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<p>This paper proposes a precise line loss rate probability density estimation method using the Bilateral Total Variation (BTV) filtering algorithm to suppress noise while preserving edge information in power data. The BTV algorithm smooths noise by considering spatial distribution, maintaining edge gradients, and improving data accuracy. The line loss rate is calculated using a combined improved equivalent resistance method with the filtered data, followed by non-parametric kernel density estimation for precise probability density results. Experiments show the method effectively filters power data, enabling accurate line loss rate calculation and reliable density estimation. Under varying conditions, the method achieves a high maximum Kendall correlation coefficient (lowest ≈ 0.88), confirming its accuracy in reflecting the true line loss rate distribution.</p>

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Research on precise estimation of line loss rate probability density based on bilateral total variation filtering algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月25日

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  • Jie Zhang,
  • Shengchun Liu,
  • Chao Feng

摘要

This paper proposes a precise line loss rate probability density estimation method using the Bilateral Total Variation (BTV) filtering algorithm to suppress noise while preserving edge information in power data. The BTV algorithm smooths noise by considering spatial distribution, maintaining edge gradients, and improving data accuracy. The line loss rate is calculated using a combined improved equivalent resistance method with the filtered data, followed by non-parametric kernel density estimation for precise probability density results. Experiments show the method effectively filters power data, enabling accurate line loss rate calculation and reliable density estimation. Under varying conditions, the method achieves a high maximum Kendall correlation coefficient (lowest ≈ 0.88), confirming its accuracy in reflecting the true line loss rate distribution.