{
  "abstract": "Fault signals in power systems often contain high-order harmonics and attenuated oscillations. which pose challenges for traditional fault time detection methods in accurately identifying the fault inception instant. To address this issue, this paper proposes an accurate fault time detection method based on Euclidean distance and Gaussian filter. Based on the phase voltage or current signal of the fault line, the improved high-order Fourier algorithm with attenuation factor is used to fit the curve of the fault phase signal data. The fault signal is compared with the imaginary non-fault signal by using Euclidean distance, and the fitting error is filtered by a Gaussian filtering algorithm to detect the fault segment. Finally, the fault is detected by pixel column, and the fault time is obtained by corresponding the pixel column and time information. The MATLAB/Simulink simulation results and the field test results show that the method can accurately detect the fault time under different harmonics, damped oscillations, transition resistance, short-circuit fault types and sampling rates, and the detection error is basically controlled within 0.1 ms, which achieves high detection accuracy. Combining the advantages of both computational efficiency and detection accuracy, the proposed method demonstrates superior practicality and cost-effectiveness in active distribution networks when compared to existing technologies.",
  "abstract_shown": true,
  "affiliations": [
    "State Grid Jiangsu Electric Power Company, Nanjing, Jiangsu, China",
    "State Grid Suzhou Power Supply Company, Suzhou, Jiangsu, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Haiou Cao"
    },
    {
      "affs": [
        1
      ],
      "name": "Yihua Meng"
    },
    {
      "name": "Chenbin Zhou"
    },
    {
      "name": "Lichen Deng"
    },
    {
      "name": "Jiayan Yu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9zms1bed9w678.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359598",
  "fulltext_url": null,
  "id": "9zms1bed9w678",
  "issns": [
    "1932-6203"
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  "landing_url": "https://doi.org/10.1371/journal.pone.0359598",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2207|Power Systems Fault Detection"
    ],
    [
      "plos",
      "/Computer and information sciences/Digital imaging/Grayscale"
    ],
    [
      "plos",
      "/Engineering and technology/Electrical engineering/Electrical faults"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing/Signal filtering"
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      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms"
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    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Damage mechanics"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms"
    ]
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  "openalex": "W7221110738",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359598&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/9zms1bed9w678.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0359598",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0359598"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:mathematics",
      "subfield": "general"
    },
    {
      "field": "physics",
      "origin": "plos:physics",
      "subfield": "general"
    }
  ],
  "title": "High-precision fault inception time detection in active distribution networks using Euclidean distance and Gaussian filtering",
  "title_text": "High-precision fault inception time detection in active distribution networks using Euclidean distance and Gaussian filtering",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/9zms1bed9w678",
  "venue": "PLOS One",
  "versions": []
}