摘要
[目的]随着分布式能源的广泛接入,配电网的拓扑结构日益复杂,同时监控数据量呈指数级增长,对故障诊断提出了新的挑战。传统故障诊断方法主要依赖监控数据和人工经验,但随着云计算和通信技术的快速发展,人工智能方法在故障诊断领域得到了广泛应用。然而,现有人工智能方法高度依赖训练数据,需要大量基础数据支撑。为此,本文基于数字孪生技术,提出一种配电网智能化故障诊断方法,以提高故障诊断的效率和准确性。[方法]利用数字孪生技术构建配电网数字孪生体,通过虚拟诊断结果指导实际系统运行。同时,采用小波包分解方法提取信号各频带能量构成特征向量,输入改进的卷积自编码器模型中进行学习,以实现故障类型的准确识别。数字孪生系统由物理层、数据层、模型层和服务层组成,实现了虚实映射功能,虚拟孪生体能够实时反映实体运行状态。在仿真实验中,以某区域10 kV配电网的三端口环网结构为基础,构建了包含7520个正常和故障样本数据的完备实验数据集。[结果]实验结果表明,经过100次迭代训练,改进的卷积自编码器模型的故障诊断准确率接近0.98。数字孪生系统的智能化诊断结果显示,本文方法能够准确识别故障类型,与实际故障类型基本一致。在对5种常见故障类型的诊断中,本文方法保持了较高的准确率,平均准确率达0.95,诊断耗时仅为5.39 s。与其他方法相比,本文方法的诊断准确率更高。[结论]通过将数字孪生技术应用于配电网智能化故障诊断,结合虚实一体化的诊断方式,显著提升了故障诊断的精确性和实时性。该方法为配电网智能化故障诊断提供了一种全新的技术手段,有助于提高配电网的可靠性和安全性,对智能电网的发展具有重要的理论意义和实践价值。此外,未来研究将重点探索应对配电网结构变化的技术方法,以进一步提升该故障诊断方法的适用性。
[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 7520 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.
作者
付慧敏
郑刚
FU Huimin;ZHENG Gang(College of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200090,China;Qingpu Power Supply Company,State Grid Shanghai Municipal Electric Power Company,Shanghai 201700,China)
出处
《沈阳工业大学学报》
北大核心
2025年第3期288-294,共7页
Journal of Shenyang University of Technology
基金
上海市科技计划项目(21DZ1208300)。
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
digital twin
distribution network
intelligent fault diagnosis
wavelet packet decomposition
improved convolutional autoencoder
distributed energy
mathematical twin
diagnostic accuracy
