Topological path identification; raw information; active paths; backup paths; digital twin
Abstract :
[en] This paper introduces a systematic approach to address the topological path identification (TPI) problem in power distribution networks.
Our approach starts by listing the DSO's raw information coming from several sources.
The raw information undergoes a transformation process using a set of transformation functions. This process converts the raw information into well-defined information exploitable by an algorithm.
Then a set of hypothetical paths is generated, considering any potential connections between the elements of the power distribution system. This set of hypothetical paths is processed by the algorithm that identifies the hypothetical paths that are compatible with the well-defined information.
This procedure operates iteratively, adapting the set of transformation functions based on the result obtained: if the identified paths fail to meet the DSO's expectations, new data is collected and/or the transformation functions found to be responsible for the discrepancies are modified.
The systematic procedure offers practical advantages for DSOs, including improved accuracy in path identification and high adaptability to diverse network configurations, even with incomplete or inaccurate data. Consequently, it emerges as a useful tool for the construction of digital twins of power distribution networks that aligns with DSO expectations.
Disciplines :
Computer science
Author, co-author :
Vassallo, Maurizio ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Bahmanyar, Alireza; Intelligent Systems Solutions, Haulogy, Neupré, Belgium
Duchesne, Laurine; Intelligent Systems Solutions, Haulogy, Neupré, Belgium
Leerschool, Adrien; Intelligent Systems Solutions, Haulogy, Neupré, Belgium
Gerard, Simon; RESA, Liège, Belgium
Wehenkel, Thomas; RESA, Liège, Belgium
Ernst, Damien ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Smart grids ; LTCI, Télécom Paris, Institut Polytechnique de Paris, France
Language :
English
Title :
A Systematic Procedure for Topological Path Identification with Raw Data Transformation in Electrical Distribution Networks
Publication date :
April 2024
Event name :
7th International Conference on Energy, Electrical and Power Engineering (CEEPE 2024)
A. Navarro-Espinosa and L. F. Ochoa, "Reconstruction of low voltage distribution networks: From GIS data to power flow models, " in 23rd International Conference on Electricity Distribution, Jun. 2015.
A. Guzmán, A. Argüello, J. Quirós-Tortós, and G. Valverde, "Processing and correction of secondary system models in geographic information systems, " IEEE Transactions on Industrial Informatics, vol. 15, no. 6, pp. 3482-3491, 2019. DOI: 10. 1109/TII. 2018. 2876356.
J. Kays, A. Seack, T. Smirek, F. Westkamp, and C. Rehtanz, "The generation of distribution grid models on the basis of public available data, " IEEE Transactions on Power Systems, vol. 32, no. 3, pp. 2346-2353, 2017. DOI: 10. 1109/TPWRS. 2016. 2609850.
G. Zepu, L. Yongjian, X. Ziwei, Y. Yilan, and Z. Lianmei, "Knowledge graph-based method for identifying topological structure of low-voltage distribution network, " The Journal of Engineering, vol. 2020, no. 12, pp. 1177-1184, 2020. DOI: https: //doi. org/10. 1049/joe. 2019. 1319.
A. Benzerga, D. Maruli, A. Sutera, A. Bahmanyar, S. Mathieu, and D. Ernst, "Low-voltage network topology and impedance identification using smart meter measurements, " in 2021 IEEE Madrid PowerTech, 2021, p. 6. DOI: 10. 1109/PowerTech46648. 2021. 9495093.
S. Bolognani and L. Schenato, "Identification of power distribution network topology via voltage correlation analysis, " in 52nd IEEE Conference on Decision and Control, 2013, pp. 1659-1664. DOI: 10. 1109/CDC. 2013. 6760120.
S. de Jongh, F. Mueller, F. Osterberg, C. A. Cañizares, T. Leibfried, and K. Bhattacharya, "Data-driven topology and parameter identification in distribution systems with limited measurements, " arXiv preprint arXiv: 2308. 09521, Aug. 2023.