中国电力2026，Vol.59Issue(5)：67-75,9.DOI:10.11930/j.issn.1004-9649.202510085
数据稀缺场景下的配电网异常数据检测方法
Abnormal data detection method for distribution networks in data scarcity scenarios
摘要
Abstract
In order to accurately detect abnormal voltage and current data in the distribution network and solve the problem of low accuracy of the detection model caused by the scarcity of abnormal data under normal operation of the distribution network,a method for detecting abnormal data based on an improved chaos optimization algorithm(ICEO)-dual attention mechanism Transformer(DAM Transformer)is proposed.This method first utilizes the strength controlled diffusion anomaly synthesis(SDAS)method to generate partial anomaly data,in order to alleviate the problem of insufficient model recognition accuracy caused by the scarcity of real anomaly samples;Secondly,an innovative DAM Transformer model was proposed,which integrates a dual attention mechanism to achieve collaborative modeling of complex patterns in different time scales and feature spaces,effectively improving the identification of multi-scale feature coupling relationships in the context of abnormal distribution network data;Finally,ICEO was used to iteratively optimize the hyperparameters of DAM Transformer,further improving the optimization efficiency and generalization performance of the model in complex scenarios.The results show that compared with traditional models,this method improves the accuracy of identifying abnormal voltage in distribution networks by 12.81%and the accuracy of identifying abnormal current by 12.22%.In data scarcity scenarios,the recognition accuracy is significantly better than traditional models.This method effectively solves the core bottleneck of sample scarcity and difficulty in modeling multi-scale features in abnormal data recognition of distribution networks,improves the accuracy of abnormal recognition and the stability of model operation,and provides key technical support for digital inspection,real-time fault warning,and operation and maintenance decision optimization of intelligent distribution networks.It has engineering application prospects.关键词
双重注意力机制/改进混沌优化算法/异常数据检测Key words
dual attention mechanism/improved chaos optimization algorithm/abnormal data detection引用本文复制引用
曾瑞江,李志勇,黄曙,王伟光..数据稀缺场景下的配电网异常数据检测方法[J].中国电力,2026,59(5):67-75,9.基金项目
国家科技重大专项项目资助(2025ZD0804405) （2025ZD0804405）
中国南方电网科技项目资助(GDKJXM20230797). This work is supported by the National Science and Technology Major Project(No.2025ZD0805902) （GDKJXM20230797）
Science and Technology Project of China Southern Power Grid Corporation(No.GDK JXM20230797). （No.GDK JXM20230797）
