期刊
IEEE TRANSACTIONS ON SMART GRID
卷 11, 期 6, 页码 4714-4726出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2020.2995164
关键词
Topology; Network topology; Computational modeling; Correlation; Smart meters; Data models; Voltage measurement; Distribution system; Markov random field; probabilistic graphical model; pseudo-likelihood; regularization; topology identification
资金
- National Natural Science Foundation of China [51907114, 71971183]
- Shanghai Science and Technology Commission Sailing Program [19YF1416900]
- Hong Kong Polytechnic University [YBY1, SB2D]
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.
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