安徽大学学报（自然科学版）2024，Vol.48Issue(6)：63-69,7.DOI:10.3969/j.issn.1000-2162.2024.06.009
基于XGBoost-CNN的多端有源配电网故障检测
Multi-terminal active distribution network fault detection based on XGBoost-CNN
摘要
Abstract
With the increasing number of distributed power sources and new energy users in the distribution grid,the traditional distribution grid has developed into a multi-source structure.In order to prevent the occurrence of reverse power accidents when other distributed power sources continued to supply power to the maintenance area in the branch fault of the multi-source active distribution grid,an improved extreme gradient boosting(XGBoost)algorithm was proposed,and a multi-source active distribution grid fault detection model based on the improved XGBoost algorithm and convolutional neural networks(CNN)was constructed.The peak values of each frequency band voltage,phase-to-phase voltage difference,and 6th harmonic component in the normal and fault states of the multi-source active distribution grid were extracted as the model's input,and the feature data was processed by the CNN network.The simulation experiment results showed that compared with the other three models,the model in this paper had better detection performance and stronger robustness.The model in this paper could effectively and accurately isolate the fault area in the distribution grid and prevent reverse power accidents from occurring.关键词
多端有源配电网/故障定位/故障检测/集成学习/神经网络Key words
multi-terminal active distribution network/fault localization/fault detection/integrated learning/neural network分类
信息技术与安全科学引用本文复制引用
郭雪丽,张健壮,龚正国,王莹,张赐源,王盼宝..基于XGBoost-CNN的多端有源配电网故障检测[J].安徽大学学报（自然科学版）,2024,48(6):63-69,7.基金项目
国家自然科学基金面上项目(52377173) （52377173）
国网河南省电力公司科技项目(B7178023K138) （B7178023K138）
