Intelligent identification of segmental line losses in low-voltage networks under graphical consistency line variation relationship analysis

Junmin Li; Yankai Zhao; Qian Wei; Liangliang Zhao; Chaonan Guo

doi:10.1117/12.3004344

25 September 2023 Intelligent identification of segmental line losses in low-voltage networks under graphical consistency line variation relationship analysis

Junmin Li, Yankai Zhao, Qian Wei, Liangliang Zhao, Chaonan Guo

Author Affiliations +

Proceedings Volume 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023); 127883G (2023) https://doi.org/10.1117/12.3004344
Event: Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The current conventional line loss identification method mainly constructs the similarity matrix by calculating the similarity of line loss data, which leads to poor identification effect due to the lack of effective extraction of line loss data features. In this regard, the intelligent identification method of low-voltage grid segmentation under the analysis of line variation relationship of mapping consistency is proposed. The similarity of voltage time series mapping of low-voltage power grid is calculated to extract line loss feature data, and the EM algorithm is used to cluster and analyze the power operation data of line loss abnormal stations, and the vector features of line loss data under different Gaussian distribution states are calculated by constructing GMM model to realize line loss identification. In the experiments, the proposed method is verified for recognition accuracy. The experimental results show that the algorithm has a high recall rate and possesses a more desirable recognition accuracy when the proposed method is used for line loss data recognition.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junmin Li, Yankai Zhao, Qian Wei, Liangliang Zhao, and Chaonan Guo "Intelligent identification of segmental line losses in low-voltage networks under graphical consistency line variation relationship analysis", Proc. SPIE 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 127883G (25 September 2023); https://doi.org/10.1117/12.3004344

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KEYWORDS

Data modeling

Power grids

Feature extraction

Detection and tracking algorithms

Mathematical modeling

Analytical research

Power supplies

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