Spectral Embedding-Based Meter-Transformer Mapping (SEMTM)

Distributed energy resources enable efficient power response but may cause transformer overload in distribution grids, calling for recovering meter-transformer mapping to provide situational awareness, i.e., the loading. The challenge lies (M.T.) two common scenarios, e.g., large distances between a meter and its parent or high similarity of meter’s consumption pattern non-parent transformer’s meter. Past methods either assume variety data as transmission grid ignore scenarios mentioned above. Therefore, we propose utilize above observation via spectral embedding by using property that inter-transformer consumptions are not same noise is limited so all $k$ smallest eigenvalues voltage-based Laplacian matrix smaller than next eigenvalue ideal matrix. We also performance guarantee Spectral Embedding-based M.T. (SEMTM). Furthermore, partially relax assumption utilizing location information aid voltage areas geographically far away, with similar voltages. Numerical simulations on IEEE test systems real feeders from our partner utility show proposed method correctly identifies mapping.