一种偏远电网中低压电能表相位分布优化方法及系统
By combining wavelet transform and Fourier spectral analysis with support vector machine classification, the phase distribution of low-voltage energy meters in remote power grids is optimized, solving the problems of phase imbalance and isolated nodes, and improving the stability and reliability of the power grid.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JIANGYIN CHANGYI GRP CO LTD
- Filing Date
- 2025-09-28
- Publication Date
- 2026-07-17
AI Technical Summary
In remote mountainous areas, the unbalanced phase distribution of low-voltage electricity meters and the problem of isolated nodes in the power grid lead to abnormal current fluctuations, power loss and equipment damage. Traditional methods are difficult to adapt to the data characteristics in complex environments, resulting in misjudgments or omissions.
Wavelet transform decomposition and Fourier spectral analysis are used to extract frequency domain features. Support vector machine classification and cross-validation are combined to optimize phase correspondence. Threshold filtering mechanism is used to determine the degree of imbalance. Random forest ensemble and neural network training are used to evaluate the overall stability and generate a stable phase distribution map.
It effectively solves the problems of uneven phase distribution and isolated node identification in remote mountainous power grids, improves the balance and reliability of power grid operation, and provides efficient technical support for power grid management in complex environments.
Smart Images
Figure CN121479538B_ABST