Illegal Action Classification in Power Grid Operation Based on Cross-domain Few-shot Learning
In order to realize the classification of illegal actions in intelligent monitoring of grid operations,improve the efficiency and safety of power system operation and maintenance,and reduce the dependence on sample labeling,this paper proposes a method of illegal actions classification of power grid operations based on cross-domain few-shot learning.In this method,an innovative cross-domain alignment mechanism is designed to generate cross-domain auxiliary data sets and target domain extended data sets by constructing an inter-domain generation mechanism and an intra-domain extension mechanism,which helps the classification model to better understand and adapt to the feature changes between different domains,and enhances the model's invariance learning ability in the target domain,so as to improve the accuracy and efficiency of the classification of illegal actions under different objective factors in the power grid operation scenario.Experimental results show that the the proposed method effectively reduces the dependence on large-scale annotated data,and through the cross-domain few-shot learning method,the unknown unlabeled sample data are input into the classification model,and the efficient and accurate classification of illegal actions in the power grid operation scenario is realized,showing a wide application prospect in the actual power grid operation and maintenance.
power grid operationsclassification of illegal actionstransfer learningcross-domain few-shot learningclassification model
万方数据
维普
