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Spectral Embedding-Based Meter-Transformer Mapping (SEMTM)
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OA
AI
DOI：10.1109/OAJPE.2023.3272647.png)
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摘要
En 中文
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 transformer loading. The challenge lies in recovering meter-transformer (M.T.) mapping for two common scenarios, e.g., large distances between a meter and its parent transformer or high similarity of a meter's consumption pattern to a non-parent transformer's meter. Past methods either assume a variety of data as in the transmission grid or ignore the two common scenarios mentioned above. Therefore, we propose to utilize the above observation via spectral embedding by using the property that inter-transformer meter consumptions are not the same and that the data noise is limited so that all the k smallest eigenvalues of the voltage-based Laplacian matrix are smaller than the next smallest eigenvalue of the ideal Laplacian matrix. We also provide a performance guarantee for Spectral Embedding-based M.T. mapping (SEMTM). Furthermore, we partially relax the assumption by utilizing location information to aid voltage information for areas geographically far away, but with similar voltages. Numerical simulations on the IEEE test systems and real feeders from our partner utility show that the proposed method correctly identifies the M.T. mapping.
Keyword:
Power systems
distribution grids
topology identification
transformers
meters
guarantees
spectral embedding
期刊
I
IF:
3.2
论文数:
401
被引数:
826
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