Name
Abstract | ||
|---|---|---|
The identification of the distribution system topology is the key concern in distribution system state estimation and the precondition for its energy management. However, lacking sufficient measurement devices, full-scale identification of entire distribution grid can hardly be achievable in practice. The frequent topology changes in distribution systems impose challenges for topology identification. This paper proposes a novel topology identification method by deeply mining the data obtained from gird terminals and smart meters at end-users premises. The proposed method starts with data processing, followed by nodal correlation analysis and topology modeling based on the Markov Random Field (MRF) method, where the pseudo-likelihood method and L2 regularization theory are introduced to improve the computation efficiency while preventing the over-fitting problem. Then the iterative screening method is developed to generate the distribution system topology of medium/low-voltage distribution systems. Finally, the incremental learning and parallel programming models are proposed to implement the algorithms on single/multi-terminal. The effectiveness of the proposed model is validated on IEEE 33-node, IEEE 123-node and actual distribution systems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TSG.2020.2995164 | IEEE Transactions on Smart Grid |
Keywords | DocType | Volume |
Distribution system,Markov random field,probabilistic graphical model,pseudo-likelihood,regularization,topology identification | Journal | 11 |
Issue | ISSN | Citations |
6 | 1949-3053 | 1 |
PageRank | References | Authors |
0.36 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Zhao | 1 | 3 | 1.07 |
| Liang Li | 2 | 172 | 19.95 |
| Zhou Xu | 3 | 66 | 15.44 |
| Xiaoyu Wang | 4 | 167 | 59.60 |
| Haobo Wang | 5 | 2 | 3.76 |
| Xianjun Shao | 6 | 1 | 0.36 |