{"title":"Risk based vulnerability assessment for HVDC transmission system","authors":"C. Weihua, Jiang Quanyuan, Cao Yijia","doi":"10.1109/IPEC.2005.207004","DOIUrl":null,"url":null,"abstract":"With the increasing utilization of HVDC system, security assessment of HVDC system has become a hot topic. This paper presents a novel approach using the framework of vulnerability to assess HVDC system security. First, equivalent models of HVDC systems, based on Markov model and logical diagram, are built to solve \"dimension disaster\" problem. Second, the risk index is used as an indicator of the level of security, and its sensitivity to a changing system parameter is used as an indicator of its trend. These two indicators are combined to determine the degree of HVDC system vulnerability to contingent disturbances. Finally, artificial neural network (ANN) is applied to the fast pattern recognition and classification of system vulnerability status. A case of Gezhouba-Shanghai HVDC system in China is presented to verify correctness and effectiveness of the approach","PeriodicalId":164802,"journal":{"name":"2005 International Power Engineering Conference","volume":"119 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 International Power Engineering Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPEC.2005.207004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
With the increasing utilization of HVDC system, security assessment of HVDC system has become a hot topic. This paper presents a novel approach using the framework of vulnerability to assess HVDC system security. First, equivalent models of HVDC systems, based on Markov model and logical diagram, are built to solve "dimension disaster" problem. Second, the risk index is used as an indicator of the level of security, and its sensitivity to a changing system parameter is used as an indicator of its trend. These two indicators are combined to determine the degree of HVDC system vulnerability to contingent disturbances. Finally, artificial neural network (ANN) is applied to the fast pattern recognition and classification of system vulnerability status. A case of Gezhouba-Shanghai HVDC system in China is presented to verify correctness and effectiveness of the approach