The integrated energy systems,usually including electric energy,natural gas and thermal energy,play a pivotal role in the energy Internet project,which could improve the accommodation of renewable energy through multi...The integrated energy systems,usually including electric energy,natural gas and thermal energy,play a pivotal role in the energy Internet project,which could improve the accommodation of renewable energy through multienergy complementary ways.Focusing on the regional integrated energy system composed of electrical microgrid and natural gas network,a fault risk warning method based on the improved RelieF-softmax method is proposed in this paper.The raw data-set was first clustered by the K-maxmin method to improve the preference of the random sampling process in the RelieF algorithm,and thereby achieved a hierarchical and non-repeated sampling.Then,the improved RelieF algorithm is used to identify the feature vectors,calculate the feature weights,and select the preferred feature subset according to the initially set threshold.In addition,a correlation coefficient method is applied to reduce the feature subset,and further eliminate the redundant feature vectors to obtain the optimal feature subset.Finally,the softmax classifier is used to obtain the early warnings of the integrated energy system.Case studies are conducted on an integrated energy system in the south of China to demonstrate the accuracy of fault risk warning method proposed in this paper.展开更多
基金Supported by National Natural Science Foundation of China(No.51777193).
文摘The integrated energy systems,usually including electric energy,natural gas and thermal energy,play a pivotal role in the energy Internet project,which could improve the accommodation of renewable energy through multienergy complementary ways.Focusing on the regional integrated energy system composed of electrical microgrid and natural gas network,a fault risk warning method based on the improved RelieF-softmax method is proposed in this paper.The raw data-set was first clustered by the K-maxmin method to improve the preference of the random sampling process in the RelieF algorithm,and thereby achieved a hierarchical and non-repeated sampling.Then,the improved RelieF algorithm is used to identify the feature vectors,calculate the feature weights,and select the preferred feature subset according to the initially set threshold.In addition,a correlation coefficient method is applied to reduce the feature subset,and further eliminate the redundant feature vectors to obtain the optimal feature subset.Finally,the softmax classifier is used to obtain the early warnings of the integrated energy system.Case studies are conducted on an integrated energy system in the south of China to demonstrate the accuracy of fault risk warning method proposed in this paper.
