A power distribution line fault diagnosis and positioning method and system

By constructing a spatiotemporally aligned multi-source data preprocessing framework and an improved adaptive wavelet entropy algorithm, combined with an improved particle swarm optimization multi-source information fusion positioning model, the problems of response lag and low positioning accuracy in power distribution line fault detection are solved, achieving high-precision fault feature extraction and positioning, and improving power supply restoration efficiency.

CN122307253APending Publication Date: 2026-06-30ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY
Filing Date
2026-04-30
Publication Date
2026-06-30

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Abstract

This invention belongs to the field of power system technology and discloses a method and system for fault diagnosis and location of distribution lines. The method includes: synchronously collecting line operation signals from all nodes of a distribution line through a hierarchical distributed multi-source sensor network; generating a time-frequency feature matrix and a fusion feature set based on multi-source heterogeneous data preprocessing and line scenario adaptation; constructing a fault feature set by strengthening the weight ratio of different fault features using dynamic weighting factors; constructing a line diagnosis model adapted to the distribution line scenario based on the fault feature set to achieve line operation status identification and fault type classification; and constructing a multi-source information fusion location model based on improved particle swarm optimization to output the fault location result of the distribution line. This invention solves the problems of difficult spatiotemporal synchronization and limited data dimensions in traditional multi-source fusion by constructing a spatiotemporally aligned multi-source data preprocessing framework, achieving panoramic monitoring and providing more comprehensive data support for fault diagnosis.
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