基于灰狼算法的配电网短路故障自动监测系统
Automatic Monitoring System for Short-circuit Fault of Distribution Network Based on Gray Wolf Algorithm
施晓敏 1徐飞 1沈磊1
作者信息
- 1. 国网安徽省电力有限公司,合肥 230022
- 折叠
摘要
针对接地短路故障中识别率较低、抗干扰性较大的问题,该文提出短路故障自动化监测系统.通过集成经验模态对零序电流做出分解,提取相应的本证模态分量,构建特征向量.结合灰狼算法对门控神经网络优化,加入相应的softmax分类优化,构建接地短路故障的自动化监测系统.经过实际算例验证,改进系统能够更稳定地进行故障识别,具有更高的准确率以及抗干扰性能.
Abstract
Aiming at the problems of low recognition rate and high anti-interference in grounding short-circuit fault,an automatic monitoring system for short-circuit fault is proposed.The zero sequence current is decomposed by integrat-ing empirical modes,and the corresponding eigenmode components are extracted.Based on the grey wolf algorithm to optimize the gated neural network and add the corresponding softmax classification optimization,the automatic moni-toring system of ground short circuit fault is constructed.The practical examples show that the improved system can identify faults more stably,with higher accuracy and anti-interference performance.
关键词
集成经验模态/神经网络/灰狼算法/故障检测/softmax层Key words
integrated empirical modality/neural networks/grey wolf algorithm/fault detection/softmax layer引用本文复制引用
出版年
2024
NETL
NSTL
万方数据