{"title":"A joint data and knowledge-driven method for power system disturbance localisation","authors":"Zikang Li, Jiyang Tian, Hao Liu","doi":"10.1049/gtd2.13331","DOIUrl":null,"url":null,"abstract":"<p>Accurate and fast disturbance localisation is critical for taking timely controls to prevent power system instability. With the increased complexity of systems, the physical model-based disturbance localisation is challenging to achieve good performance due to model deficiency. Phasor measurement unit (PMU)-based approaches are developed but their performance has been significantly affected by the number of PMUs. To this end, this article proposes a joint data and knowledge-driven disturbance localisation method. A spatiotemporal graph convolutional network is proposed to effectively capture the spatiotemporal dependence with a limited number of PMU measurements. By integrating the physical constraints of disturbance type-topology information and localisation cost characteristics, a composite constraint loss function is proposed that embed physical knowledge into the data-driven method. This leads to the development of the disturbance localisation method and allows quick identification, improved localisation accuracy, and interpretability of the algorithm. Simulation results carried out on the IEEE 39-bus system and IEEE 118-bus system verify the effectiveness and robustness of the proposed method.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"18 24","pages":"4078-4089"},"PeriodicalIF":2.0000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13331","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iet Generation Transmission & Distribution","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/gtd2.13331","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate and fast disturbance localisation is critical for taking timely controls to prevent power system instability. With the increased complexity of systems, the physical model-based disturbance localisation is challenging to achieve good performance due to model deficiency. Phasor measurement unit (PMU)-based approaches are developed but their performance has been significantly affected by the number of PMUs. To this end, this article proposes a joint data and knowledge-driven disturbance localisation method. A spatiotemporal graph convolutional network is proposed to effectively capture the spatiotemporal dependence with a limited number of PMU measurements. By integrating the physical constraints of disturbance type-topology information and localisation cost characteristics, a composite constraint loss function is proposed that embed physical knowledge into the data-driven method. This leads to the development of the disturbance localisation method and allows quick identification, improved localisation accuracy, and interpretability of the algorithm. Simulation results carried out on the IEEE 39-bus system and IEEE 118-bus system verify the effectiveness and robustness of the proposed method.
期刊介绍:
IET Generation, Transmission & Distribution is intended as a forum for the publication and discussion of current practice and future developments in electric power generation, transmission and distribution. Practical papers in which examples of good present practice can be described and disseminated are particularly sought. Papers of high technical merit relying on mathematical arguments and computation will be considered, but authors are asked to relegate, as far as possible, the details of analysis to an appendix.
The scope of IET Generation, Transmission & Distribution includes the following:
Design of transmission and distribution systems
Operation and control of power generation
Power system management, planning and economics
Power system operation, protection and control
Power system measurement and modelling
Computer applications and computational intelligence in power flexible AC or DC transmission systems
Special Issues. Current Call for papers:
Next Generation of Synchrophasor-based Power System Monitoring, Operation and Control - https://digital-library.theiet.org/files/IET_GTD_CFP_NGSPSMOC.pdf