基于谐振敏感频次的MVL级联电缆电容参数估计方法
By utilizing resonant sensitive frequency and fundamental frequency measurement data in multi-voltage cascaded cable networks, combined with neural networks, the problems of accuracy and computational efficiency in capacitance parameter estimation in existing technologies have been solved, enabling fast and accurate capacitance parameter estimation in ultra-large urban power grids.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SICHUAN UNIV
- Filing Date
- 2026-05-12
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies struggle to comprehensively consider the combined effects of different voltage levels on disturbance propagation and resonance characteristics in multi-voltage cascaded cable networks, and their computational complexity is high, making them unsuitable for frequent or real-time use in ultra-large urban power grids.
The method for estimating the capacitance parameters of MVL cascaded cables based on resonant sensitive frequencies uses fundamental frequency measurement data and network-wide resonant sensitive frequency information, combined with neural networks, to construct a weighted current residual least squares model and a multi-layer feedforward neural network, thereby achieving rapid estimation and correction of cable capacitance parameters.
This invention improves the accuracy and computational efficiency of capacitance parameter estimation in cascaded cable systems with multiple voltage levels, making it suitable for frequent or near-real-time parameter updates in ultra-large urban power grids and reducing the reliance on harmonic power flow calculations.
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Figure CN122178360B_ABST