A sparse recovery model with fast decoupled solution for distribution state estimation and its performance analysis

doi:https://doi.org/10.1007/s40565-019-0522-9

Home > Archive>Volume 7, Issue 6, 2019 >1411-1421. DOI:https://doi.org/10.1007/s40565-019-0522-9

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A sparse recovery model with fast decoupled solution for distribution state estimation and its performance analysis

DOI:

https://doi.org/10.1007/s40565-019-0522-9

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1 Department of Electrical Engineering, Tsinghua University, Beijing 100084, China 2 Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China 3 College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Fund Project:

This work was supported in part by the National Key Research and Development Plan of China (No.2018YFB0904200) and in part by the National Natural Science Foundation of China (No. 51725703).

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Abstract:

This paper introduces a robust sparse recovery model for compressing bad data and state estimation (SE), based on a revised multi-stage convex relaxation (R-Capped- L1) model. To improve the calculation efficiency, a fast decoupled solution is adopted. The proposed method can be used for three-phase unbalanced distribution networks with both phasor measurement unit and remote terminal unit measurements. The robustness and the computational efficiency of the R-Capped-L1 model with fast decoupled solution are compared with some popular SE methods by numerical tests on several three-phase distribution networks.

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Online: November 27,2019
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