基于变分模态分解-布谷鸟搜索-支持向量回归的变压器油中溶解气体浓度预测方法

Prediction of dissolved gas content in transformer oil based on variational mode decomposition-cuckoo search-support vector regression model

针对电力变压器油中溶解气体浓度预测过程中存在的时间序列内部复杂和预测困难等问题,研究了时间序列分解预测重构方式,提出变分模态分解,结合布谷鸟搜索-支持向量回归组合预测方法。首先采用VMD将原始溶解气体浓度分解成为一组平稳的模态分量,降低了预测的复杂度。之后利用预测性能较好的SVR对各个模态分量分别进行预测。最后使用CS开展全局搜索对SVR参数进行优化选取,将得到的溶解气体浓度预测结果进行叠加重构。通过对油中溶解气体中H_(2)的仿真实验,得到VMD-CS-SVR组合模型预测结果的均方根误差为0.124μL/L,平均绝对百分比误差为1.19%,有效提升了预测精度。通过对CO和C 2H 4建模预测,进一步验证了本文所提模型的有效性。

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 H_(2) content,the root mean square error is 0.124μL/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 C 2H 4.The results indicate that the VMD-CS-SVR model has high accuracy and is suitable for predicting dissolved gas concentration in transformer oil.