Research on Abnormal Warning of Substation Operation Based on Multi Feature Fusion
In the conventional research on the early warning of abnormal operation of substations,the data source of operation state trend is not analyzed,which leads to insufficient sample data in the early warning,which can not re-duce the time consumption in the algorithm and achieve rapid early warning.Therefore,the research on early warn-ing of abnormal operation of substation based on multi-feature fusion is proposed.The data source of early warning of substation operation trend is analyzed by discrete time series,and the abnormal situation model of substation based on multi-feature and multi-feature fusion is established on the basis of feature extraction of abnormal data of substa-tion operation.Finally,the early warning research of abnormal operation of substation is realized through training samples.In the experiment,when there is an emergency abnormal situation in the 220kV high-voltage room,the ex-perimental group needs to spend 9 seconds to detect abnormal data and transmit information,while the control group needs to spend 12 seconds in the same situation,and the information transmission time of the experimental group is 3 seconds faster than that of the control group.The results show that the abnormal early warning research based on multi-feature fusion takes less time and is more conducive to the transmission of early warning information.
万方数据
维普
