A household-transformer relationships and phase identification method of low-voltage distribution networks based on graph transformation and transfer learning
A method based on graph transformation and transfer learning is proposed to further enhance the accuracy of household-transformer relationships and phase identification in low-voltage distribution networks.Firstly,a graph transformation method based on Gramian angular field(GAF)is introduced to convert electricity consumption data into a two-dimensional representation,facilitating the identification of differences in one-dimensional time-series electricity consumption data.Next,to address challenges such as sparse user data in low-voltage distribution net-works,limited data acquisition methods,and a scarcity of samples,a deep learning model suitable for household-transformer relationship and phase identification is constructed using transfer learning and leveraging pre-trained pa-rameter weights.Experimental validation demonstrates that the proposed model outperforms mainstream methods in both household-transformer relationship and phase identification,exhibiting improved accuracy and stability.
low-voltage distribution networkshousehold-transformer relationship and phase recognitionGramian angular summation fieldtransfer learningdeep learning model
维普
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