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A power line loss analysis method based on boost clustering

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Abstract

The estimation of feeder line losses plays a crucial guiding role in power system planning, design, production technology, and management level of a distribution system. It is also most important for the monitor of abnormal electricity consumption, such as electricity leakage or theft. To achieve this goal, the paper proposes a boost k-means based method for the estimation of feeder line losses. The index system for the power line loss analysis was established, and the characteristic indicators including the average line loss rate, line loss rate range, line loss rate fluctuation, and line loss rate gradient are calculated depending upon the line loss intensity and time change information. Furthermore, the established characteristic indicators are input to the boost k-means algorithm for the clustering calculation, and the final results are obtained along with the line loss characteristic analysis of each cluster. The proposed method is simple, fast, practical, and outperforms the traditional method with a highest exceeding accuracy of 20.37%. The experimental findings demonstrate the effectiveness and feasibility of the proposed method.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is partly supported by the Fundamental Research Funds for the Central Universities (#20720181004). The authors would also like to thank all the editors and anonymous reviewers for their constructive advice.

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Authors and Affiliations

  1. Department of Electronic Commerce, Xiangtan University, Xiangtan, 411105, China

    Junde Chen

  2. School of Informatics, Xiamen University, Xiamen, 361005, China

    Junde Chen, Adnan Zeb & Defu Zhang

  3. Spira Inc, Los Angeles, CA, 90032, USA

    Yuandong Sun

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  1. Junde Chen
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  2. Adnan Zeb
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  3. Yuandong Sun
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  4. Defu Zhang
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Correspondence to Defu Zhang.

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Chen, J., Zeb, A., Sun, Y. et al. A power line loss analysis method based on boost clustering. J Supercomput 79, 3210–3226 (2023). https://doi.org/10.1007/s11227-022-04777-w

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