互信息和核熵成分分析的油中溶解气体浓度建模

针对变压器油中溶解气体浓度的预测问题,提出了一种基于互信息和核熵成分分析(KECA)的油中溶解气体浓度预测建模方法。首先,用标准互信息变量选择方法确定模型的输入变量并对选取的输入变量进行相重构;然后,利用Renyi熵信息测度确定KECA核参数并采用KECA对相空间进行特征提取;最后,以核熵成分作为机器学习极限学习机(ELM)的输入,建立变压器油中溶解气体浓度的预测模型。实验结果表明,与灰色模型、支持向量机、BP神经网络建模方法相比,本文提出的方法能够充分利用油中溶解气体浓度信息,因而具有较优的预测精度和泛化能力。

Modeling for dissolved gases concentration based on mutual information and kernel entropy component analysis

Aiming at the testing problem of dissolved gases concentration in transformer oil, a new prediction modeling method based on mutual information(MI)and kernel entropy component analysis(KECA)was proposed. Firstly, normalized mutual information feature selection method was used to select input variables and the phase reconstruction space were reconstructed for them. Then, feature extraction was carried out in the phase reconstruction space by using KECA, meanwhile, the kernel parameter of KECA was determined by Renyi information entropy. At last, kernel entropy components were extracted by KECA and then they were used as the inputs of extreme learning machine(ELM)which was employed to forecast dissolved gases concentration. Experimental results show that compared with grey model, support vector machine(SVM)and BP neural network(BPNN), the proposed model can sufficiently utilize the dissolved gases information, thus it has a better prediction and generalization.