沈阳工业大学学报2025，Vol.47Issue(3)：288-294,7.DOI:10.7688/j.issn.1000-1646.2025.03.03
基于数字孪生的配电网智能化故障诊断方法
Intelligent fault diagnosis method for distribution network based on digital twin technology
摘要
Abstract
[Objective]With the widespread integration of distributed energy,the complex topology and exponentially increased monitoring data of distribution networks pose new challenges to fault diagnosis.Traditional fault diagnosis methods mainly rely on monitoring data and human experience.With the rapid development of cloud computing and communication technology,artificial intelligence methods are widely applied in the field of fault diagnosis.However,existing artificial intelligence methods have a high dependence on training data,requiring a large number of basic data as support.Therefore,an intelligent fault diagnosis method for distribution networks was proposed by leveraging digital twin technology to improve the efficiency and accuracy of fault diagnosis.[Methods]A digital twin of the distribution network was constructed using digital twin technology,and virtual diagnosis results were used to guide actual system operation.Additionally,wavelet packet decomposition was utilized to obtain the energy of each frequency band of the signal to construct feature vectors,which were input into the improved convolutional autoencoder(CAE)model for learning to identify the fault type.The digital twin system included a physical layer,a data layer,a model layer,and a service layer,achieving virtual-real mapping,with the virtual twin reflecting the state of the physical entity in real time.In the simulation experiment,the three-port ring network structure of a 10 kV distribution network in an area was used as the basis,and a complete experimental dataset was constructed,including 7 520 pieces of normal and fault sample data.[Results]The performance analysis results of the proposed model show that after 100 iterations of training,the diagnostic accuracy of the improved CAE model is close to 0.98.Moreover,the intelligent diagnosis results of the digital twin system demonstrate that the fault types diagnosed by the proposed method are basically consistent with the actual fault types,and for five common fault types,it maintains an ideal diagnostic accuracy.The average accuracy reaches 0.95,and the diagnosis time is only 5.39 s.A comparison of diagnoses using different methods indicates that the proposed method has a higher diagnostic accuracy.[Conclusion]The application of digital twin technology to the intelligent fault diagnosis of distribution networks,by adopting the approach of virtual-real integration,further improves the accuracy and real-time performance of fault diagnosis,thus providing a new technical means for the intelligent fault diagnosis of distribution networks.This contributes to enhancing the reliability and safety of distribution networks and holds important theoretical and practical value for the development of smart grids.Furthermore,future research will focus on how to cope with the changes in the structure of distribution networks to improve the applicability of the proposed fault diagnosis method.关键词
数字孪生/配电网/智能化故障诊断/小波包分解/改进卷积自编码器/分布式能源/数字孪生体/诊断准确率Key words
digital twin/distribution network/intelligent fault diagnosis/wavelet packet decomposition/improved convolutional autoencoder/distributed energy/mathematical twin/diagnostic accuracy分类
信息技术与安全科学引用本文复制引用
付慧敏,郑刚..基于数字孪生的配电网智能化故障诊断方法[J].沈阳工业大学学报,2025,47(3):288-294,7.基金项目
上海市科技计划项目(21DZ1208300). （21DZ1208300）
