A household-transformer relationships and phase identification method of low-voltage distribution networks based on graph transformation and transfer learning
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.
关键词
低压配电网/户变关系和相位识别/格拉姆角和场/迁移学习/深度学习模型
Key words
low-voltage distribution networks/household-transformer relationship and phase recognition/Gramian angular summation field/transfer learning/deep learning model
维普
万方数据