基于多特征融合和随机森林的串联电弧故障检测方法
Series Arc Fault Detection Method Based on Multi-feature Fusion and Random Forest
郭敏 1郭小璇 1吴宁 1卢健斌 1陈卫东1
作者信息
- 1. 广西电网有限责任公司电力科学研究院,南宁 530023;广西电力装备智能控制与运维重点实验室,南宁 535023
- 折叠
摘要
串联电弧故障在线路负载的限制下,其故障电流比较小,使得传统电气保护装置如低压断路器、熔断器等无法发生动作,因此,串联电弧故障的检测成为目前电弧故障检测研究的热点问题.为此,通过搭建电弧故障平台进行电弧实验,获得典型负载在正常工作和电弧故障时的电流数据;然后,在电弧多特征融合的基础上,结合特征的信息增益、增益率对电弧特征进行重要性排序,构建更加合适的串联电弧特征指标集;最后,在此基础上建立随机森林算法,并采用粒子群算法对参数进行优化.研究结果表明:与传统随机森林和BP神经网络相比,该方法有更高的识别率,其准确率和查全率都达到了99％,能够实现对串联故障电弧的有效诊断.
Abstract
Under the limitation of the line load,the fault current of the series arc fault is relatively small,which makes such traditional electrical protection devices as low-voltage circuit breakers,fuses cannot operate,therefore,the detection of series arc faults has become a hot issue in the current arc fault detection research.For this purpose,the arc experiment is performed by setting up arc fault platform and the current data of typical loads during normal operation and arc faults are obtained;Then,on the basis of arc multi-feature fusion,the information gain and the gain rate of the features are combined to sort the importance of the arc features so to construct a more suitable set of series arc features;Finally,on this basis,a random forest algorithm is set up and the particle swarm algorithm is used to optimize the parameters.The research results show that compared with the traditional random forest and BP neural network,the proposed method has a higher identification rate,and both its accuracy rate and completion rate reach 99％,which can achieve effective diagnosis of series fault arc.
关键词
串联电弧/故障识别/特征分析/特征选择/随机森林Key words
series arc fault/fault recognition/feature analysis/feature selection/random forest引用本文复制引用
基金项目
南方电网公司科技项目(GXKJXM20220099)
出版年
2024
NETL
NSTL
万方数据