Non-contact detection method and system for aging state of lightning arrester
By constructing a spatial magnetic field gradient tensor matrix and performing thermo-electrodynamic phase space trajectory analysis, the problems of weak current extraction and false alarms in non-contact detection of surge arresters were solved, enabling accurate aging status assessment and lifespan prediction of surge arresters, and improving the anti-interference capability and diagnostic confidence of the detection.
Patent Information
- Application Number
- CN202610492033.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-15
- Publication Date
- 2026-06-09
AI Technical Summary
Existing non-contact detection technologies for surge arresters are difficult to effectively extract weak leakage currents in complex electromagnetic environments, are easily affected by external environmental factors and generate false alarms, and lack quantitative assessment of the internal nonlinear degradation state of the equipment and prediction of its remaining life.
By acquiring the multi-node spatial magnetic induction intensity vector and two-dimensional temperature distribution matrix of multiple spatial nodes around the surge arrester, and combining temperature drift compensation and node differential calculation, a spatial magnetic field gradient tensor matrix is constructed to remove far-field interference, reconstruct the near-field resistive current signal, and extract topological geometric distortion parameters through spatial cross-phase-locked verification and thermo-electrodynamic phase spatial trajectory analysis, thereby realizing the prediction of the remaining effective life of the equipment.
Accurate extraction of weak leakage current inside surge arresters in complex electromagnetic environments reduces false alarm rates, enables keen detection of nonlinear degradation characteristics of equipment and quantitative prediction of remaining life, and promotes predictive health management of surge arresters.