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  4. Uncertainty quantification for branch-current state estimation in power distribution systems
 
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Uncertainty quantification for branch-current state estimation in power distribution systems

Publikationstyp
Conference Paper
Date Issued
2024-10
Sprache
English
Author(s)
Strobel, Florian Thorsten Lutz  
Elektrische Energietechnik E-6  
Babazadeh, Davood  orcid-logo
Electrical Power and Energy Technology E-6  
Stock, Simon  orcid-logo
Elektrische Energietechnik E-6  
Becker, Christian  orcid-logo
Elektrische Energietechnik E-6  
TORE-URI
https://hdl.handle.net/11420/54308
Citation
IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2024
Contribution to Conference
IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2024  
Publisher DOI
10.1109/isgteurope62998.2024.10863319
Publisher
IEEE
The growing number of prosumers in the distribution grid pushes the system to the edge of its operational capacities. This makes it essential to assess the current state of the system. However, the availability of grid measurements is often times not sufficient. To achieve observability, inaccurate pseudo measurements can be included in the state estimation. The obtained results are subject to high uncertainty. This paper proposes an algorithm for quantifying the uncertainty of the branch current and node voltage estimate in a branch current-based weighted least squares state estimation. The algorithm uses the same inputs as the state estimation, including the measurement vector, its standard deviation, and the grid parameters. The uncertainty quantification is twofold: first, the uncertainty of the currents is estimated using the diagonal of the gain matrix. Second, the voltage uncertainty is obtained using a novel error propagation calculation. The proposed algorithm is based on a modified version of the branch current state estimation algorithm.We evaluate the proposed algorithm in a variety of scenarios with different measurement availabilities and pseudo measurement uncertainty distributions. As a guiding example, we utilize a benchmark distribution system from the Simbench dataset. The algorithm demonstrates high accuracy when dealing with Gaussian distributed input uncertainty
Subjects
current state estimation | uncertainty quantification | low voltage grid | error propagation
DDC Class
600: Technology
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