{"title":"基于多源信息融合的母线故障诊断方法","authors":"Xuebao Jiang, Haiou Cao, Chenbin Zhou, Xuchao Ren, Jiaoxiao Shen, Jiayan Yu","doi":"10.3389/fenrg.2024.1443570","DOIUrl":null,"url":null,"abstract":"Against the backdrop of smart grid development, the electric power system demands higher accuracy and comprehensiveness in fault analysis. Establishing a digital twin platform for multiple equipment faults represents the future direction of power system development. Presently, while many researchers employ artificial intelligence algorithms to diagnose faults in key equipment such as transmission lines and transformers, intelligent diagnostic methods for busbar faults remain insufficient. Therefore, this paper proposes a busbar fault diagnosis method based on multi-source information fusion. Initially, the diagnostic method for busbar faults is explored, conducting both time-domain and frequency-domain analyses on simulated fault data. The data of this model are optimized using Dempster-Shafer evidence theory to enhance algorithm training speed. Subsequently, BP neural network training is implemented. Finally, validation testing of fault data demonstrates a fault recognition accuracy of 99.1% for this method. Experimental results illustrate the method’s feasibility and low computational costs, thereby advancing the development of digital twin platforms for power system fault diagnosis.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"72 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Busbar fault diagnosis method based on multi-source information fusion\",\"authors\":\"Xuebao Jiang, Haiou Cao, Chenbin Zhou, Xuchao Ren, Jiaoxiao Shen, Jiayan Yu\",\"doi\":\"10.3389/fenrg.2024.1443570\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Against the backdrop of smart grid development, the electric power system demands higher accuracy and comprehensiveness in fault analysis. Establishing a digital twin platform for multiple equipment faults represents the future direction of power system development. Presently, while many researchers employ artificial intelligence algorithms to diagnose faults in key equipment such as transmission lines and transformers, intelligent diagnostic methods for busbar faults remain insufficient. Therefore, this paper proposes a busbar fault diagnosis method based on multi-source information fusion. Initially, the diagnostic method for busbar faults is explored, conducting both time-domain and frequency-domain analyses on simulated fault data. The data of this model are optimized using Dempster-Shafer evidence theory to enhance algorithm training speed. Subsequently, BP neural network training is implemented. Finally, validation testing of fault data demonstrates a fault recognition accuracy of 99.1% for this method. Experimental results illustrate the method’s feasibility and low computational costs, thereby advancing the development of digital twin platforms for power system fault diagnosis.\",\"PeriodicalId\":12428,\"journal\":{\"name\":\"Frontiers in Energy Research\",\"volume\":\"72 1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Energy Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3389/fenrg.2024.1443570\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Energy Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3389/fenrg.2024.1443570","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
摘要
在智能电网发展的背景下,电力系统对故障分析的准确性和全面性提出了更高的要求。建立多设备故障数字孪生平台是未来电力系统发展的方向。目前,许多研究人员采用人工智能算法诊断输电线路和变压器等关键设备的故障,但母线故障的智能诊断方法仍然不足。因此,本文提出了一种基于多源信息融合的母线故障诊断方法。首先,探讨了母线故障诊断方法,对模拟故障数据进行了时域和频域分析。利用 Dempster-Shafer 证据理论对该模型的数据进行了优化,以提高算法训练速度。随后,实施了 BP 神经网络训练。最后,故障数据的验证测试表明,该方法的故障识别准确率达到 99.1%。实验结果表明了该方法的可行性和低计算成本,从而推动了用于电力系统故障诊断的数字孪生平台的发展。
Busbar fault diagnosis method based on multi-source information fusion
Against the backdrop of smart grid development, the electric power system demands higher accuracy and comprehensiveness in fault analysis. Establishing a digital twin platform for multiple equipment faults represents the future direction of power system development. Presently, while many researchers employ artificial intelligence algorithms to diagnose faults in key equipment such as transmission lines and transformers, intelligent diagnostic methods for busbar faults remain insufficient. Therefore, this paper proposes a busbar fault diagnosis method based on multi-source information fusion. Initially, the diagnostic method for busbar faults is explored, conducting both time-domain and frequency-domain analyses on simulated fault data. The data of this model are optimized using Dempster-Shafer evidence theory to enhance algorithm training speed. Subsequently, BP neural network training is implemented. Finally, validation testing of fault data demonstrates a fault recognition accuracy of 99.1% for this method. Experimental results illustrate the method’s feasibility and low computational costs, thereby advancing the development of digital twin platforms for power system fault diagnosis.
期刊介绍:
Frontiers in Energy Research makes use of the unique Frontiers platform for open-access publishing and research networking for scientists, which provides an equal opportunity to seek, share and create knowledge. The mission of Frontiers is to place publishing back in the hands of working scientists and to promote an interactive, fair, and efficient review process. Articles are peer-reviewed according to the Frontiers review guidelines, which evaluate manuscripts on objective editorial criteria