content,the root mean square error is 0.124 μL/L and the average absolute percentage error is 1.19％,effectively enhancing prediction accuracy.Further validation of the model's effectiveness is conducted through modeling and predicting CO and C2H4.The results indicate that the VMD-CS-SVR model has high accuracy and is suitable for predicting dissolved gas concentration in transformer oil.
关键词
电力变压器/油中溶解气体浓度/支持向量回归/布谷鸟搜索/模态分解
Key words
power transformer/dissolved gas content/support vector regression/cuckoo search/modal decomposition
维普
万方数据