Research on Fault Detection and Self-Healing System in Intelligent Distribution Network
The traditional grounding fault detection and self repair methods in power systems are often challenged due to slow response and low accuracy.Firstly,this paper constructs a framework for identifying one-phase ground-ing faults,with a focus on the key indicator of zero sequence current,which processes the zero sequence current sig-nal in multiple dimensions,converts it into characteristic vectors in the time domain,frequency domain,and wave-let domain,and assigns different weights to these features using Random Forest Algorithm.Then,the LightGBM al-gorithm is used to perform deep learning training on these classified feature vectors,in order to achieve accurate fault prediction.This algorithm can effectively resist the influence of transition resistance and initial phase angle changes on the prediction results,with an accuracy performance of 98.9%,far exceeding other comparative algo-rithms.
万方数据
维普
